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10.3389/fonc.2024.1373127
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Comparison of chemotherapy and chidamide combined with chemotherapy in patients with untreated angioimmunoblastic T-cell lymphoma
|
Background Angioimmunoblastic T-cell lymphoma (AITL) is characterized by high recurrence rates and poor prognosis, and effective first-line treatment is lacking. Recently, histone deacetylase inhibitors (HDACi), such as chidamide, have been found to induce durable remissions in AITL patients. Methods Patients with untreated AITL from March 2015 to March 2023 were retrospectively collected and divided into chemotherapy (ChT) group and chidamide combined with chemotherapy (C-ChT) group based on the first-line treatment received. The comparison of efficacy and safety between the two groups was conducted. Results 86 patients with newly diagnosed AITL were enrolled, in which 35 patients were in the ChT group and 51 in the C-ChT group. The objective response rate (ORR) of C-ChT group was significantly higher than that of ChT group (84.3% vs. 60%, P= 0.011), and had superior progression-free survival (PFS) (27 months vs. 12 months, P= 0.025). However, no significant difference in overall survival (OS) was observed between the two groups (P= 0.225). In addition, the responding patients who received autologous stem cell transplantation (ASCT) had superior PFS compared to those who did not (P= 0.015). Conclusions Compared with ChT regimen, C-ChT regimen was well tolerated and had superior ORR and PFS in patients with untreated AITL. ASCT may contribute to longer PFS in remission patients.
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2234943X
|
ONCOLOGY
|
10.1007/s44196-024-00453-4
|
Leveraging Model Scaling and Butterfly Network in the Bone Scan Image Segmentation
|
As we all know, cancer is one of the leading causes of death worldwide and the second leading cause of death overall. This is why regular screenings or health checks are necessary to detect cancer lesions early. Since bone scan images have become the primary means of detecting the emergence of cancer lesions on bone, high segmentation accuracy is essential for establishing the model of some predefined regions in bone scan images where cancer metastasis was predicted to appear. Consequently, robust localization and identification of the specific region in bone scan images are required for automated metastasis detection. To this end, we propose Efficient-BtrflyNet, a new deep learning-based architecture for skeleton segmentation of whole-body bone scan images. The proposed architecture exploits the benefits of EfficientNet’s model scaling and the encoder–decoder design of butterfly-type networks. We added EfficientNetB7 to the encoder section to obtain more specific features. The proposed architecture simultaneously processes anterior and posterior whole-body bone scan images. Using 37 bone scan images, we evaluated the performance of our proposed skeleton segmentation system using the Dice score. Efficient-BtrflyNet achieves superior segmentation performance compared to the existing representative method.
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18756883
|
AI
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10.3390/ai5020028
|
Artificial Intelligence in Healthcare: ChatGPT and Beyond
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Artificial intelligence (AI), the simulation of human intelligence processes by machines, is having a growing impact on healthcare
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26732688
|
AI
|
10.3390/ai5020029
|
Development of an Attention Mechanism for Task-Adaptive Heterogeneous Robot Teaming
|
The allure of team scale and functional diversity has led to the promising adoption of heterogeneous multi-robot systems (HMRS) in complex, large-scale operations such as disaster search and rescue, site surveillance, and social security. These systems, which coordinate multiple robots of varying functions and quantities, face the significant challenge of accurately assembling robot teams that meet the dynamic needs of tasks with respect to size and functionality, all while maintaining minimal resource expenditure. This paper introduces a pioneering adaptive cooperation method named inner attention (innerATT), crafted to dynamically configure teams of heterogeneous robots in response to evolving task types and environmental conditions. The innerATT method is articulated through the integration of an innovative attention mechanism within a multi-agent actor–critic reinforcement learning framework, enabling the strategic analysis of robot capabilities to efficiently form teams that fulfill specific task demands. To demonstrate the efficacy of innerATT in facilitating cooperation, experimental scenarios encompassing variations in task type (“Single Task”, “Double Task”, and “Mixed Task”) and robot availability are constructed under the themes of “task variety” and “robot availability variety.” The findings affirm that innerATT significantly enhances flexible cooperation, diminishes resource usage, and bolsters robustness in task fulfillment.
|
26732688
|
AI
|
10.1007/s44196-024-00502-y
|
Alzheimer’s Disease Detection via Multiscale Feature Modelling Using Improved Spatial Attention Guided Depth Separable CNN
|
Early detection of Alzheimer's disease (AD) is critical due to its rising prevalence. AI-aided AD diagnosis has grown for decades. Most of these systems use deep learning using CNN. However, a few concerns must be addressed to identify AD: a. there is a lack of attention paid to spatial features; b. there is a lack of scale-invariant feature modelling; and c. the convolutional spatial attention block (C-SAB) mechanism is available in the literature, but it exploits limited feature sets from its input features to obtain a spatial attention map, which needs to be enhanced. The suggested model addresses these issues in two ways: through a backbone of multilayers of depth-separable CNN. Firstly, we propose an improved spatial convolution attention block (I-SAB) to generate an enhanced spatial attention map for the multilayer features of the backbone. The I-SAB, a modified version of the C-SAB, generates a spatial attention map by combining multiple cues from input feature maps. Such a map is forwarded to a multilayer of depth-separable CNN for further feature extraction and employs a skip connection to produce an enhanced spatial attention map. Second, we combine multilayer spatial attention features to make scale-invariant spatial attention features that can fix scale issues in MRI images. We demonstrate extensive experimentation and ablation studies using two open-source datasets, OASIS and AD-Dataset. The recommended model outperforms existing best practices with 99.75% and 96.20% accuracy on OASIS and AD-Dataset. This paper also performed a domain adaptation test on the OASIS dataset, which obtained 83.25% accuracy.
|
18756883
|
AI
|
10.1007/s00432-024-05767-6
|
Construction of a prognostic model for extensive-stage small cell lung cancer patients undergoing immune therapy in northernmost China and prediction of treatment efficacy based on response status at different time points
|
Background and purpose: Recently, the emergence of immune checkpoint inhibitors has significantly improved the survival of patients with extensive-stage small cell lung cancer. However, not all patients can benefit from immunotherapy; therefore, there is an urgent need for precise predictive markers to screen the population for the benefit of immunotherapy. However, single markers have limited predictive accuracy, so a comprehensive predictive model is needed to better enable precision immunotherapy. The aim of this study was to establish a prognostic model for immunotherapy in ES-SCLC patients using basic clinical characteristics and peripheral hematological indices of the patients, which would provide a strategy for the clinical realization of precision immunotherapy and improve the prognosis of small cell lung cancer patients. Methods: This research retrospectively collected data from ES-SCLC patients treated with PD-1/PD-L1 inhibitors between March 1, 2019, and October 31, 2022, at Harbin Medical University Cancer Hospital. The study data was randomly split into training and validation sets in a 7:3 ratio. Variables associated with patients’ overall survival were screened and modeled by univariate and multivariate Cox regression analyses. Models were presented visually via Nomogram plots. Model discrimination was evaluated by Harrell’s C index, tROC, and tAUC. The calibration of the model was assessed by calibration curves. In addition, the clinical utility of the model was assessed using a DCA curve. After calculating the total risk score of patients in the training set, patients were stratified by risk using percentile partitioning. The Kaplan–Meier method was used to plot OS and PFS survival curves for different risk groups and response statuses at different milestone time points. Differences in survival time groups were compared using the chi-square test. Statistical analysis software included R 4.1.2 and SPSS 26. Results: This study included a total of 113 ES-SCLC patients who received immunotherapy, including 79 in the training set and 34 in the validation set. Six variables associated with poorer OS in patients were screened by Cox regression analysis: liver metastasis (P = 0.001), bone metastasis (P = 0.013), NLR < 2.14 (P = 0.005), LIPI assessed as poor (P < 0.001), PNI < 51.03 (P = 0.002), and LDH ≥ 146.5 (P = 0.037). A prognostic model for immunotherapy in ES-SCLC patients was constructed based on the above variables. The Harrell’s C-index in the training and validation sets of the model was 0.85 (95% CI 0.76–0.93) and 0.88 (95% CI 0.76–0.99), respectively; the AUC values corresponding to 12, 18, and 24 months in the tROC curves of the training set were 0.745, 0.848, and 0.819 in the training set and 0.858, 0.904 and 0.828 in the validation set; the tAUC curves show that the overall tAUC is > 0.7 and does not fluctuate much over time in both the training and validation sets. The calibration plot demonstrated the good calibration of the model, and the DCA curve indicated that the model had practical clinical applications. Patients in the training set were categorized into low, intermediate, and high risk groups based on their predicted risk scores in the Nomogram graphs. In the training set, 52 patients (66%) died with a median OS of 15.0 months and a median PFS of 7.8 months. Compared with the high-risk group (median OS: 12.3 months), the median OS was significantly longer in the intermediate-risk group (median OS: 24.5 months, HR = 0.47, P = 0.038) and the low-risk group (median OS not reached, HR = 0.14, P = 0.007). And, the median PFS was also significantly prolonged in the intermediate-risk group (median PFS: 12.7 months, HR = 0.45, P = 0.026) and low-risk group (median PFS not reached, HR = 0.12, P = 0.004) compared with the high-risk group (median PFS: 6.2 months). Similar results were obtained in the validation set. In addition, we observed that in real-world ES-SCLC patients, at 6 weeks after immunotherapy, the median OS was significantly longer in responders than in non-responders (median OS: 19.5 months vs. 11.9 months, P = 0.033). Similar results were obtained at 12 weeks (median OS: 20.7 months vs 11.9 months, P = 0.044) and 20 weeks (median OS: 20.7 months vs 11.7 months, P = 0.015). Finally, we found that in the real world, ES-SCLC patients without liver metastasis (P = 0.002), bone metastasis (P = 0.001) and a total number of metastatic organs < 2 (P = 0.002) are more likely to become long-term survivors after receiving immunotherapy. Conclusion: This study constructed a new prognostic model based on basic patient clinical characteristics and peripheral blood indices, which can be a good predictor of the prognosis of immunotherapy in ES-SCLC patients; in the real world, the response status at milestone time points (6, 12, and 20 weeks) can be a good indicator of long-term survival in ES-SCLC patients receiving immunotherapy.
|
14321335
|
ONCOLOGY
|
10.1186/s40594-024-00476-5
|
The impact of changing engineering perceptions on women’s attitudes and behavioral intentions towards engineering pursuits
|
Background: Women are underrepresented in the field of engineering within academic and professional settings. Based upon premises outlined by social role theory and goal congruity theory, a key factor that contributes to this underrepresentation is a gendered societal belief that there is a disconnect between engineering (seen as more agentic, or self-oriented) and women’s values and abilities (which are believed to be more communal, or other-oriented). While there is evidence that this perceived disconnect influences women’s pursuit of engineering, the extent to which an intervention could realistically counter these perceptions at key points along the engineering pathway has not been explored. Across two studies, we examine the impact of a communal-based intervention (in which we frame engineering majors and careers in more, though not exclusively, communally oriented ways) on women’s engineering-related attitudes and behavioral intentions at two points along the academic-employment pathway: women’s major selection and women’s job selection. Results: Study 1 found that women with undeclared majors had more positive attitudes (confidence and interest) towards engineering majors when engineering major descriptions were framed as more communal versus more agentic. However, there was no impact on their behavioral intentions to pursue the major. Study 2 found that women with engineering majors were more confident in their ability to be successful in a job role and were more likely to apply when the job role was framed as more communal as compared to more agentic. However, they did not indicate greater interest in the job role. Conclusions: Testing this intervention on relevant populations advances the literature by providing greater evidence for the potential of such an intervention to meaningfully address women’s underrepresentation at multiple points along the engineering pathway. Furthermore, this study provides evidence that a messaging-based intervention is impactful with a realistic representation of engineering as both an agentic and communally oriented field, which ensures that the retention of those attracted to the field is not negatively impacted by idealistic messaging. While addressing women’s pursuit of engineering is important, work must continue to seek ways to always improve women’s experience in engineering contexts as well.
|
21967822
|
EDUCATION
|
10.3389/fpsyg.2024.1389935
|
Beauty ideals and body positivity: a qualitative investigation of young women’s perspectives on social media content in China
|
Much of the existing knowledge regarding the impact of beauty ideals and body positive social media content on women’s body image is based on the Western cultural context. This limits our understanding of the issue in other cultures, such as China, among others. Therefore, to address this gap, this study examined young Chinese women’s perspectives on beauty ideals and body positivity in social media through a qualitative investigation. Female university students in China (N = 24) participated in individual interviews. A thematic analysis revealed four primary themes: (1) characteristics of mainstream beauty ideals in Chinese social media; (2) impact of beauty ideals on young women; (3) perspectives on the content and roles of body positivity; (4) influences of body positive social media content on young women. These findings indicate that young Chinese women are aware of the beauty ideals in social media and their negative impact on their body image. Furthermore, young Chinese women generally expressed a favorable outlook on body positivity but noted its limitations.
|
16641078
|
PSYCHOLOGY
|
10.1007/s00432-024-05859-3
|
FERMT1 suppression induces anti-tumor effects and reduces stemness in glioma cancer cells
|
Objective: Glioma is a leading cause of mortality worldwide, its recurrence poses a major challenge in achieving effective treatment outcomes. Cancer stem cells (CSCs) have emerged as key contributors to tumor relapse and chemotherapy resistance, making them attractive targets for glioma cancer therapy. This study investigated the potential of FERMT1 as a prognostic biomarker and its role in regulating stemness through cell cycle in glioma. Methods: Using data from TCGA-GBM, GSE4290, GSE50161 and GSE147352 for analysis of FERMT1 expression in glioma tissues. Then, the effects of FERMT1 knockdown on cell cycle, proliferation, sphere formation ability, invasion and migration were investigated. The influences of FERMT1 on expression of glycolysis-related proteins and levels of ATP, glucose, lactate and G6PDH were also explored. Furthermore, the effects of FERMT1 knockdown on cellular metabolism were evidenced. Results: Significant upregulation of FERMT1 in glioma tissues was observed. Silencing FERMT1 not only affected the cell cycle but also led to a notable reduction in proliferation, invasion and migration. The expression of glycolysis-associated proteins including GLUT1, GLUT3, GLUT4, and SCO2 were reduced by FERMT1 knockdown, resulted in increased ATP and glucose as well as decreased lactic acid and G6PDH levels. FERMT1 knockdown also inhibited cellular metabolism. Moreover, FERMT1 knockdown significantly reduced sphere diameter, along with inhibiting the expression of transcription factors associated with stemness in glioma cells. Conclusion: These findings demonstrated that FERMT1 could be an ideal target for the advancement of innovative strategies against glioma treatment via modulating cellular process involved in stemness regulation and metabolism.
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14321335
|
ONCOLOGY
|
10.1186/s40594-024-00488-1
|
A microgenetic analysis of teachers’ learning through teaching
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Background: What and how teachers learn through teaching without external guidance has long been of interest to researchers. Yet limited research has been conducted to investigate how learning through teaching occurs. The microgenetic approach (Siegler and Crowley, American Psychologist 46:606–620, 1991) has been useful in identifying the process of student learning. Using this approach, we investigated the development of teacher knowledge through teaching as well as which factors hinder or promote such development. Results: Our findings suggest that teachers developed various components of teacher knowledge through teaching without external professional guidance. Further, we found that the extent to which teachers gained content-free or content-specific knowledge through teaching depended on their robust understanding of the concept being taught (i.e., content knowledge), the cognitive demand of the tasks used in teaching, and the lesson structure chosen (i.e., student centered vs. teacher centered). Conclusions: In this study, we explored teacher learning through teaching and identified the sources leading to such learning. Our findings underscore the importance of teachers’ robust understanding of the content being taught, the tasks used in teaching, and a lesson structure that promotes teachers’ learning through teaching on their own.
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21967822
|
EDUCATION
|
10.3389/feduc.2024.1347052
|
Enhancing doctoral learning through virtual communities of practice: an autoethnographic perspective
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This article explores the role of virtual communities of practice in enhancing the doctoral experience, particularly in the contemporary digital era. The author emphasizes the multifaceted benefits, including elevating academic networking, optimizing knowledge management, and supporting the mental well-being of remote learners. The establishment of clear shared objectives, dynamic leadership, and a conducive environment for collaborative innovation are identified as key prerequisites for building successful virtual communities of practice. As remote doctoral education becomes more prevalent, virtual communities of practice not only facilitate academic engagement but also foster mutual support and advocacy among doctoral students. The researcher, as a final year PhD student employed autoethnography as a research method to offer an intimate and reflective exploration of her personal experiences within virtual communities of practice. This unique insider perspective adds depth to the discussion on elevating academic networking, optimizing knowledge management, and supporting the mental well-being of remote learners. Furthermore, her ongoing doctoral research focuses on the socialization process and the development of a sense of belonging among doctoral students. Motivated by her research topics, she commenced her doctoral studies during the epidemic and cultivated the practice of consistently maintaining a researcher’s reflection diary. This perspective article examines her diary, elucidating her experiences, opinions, and feelings. The researcher utilized a thematic approach to thoroughly analyze the author’s research diaries covering the period from December 2020 to August 2023. The article concludes by calling for further research into the professional identity development of doctoral students within virtual learning communities, exploring potential challenges and effective coping mechanisms to achieve inclusive practices in the complex and diverse digital era of academia.
|
2504284X
|
EDUCATION
|
10.3389/feduc.2024.1404076
|
Investigating how early academic performance and parental socio-economic status predict and explain successful completion of secondary education in Germany
|
In educational sociology, it is of greatest interest to explain why some students are more successful than others and obtain higher educational qualifications or receive better grades, which can have long-lasting consequences. The present study compares the influence of early academic performance, which can be regarded as a proxy of overall intelligence, to the socio-economic status (SES) of the family, which measures how much a family can invest in the education of their offspring. Using large-scale German NEPS panel data (N = 5,208), the analyses test statistically how much variance of two outcome variables (acquisition of higher education eligibility and final grade) are explained by academic performance and SES; both measured approximately 9 years earlier at the beginning of secondary education. Dominance analyses reveal that performance has a larger influence (ca. 14% for both outcomes) than SES (ca. 8% for eligibility and ca. 4% for grades). Regression analyses show that high performance can better compensate for low SES than vice versa. These results indicate that performance is probably more relevant for academic success than the SES of one’s own family.
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2504284X
|
EDUCATION
|
10.3390/educsci14080809
|
Teaching Experience as a Key Factor in Dealing with Digital Teaching Stress
|
Digital pandemic stress among university faculty has become a key issue in the contemporary era, marked by the rapid transition to online teaching. This study conducts a quantitative investigation into the teaching experience as a key explanatory variable in explaining the levels of such stress. For this purpose, a validated instrument has been used, which has been answered by a sample of 1240 university professors. The results show that, although the participating professors do not express high self-concepts of their digital competence or professional aspects, they do not express high levels of digital stress due to the pandemic. However, strong divergences have been identified between the levels of digital pandemic stress of more experienced professors and those of younger professors. Specifically, more experienced professors report lower levels of stress than younger professors, although there are no significant differences in their respective digital competencies. Consequently, the results suggest that teaching experience mitigates teaching digital stress, even when this greater experience does not concur with greater digital competence. It has also been found that the evolution of ratings with teaching experience depends on whether the professor is a specialist in scientific–technical or humanistic–social areas. Specifically, professors in scientific–technical areas with 15 to 25 years of experience are those who suffer more digital stress. Moreover, the digital stress of professors in scientific–technical areas increases between 10 and 25 years of experience, while it decreases among professors with less than 10 years of experience. In contrast, among professors in humanistic–social areas, the trend in the evolution of digital stress is the opposite: it increases among those with less than 10 years of experience and decreases among those with more than 10 years of experience.
|
22277102
|
EDUCATION
|
10.1007/s44196-024-00607-4
|
MLAWSMOTE: Oversampling in Imbalanced Multi-label Classification with Missing Labels by Learning Label Correlation Matrix
|
Missing labels in multi-label datasets are a common problem, especially for minority classes, which are more likely to occur. This limitation hinders the performance of classifiers in identifying and extracting information from minority classes. Oversampling is an effective method for addressing imbalanced multi-label problems by generating synthetic instances to create a class-balanced dataset. However, the existing oversampling algorithms mainly focus on the location of the generated data, and there is a lack of design on how to complete the labels of the synthetic data. To address this issue, we propose MLAWSMOTE, a synthetic data generation algorithm based on matrix factorization weights. We introduce a weak supervised learning method in the oversampling method, optimize the weights of features and labels by using label correlation, and iteratively learn the ideal label weights. The mapping relationship between features and labels is learned from the dataset and the label correlation matrix. The oversampling ratio is defined based on the discrepancy between observed labels and the ideal label of synthetic instances. It mitigates the impact of missing minority labels on the model’s predictions. The labeling of synthetic instances is performed based on label prediction, and the potential labeling distribution is complemented. Experimental results on multiple multi-label datasets under different label missing ratios demonstrate the effectiveness of the proposed method in terms of ACC, Hamming loss, MacroF1 and MicroF1. In the validation of the four classifiers, MacroF1 decreased by 24.78%, 17.81%, 3.8% and 19.56%, respectively, with the increase of label loss rate. After applying MLAWSMOTE only decreased by 15.79%, 13.63%, 3.78% and 15.21%.
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18756883
|
AI
|
10.3389/feduc.2024.1433184
|
“It actually helped”: students’ perceptions of feedback helpfulness prior to and following a teacher professional learning intervention
|
This study investigated the effects of a teacher professional learning intervention, underpinned by a student-centred model of feedback, on student perceptions of feedback helpfulness. The study was conducted in the context of primary education English writing in Queensland, Australia. No overall differences in feedback perceptions of students in 13 intervention and 9 comparison schools were identified following the intervention. However, more detailed analyses revealed significantly greater increases in perceived helpfulness among intervention group students for six feedback strategies. This suggests the intervention changed teachers’ feedback practices, enhancing student perceptions of feedback helpfulness. Student focus group data provided valuable qualitative insights into student feedback perceptions. Overall findings highlight the interrelatedness between feedback strategies across the feedback cycle for enhancing student learning.
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2504284X
|
EDUCATION
|
10.1007/s44196-024-00608-3
|
Construction of Risk Prediction Models for Enterprise Finance Sharing Operations Using K-Means and C4.5 Algorithms
|
The evaluation of financial sharing centres in enterprises typically relies on outdated financial data, lacks comprehensive assessment, and presents risks such as employee misconduct. To address these challenges, we propose a risk prediction model for enterprise financial sharing operations based on the K-means clustering algorithm for performance evaluation and the C4.5 algorithm for managing employee risks. Our approach enhances the accuracy and objectivity of performance evaluation while improving the efficiency of personnel risk management. Results indicate that the K-means algorithm classifies employee performance into five levels, facilitating comprehensive performance evaluation. Furthermore, through risk management optimisation, accuracy and recall rates increase to 0.905 and 0.890, respectively. The proposed risk prediction model achieves high accuracy rates of 90.5% and 92.4% in the training and test sets, respectively. Practical application of our methodology and model in A Group's financial sharing centre demonstrates their effectiveness and potential for enhancing the operation and management of enterprise financial sharing centres.
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18756883
|
AI
|
10.3389/frai.2024.1424924
|
A methodology for planning, implementation and evaluation of skills intelligence management – results of a design science project in technology organisations
|
Introduction: The evolving labour market requirements amidst digital transformation necessitate robust skills intelligence for informed decision-making and adaptability. Novel technologies such as Big Data, Machine Learning, and Artificial Intelligence have significant potential for enhancing skills intelligence.Methods: This study bridges the gap between theory and practice by designing a novel software artefact for skills intelligence management. With its systematic framework for identifying skills intelligence elements, an assessment instrument, and an implementation methodology, the artefact ensures a thorough approach to skills intelligence management.Results: The artefact was demonstrated in 11 organisations. Feedback collected from interviews, focus group sessions, and observations (N = 19) indicated that the artefact is a feasible starting point for implementing or systematising skills intelligence management. Participants suggested improvements but concurred that the systematic approach enhances skills intelligence data collection and quality.Discussion: The study shows that the artefact facilitates the application of advanced technologies in skills intelligence management. Additionally, it contributes a set of principles for effective skills intelligence management, fostering a broader conversation on this critical topic. Participants’ feedback underscores the artefact’s potential and provides a basis for further refinement and application in diverse organisational contexts.
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26248212
|
AI
|
10.3390/cancers16162816
|
Analysis by TeloView® Technology Predicts the Response of Hodgkin’s Lymphoma to First-Line ABVD Therapy
|
Classic Hodgkin’s lymphoma (cHL) is a curable cancer with a disease-free survival rate of over 10 years. Over 80% of diagnosed patients respond favorably to first-line chemotherapy, but few biomarkers exist that can predict the 15–20% of patients who experience refractory or early relapsed disease. To date, the identification of patients who will not respond to first-line therapy based on disease staging and traditional clinical risk factor analysis is still not possible. Three-dimensional (3D) telomere analysis using the TeloView® software platform has been shown to be a reliable tool to quantify genomic instability and to inform on disease progression and patients’ response to therapy in several cancers. It also demonstrated telomere dysfunction in cHL elucidating biological mechanisms related to disease progression. Here, we report 3D telomere analysis on a multicenter cohort of 156 cHL patients. We used the cohort data as a training data set and identified significant 3D telomere parameters suitable to predict individual patient outcomes at the point of diagnosis. Multivariate analysis using logistic regression procedures allowed for developing a predictive scoring model using four 3D telomere parameters as predictors, including the proportion of t-stumps (very short telomeres), which has been a prominent predictor for cHL patient outcome in a previously published study using TeloView® analysis. The percentage of t-stumps was by far the most prominent predictor to identify refractory/relapsing (RR) cHL prior to initiation of adriamycin, bleomycin, vinblastine, and dacarbazine (ABVD) therapy. The model characteristics include an AUC of 0.83 in ROC analysis and a sensitivity and specificity of 0.82 and 0.78 respectively.
|
20726694
|
ONCOLOGY
|
10.1186/s40359-024-01926-z
|
The moderating effect of altruism on the relationship between occupational stress and turnover intentions: a cross-sectional study of community rehabilitation workers in China
|
Background: In China, community rehabilitation workers are facing a growing challenge related to heavy occupational stress, which is having an impact on employment turnover. Previous studies have explored the effect of the public service motivation of workers in “helping” jobs on occupational stress or turnover intention, but there is a lack of clarification of the impact of altruism on turnover intention in the case of complex pathways involving various factors. Methods: A stratified sampling method was used, and a total of 82 community rehabilitation workers who assist disabled people from 34 community health centres in Jiangmen city were included in the study from August to October 2022. The turnover intention, occupational stress, burnout, quality of life, altruism, and certain sociodemographic information of community rehabilitation workers were measured using a structured questionnaire. The partial least squares method was employed to construct and test the structural equation model. Results: Although altruism had no direct impact on occupational stress or turnover intention, altruism moderated the effect of occupational stress on burnout (βMod = −0.208) and quality of life (βMod = 0.230) and weakened the mediation of burnout and quality of life between occupational stress and turnover intention. Conclusions: This study proposes to address the dilemma of “strong function” and “weak specialty” in community rehabilitation services and to conduct positive psychological interventions for community rehabilitation workers through the guidance of altruistic values.
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20507283
|
PSYCHOLOGY
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10.3389/frai.2024.1431156
|
Person-based design and evaluation of MIA, a digital medical interview assistant for radiology
|
Introduction: Radiologists frequently lack direct patient contact due to time constraints. Digital medical interview assistants aim to facilitate the collection of health information. In this paper, we propose leveraging conversational agents to realize a medical interview assistant to facilitate medical history taking, while at the same time offering patients the opportunity to ask questions on the examination.Methods: MIA, the digital medical interview assistant, was developed using a person-based design approach, involving patient opinions and expert knowledge during the design and development with a specific use case in collecting information before a mammography examination. MIA consists of two modules: the interview module and the question answering module (Q&A). To ensure interoperability with clinical information systems, we use HL7 FHIR to store and exchange the results collected by MIA during the patient interaction. The system was evaluated according to an existing evaluation framework that covers a broad range of aspects related to the technical quality of a conversational agent including usability, but also accessibility and security.Results: Thirty-six patients recruited from two Swiss hospitals (Lindenhof group and Inselspital, Bern) and two patient organizations conducted the usability test. MIA was favorably received by the participants, who particularly noted the clarity of communication. However, there is room for improvement in the perceived quality of the conversation, the information provided, and the protection of privacy. The Q&A module achieved a precision of 0.51, a recall of 0.87 and an F-Score of 0.64 based on 114 questions asked by the participants. Security and accessibility also require improvements.Conclusion: The applied person-based process described in this paper can provide best practices for future development of medical interview assistants. The application of a standardized evaluation framework helped in saving time and ensures comparability of results.
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26248212
|
AI
|
10.3389/frai.2024.1384709
|
Deep learning models for the early detection of maize streak virus and maize lethal necrosis diseases in Tanzania
|
Agriculture is considered the backbone of Tanzania’s economy, with more than 60% of the residents depending on it for survival. Maize is the country’s dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.
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26248212
|
AI
|
10.3389/fonc.2024.1452559
|
Fibrosis to carcinogenesis: unveiling the causal dynamics between pulmonary fibrosis and lung cancer
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Background: Previous clinical evidence has shown a correlation between pulmonary fibrosis (PF) and lung cancer (LC), but their causal relationship remains unknown.Methods: This study utilized a bidirectional two-sample Mendelian randomization (MR) approach to explore the causal relationship between PF and LC, including its subtypes. Genetic data were obtained from the IEU and FinnGen Genome-Wide Association Studies (GWAS). SNPs with genome-wide significance were selected, and analyses were conducted using Inverse-Variance Weighted (IVW), MR Egger, and Weighted Median methods. The IVW results for various subtypes of lung cancer and PF were used in a meta-analysis to investigate the overall causal effect between PF and lung cancer. Sensitivity analysis was used for both MR and meta-analysis to investigate the robustness of the results.Results: The bidirectional MR analysis showed no significant causal relationship between PF and overall, LC or its subtypes, except for SCLC, which had a significant positive association (OR = 1.29, 95% CI 1.07-1.57, p = 0.009). The meta-analysis results indicated no overall causal effect (OR = 1.067, 95% CI: 0.952-1.195, P = 0.265, I² = 57.3%). In the reverse MR analysis, NSCLC and LUSC showed significant associations with PF (OR = 1.12, 95% CI 1.01-1.23, p = 0.028 and OR = 1.04, 95% CI 1.01-1.08, p = 0.012, respectively), while the meta-analysis results indicated no significant causal effect (OR = 1.006, 95% CI: 0.973-1.040, P = 0.734, I² = 55.9%). Sensitivity analyses indicated no evidence of horizontal pleiotropy or significant heterogeneity.Conclusion: This study suggests a potential causal relationship between PF and SCLC, as well as between NSCLC and LUSC with PF. However, the overall causal relationship between PF and LC was not statistically significant, possibly due to individual variability and other influencing factors. Further research using data from diverse populations is needed to validate these findings.
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2234943X
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ONCOLOGY
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10.3389/feduc.2024.1408275
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The significance of school bullying prevention program: a narrative inquiry from the perspective of a school police officer at a Youth Police Academy in Korea
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The need for effective school bullying prevention programs is more pronounced than ever. To address school bullying, Korea has operated the Youth Police Academy (YPA) since 2014. Although the School Police Officers (SPOs) in charge at YPA can provide valuable insights into the significance of school bullying prevention programs, there has been limited research in this area. The purpose of this study is to explore the relevance of school bullying prevention programs and delineate the role of YPA in preventing school bullying, based on the professional experiences and perspectives of YPA’s SPOs. We employed narrative analysis based on interviews with SPOs. The findings revealed that while the majority of SPOs experienced career crises, they overcame these challenges and developed professional perspectives on the YPA program and anti-bullying program. SPOs perceive that school bullying prevention program should focus on “resolving relationships,” “collaborative care,” and “teaching coping behaviors.” Accordingly, YPA can function as a “place of reconciliation,” “place helping students understand others’ perspectives through experiential and case-based educational approaches,” “hub for school bullying prevention education grounded in collaboration with relevant institutions and local experts,” “provider of coping information,” and an “active protector of victims.”
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2504284X
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EDUCATION
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10.3389/feduc.2024.1360848
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Task-irrelevant visual distractions and mindful self-regulated learning in a low-stakes computer-based assessment
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Introduction: There is a growing concern about the threat of distractions in online learning environments. It has been suggested that mindfulness may attenuate the effects of distraction. The extent to which this translates to academic performance is under investigation. We aimed to investigate the relationship between task-irrelevant visual distraction, time pressure, and mindful self-regulated learning in the context of a low-stake computer-based assessment.Methods: The study sampled 712 registered users of Prolific.co who were prescreened, current undergraduate university students. After data quality screening, 609 were retained for analyses. A 2 × 2 between-subjects design was used. Participants were randomly assigned to the following groups: (1) a control condition, (2) a distract condition, (3) a time pressure condition, or (4) a distract and time pressure condition. All participants completed reading comprehension questions, demographic questions, and the Mindful Self-Regulated Learning Scale.Results: Presenting a visual distraction increased self-reported distraction and having a clock present increased self-reported time pressure. The distraction did not have a statistically significant effect on test performance. Mindfulness was negatively correlated with test performance, self-reported distraction, and self-reported time pressure.Discussion: Continuous task-irrelevant visual distractions may not be distracting enough to influence low-stakes testing performance, but they do influence self-perceptions.
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2504284X
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EDUCATION
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10.3389/feduc.2024.1352959
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Sustainability in undergraduate course curricula at Andalusian (Spain) universities: a critical analysis
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Education is one of the main tools used to implement sustainable development goals (SDGs). Higher education institutions (HEIs) have a major social responsibility regarding sustainability given the relevance and impact of their educational work and the creation of knowledge through their research. Sustainability is promoted and linked to values, teaching-learning methodologies, and studying of global–local problems. Within this framework, the objective of our research is to determine the presence and means by which sustainability appears in the course curricula of university bachelor’s degrees of the public universities of Andalusia (Spain). The study used quantitative methodology. As in other studies, major deficiencies have been revealed in terms of the inclusion of sustainability in the universities, determining a limited presence of local problems to address sustainability. Thus, Andalusian universities distance themselves from the society and community in which they exist. This may also limit student knowledge of sustainability issues in which they could potentially be relevant participants.
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2504284X
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EDUCATION
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10.3390/ai5030074
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Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges
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Physics-informed neural networks (PINNs) represent a significant advancement at the intersection of machine learning and physical sciences, offering a powerful framework for solving complex problems governed by physical laws. This survey provides a comprehensive review of the current state of research on PINNs, highlighting their unique methodologies, applications, challenges, and future directions. We begin by introducing the fundamental concepts underlying neural networks and the motivation for integrating physics-based constraints. We then explore various PINN architectures and techniques for incorporating physical laws into neural network training, including approaches to solving partial differential equations (PDEs) and ordinary differential equations (ODEs). Additionally, we discuss the primary challenges faced in developing and applying PINNs, such as computational complexity, data scarcity, and the integration of complex physical laws. Finally, we identify promising future research directions. Overall, this survey seeks to provide a foundational understanding of PINNs within this rapidly evolving field.
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26732688
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AI
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10.3389/feduc.2024.1438721
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Exploring the assessment of musical praxis through ICT in the academic context
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The evaluation of musical praxis involves a nuanced assessment of performer competencies within the intricate dynamics of musical elements, often hindered by subjective influences and the transient nature of performance. This study investigates the integration of Information and Communication Technology (ICT) tools to enhance instrumental praxis evaluation, focusing on French horn applicants to the University of Valencia Philharmonic Orchestra (OFUV). Employing a descriptive observational methodology and utilizing the MAXQDA application for analysis, the study examines key aspects of interpretation through individual recordings. Results demonstrate that ICT applications facilitate transparent and precise evaluation of performance aspects, underscoring the importance of incorporating these tools in performative education. In this regard, 89% of participants found the feedback to be very useful. Leveraging audio-video recordings offers a promising avenue for comprehensive analysis, providing clearer feedback and advocating for their integration by educational authorities and instructors to foster objective evaluation and enhance musical pedagogy.
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2504284X
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EDUCATION
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10.3390/cancers16183178
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Clinical Characteristics and Outcomes of Tympanomastoid Paragangliomas: A Report from Slovenia
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(1) Background: Head and neck paragangliomas are neuroendocrine tumors that typically originate from the parasympathetic nervous system and are predominantly non-secretory. Their clinical manifestations result from their mass effect on the surrounding tissues. The approach to treating these tumors depends on factors such as their location, size, impact on adjacent structures, and the patient’s overall health and preferences. (2) Methods: A retrospective analysis of the management of temporal bone paraganglioma classes A and B (according to the modified Fisch classification) was performed at the University Medical Centre, Ljubljana, between 2011 and 2023. (3) Results: We analyzed 23 cases, 19 of which underwent surgery; complete tumor removal was achieved in 18 of them. Four patients were irradiated due to tumor progression to class C. Three of these four patients initially refused surgery and were treated with radiotherapy (RT) 7, 13, and 18 years after diagnosis. In the fourth patient, complete surgical resection was not achieved and she was treated with RT four years after surgery, due to the growth of the tumor to class C. The average follow-up time from diagnosis was 8.9 years (median 6 years; range 1–26 years). (4) Conclusions: The surgical treatment of patients with class A and B paragangliomas is effective and safe. In cases where surgery is refused but the tumor continues to grow to class C, RT is an alternative and efficient method of controlling tumor growth.
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20726694
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ONCOLOGY
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10.1007/s00432-024-05953-6
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A prospective study to compare the diagnostic accuracy of 99mTc-CNDG SPECT/CT and contrast-enhanced CT in staging of non-small cell lung cancer
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Objective To explore the value of 99mTc-isonitrile deoxyglucosamine (CNDG) SPECT/CT in the staging and resectability diagnosis of non-small cell lung cancer (NSCLC) compared with contrast-enhanced CT (CECT). Methods This research was approved by the hospital ethics review committee. Sixty-three patients with NSCLC received 99mTc-CNDG SPECT/CT, CECT and initial TNM staging before treatment. Thirty-three patients who underwent radical surgery underwent postoperative pathological TNM staging as the reference standard. Another thirty patients underwent radiochemotherapy; among them, the reference standard of 7 patients of N staging and 5 patients of M staging was based on biopsy pathology, and the diagnosis of the remaining lesions was confirmed by at least one different image or clinical imaging follow-up for more than 3 months. The McNemar test and receiver operating characteristic (ROC) curve analysis were used to compare the diagnostic accuracy of staging and resectability of 99mTc-CNDG SPECT/CT and CECT in NSCLC, respectively. Results For all patients and surgical patients, the accuracies of 99mTc-CNDG SPECT/CT in diagnosing the T stage and N stage were higher than those of CECT (all patients: 90.5%, 88.9% vs. 79.4%, 60.3%; surgical patients: 81.8%, 78.8% vs. 60.6%, 51.5%), and the differences were statistically significant (all patients: T stage, P = 0.016; N stage, P = 0.000; surgical patients: T stage, P = 0.016; N stage, P = 0.004). For all patients, the accuracy of 99mTc-CNDG SPECT/CT in diagnosing the M stage was higher than that of CECT (96.8% vs. 90.5%), but the difference was not statistically significant (P = 0.289). ROC curve analysis showed that the accuracy of 99mTc-CNDG SPECT/CT in diagnosing the potential resectability of NSCLC was significantly better than that of CECT (P = 0.046). Conclusion This preliminary clinical study shows that 99mTc-CNDG SPECT/CT is of great value for accurate clinical staging of NSCLC compared with CECT and can significantly improve the accuracy of resectability diagnosis.
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14321335
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ONCOLOGY
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10.1007/s44196-024-00651-0
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An Energy-Efficient Bio-Inspired Mobility-Aware Cluster p-WOA Algorithm for Intelligent Whale Optimization and Fuzzy-Logic-Based Zonal Clustering Algorithm in FANET
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The newest research topic is flight ad hoc network (FANET). The primary obstacles faced by unmanned aerial vehicles (UAVs) are their limited flight duration and inefficient routes resulting from their great mobility and low battery power. Compared to MANETs or VANETs, FANETS routing is thought to be more difficult because of these topological restrictions. Artificial intelligence (AI)-based clustering techniques can be applied to resolve intricate routing issues in situations when both static and dynamic routing are ineffective. To overcome these path difficulties, clustering techniques based on evolutionary algorithms, including intelligent, probabilistic, bio-inspired whale optimization algorithms (p-WOAs), we suggest fuzzy-logic-based zonal clustering-based routing algorithms in this study to be used in FANET to build clusters. In addition to requiring fewer cluster heads (CHs) for routing, p-WOA offers good coverage and low energy consumption. The stochastic whale optimization technique, which draws inspiration from nature, is utilized in this paper to build networks and deploy nodes. The next step is to choose cluster heads using a region clustering technique based on fuzzy logic. By selecting the right cluster head, you can decrease routing traffic and increase cluster longevity. Routing overhead is also decreased. The data are then sent to the best path using a reference point group mobility model. The proposed p-WOA was used to test fuzzy integral and fuzzy logic ant optimization, fuzzy integral and neural network interference system, fuzzy integral and whale optimization algorithm (ANFIS-WOA), and fuzzy integral and FL-ALO. An array of indicators, such as cluster count, longevity, cluster configuration time, cluster head consistency, and energy usage, are employed to assess the effectiveness of the suggested methodology. The suggested algorithm works better than the most advanced techniques available today, as demonstrated by the experimental findings presented in this paper.
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18756883
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AI
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10.3389/feduc.2024.1389592
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Unskilled and unaware? Differences in metacognitive awareness between high and low-ability students in STEM
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Introduction: Metacognition, or the ability to monitor and control one's cognitive processes, is critical for learning in self-regulated contexts, particularly in introductory STEM courses. The ability to accurately make predictions about one's ability and performance can determine the effectiveness in which students effectively prepare for exams and employ good study strategies. The Dunning-Kruger pattern, where low-performing individuals are more overconfident and less accurate at the ability to predict their performance than high-performing individuals, is robustly found in studies examining metacognitive monitoring. The extent to which the Dunning-Kruger pattern can be explained by the lack of metacognitive awareness is not yet established in the literature. In other words, it is unclear from prior work whether low-performing students are “unskilled and unaware” or simply “unskilled but subjectively aware.” In addition, arguments about whether this pattern is a psychological phenomenon or a statistical artifact of the measurement of metacognition can be found in the literature.Methods: Students enrolled in three different physics courses made predictions about their exam scores immediately before and after taking each of the three exams in the course. Student predictions were compared to their exam scores to exam metacognitive accuracy. A new method for examining the cause of the Dunning-Kruger effect was tested by examining how students adjust their metacognitive predictions after taking exams.Results: In all contexts low-performing students were more overconfident and less accurate at making metacognitive predictions than high-performing students. In addition, these students were less able to efficiently adjust their metacognitive predictions after taking an exam.Discussion: The results of the study provide evidence for the Dunning-Kruger effect being a psychological phenomenon. In addition, findings from this study align with the position that the skills needed to accurately monitor one's performance are the same as those needed for accurate performance in the first place, thus providing support for the “unskilled and unaware” hypothesis.
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2504284X
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EDUCATION
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10.1186/s40359-024-02073-1
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Factors affecting the quality of work life for industrial labour force: empirical evidence from a developing country
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The success of any organization requires a skilled, competent, and satisfied workforce. If the workforce can be provided with the necessary components to ensure a high quality of working life, they will become permanent assets. Various factors undoubtedly affect the quality of workers' work lives. This study aims to investigate the drivers of the quality of work life in industrial labour force in a developing country, Bangladesh. It enumerated the elements that have an impact on industrial labour force’s quality of work life (QWL). A structured questionnaire was administered to 420 Bangladeshi workers across diverse industries, yielding a commendable response rate of 93.33%. The collected data underwent analysis employing the partial least squares structural equation modeling (PLS-SEM) technique. Representative industries and respondents were chosen by random selection. The results revealed that work environment, organizational culture and climate, relationships and cooperation, compensation and rewards, adequacy of resources, autonomy of work, job satisfaction, and security are directly related to the QWL. Training and development, and facilities do not significantly affect QWL. The research results can be used to improve the quality of work life for those working in the industrial sector. An industry may accomplish long-term and short-term goals by maintaining a pleasant workforce. The study's findings will provide policymakers and regulatory authorities of Bangladesh's industrial sector with strategic references and strategies to boost industrial productivity and economic growth for sustainable development by ensuring industrial employees' quality of work life that can serve as a template for Bangladesh.
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20507283
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PSYCHOLOGY
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10.3389/frai.2024.1447171
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Political ideology shapes support for the use of AI in policy-making
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In a world grappling with technological advancements, the concept of Artificial Intelligence (AI) in governance is becoming increasingly realistic. While some may find this possibility incredibly alluring, others may see it as dystopian. Society must account for these varied opinions when implementing new technologies or regulating and limiting them. This study (N = 703) explored Leftists’ (liberals) and Rightists’ (conservatives) support for using AI in governance decision-making amidst an unprecedented political crisis that washed through Israel shortly after the proclamation of the government’s intentions to initiate reform. Results indicate that Leftists are more favorable toward AI in governance. While legitimacy is tied to support for using AI in governance among both, Rightists’ acceptance is also tied to perceived norms, whereas Leftists’ approval is linked to perceived utility, political efficacy, and warmth. Understanding these ideological differences is crucial, both theoretically and for practical policy formulation regarding AI’s integration into governance.
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26248212
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AI
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10.3389/feduc.2024.1380295
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Data based individualization in early writing: the importance and measurement of implementation fidelity
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In this paper we describe the process of monitoring fidelity of implementation for a teacher-implemented early writing intervention. As part of a large, federally funded project, teachers who worked with students in grades 1 through 3 in schools across two states in the US were recruited and then randomly assigned to implementation and control conditions. Using Data-Based Individualization (DBI) as a framework for best practice in assessment and intervention, teachers in the implementation group received professional development on early writing intervention and assessment and then implemented these practices with their students who had significant writing challenges. Coaches, who were part of the research project, supported teachers and also observed teachers in both the implementation and control conditions at least twice during the course of the 20-week study. This paper focuses on the results of the fidelity measures that were administered throughout the project. An overview of the importance of fidelity checks is followed by a description of the fidelity tools used, as well as data from those tools. Areas of strength and challenge for teachers when implementing early writing assessment and intervention and engaging in data-based decision making with fidelity are discussed, along with recommendations regarding the practical and research importance of fidelity checks.
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2504284X
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EDUCATION
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10.1007/s44196-024-00675-6
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Application and Empirical Analysis of Fuzzy Neural Networks in Mining Social Media Users’ Behavioral Characteristics and Formulating Accurate Online Marketing Strategies
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In the current digital social environment, social media platforms have become an important position for user behavior insights and precision marketing. User behavioral data on social media contain rich information, but they are often fuzzy, uncertain and highly complex. Fuzzy neural network (FNN), as an advanced model combining fuzzy logic and neural network theory, provides a powerful tool for processing and analyzing social media user behavioral features. This study is dedicated to exploring the application of fuzzy neural networks in social media user behavior analysis and their key role in the design of accurate online marketing strategies. We construct and optimize a fuzzy neural network model by meticulously classifying and quantifying user behavioral features, including behavioral frequency features, content topic features, social interaction features, and time series features, as well as applying fuzzy set theory to deal with fuzzy features such as emotional states. Through empirical analysis, we will show how fuzzy neural networks can reveal the intrinsic laws behind user behaviors, and how these insights can be used to design and implement precise online marketing strategies to improve advertising effectiveness, user engagement, and brand loyalty.
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18756883
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AI
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10.3390/cancers16223762
|
The Impact of Bone Marrow Involvement on Prognosis in Diffuse Large B-Cell Lymphoma: An 18F-FDG PET/CT Volumetric Segmentation Study
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Background: This study assessed the prognostic value of tumor burden in bone marrow (BM) and total disease (TD), as depicted on 18F-FDG PET/CT in 140 DLBCL patients, for complete remission after first-line systemic treatment (iCR) and 3- and 5-year overall survival (OS3 and OS5). Methods: Baseline 18F-FDG PET/CT scans of 140 DLBCL patients were segmented to quantify metabolic tumor volume (MTV), total lesion glycolysis (TLG), and SUVmax in BMI, findings elsewhere (XL), and TD. Results: Bone marrow involvement (BMI) presented in 35 (25%) patients. Median follow-up time was 47 months; 79 patients (56%) achieved iCR. iCR was significantly associated with TD MTV, XL MTV, BM PET positivity, and International Prognostic Index (IPI). OS3 was significantly worse with TD MTV, XL MTV, IPI, and age. OS5 was significantly associated with IPI, but not with MTVs and TLGs. Univariate factors predicting OS3 were XL MTV (hazard ratio [HR] = 1.29), BMI SUVmax (HR = 0.56), and IPI (HR = 1.92). By multivariate analysis, higher IPI (HR = 2.26) and BMI SUVmax (HR = 0.91) were significant independent predictors for OS3. BMI SUVmax resulted in a negative coefficient and hence indicated a protective effect. Conclusions: Baseline 18F-FDG PET/CT MTV is significantly associated with survival. BMI identified on 18F-FDG PET/CT allows appropriate treatment that may improve survival.
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20726694
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ONCOLOGY
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10.3389/feduc.2024.1420048
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Toward a “pluriversal” international relations studies in Indonesia
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What are the grounds of International Relations (IR) studies? Scholars have pointed out the strong connection between IR and Western knowledge, philosophies, and histories (Barasuol & da Silva, 2016;Blaney, 2002;Blaney & Tickner, 2017a, 2017b;Liu, 2016). Highlighting a Western-centered discipline, recent scholarship in IR has concluded the lack of plurality in IR theorizing, with the call to adopt more diverse means of understanding how the world works in a political sense (Acharya, 2014(Acharya, , 2016)). The consequence of a Western-centered discipline has been that the voices from the Global South are underrepresented and excluded from IR knowledge formation. In addition, the recent exploration of critical theories in IR (Critical Theory, Feminism, Marxism, etc.) has not been perceived as sufficient to eliminate biases in the field, as many have argued about the 'epistemic violence' encountered by scholars in the Global South (Ala et al., 2021;Odoom & Andrews, 2017). With the presence of biases in IR knowledge, this opinion article calls for re-evaluating the pedagogy of IR studies, especially in parts of the globe that do not share a common perspective with the Global North.The historical, cultural, and political contexts of the Global North (Western states) differ from the Global South (Small and Middle powers, primarily located in Africa, Latin America, and Asia). In Latin American, African, and Asian countries, perceptions of how the world works and what matters in global politics contrast with the common literature produced in IR. However, a general belief, predominantly adopted in the Global North, is that IR theories are "…universally applicable, irrespective of the local context, culture, and society" (Ala et al., 2021, p.38). The universal applicability of IR studies impacts the teaching and learning processes in the Global South, as there is a lack of convergence between what is taught and the socio-political realities in their countries. The problem associated with the tendency to universalize this Western-based knowledge is multiplied when higher education curriculums are geared to adopt Western-based perspectives, epistemologies, and ontologies in IR without exploring more diverse perspectives in the field. The core of Western-based knowledge includes IR grand theories, including realism, liberalism, constructivism, and the assessment of empirical investigations from the West to support the claims of those theories.In brief, this opinion article extends the applicability of the 'global pluriversal IR' echoed in Ala, Inoue, and Valencia's 2021 study. It argues that Indonesia, as a country of the Global South, has similarities to Brazil and South Africa regarding the prospects and challenges of diversifying the IR curriculum in the country. Eventually, this article echoes the importance of revealing the potential of Indonesian philosophies as an alternative means to understanding IR theories, transcending the dominant western-centrist IR studies currently adopted in Indonesia. It is further argued that knowledge and ontologies can benefit from plurality through Indonesia's IR worldviews, leading to a higher connection to the social realities in the Global South. The focus is on five prominent undergraduate IR programs, including those under Universitas Hasanuddin, Universitas Padjajaran, Binus University, Universitas Airlangga, and Universitas Indonesia. The study programs are hosted by universities consistently ranked in the top 15 among Indonesian Universities according to the world university rankings of Quacquarelli Symonds and Times Higher Education (QS, 2024;THE, 2024).The argument put forward is as follows. First, the dominance of Western-centered IR theories and sub-areas of IR and the seclusion of Global South perspectives. Second, this article provides some suggestions on measures that can be taken by Indonesian higher education institutions to achieve a 'pluriversal' IR in Indonesia. This includes bridging local Indonesian values to interpret regional Southeast Asian affairs, and as the basis to establish alternative interpretations to world affairs.IR students have been exposed to this Western-centered IR since the early years of their undergraduate studies. Students are expected to be introduced to the Great Debate among IR scholars in the twentieth century, connected to European and US history, Western Powers, and how the Global North perceives the other parts of the globe. Following this, sub-areas of IR are primarily dominated by the American academy, focused on foreign policy, political economy, and international security (Acharya & Buzan, 2010;Baylis et al., 2019;Griffiths, 2020;Putra, 2023a). Consequently, if not left out, local knowledge, such as the norms and contexts that influence Indonesia's foreign policies, has become a minor theme in the IR curriculum. In addition, this article also identifies the problem that IR theories introduced in the early years of an IR student are focused on those Western IR theories, thus shaping the foundations of an IR student's understanding of IR studies. , 2024). These courses form the foundation of how Indonesian IR students think about the study, including the types of theories that would be utilized as analytical tools in assessing cases in the following years.However, the substantive would predominantly be Western-centered, leading to the perception that what matters in the study are the variables highlighted by realism, liberalism, and constructivism. Paradigms of the Global South, for example, decolonization materials or how non-Western states perceive IR, tend to occupy a minor aspect in the foundational stages of IR teaching. Indonesian IR study programs are members of the Association of International Relations Indonesia (AIHII), which facilitates benchmarking curriculums from leading IR programs in the state. It is thus viable to conclude that the curriculum structure adopted in those five IR...
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2504284X
|
EDUCATION
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10.3390/ai5040120
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A Novel Multi-Objective Hybrid Evolutionary-Based Approach for Tuning Machine Learning Models in Short-Term Power Consumption Forecasting
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Accurately forecasting power consumption is crucial important for efficient energy management. Machine learning (ML) models are often employed for this purpose. However, tuning their hyperparameters is a complex and time-consuming task. The article presents a novel multi-objective (MO) hybrid evolutionary-based approach, GA-SHADE-MO, for tuning ML models aimed at solving the complex problem of forecasting power consumption. The proposed algorithm simultaneously optimizes both hyperparameters and feature sets across six different ML models, ensuring enhanced accuracy and efficiency. The study focuses on predicting household power consumption at hourly and daily levels. The hybrid MO evolutionary algorithm integrates elements of genetic algorithms and self-adapted differential evolution. By incorporating MO optimization, GA-SHADE-MO balances the trade-offs between model complexity (the number of used features) and prediction accuracy, ensuring robust performance across various forecasting scenarios. Experimental numerical results show the superiority of the proposed method compared to traditional tuning techniques, and random search, showcasing significant improvements in predictive accuracy and computational efficiency. The findings suggest that the proposed GA-SHADE-MO approach offers a powerful tool for optimizing ML models in the context of energy consumption forecasting, with potential applications in other domains requiring precise predictive modeling. The study contributes to the advancement of ML optimization techniques, providing a framework that can be adapted and extended for various predictive analytics tasks.
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26732688
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AI
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10.3389/fonc.2024.1491167
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A real−world pharmacovigilance study of FDA Adverse Event Reporting System events for pralsetinib
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Background: Pralsetinib, a selective oral inhibitor of rearranged during transfection (RET) fusion proteins and oncogenic RET mutants, has shown significant efficacy in treating RET fusion-positive non-small cell lung cancer and thyroid cancer. However, since pralsetinib was approved in the United States in September 2020, there have been limited reports of post-marketing adverse events (AEs). In this study, we aimed to analyze the AE signals with pralsetinib on the basis of the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) to provide instructions in clinical practice.Methods: All AE reports were obtained from the FAERS database from the first quarter (Q3) of 2020 to the second quarter (Q2) of 2024. Various signal quantification techniques were used for analysis, including reporting odds ratios, proportional reporting ratios, Bayesian confidence propagation neural network, and multi-item gamma Poisson shrinker (MGPS)-based empirical Bayesian geometric mean.Results: Out of 8,341,673 case reports in the FAERS database, 1,064 reports of pralsetinib as the “primary suspected (PS)” AEs were recorded, covering 26 system organ classes and 256 preferred terms. Of the reports, 62.5% were from consumers rather than healthcare professionals. The most common systems were general disorders and administration site conditions (n = 704), investigations (n = 516), and gastrointestinal disorders (n = 405). A total of 95 significant disproportionality preferred terms (PTs) conformed to the four algorithms simultaneously. AEs that ranked the top three at the PT level were hypertension (n = 80), asthenia (n = 79), and anemia (n = 65). Of the 95 PTs with significant disproportionation, unexpected significant AEs such as increased blood calcitonin, increased myocardial necrosis marker, and bacterial cystitis were observed, which were not mentioned in the drug’s instructions. The median onset time of pralsetinib-associated AEs was 41 days [interquartile range (IQR) 14–86 days]. The majority of the AEs occurred in 30 days (42.86%).Conclusion: Our pharmacovigilance analysis of real-world data from the FEARS database revealed the safety signals and potential risks of pralsetinib usage. These results can provide valuable evidence for further clinical application of pralsetinib and are important in enhancing clinical medication safety.
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2234943X
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ONCOLOGY
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10.3389/feduc.2024.1288723
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Generative AI and education: dynamic personalization of pupils’ school learning material with ChatGPT
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The widespread use of generative AI tools like ChatGPT has seen significant growth. This rise prompted discussions on integrating these technologies into school education. However, the practical implementation, testing, and assessment of generative AI in primary and secondary education remained largely unexplored. This article examines the application of ChatGPT-3.5 and 4 in primary school education. A study involving 110 students aged 8–14 across grades 4–6 in two Uruguayan schools was conducted. The focus was on using generative AI for dynamic personalization of educational content during classroom lessons. In these sessions, instructional content followed the curriculum goals, and text, illustrations, and exercises were generated and dynamically adjusted based on generative AI. The findings indicate that generative AI effectively tailors school materials to match varying pupil knowledge levels. Real-time adjustments during lessons cater to individual learning needs, enhancing cognitive ergonomics. This approach not only boosts pupil motivation but also improves their performance, facilitating more effective achievement of the curriculum’s learning objectives. These results suggest a promising avenue for leveraging generative AI to personalize and optimize primary school education.
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2504284X
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EDUCATION
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10.3389/fonc.2024.1513956
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Corrigendum: Exogenous let-7a-5p induces A549 lung cancer cell death through BCL2L1-mediated PI3Kγ signaling pathway
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In the published article, there were errors in Figure 3B-C, Figure 4E-F, and Figure 7A-B as published. During the transwell assay and scratch test procedures, we used the equipment's default image naming system for batch exports, which led to difficulties in distinguishing between intervention groups during image selection and resulted in incorrect image placement. Given that a significant amount of time has elapsed since the publication, the original data associated with these results are no longer available, we therefore carried out independent repeat experiments and achieved consistent outcomes with the initial findings. As a result, the relevant images and their quantitative data in Figure 3B-E The corrected Figure 7 and its caption appear below.
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2234943X
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ONCOLOGY
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10.3389/frai.2024.1476950
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What is in a food store name? Leveraging large language models to enhance food environment data
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Introduction: It is not uncommon to repurpose administrative food data to create food environment datasets in the health department and research settings; however, the available administrative data are rarely categorized in a way that supports meaningful insight or action, and ground-truthing or manually reviewing an entire city or neighborhood is rate-limiting to essential operations and analysis. We show that such categorizations should be viewed as a classification problem well addressed by recent advances in natural language processing and deep learning—with the advent of large language models (LLMs).Methods: To demonstrate how to automate the process of categorizing food stores, we use the foundation model BERT to give a first approximation to such categorizations: a best guess by store name. First, 10 food retail classes were developed to comprehensively categorize food store types from a public health perspective.Results: Based on this rubric, the model was tuned and evaluated (F1micro = 0.710, F1macro = 0.709) on an extensive storefront directory of New York City. Second, the model was applied to infer insights from a large, unlabeled dataset using store names alone, aiming to replicate known temporospatial patterns. Finally, a complimentary application of the model as a data quality enhancement tool was demonstrated on a secondary, pre-labeled restaurant dataset.Discussion: This novel application of an LLM to the enumeration of the food environment allowed for marked gains in efficiency compared to manual, in-person methods, addressing a known challenge to research and operations in a local health department.
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26248212
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AI
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10.3389/frai.2024.1385522
|
Frugal innovation in the business environment: a literature review and future perspectives
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Introduction: This research aims to explore the growing field of frugal innovation within the business environment, particularly its intersection with sustainability and artificial intelligence.Methods: Through a comprehensive literature review, the study analyzes key research trends and methodologies from 420 scholarly articles published between 2012 and August 2024. A bibliometric review traces the evolution of frugal innovation, while a content analysis provides insights into its practical applications across various industries, especially in resource-constrained settings.Results: The findings highlight the significant role of frugal innovation in addressing global challenges, such as reducing environmental impact and promoting social inclusion, especially through the adoption of cleaner technologies and socially responsible business practices. The study also emphasizes the transformative potential of AI in enhancing the scalability and efficiency of frugal solutions.Discussion: This research contributes to the ongoing conversation on sustainable development by identifying knowledge gaps and proposing future strategies for leveraging frugal innovation to drive inclusive growth. The implications of this research are valuable for academics, practitioners, and policymakers aiming to foster sustainable innovation in diverse socio-economic contexts.
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26248212
|
AI
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10.3389/fpsyg.2024.1514482
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Research on the driving mechanism of tourists’ ecological protection behavior in intangible cultural heritage sites
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Despite the increasing focus on intangible cultural heritage tourism, there is a lack of research on the ecological protection behaviors of tourists in these contexts. With UNESCO’s continuous refinement of the World Heritage system, intangible cultural heritage has gradually become a focal point for tourism development and protection. While such tourism can promote the preservation and transmission of heritage, it also introduces ecological environmental issues that need to be addressed. Therefore, exploring the driving mechanisms of tourists’ ecological protection behavior holds significant practical value. Based on the Theory of Planned Behavior (TPB), this study constructs a driving model of tourists’ ecological protection behavior. It examines the influence of behavioral attitude, subjective norms, perceived behavioral control, and personal norms on tourists’ willingness to engage in ecological protection. By distributing questionnaires both offline and online, we analyzed data from 312 valid responses. The results indicate that all four factors have a significant positive impact on tourists’ willingness to engage in ecological protection behavior. Among these factors, personal norms and behavioral attitude have a relatively larger influence. The findings provide valuable references for intangible cultural heritage sites in China and regions with similar cultural and tourism dynamics.
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16641078
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PSYCHOLOGY
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10.3389/fpsyg.2024.1433171
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Empowering young athletes: the influence of autonomy-supportive coaching on resilience, optimism, and development
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Introduction: The present study investigates how autonomy-supportive coaching style influences youth athlete development through psychological resilience and dispositional optimism. Despite growing interest in factors that contribute to athlete development, gaps remain in understanding how coaching approaches interact with psychological traits to foster youth athletes’ growth. This study addresses these gaps by proposing a serial mediation model in which autonomy-supportive coaching indirectly enhances athlete development through resilience and optimism.Methods: Data were collected from 325 youth athletes and their coaches across training facilities and schools in China, and analyzed using structural equation modeling in SmartPLS.Results: Results indicate that autonomy-supportive coaching style significantly increases psychological resilience, which in turn boosts dispositional optimism, positively impacting athlete development. Both resilience and optimism serially mediate the link between coaching style and athlete growth.Discussion: These findings emphasize the importance of autonomy-supportive coaching in creating psychologically supportive environments that foster resilience, optimism, and developmental pathways in youth sports.
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16641078
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PSYCHOLOGY
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10.1007/s44196-024-00726-y
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A Novel Conflict Deduction Algorithm Based on Contradiction Separation Inference Rule
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Automated reasoning, a significant field within artificial intelligence, has attracted increased attention in recent years due to the rising demand for trustworthy AI. Binary resolution, among other inference rules, is crucial in automated reasoning of first-order logic, including the new conflict resolution method. Conflict resolution processes only two clauses in each deduction step and eliminates a complementary pairs of literals from input clauses. This paper proposes a contradiction separation conflict deduction (CSCD) method based on the contradiction separation rule to address these limitations. This novel resolution methodology, together with its automated reasoning theory and method, handles several clauses in each deduction step to seek for conflicts and generates learnt clauses through synergized deduction. Thus, the approach improves deduction by detecting conflicts more effectively, especially with lengthier input clauses. CSCD and conflict resolution are analyzed in detail, then how to create a practical CSCD algorithm and its implementation is summarized. We tested the CSCD algorithm to solve the CASC-26 problems and also applied it to the current leading ATP system (Eprover). Experimental results show that the CSCD deduction approach improves reasoning capability of conflict deduction method. Additionally, the Eprover with the proposed CSCD algorithm improves its performance and has solved various problems with a rating of 1 from the benchmark database TPTP.
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18756883
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AI
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10.3389/feduc.2024.1380942
|
The attractiveness of the teaching profession: a integrative literature review
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The widespread shortage of teachers highlights the urgent need to examine the factors influencing the attractiveness of the teaching profession. This issue is driven by high rates of early-career attrition, an ageing workforce, and a decline in candidates entering teacher education programs. Understanding the factors that make the profession appealing—or unappealing—has become essential for ensuring educational quality and equity. An integrative literature review was conducted to identify the key themes related to the attractiveness of the teaching profession, synthesizing evidence from multiple studies and highlighting research gaps. Findings reveal that teaching still attracts candidates driven by intrinsic motivations and social utility. However, external factors such as low salaries, challenging working conditions, and limited career progression remain significant deterrents. The social image of teaching, shaped by media and community perceptions, also influences career choices. The intersection of demographic shifts and educational policy changes highlights the complexity of addressing teacher shortages. Despite increased attention from policymakers, significant gaps remain, particularly in relation to interventions that reduce early-career attrition and support teacher retention. Future research should explore targeted strategies to support early-career teachers and examine the socio-economic factors that influence career decisions. Addressing these issues is critical to developing sustainable policies that enhance the attractiveness of the teaching profession and promote educational equity.
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2504284X
|
EDUCATION
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10.3389/frai.2024.1473837
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Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm
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Background: The Department of Rehabilitation Medicine is key to improving patients’ quality of life. Driven by chronic diseases and an aging population, there is a need to enhance the efficiency and resource allocation of outpatient facilities. This study aims to analyze the treatment preferences of outpatient rehabilitation patients by using data and a grading tool to establish predictive models. The goal is to improve patient visit efficiency and optimize resource allocation through these predictive models.Methods: Data were collected from 38 Chinese institutions, including 4,244 patients visiting outpatient rehabilitation clinics. Data processing was conducted using Python software. The pandas library was used for data cleaning and preprocessing, involving 68 categorical and 12 continuous variables. The steps included handling missing values, data normalization, and encoding conversion. The data were divided into 80% training and 20% test sets using the Scikit-learn library to ensure model independence and prevent overfitting. Performance comparisons among XGBoost, random forest, and logistic regression were conducted using metrics, including accuracy and receiver operating characteristic (ROC) curves. The imbalanced learning library’s SMOTE technique was used to address the sample imbalance during model training. The model was optimized using a confusion matrix and feature importance analysis, and partial dependence plots (PDP) were used to analyze the key influencing factors.Results: XGBoost achieved the highest overall accuracy of 80.21% with high precision and recall in Category 1. random forest showed a similar overall accuracy. Logistic Regression had a significantly lower accuracy, indicating difficulties with nonlinear data. The key influencing factors identified include distance to medical institutions, arrival time, length of hospital stay, and specific diseases, such as cardiovascular, pulmonary, oncological, and orthopedic conditions. The tiered diagnosis and treatment tool effectively helped doctors assess patients’ conditions and recommend suitable medical institutions based on rehabilitation grading.Conclusion: This study confirmed that ensemble learning methods, particularly XGBoost, outperform single models in classification tasks involving complex datasets. Addressing class imbalance and enhancing feature engineering can further improve model performance. Understanding patient preferences and the factors influencing medical institution selection can guide healthcare policies to optimize resource allocation, improve service quality, and enhance patient satisfaction. Tiered diagnosis and treatment tools play a crucial role in helping doctors evaluate patient conditions and make informed recommendations for appropriate medical care.
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26248212
|
AI
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10.3389/fonc.2024.1474105
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Application value of 18F-FDG PET/CT in soft tissue metastasis of intrahepatic cholangiocarcinoma: a case report and literature review
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Intrahepatic cholangiocarcinoma (ICC)originates from the epithelial cells of the intrahepatic bile ducts, with insidious onset and strong invasiveness, and most of the cases are found in the advanced stage, with extremely poor prognosis. In advanced stages, distant metastases to the lungs, bones, and brain are common, but distant soft tissue (subcutaneous and skeletal muscle) and breast metastases are rare, and simultaneous metastases to all three rare sites had not been reported. We report a 69-year-old woman with right upper abdominal pain who underwent a plain and enhanced CT scan of the upper abdomen, which revealed an intrahepatic space-occupying lesion, as well as subcutaneous and peritoneal nodules in the abdomen. To further evaluate the presence of other metastases, an 18F-FDG PET/CT scan was performed, which showed abnormal FDG uptake in the liver, peritoneum, left upper femur, right breast, subcutaneous tissues of the thoracic and abdominal regions, and skeletal muscle, while the corresponding CT densities of part of the skeletal muscle and the left upper femur did not show any significant abnormality. Pathologic confirmation of ICC with multiple metastases was obtained by puncture biopsy of the liver and subcutaneous nodes. This case demonstrates the advantages of 18F-FDG PET/CT in comprehensively evaluating systemic metastasis of ICC and detecting occult metastases, which is of great significance in its clinical diagnosis and staging.
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2234943X
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ONCOLOGY
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10.1186/s40359-025-02377-w
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The effects of physical activity on social physique anxiety in college students—the mediating and moderating role of mental toughness and negative physical self
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To examine the effects of physical activity on college students' social physique anxiety and the mediating and moderating roles of negative physical self and mental toughness in it, and to provide empirical evidence that physical activity improves college students' social physique anxiety. Stratified whole cluster convenience sampling was used to survey 1177 university students, 53.8% male and 46.2% female, with a mean age of (19.12 ± 1.21) years. Mediated and moderated effects were analysed using SPSS 26.0 and AMOS 28.0. (1) Physical activity negatively predicted social physique anxiety (2) Negative physical self and mental toughness (individual power) played a significant partial mediating role between physical activity and social physique anxiety, with mediating effects accounting for 40.09% and 27.11% of the total effect, respectively; (3) The R2 change in the interaction term of physical activity and family support in mental toughness (supporting force) reached a significant level, and family support played a significant negative moderating role between physical activity and social physique anxiety, whereas the interaction term of interpersonal assistance and physical activity was not significant and could not moderate the interrelationship between physical activity and social physique anxiety. Physical activity affects college students' levels of social physique anxiety through negative physical self and mental toughness (individual power); the effect of physical activity on social physique anxiety is negatively moderated by the family support dimension of mental toughness (supporting force).
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20507283
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PSYCHOLOGY
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10.3389/feduc.2025.1431793
|
Supporting a state in developing a working theory of improvement for promoting equity in science education
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Introduction: This study was undertaken to explore the potential of developing a working theory of improvement for creating a more equitable system of science education at the level of a US state. We ask: How can tools from a long-term research-practice partnership support a state team in initiating improvement research toward promoting a more equitable system of science education?Methods: This design study took place in winter 2024 in a single state. External partners supported leaders of a single state in the US Northeast to support a process of articulating aims, specifying primary and secondary drivers, and identifying change strategies to promote a more equitable system of science education in the state, grounded in the vision of A Framework for K-12 Science Education (National Research Council, 2012). In this paper, we rely on descriptive analyses of joint meetings and a focus group with state leaders describe the tools supporting the process of development, the team’s use of the tools to generate an early draft of the Driver Diagram, and issues surfaced while developing it with a team of interest holders in the state.Results: Two meanings of equity emerged as significant within the series of meetings: that of the importance of universal access to professional learning and the importance of students having opportunities to experience culturally relevant instruction. The issues surfaced highlighted the need for infrastructures for professional learning to reach a diverse group of interest holders in science, including teachers, school leaders, and district leaders across the state. They also saw curriculum materials that connect to students’ everyday lives and community priorities as key drivers for equitable change in the system, around which professional learning activities should be organized. The team also surfaced several policy changes needed to implement change strategies, only some of which team members felt they had some authority.Discussion: Where past researchers have observed that equity can disappear as a focus during implementation of reforms, this study found that developing an aim statement and driver diagram helped energize and refocus a team’s implementation efforts geared toward a vision for science teaching and learning that is focused on ensuring all students can engage in meaningful science learning that is culturally and locally relevant to them.
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2504284X
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EDUCATION
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10.1007/s00432-025-06130-z
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Retraction Note: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6
| null |
14321335
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ONCOLOGY
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10.3389/feduc.2025.1546448
|
Factors influencing the implementation of a teacher professional development program to improve teaching quality
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In this study, we examined why a Teacher Professional Development (TPD) program, designed to support teachers in using students’ perceptions of teaching quality (SPTQ) data, faced significant implementation challenges in 17 secondary schools in Chile. Despite voluntary participation and initial interest, 15 of the 17 schools dropped out within 2–3 months of starting the program. Through 12 semi-structured interviews with professional learning community coordinators from nine schools, we investigated four key attributes of the TPD program to understand implementation challenges: its added value, compatibility, clarity, and tolerance. While coordinators valued several aspects of the program (including its structured manual, evidence-based teaching strategies, and integration of SPTQ data) significant implementation barriers emerged. Time constraints, lack of technological infrastructure, and insufficient organizational routines made the implementation of the TPD program too burdensome for most schools. We discuss how compatibility between TPD programs and schools’ existing structures and routines acts as a critical bottleneck that can prevent successful implementation, even when participants see value in the program. This study provides important insights into the conditions necessary for successful TPD implementation in a global south country.
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2504284X
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EDUCATION
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10.1186/s40594-025-00535-5
|
A more positive mindset context is associated with better student outcomes in STEM, particularly for traditional-age students
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Students' beliefs about their ability to grow in STEM disciplines have been linked to better course outcomes. However, such mindset beliefs are subject to the environmental cues projected by the instructor in the classroom, which we refer to as the mindset context. Recent meta-analyses indicated heterogeneity in the benefits of student mindset interventions, which the classroom environment may shape. In this work, we use structural equation modeling (SEM) to investigate the mindset context and its impact on students’ affect and performance in STEM courses, particularly for students from marginalized groups who may be disproportionately affected by these factors. We collected student perceptions of their instructors’ universality beliefs about student abilities (all people or only some people can reach excellence in STEM), students’ growth beliefs, sense of belonging (as measured by peer support, faculty support, and classroom comfort) and course grades. The sample was collected from courses in a STEM college within a demographically diverse, moderately selective institution in the Southern United States (N = 625). We found that student perceptions of the mindset context did not directly predict course grades, but ACT scores did (standardized exams used for college entry in the USA). However, SEM analysis revealed that when students perceived instructors to believe only some students can succeed in STEM (endorse more non-universal beliefs), they reported fewer growth beliefs about their abilities in STEM. This led to less classroom comfort in contributing to class discussions, ultimately lowering STEM grades. Multigroup moderation analysis showed no differences in paths based on race, gender, and generational status. However, the mindset context impacted traditional students’ (age of 18–22) growth beliefs to a greater extent than non-traditional students (> 22 years old). Additionally, classroom comfort significantly predicted grades for traditional students but not for non-traditional students. Our finding suggests that when students perceive the mindset context more positively, their outcomes improve, especially for traditional students who may be more sensitive to classroom cues. Thus, mindset interventions for faculty (coupled with student interventions) may also be beneficial to supporting student success. Additionally, we recommend improving student content preparation to enhance foundational knowledge, considering that indicators of prior preparation (ACT scores) play a more direct role in predicting student grades.
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21967822
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EDUCATION
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10.1186/s40594-025-00538-2
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Analysis of two pedagogical approaches to foster discipline integrations in an educational data mining class using communities of practice
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This paper describes research into two pedagogical approaches to foster transdisciplinarity in a graduate engineering course that involves education and computer science. Leveraging the Communities of Practice framework, we examine how students majoring in computer science can integrate new knowledge from education and computer science to engage in an educational data mining project. The first course iteration sought to connect students from education and computer science disciplines through a blend of problem-based learning and traditional lectures. The second course iteration involved computer science students only, but included two instructors, one from computer science and the other from education. To evaluate these approaches, we conducted multiple student interviews and classroom observations. We found that pursuing interdisciplinary through student brokers had a localized student impact on discipline integration without creating an entire class transdisciplinary environment, proving particularly effective for students with backgrounds outside of computer science. However, it fell short of achieving an overarching integration of education knowledge across the entire class. In contrast, the co-teaching approach influenced class dynamics significantly as instructors honed their brokerage skills and introduced crucial components to the multidisciplinary toolkit. Students reinterpreted these elements within the context of their projects, leading to a deeper integration of education and computer science disciplines. However, while students did acquire more knowledge from both disciplines, they did not always achieve a comprehensive practical understanding of the class outcomes. Findings suggest that differences in instructional design can significantly impact how interdisciplinary integration forms within a class. Using CoP, we identified various models to foster disciplinary integration. The two pedagogical approaches used—student brokers and co-instructors—achieved some disciplinary integration, highlighting multidisciplinary, interdisciplinary, and transdisciplinary integration. Engaging in projects with multidisciplinary teams allows students to interact one-on-one while working on real projects, enabling them to negotiate their participation with peers and resulting in a deeper integration of the involved disciplines. This paper discusses the merits and the drawbacks of employing both approaches to build an interdisciplinary class.
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21967822
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EDUCATION
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10.1007/s44196-025-00771-1
|
Advanced Hybrid Machine Learning Model for Accurate Detection of Cardiovascular Disease
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Cardiovascular disease (CVD) is one of the foremost reasons behind the death of people worldwide. Prevention and early diagnosis are the only ways to control its progression and onset. Thus, there is an urgent need for a detection model comprising intelligent technologies, including Machine Learning (ML) and deep learning, to predict the future state of an individual suffering from cardiovascular disease by effectively analyzing patient data. This study aims to propose a hybrid model that provides a deep insight into the data under consideration to enhance model accuracy for effectively detecting cardiovascular disease. This current research proposes a hybrid model comprising four stages. In the first stage of the proposed hybrid model, the data imbalance problem is solved using a hybrid sampling technique named Synthetic Minority Oversampling Technique-Edited Nearest Neighbors Rule. In the second stage, the Chi-square is applied as a feature selection method to select the highly relevant features from the records of 1190 with 11 clinical features, curated by combining the 5 most popular datasets, including Long Beach VA, Hungarian, Switzerland, and Statlog (Heart). In the third stage, the preprocessed dataset is passed to a stacking ensemble model comprising three base learners: Random Forest Tree (RFT), K-Nearest Neighbor (K-NN), and AdaBoost classifier and one meta-learner: Logistic Regression (LR), optimized with Grid Search Cross-Validation (GSCV) optimization approach, whose performance is evaluated against individual classifier. In the fourth stage, the performance is evaluated in terms of accuracy, sensitivity, specificity, F1 score, and ROC_AUC score.. The comparative results prove that the proposed hybrid model scored the highest accuracy of 97.8%, 96.15% sensitivity, and 96.75% specificity and 98.6% ROC_AUC score when compared with the existing techniques and models after applying the SMOTE–ENN (for data balancing) and Chi-square (for feature selection) methods for the efficient detection of cardiovascular disease. The implementation results demonstrate that the suggested hybrid model may accurately identify cardiovascular disease among patients. It facilitates the application of robust clinical treatment strategies.
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18756883
|
AI
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10.3389/frai.2025.1496109
|
Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease
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Background: The diagnostic power of exercise stress electrocardiography (ExECG) remains limited. We aimed to construct an artificial intelligence (AI)-based method to enhance ExECG performance to identify patients with significant coronary artery disease (CAD).Methods: We retrospectively collected 818 patients who underwent both ExECG and coronary angiography (CAG) within 6 months. The mean age was 57.0 ± 10.1 years, and 614 (75%) were male patients. Significant coronary artery disease was seen in 369 (43.8%) CAG reports. We also included 197 individuals with normal ExECG and low risk of CAD. A convolutional recurrent neural network algorithm, integrating electrocardiographic (ECG) signals and features from ExECG reports, was developed to predict the risk of significant CAD. We also investigated the optimal number of inputted ECG signal slices and features and the weighting of features for model performance.Results: Using the data of patients undergoing CAG for training and test sets, our algorithm had an area under the curve, sensitivity, and specificity of 0.74, 0.86, and 0.47, respectively, which increased to 0.83, 0.89, and 0.60, respectively, after enrolling 197 subjects with low risk of CAD. Three ECG signal slices and 12 features yielded optimal performance metrics. The principal predictive feature variables were sex, maximum heart rate, and ST/HR index. Our model generated results within one minute after completing ExECG.Conclusion: The multimodal AI algorithm, leveraging deep learning techniques, efficiently and accurately identifies patients with significant CAD using ExECG data, aiding clinical screening in both symptomatic and asymptomatic patients. Nevertheless, the specificity remains moderate (0.60), suggesting a potential for false positives and highlighting the need for further investigation.
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26248212
|
AI
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10.3390/ejihpe15040045
|
Relations Between Medical Students’ Motivational Persistence Skills and Their Acceptance of Specific Blended Learning Tools
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The concept of blended education, which refers to the intensive integration of digital resources into the teaching process and its mixed online and on-site delivery, combining as much as possible the advantages of both methods in an optimal way, is becoming increasingly popular among teaching tools. There is no universal recipe for designing a successful blended course; the success of such courses is measured entirely through their degree of acceptance among students, defined by their emotional motivation to learn and the obtained practical results. Our study aimed to evaluate the motivational persistence degree (MPS) of medical students in connection with the students’ acceptance of different didactic tools involved in blended-learning approaches. Materials and Method: We investigated a sample comprising 523 students in Dental Medicine or General Medicine, belonging to all years of study, from four main Universities in Romania; we classified them according to their motivational persistence profile (using k-means data clustering) and we comparatively investigated the main relevant features of students in each cluster—gender, age group, opinions about the general usefulness of multimedia resources in the learning process, and their degree of acceptance of specific types of instructional materials involved in blended learning. Results: We found that the students who mostly enjoy the traditional learning style have average motivational persistence skills; they are perseverant and competitive, but they are not so good at planning their daily tasks. They enjoy external directions, set by teachers. The students who most enjoy PowerPoint presentations and those who enjoy instructional videos show similar behavior, both belonging to the cluster with the highest MPS score. They have the best motivational persistence skills amongst all categories; they are particularly excellent at planning and fulfilling daily tasks, as well as following their goals in the long term. The students who mostly enjoy online documentary sources belong also to a cluster with above average MPS score; they excel in fulfilling daily tasks, but exhibit weaker performance in recalling unachieved goals. These results are similar to those already reported in the literature; the strength of our study is in that it provides much more specific evaluations oriented to the motivational persistence degree, which is highly significant in the case of medical students, because it is a measure of their commitment in fulfilling certain tasks. Conclusions: Our results have the potential to highlight reasons for academic success or failure according to a student’ s profile, and will prove helpful in selecting the most appropriate didactic tools.
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22549625
|
PSYCHOLOGY
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10.3389/fonc.2025.1489169
|
The relationship of depression and quality of life with mediating role of death anxiety, silver lining and religious coping among women cancer patients in Pakistan
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Objective: Pakistani women are among those most likely to be diagnosed with cancer. Cancer patients experience significant changes that impact their mental and physical health, primarily due to the increased burden of the disease. This study aims to explore the relationship between depression and quality of life (QOL) in cancer patients, as well as how religious coping (RC), silver lining (SL), and death anxiety (DA) influence this connection.Materials and methods: A total of 450 individuals diagnosed with cancer were recruited from outpatient departments of various hospitals in Islamabad. Out of these, 421 patients who met the inclusion criteria were included in the study. Three types of cancer were considered for data collection there was 132 (31.4%) breast cancer, 154 (36.6%) blood cancer and 135 (32.1%) lung cancer patients Participants were assessed using the following measurement tools: the Demographic Form, The Short Muslim Religious Practice and Religious Belief, the Patient Health Questionnaire (PHQ-9, 2011), the Death Anxiety Scale, the Silver Lining Questionnaire, and the WHOQOL-BREF Questionnaire.Results: The findings of the current study revealed a negative association between depression and quality of life (QOL). Additionally, death anxiety (DA) was positively correlated with both depression and QOL. Conversely, silver lining (SL) and religious coping (RC) were negatively associated with depression and positively associated with QOL. Path analysis indicated that DA, SL, and RC served as mediators in the relationship between depression and QOL among cancer patients.Conclusion: The study concluded that cancer patients can better manage their depression and enhance their quality of life by strengthening their silver lining (SL) and religious coping (RC). These findings should be considered when developing strategies to manage depression and other psychological issues in cancer patients, thereby providing more effective treatments for this population
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2234943X
|
ONCOLOGY
|
10.3390/educsci15040430
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Boosting Active Learning Through a Gamified Flipped Classroom: A Retrospective Case Study in Higher Engineering Education
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Active learning and associated techniques such as flipped classes have been demonstrated to have positive impacts on student learning and performance. Active learning faces several challenges when learners apply weak learning styles. Weak learning might happen when a student is not motivated to carry out any pre-class content activity, actively participate in the class activity, or reflect and reinforce the learned content during and after the class. This study explores how a gamified flipped classroom affects active learning performance and learning outcomes. The case is related to a technical course in the Maintenance Engineering Field, which is well known for a high rate of misunderstanding and low learning outcomes. It is found that sequential game-boosting activities in the flipped classroom have managed to level up students’ learning outcomes by explaining almost all concepts with low levels of misconceptions.
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22277102
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EDUCATION
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10.1186/s40594-025-00542-6
|
Investigating the emotion regulation of STEM teachers: a scoping review
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A significant tension exists between the necessity for teachers to regulate their emotions and the tendency to overlook these emotions in STEM education. Teachers’ emotion regulation is inherently context-sensitive and discipline-specific. Therefore, it is crucial for researchers to explore the particularities of teachers’ emotion regulation in the context of STEM education. This study presents a scoping review of existing research on STEM teachers’ emotion regulation, focusing on theoretical underpinnings, methodological approaches, and research foci. Following scoping review guidelines, a corpus of 23 studies published between 2004 and 2023 was collected and analyzed. Emotional intelligence emerged as the most frequently employed theoretical underpinning for conceptualizing STEM teachers’ emotion regulation, followed by emotional labor and emotion regulation. Among the four research approaches—quantitative, qualitative, mixed, and conceptual—the majority of studies adopted quantitative and qualitative methods to investigate the nature and the relational model of teachers’ emotion regulation in STEM education. The findings indicate that research on STEM teachers’ emotion regulation exhibits imbalances in theoretical and methodological approaches. Although various contexts, antecedents, and outcomes of STEM teachers’ emotion regulation have been identified, the scope and depth of these investigations remain limited. Research on STEM teachers’ emotion regulation is still in its early stages for several reasons: the paucity of studies in this area, a reliance on broad emotional constructs rather than specific emotion regulation perspectives, and the lack of tailored theoretical frameworks addressing STEM teachers’ emotion regulation. This scoping review maps existing knowledge on teachers’ emotion regulation in STEM education, elucidates the underlying philosophical standpoints of prior research, and offers recommendations for future research directions.
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21967822
|
EDUCATION
|
10.3389/fonc.2025.1587069
|
Safety and efficacy of apatinib in combination treatment versus apatinib as second-line treatment for advanced gastric cancer
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Background: Apatinib is a systemic therapeutic agent for advanced gastric adenocarcinoma (GAC) and gastroesophageal junction adenocarcinoma (GEJA). Its efficacy can be enhanced by applying it as a combination therapy, but the evidence supporting its combination application as a second-line treatment is not well documented. In the current study, we aimed to assess the efficacy and safety profile of apatinib, both as a monotherapy and in combination regimens, for second-line treatment of GAC and GEJA in real-world settings.Methods: In this retrospective cohort analysis, we analyzed clinical data from 96 patients with advanced GAC or GEJA who received second-line apatinib monotherapy or combination therapy. Cox regression analysis was performed to identify prognostic factors influencing clinical outcomes of different treatment approaches (apatinib combination with other drugs).Results: The results indicated that the overall objective response rate (ORR) and disease control rate (DCR) for second-line apatinib therapy were 19.8% and 31.3%, respectively. The median progression-free survival (mPFS) was 4.8 months (95% CI: 4.3-6.2m), while the median overall survival (mOS) was 10.3 months (95% CI: 8.9-12.4m). Multivariable Cox regression analysis identified gender, liver metastasis, and peritoneal metastasis as independent predictors of inferior PFS and OS outcomes. In terms of safety, the primary adverse reactions included myelosuppression, elevated AST and ALT levels, hypertension, hand-foot syndrome, hyperbilirubinemia, proteinuria, fatigue, and vomiting, with a low incidence of grade 3–4 toxicities.Conclusions: Apatinib-based combination therapy significantly enhances both progression-free survival and overall survival in patients with advanced gastric cancer when compared to monotherapy, while also demonstrating a safe and reliable profile.
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2234943X
|
ONCOLOGY
|
10.1007/s00432-025-06202-0
|
Increased expression of DNAJC7 promotes the progression of hepatocellular carcinoma by influencing the cell cycle and immune microenvironment
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Background Hepatocellular carcinoma (HCC) is the leading cause of cancer-related mortality worldwide owing to the lack of effective and early diagnostic tools and therapeutic approaches. DNAJC7, a member of the DnaJ heat shock family, is crucial in protein folding and stability; however, its specific functions and mechanisms in HCC remain unclear. Objective This study aimed to explore the role of DNAJC7 in HCC progression and evaluate its potential clinical significance as a prognostic marker. Methods Public databases (TCGA, ICGC, GEO, and TIMER) were used to assess DNAJC7 expression, correlations with clinical parameters, and related signaling pathways. Proliferation, migration, invasion, and cell cycle assays were performed to evaluate the function of DNAJC7 in HCC. Immune infiltration and associations with checkpoint proteins were analyzed using TIMER, and a Gene Set Enrichment Analysis (GSEA) was used to explore enriched pathways. Results DNAJC7 expression was higher in HCC tissues than in adjacent normal tissues and was associated with advanced malignancy and poor prognosis, including a lower overall survival, progression-free survival, and disease-free survival. DNAJC7 knockdown resulted in reduced malignant behavior of HCC cells, leading to S-phase cell cycle arrest. Increased DNAJC7 expression was associated with immune cell infiltration and the presence of immunological checkpoint molecules, including CTLA4 and PD-1. GSEA highlighted the activation of key pathways, including WNT signaling and cell cycle regulation. Conclusion DNAJC7 regulates tumor cell proliferation, migration, invasion, and immune evasion by acting as an oncogene in HCC. It can serve as a diagnostic and prognostic biomarker and potential treatment target for HCC.
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14321335
|
ONCOLOGY
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10.3390/ai6050097
|
FASTSeg3D: A Fast, Efficient, and Adaptive Ground Filtering Algorithm for 3D Point Clouds in Mobile Sensing Applications
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Background: Accurate ground segmentation in 3D point clouds is critical for robotic perception, enabling robust navigation, object detection, and environmental mapping. However, existing methods struggle with over-segmentation, under-segmentation, and computational inefficiency, particularly in dynamic or complex environments. Methods: This study proposes FASTSeg3D, a novel two-stage algorithm for real-time ground filtering. First, Range Elevation Estimation (REE) organizes point clouds efficiently while filtering outliers. Second, adaptive Window-Based Model Fitting (WBMF) addresses over-segmentation by dynamically adjusting to local geometric features. The method was rigorously evaluated in four challenging scenarios: large objects (vehicles), pedestrians, small debris/vegetation, and rainy conditions across day/night cycles. Results: FASTSeg3D achieved state-of-the-art performance, with a mean error of <7%, error sensitivity < 10%, and IoU scores > 90% in all scenarios except extreme cases (rainy/night small-object conditions). It maintained a processing speed 10× faster than comparable methods, enabling real-time operation. The algorithm also outperformed benchmarks in F1 score (avg. 94.2%) and kappa coefficient (avg. 0.91), demonstrating superior robustness. Conclusions: FASTSeg3D addresses critical limitations in ground segmentation by balancing speed and accuracy, making it ideal for real-time robotic applications in diverse environments. Its computational efficiency and adaptability to edge cases represent a significant advancement for autonomous systems.
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26732688
|
AI
|
10.3389/feduc.2025.1552760
|
Exploring teachers’ pedagogical reasoning in mathematics education using the TPACK framework
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Effective integration of technology in mathematics education requires teachers to blend content knowledge, pedagogical strategies, and digital tools. The Technological Pedagogical Content Knowledge (TPACK) framework offers a lens for understanding teachers’ pedagogical reasoning when designing technology-enhanced lessons. However, the ways in which TPACK informs instructional planning and the challenges educators face remain under - synthesized. A systematic review following PRISMA guidelines was conducted using Scopus (2015–2024). Search terms included “pedagogical reasoning,” “instructional reasoning,” “TPACK,” and “math*.” From 118 records retrieved, title/abstract screening and full - text eligibility assessments yielded eight empirical studies examining TPACK and pedagogical reasoning in mathematics contexts. The included studies employed predominantly qualitative case studies and mixed - methods designs to capture teachers’ decision - making processes. Findings indicate that educators leverage TPACK to enhance conceptual understanding and student engagement via dynamic visualizations, interactive simulations, and scaffolded digital tasks. Common obstacles include limited subject - specific professional development, resource constraints, and heterogeneity in teachers’ TPACK proficiency. Evidence also highlights TPACK’s capacity to foster inquiry - based learning and develop teachers’ adaptive expertise. Sustained, targeted professional development and equitable access to technology are essential for deepening TPACK enactment. Implications for practice include designing PD programs that integrate subject - specific technology applications and creating institutional support structures. Future research should investigate longitudinal impacts of TPACK on teachers’ reflective practices and student outcomes, and develop standardized assessment tools tailored to mathematics instruction.
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2504284X
|
EDUCATION
|
10.3389/frai.2025.1431003
|
Co-Learning: code learning for multi-agent reinforcement collaborative framework with conversational natural language interfaces
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Online question-and-answer (Q&A) systems based on the Large Language Model (LLM) have progressively diverged from recreational to professional use. However, beginners in programming often struggle to correct code errors independently, limiting their learning efficiency. This paper proposed a Multi-Agent framework with environmentally reinforcement learning (E-RL) for code correction called Code Learning (Co-Learning) community, assisting beginners to correct code errors independently. It evaluates the performance of multiple LLMs from an original dataset with 702 error codes, uses it as a reward or punishment criterion for E-RL; Analyzes input error codes by the current agent; selects the appropriate LLM-based agent to achieve optimal error correction accuracy and reduce correction time. Experiment results showed that 3% improvement in Precision score and 15% improvement in time cost as compared with no E-RL method respectively. The results indicate that integrating E-RL with a multi-agent selection strategy can effectively enhance both the accuracy and efficiency of LLM-based code correction systems, making them more practical for educational and professional programming support scenarios.
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26248212
|
AI
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10.3389/feduc.2025.1555167
|
Problem-solving: development and validation of a short instrument for higher education
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Problem-solving is becoming more and more seen as an important skill for college students to learn to build metacognitive skills, critical thought, and the ability to learn on their own. Even though this skill is very important, there aren’t many approved tools that can be used to test it in schools, especially in Peru. The goal of this study is to fill in that gap by creating and testing a short problem-solving scale based on the Rational Problem-Solving Style, which stresses taking a planned and organized approach to problems. 733 Peruvian college students (mean age: 21.56 years, standard deviation: 4.15 years; 59.89% female) took part. A 15-item Problem-Solving Questionnaire and used experimental (EFA) and confirmatory factor analysis (CFA) to test it. The scale’s validity and reliability were checked, along with its link to academic self-efficacy. There were four parts to the Problem-Solving Questionnaire: Solution Analysis and Planning, Critical Evaluation of Solutions, Generation and Evaluation of Alternatives, and Prioritization and Review of Alternatives. Fit scores from CFA (like CFI = 0.98 and RMSEA = 0.062) and reliability coefficients (ω = 0.73–0.90) showed that it was a reliable educational tool. There was proof of concept validity in the form of correlations with academic self-efficacy (r = 0.36–0.80). The scale is a validity and effective way to test the problem-solving skills of university students in Peru. Due to its brevity and emphasis on logical methods, it is suitable for use in both education and research, aligning with global goals for quality education.
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2504284X
|
EDUCATION
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