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In vitro dissolution testing plays a key role in controlling the quality and optimizing the formulation of solid dosage pharmaceutical products. Data-driven dissolution models can improve the efficiency of testing: their predictions can act as surrogates to physical experiments and help identify key material attributes / processing parameters that impact product dissolution. Reducing the data (size) requirements of developing such models would significantly improve the utility of dissolution models. In this study, we investigate how Gaussian process regression (GPR) and active learning can reduce the dataset-size requirements for developing predictive models and identifying important processing parameters compared to common practice methods. Initially, we perform a DoE study over five processing parameters and measure the dissolution of compound B to generate a dataset. Using this dataset, we find that GPR provides higher fidelity predictions of dissolution than polynomial models when trained on the same data. In addition, we use Shapley additive explanations to interpret our GPR model and assess processing parameter importance. Through a retrospective analysis, we find that active learning can target a reduced set of experiments (compared to a full DoE) which are particularly conducive for model development and identification of important processing parameters., Competing Interests: Declaration of competing interest Siddharth S. Kesharwani and Fady Ibrahim are Sanofi employees and may hold shares and/or stock options in the company., (Copyright © 2025 Elsevier B.V. All rights reserved.)

Bayesian optimization is a stochastic, global black-box optimization algorithm. By combining Machine Learning with decision-making, the algorithm can optimally utilize information gained during experimentation to plan further experiments-while balancing exploration and exploitation. Although Design of Experiments has traditionally been the preferred method for optimizing bioprocesses, AI-driven tools have recently drawn increasing attention to Bayesian optimization within bioprocess engineering. This review presents the principles and methodologies of Bayesian optimization and focuses on its application to various stages of bioprocess engineering in upstream and downstream processing., (© 2025 The Author(s). Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

Background: University students accumulate lots of sedentary time without interrupting and comprehensive approaches to reduce time spent sedentary are lacking. The implementation of physically active university teaching needs practicable approaches and the support by lecturers. However, there is little research on which physically activating strategies lecturers actually use and what barriers they become aware to implement these. This exploratory, qualitative study aims to identify physically activating strategies as well as barriers for reducing sitting time and physically active university teaching from the perspective of lecturers., Methods: We conducted semi-structured interviews with 16 lecturers to explore potential physically activating strategies in university teaching, assess their degree of utilization, and identify barriers to implementation. The data were analysed by a structured content analysis of the interview transcripts using MAXQDA 2020 software., Results: Physically activating strategies are hardly known among university lecturers and are seldomly used on a regular and conscious basis. We identified two types of strategies with physically activating measures and teaching methods. Lecturers highlighted two specific types of physically activating measures: physical activity breaks and the use of physically activating furniture. All together, we identified 18 distinct teaching methods (e.g. group work, gallery walk) that integrate learning processes with physical activity in a pedagogical-didactic manner. The main barriers to implementation identified were lack of space, lack of time, students' unwillingness to move; organizational social norms, and lecturers' uncertainty about how to implement these strategies effectively., Conclusions: University lecturers are generally unfamiliar with and rarely use physically activating strategies to reduce sedentary behavior in students. However, lecturers identified 18 potential teaching methods that integrate physical activity with pedagogical-didactic principles, offering a new approach to physically active university teaching. These methods present an untapped potential for the low-threshold integration of physical activity and breaks from sitting into university teaching, aligning with "stealth health" strategies that incidentally promote health while assuring a "high-quality education" as the core concern of higher education teaching. Understanding and addressing the barriers to implementation, such as lack of space, time, social and organizational norms, is crucial for the effective planning and implementation of interventions., Competing Interests: Declarations. Ethics approval and consent to participate: The study complies with ethical and legal data protection regulations and was approved by the Ethics Committee of the Institute of Sport Science of the University of Würzburg (EV2024/2–2305). All research methods in this study have been performed in accordance with the Declaration of Helsinki. Prior to participation in the study, all participants were provided with written and verbal information about the study, and written informed consent was obtained from all participants to participate in the study. The study ensured that quotes were anonymized and no conclusions could be drawn about the respondents. We affirm that the interview guide was developed for this study and that the results of the study presented have not been published elsewhere. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Recent advancements in deep learning have revolutionized digital dentistry, highlighting the importance of precise dental segmentation. This study leverages active learning with the three-dimensional (3D) nnU-net and multi-labels to improve segmentation accuracy of dental anatomies, including the maxillary sinuses, maxilla, mandible, and inferior alveolar nerves (IAN), which are important for implant planning, in 3D cone-beam computed tomography (CBCT) scans. Segmentation accuracy was compared using single-label, adjacent pair-label, and multi-label relevant anatomic structures with 60 CBCT scans from Kooalldam Dental Hospital and externally validated using data from Seoul National University Dental Hospital. The dataset was divided into three training stages for active learning. The evaluation metrics were assessed through the Dice similarity coefficient (DSC) and mean absolute difference. The overall internal test set DSCs from the multi-label, single-label, and pair-label models were 95%, 91% (paired t-test; p = 0.01), and 93% (p = 0.03), respectively. The DSC of the IAN in the internal and external datasets increased from 83% to 79%, 87% and 81%, to 90% and 86% for the single-label, pair-label, and multi-label models, respectively (all p = 0.01). Prediction accuracy improved over time, significantly reducing the manual correction time. Our active learning and multi-label strategies facilitated accurate automatic segmentation., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethical approval: This study was approved by the Institutional Review Board of the Asan Medical Center (IRB No. 2024 − 0485) and Seoul National University Hospital (IRB no. ERI20022) and performed according to the principles of the Declaration of Helsinki. It was based on a review of retrospective charts of patients at Kooalldam Dental Hospital between 2017 and 2020 and at Seoul National University Dental University Hospital between 2010 and 2017. The IRB Committee waived the requirement for informed consent from both institutions, which approved the study due to retrospective observational studies. We received anonymized head and neck CBCT images of patients who don’t have severe maxillofacial deformities, edentulism, or multiple missing teeth with 0.3 mm slice thickness., (© 2025. The Author(s).)

Purpose: This study examined whether peer instruction enhanced the retention of content for learners who participated in a review session utilizing an Audience Response System (ARS)., Methods: Review sessions for two groups of students taking the same course were conducted. Both groups utilized ARS to answer questions presented in the session, while only one group also utilized an educational method known as peer instruction-otherwise, sessions were identical. Performance on quizzes covering the same subject matter, as well as course exam, was analyzed to determine the potential long- and short-term effects of the combination of peer instruction with ARS on course performance and satisfaction., Results: There was no significant difference in performance on administered quiz questions between the groups for the pre-quiz (before the review session), the post-quiz (directly after the review session), nor the end-of-semester quiz. The group that utilized peer instruction had a statistically significant increase (p = 0.008767) in performance on the administered course exam. Both groups expressed satisfaction for the course as well as peer instruction, regardless if their particular group utilized peer instruction in their review session or not., Conclusions: The combination of peer instruction with ARS may be an effective tool to improve short-term performance outcomes as evidenced by the difference in course exam performance between groups. Further research should be conducted to determine techniques that can have a greater impact on long-term performance., (© 2025 American Dental Education Association.)

Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this paper introduces an innovative approach that combines active learning and off-policy Deep Reinforcement Learning (DRL) to improve image steganalysis with minimal labeled data. Active learning allows the model to intelligently choose which unlabeled images should be annotated, thus reducing the amount of labeled data needed for effective training. Traditional active learning strategies often use static selection methods that restrict flexibility and do not adjust well to dynamic environments. To overcome this, our method incorporates off-policy DRL for strategic data selection. The off-policy in DRL can increase sample efficiency and significantly enhance learning outcomes. We also use the Differential Evolution (DE) algorithm to fine-tune the hyperparameters of the model, reducing its sensitivity to different settings and ensuring more stable results. Our testing on the extensive BossBase 1.01 and BOWS-2 datasets demonstrates the robust ability of the approach to distinguish between unaltered and steganographic images, achieving an average F-measure of 93.152% on BossBase 1.01 and 91.834% on the BOWS-2 dataset. In summary, this research enhances digital security by employing advanced image steganalysis to detect hidden data, significantly improving detection accuracy with minimal labeled data., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Objective: Automate the extraction of adverse events from the text of electronic medical records of patients hospitalized for cardiac catheterization., Methods: We focused on events related to cardiac catheterization as defined by the NCDR-IMPACT registry. These events were extracted from the Necker Children's Hospital data warehouse. Electronic health records were pre-screened using regular expressions. The resulting datasets contained numerous false positives sentences that were annotated by a cardiologist using an active learning process. A deep learning text classifier was then trained on this active learning-annotated dataset to accurately identify patients who have suffered a serious adverse event., Results: The dataset included 2,980 patients. Regular expression based extraction of adverse events related to cardiac catheterization achieved a perfect recall. Due to the rarity of adverse events, the dataset obtained from this initial pre-screening step was imbalanced, containing a significant number of false positives. The active learning annotation enabled the acquisition of a representative dataset suitable for training a deep learning model. The deep learning text-classifier identified patients who underwent adverse events after cardiac catheterization with a recall of 0.78 and a specificity of 0.94., Conclusion: Our model effectively identified patients who experienced adverse events related to cardiac catheterization using real clinical data. Enabled by an active learning annotation process, it shows promise for large language model applications in clinical research, especially for rare diseases with limited annotated databases. Our model's strength lies in its development by physicians for physicians, ensuring its relevance and applicability in clinical practice., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Orthopaedic researchers need new strategies for engaging underrepresented minority (URM) students. Our field has demonstrated noticeable gaps in racial, ethnic, and gender diversity, which inhibit our ability to innovate and combat the severe socioeconomic burden of musculoskeletal disorders. Towards this goal, we designed, implemented, and evaluated Learning on a Limb (LoaL), an orthopaedic research outreach module to teach URM high school students about orthopaedic research. During the 4-h module, students completed hands-on activities to learn how biomechanical testing, microcomputed tomography, cell culture, and histology are used in orthopaedic research. Over 3 years, we recruited 32 high school students from the Greater Philadelphia Area to participate in LoaL. Most participants identified as racial/ethnic or gender minorities in orthopaedic research. Using pre/post-tests, we found that students experienced significant learning gains of 51 percentage points from completing LoaL. In addition to teaching students about orthopaedic research, post-survey data demonstrated that participating in LoaL strongly influenced students' interest in orthopaedic research and scientific confidence. Several students acted on this interest by completing summer research experiences in the McKay Orthopaedic Research Laboratory at the University of Pennsylvania. LoaL instructors also benefited by having the opportunity to "pay it forward" to the next generation of students and build community within their department. Empowering institutions to host modules like LoaL would synergistically inspire URM high school students and strengthen community within orthopaedic departments to ultimately enhance orthopaedic research innovations., (© 2024 The Author(s). Journal of Orthopaedic Research ® published by Wiley Periodicals LLC on behalf of Orthopaedic Research Society.)

Active Learning (AL) represents a transformative instructional approach that departs from traditional methods by immersing students in experiential learning activities such as problem-solving, discussions, role-plays, interactive engagement, and case studies. Despite its widely recognized potential, the effects of AL on psycho-affective constructs in English as a Foreign Language (EFL) contexts remain underexplored. Hence, this study explored the impact of AL on EFL learners' motivation, attitudes, and anxiety in Iran. A sample of 110 pre-intermediate EFL learners from the Iran Language Institute in Borujerd City was recruited through random sampling. Following participant homogenization via the Oxford Quick Placement Test and outlier exclusion, 51 learners were randomly assigned to an experimental group (EG, n = 26) receiving AL-based instruction, and a control group (CG, n = 25) exposed to traditional lecture-based instruction. Over 18 one-hour sessions, conducted twice weekly, the groups underwent distinct interventions. The outcomes of independent samples t-tests revealed that AL-based instruction significantly enhanced the EFL learners' motivation, fostered more positive attitudes toward English language learning, and effectively reduced anxiety in the EG compared to the CG. The study's findings approved the pedagogical value of AL, specifically its capacity to create an engaging and emotionally supportive classroom environment that promotes learner confidence, sustained motivation, and a positive disposition toward English learning. Different stakeholders in EFL education are encouraged to integrate AL strategies into EFL curricula to improve psycho-affective outcomes., Competing Interests: Declaration of competing interest The corresponding author declares that there is no conflict of interest., (Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: The field of medical education has seen a growing interest in lecture free curriculum. However, it comes with its own set of challenges and obstacles. In this article, we aim to identify the prerequisites, facilitators, challenges, and barriers of lecture-free curriculum in medical education and examine their interrelationships using interpretive structural modeling (ISM) technique., Methods: In this mixed-method study initially, we performed a scoping review and semi-structured interviews and determined the main prerequisites, facilitators, challenges, and barriers of lecture-free curriculum in medical education using qualitative content analysis approach. The interrelationships among these components were investigated using ISM. Therefore, self-interactive structural matrices were formed, initial and final reachability matrices were achieved, and MICMAC analysis was conducted to classify the factors., Results: Finally, two ISM models of prerequisites and facilitators with 27 factors in 10 levels and challenges and obstacles with 25 factors in eight levels were developed. Each of the models was divided into three parts: key, strategic, and dependent factors. 'Providing relevant evidence regarding lecture free curriculum' emerged as the most important prerequisite and facilitator, and 'insufficient support from the university' was identified as the most critical barrier and challenge., Conclusions: The study highlights the significant importance of lecture-free curriculum in medical education and provides insights into its prerequisites, facilitators, challenges, and barriers. The findings can be utilized by educational managers and decision-makers to implement necessary changes in the design and implementation of lecture-free in medical education, leading to more effective improvements in the quality and success of education.

Background: The flipped classroom (FC) approach has demonstrated efficacy in enhancing the learning process, including within higher education contexts such as medical education. Recently, FC has emerged as potential alternative to traditional teaching models across various disciplines, particularly due to its more engaging nature. However, there is limited data available regarding its impact on student performance, particularly in the context of long-term knowledge retention in pharmacology. In this study, our objective was to assess the short- and long-term impact of FC on student performance in Hungarian pharmacology teaching at medical faculty in Semmelweis University (Budapest, Hungary)., Methods and Results: 161 medical students and 10 teachers were involved in this study. We flipped four seminars, then we assessed the academic performance by using multiple choice tests immediately and two weeks after the flipped class. A follow-up assessment was conducted six months after the initial two FC sessions. Our findings indicated that the FC approach enhanced both short- and long-term knowledge retention across most topics. Notably, this long-term improvement was evident even six months after the original seminars on specific subjects. However, despite these topic-specific benefits, the overall performance, including exam grades, did not show significant improvement when compared to the conventional teaching approach. Additionally, we assessed student and teacher perceptions using two questionnaires immediately after practice and at the end of the year. According to the questionnaire responses, students perceived positively the FC approach, emphasizing its interactive and thought-provoking aspects. However, they identified the time-consuming nature of preparation as a significant concern. Teachers also viewed the FC approach favorably, particularly appreciating its interactivity and potential for greater effectiveness. Interestingly, more experienced teachers were less receptive to the FC method, and their perceptions of it were less favorable compared to those of their younger colleagues., Conclusions: The flipped classroom approach presents a viable strategy for teaching pharmacology, with the potential to enhance student performance and engagement. However, student occupation and faculty resistance may pose significant challenges to the implementation of such alternative teaching methods., Competing Interests: Declarations. Ethics approval and consent to participate: All participants have signed a written consent after being informed in detail to participate in this present study (see Appendix 1). Study design and investigation was in compliance with Helsinki Declaration and approved by Regional and Institutional Committee of Science and Research Ethics of Semmelweis University (SE-RKEB-193/2023). Consent for publication: The authors declare that the manuscript contains no individual person’s data. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Purpose/objectives: The aim of the study was to assess the improving effect of technology-assisted peer role-play on dental students' theoretical knowledge and practical skills in medical history taking, vital sign monitoring, and patient-specific local anesthetic (LA) dose calculation within the scope of medical assessment., Methods: The study involved 93 fourth-year dental students who received 22 h of theoretical and practical training on medical assessment. During training, digital monitoring simulations were conducted using ResusMonitor. In the peer role-play group (Group RP, n = 46) students alternated the roles of patient and dentist and in the real patient practice group (Group PP, n = 47) students practiced on real patients. Students' theoretical knowledge and practical skills were evaluated before and after the training using questionnaires and simulated patient assessments., Results: Prior to training, groups had similar knowledge and skills. After training, both groups showed significant improvement (p < 0.001). Peer role-playing notably enhanced theoretical knowledge and practical skills in medical history taking and monitoring (p < 0.05). After the study, the theoretical knowledge quality of the students about basic and detailed medical history taking improved. Monitoring competence rose from below 30% to 93.5% in Group RP and 89.4% in Group PP post-training. All students became competent in calculating patient-specific LA dose after training, whereas they were previously inadequate., Conclusion: Peer role-play with ResusMonitor was more effective than real patient practice for improving theoretical knowledge and practical skills in medical assessment. Technology-enhanced peer role-play was recommended as an economical and accessible method for improving medical assessment training in dental education., (© 2025 American Dental Education Association.)

This paper presents the smart distributed data factory (SDDF), an AI-driven distributed computing platform designed to address challenges in drug discovery by creating comprehensive datasets of molecular conformations and their properties. SDDF uses volunteer computing, leveraging the processing power of personal computers worldwide to accelerate quantum chemistry (DFT) calculations. To tackle the vast chemical space and limited high-quality data, SDDF employs an ensemble of machine learning (ML) models to predict molecular properties and selectively choose the most challenging data points for further DFT calculations. The platform also generates new molecular conformations using molecular dynamics with the forces derived from these models. SDDF makes several contributions: the volunteer computing platform for DFT calculations; an active learning framework for constructing a dataset of molecular conformations; a large public dataset of diverse ENAMINE molecules with calculated energies; an ensemble of ML models for accurate energy prediction. The energy dataset was generated to validate the SDDF approach of reducing the need for extensive calculations. With its strict scaffold split, the dataset can be used for training and benchmarking energy models. By combining active learning, distributed computing, and quantum chemistry, SDDF offers a scalable, cost-effective solution for developing accurate molecular models and ultimately accelerating drug discovery., Competing Interests: Declarations. Competing interests: The authors of this paper are employees of Deep Origin Inc. and hold shares of the company. The authors have no other relevant affiliation or financial involvement with any organization/entity that has a financial interest in or financial conflict with the subject matter and materials discussed in this paper., (© 2025. The Author(s).)

Background: Team-based learning methods enhance course achievement, student success, and student satisfaction and are frequently used in nursing curricula. However, there is no valid and reliable tool for evaluating team-based learning in Türkiye. Adapting the Team-based Learning Student Assessment (TBL-SAI) into Turkish can provide a standardized and culturally appropriate evaluation tool, supporting nursing education practice and research. This study aimed to assess the validity and reliability of the Turkish version of the assessment (TBL-SAI-TR) among nursing students., Methods: This descriptive and psychometrics study was conducted between October and November 2022. The sample consisted of 430 nursing students. The translation-back-translation method was used to determine the language accuracy of the scale. The content validity, construct validity, item analysis, and internal consistency analysis were used as psychometrics tests of the study., Results: The Cronbach's α value of the TBL-SAI-TR was .90. The item-total correlations of the 23-item three-factor scale, validated by confirmatory factor analysis, were between .30 and .74, and the intraclass correlations were between .88 and .90., Conclusion: This study showed that the TBL-SAI-TR is a valid and reliable tool for assessing team-based learning among Turkish nursing students., Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the Non-Invasive Clinical Research Ethics Committee of the involved university (Application number: 202206Y0267 Tarih: 08.07.2022) and was conducted in accordance with the principles outlined in the Declaration of Helsinki. First, the rationale and purpose of the study were explained to the volunteer nursing students participating in the study, after which the survey link was shared. The survey was administered only to those who provided informed consent in writing. The participants were assured of the anonymity of their data, that the survey data would not be used for purposes other than the research, and that the data would be discarded after the completion of the study. Permission was obtained via e-mail from the person who developed the scale for its translation into Turkish, validity and reliability study, and use of the original title. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Objective: This study investigates how different team grouping methods and task assignments affect group dynamics, specifically focusing on the presence of free riders and active learners (those who fully engage and contribute to the group)., Methods: This study was conducted in 2020 within a Problem-Based Learning program focused on physical chemistry and pharmaceutics. A total of 255 second-year students were divided into three groups based on their career prospects, their grades, or randomly: (1) Motivation group, (2) Grade group, and (3) Randomized group. Data was collected through post-program questionnaires that included demographic questions, learning behaviors, and peer evaluations. Statistical analyses, including chi-square tests and analysis of variance, were employed to compare group dynamics and learner characteristics. Qualitative comments on team performance were analyzed through coding and categorizing., Results: The Grade group had the highest number of free riders, while the Motivation group had the most active learners. Free riders demonstrated lower grades and self-evaluations, while active learners exhibited higher grades and leadership roles. Motivation based grouping, aligning team tasks with students' career prospects, was associated with higher engagement and fewer free riders. Grouping students based on career prospects promotes active learning, while grouping by academic performance may increase free riding., Conclusion: The grouping method that combined students with various Grades may not yield positive effect, and the grouping method based on career prospects have the potential to increase student motivation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025. Published by Elsevier Inc.)

Gamification and social networking can be used as active learning tools, as they have been shown to increase learner motivation. Nevertheless, it is unclear whether they impact students' knowledge acquisition. This study aimed to share an educational experience for medical students using Kahoot and TikTok as active teaching/learning tools and assessed whether they improve academic performance. A retrospective quasi-experimental study was conducted with 176 students enrolled in radiation oncology in the 2021/22 academic year (control group), who received traditional teaching, and 166 students in the 2022/23 academic year (experimental group), where Kahoot and TikTok were added to traditional teaching. Final exam results of both groups were compared to assess learning differences, as well as those of the experimental group in terms of participation in Kahoot and TikTok and their relationship with the final grade. Comparing the average exam scores of the experimental group to the control group showed an increase of 12.9% in the first group compared to the traditional group (6.02 vs. 7.31 points, p = 0.0001). In the experimental group, students who participated in Kahoot or TikTok increased their mean score by 0.54 points (p = 0.021) and 1.57 points (p = 0.040), respectively, compared to those who did not. A correlated increase in mean test scores was observed based on tool usage: none, Kahoot or TikTok, or both (6.19, 6.88 ± 1.48, and 7.51 ± 1.20 points, p = 0.019). Using Kahoot and TikTok as active learning tools for medical students improved academic performance when integrated with traditional methodology., Competing Interests: Declarations. Ethics Approval: This study received approval from the Ethics Committee of the University of Malaga (CEUMA), code 137/202. Consent to Participate: All users voluntarily participated in sending questionnaires and signed the informed consent. All data has been treated anonymously. Consent for Publication: The authors affirm that the research participants provided informed consent for the publication of de-identified data. Conflict of Interest: The authors declare no competing interests., (© 2025. The Author(s).)

Collaboration and communication are important competencies for undergraduate life science education, as noted in the Vision and Change in Undergraduate Biology Education report. However, initiating collaboration and communication in the classroom can be an anxiety-inducing experience for many students. In contrast to traditional-style icebreakers, we introduce a course content-focused icebreaker activity that served as a group-forming undertaking on the first day of class. We developed four sets of handouts (icebreaker tickets), each having a common course theme (e.g., microbiology, cell biology, physiological system infections/disorders, virology). Students were randomly provided with a ticket at the beginning of the course, and they worked to establish groups with their peers, based on their own interpretation of the ticket's content and rationalization of a grouping scheme. Student feedback and engagement data collected from implementation at three independent institutions were largely positive, where students reported the activity to be an effective tool for building a course content-focused community of learners. The icebreaker tickets and instructor's notes disseminated in this manuscript can be adapted to fit educators' course goals and help set the tone for the first day of the class and beyond that fosters communication and collaboration among students.

Background: Chronic Kidney Disease (CKD) is a global health concern and is frequently underdiagnosed due to its subtle initial symptoms, contributing to increasing morbidity and mortality. A comprehensive understanding of CKD comorbidities could lead to the identification of risk-groups, more effective treatment and improved patient outcomes. Our research presents a two-fold objective: developing an effective machine learning (ML) workflow for text classification and entity relation extraction and assembling a broad list of diseases influencing CKD development and progression., Methods: We analysed 39,680 abstracts with CKD in the title from the Embase library. Abstracts about a disease affecting CKD development and/or progression were selected by multiple ML classifiers trained on a human-labelled sample. The best classifier was further trained with active learning. Disease names in question were extracted from the selected abstracts using a novel entity relation extraction methodology. The resulting disease list and their corresponding abstracts were manually checked and a final disease list was created., Findings: The SVM model gave the best results and was chosen for further training with active learning. This optimised ML workflow enabled us to discern 68 comorbidities across 15 ICD-10 disease groups contributing to CKD progression or development. The reading of the ML-selected abstracts showed that some diseases have direct causal effect on CKD, while others, like schizophrenia, has indirect causal effect on CKD., Interpretation: These findings have the potential to guide future CKD investigations, by facilitating the inclusion of a broader array of comorbidities in CKD prognostic models. Ultimately, our study enhances understanding of prognostic comorbidities and supports clinical practice by enabling improved patient monitoring, preventive strategies, and early detection for individuals at higher CKD development or progression risk., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2025 Sághy, Elsharkawy, Moriarty, Kovács, Wittmann and Zemplényi.)

Large labeled data bring significant performance improvement, but acquiring labeled medical data is particularly challenging due to the laborious, time-consuming, and medically qualified annotation. Semi-supervised learning has been employed to leverage unlabeled data. However, the quality and quantity of annotated data have a great influence on the performance of the semi-supervised model. Selecting informative samples through active learning is crucial and could improve model performance. Therefore, we propose a unified semi-supervised active learning architecture (RL-based SSAL) that alternately trains a semi-supervised network and performs active sample selection. Semi-supervised model is first well trained for sample selection, and selected label-required samples are annotated and added to the previously labeled dataset for subsequent semi-supervised model training. To learn to select the most informative samples, we adopt a policy learning-based approach that treats sample selection as a decision-making process. A novel reward function based on the product of predictive confidence and uncertainty is designed, aiming to select samples with high confidence and uncertainty. Comparisons with a semi-supervised baseline on collected lumbar disc herniation dataset demonstrate the effectiveness of the proposed RL-based SSAL, achieving over 3% promotion across different amounts of labeled data. Comparisons with other active learning methods and ablation studies reveal the superiority of proposed policy learning based on active sample selection and reward function. Our model trained with only 200 labeled data achieves an accuracy of 89.32% which is comparable to the performance achieved with the entire labeled dataset, demonstrating its significant advantage., Competing Interests: Declarations. Ethics Approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Review Committees of the Fifth Affiliated Hospital of Sun Yat-sen University (No. 2021 K53-1). Consent to Participate: The study was done after agreement from the hospital ethics committee and with the informed consent of the individual participants. Consent for Publication: The work is original and not previously published. All authors agreed to submit this manuscript to this journal. The authors affirm that human research participants provided informed consent for publication. Competing Interests: The authors declare no competing interests., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Objective: To assess the impact of wearing a continuous glucose monitoring (CGM) device on student pharmacist counseling ability (primary), knowledge, confidence, and empathy (secondary)., Methods: Students from two institutions were randomized to wear a CGM device (intervention) vs not (control). All received CGM education via lecture and live demonstration. Intervention group students were provided a CGM device. After two weeks, all students completed a standardized patient (SP) encounter regarding CGM prescription counseling. Pre-post CGM-related knowledge, confidence, and empathy were assessed. Analysis of variance was used to compare between-group counseling score difference, and analysis of covariance was used to compare changes in pre-post knowledge, confidence, and empathy scores. Analyses were performed using Stata/BE17.0., Results: Of 86 students enrolled, 63 consented and completed surveys (32 intervention, 31 control). The intervention group demonstrated higher average SP counseling score vs control (82.4 % vs 77.7 %, p = .046). The intervention group had greater change in confidence vs control from pre to post survey (+ 2.3 vs + 1.7, p = 0.03, range 1-5). No between-group differences were observed in knowledge or empathy., Conclusion: The opportunity to wear a CGM device resulted in higher performance on CGM counseling and greater rise in CGM-related confidence. This study supports the use of resources to provide hands-on CGM training., Practice Implications: Hands-on education about new diabetes technology can help students prepare to care for and counsel patients., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Christina Sherrill, Heather Folz reports financial support was provided by National Association of Chain Drug Stores Foundation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Conflicts of interest The authors have no relevant financial or non-financial interests to disclose., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction: In recent years, podcasts have been increasingly deployed in medical education. However, studies often fail to evaluate the learning outcomes from these podcasts effectively. The aim of this study was to determine whether the active production of podcasts enhances students' knowledge compared to the passive consumption of student-produced podcasts, as it increases the engagement with the learning content through active learning., Methods: The study was conducted at a German medical school during the fifth-year paediatrics block internship. The final sample consisted of 86 students who were divided into small groups that produced podcasts on one paediatric topic on the first day and listened to a podcast on another topic on the second day., Results: The first group, which produced a podcast on topic A and only listened to a podcast on topic B, performed significantly better on questions on topic A than on questions on topic B, (p < 0.01, d = 0.63). This effect was not observed in the second group, which produced a podcast on topic B and listened to a podcast on topic A (p = 0.81, d = 0.04). Additionally, it was found that a longer time interval between podcast production and the knowledge test led to poorer memory performance (β = 1.10, p = 0.04)., Conclusion: While the differing results between the two groups may be due to a lack of power to detect meaningful differences, this study nonetheless provides initial evidence that the active production of podcasts may enhance the learning outcomes of medical students., Trial Registration: Not applicable., (© 2025 The Author(s). The Clinical Teacher published by Association for the Study of Medical Education and John Wiley & Sons Ltd.)

Self-study is essential for student learning, and active learning methods aim to facilitate constructive self-study time usage. How active learning strategies actually affect student self-study in medical education, however, remains partly unknown. The aim of this study was to examine medical students' use of self-study time in a longitudinal integrated clerkship employing active learning strategies. Week-long time logs were used to collect quantitative data on students' scholastic activities. Furthermore, semistructured individual interviews were conducted for in-depth exploration of different learning activities' influence on students' self-study behaviour. Preparation for and follow-up work after scheduled learning activities were specifically addressed. Students spent on average 42.1 h per week on educational activities, of which self-study comprised 23 h. Preparation for upcoming learning activities was highly prioritized by the students. Team-based learning (TBL) generated most self-study hours, closely followed by patient encounters, while problem-based learning (PBL) induced least amounts of self-study. 50% of self-study time was done without specific relation to scheduled learning activities. Active learning methods appear to promote preparatory work, in accordance with their pedagogic theory. Type of learning activity appears to be decisive in determining the amount of self-study students choose to undertake. Future research should address how quantity and content of different learning activities influence self-study behaviour., (© 2024 The Author(s). The Clinical Teacher published by Association for the Study of Medical Education and John Wiley & Sons Ltd.)

Federated learning (FL) enables multiple institutes to train models collaboratively without sharing private data. Current FL research focuses on communication efficiency, privacy protection, and personalization and assumes that the data of FL have already been ideally collected. However, in medical scenarios, data annotation demands both expertise and intensive labor, which is a critical problem in FL. Active learning (AL) has shown promising performance in reducing the number of data annotations in medical image analysis. We propose a federated AL framework in which AL is executed periodically and interactively under FL. We exploit a local model in each hospital and a global model acquired from FL to construct an ensemble. We use ensemble entropy-based AL as an efficient data-annotation strategy in FL. Therefore, our federated AL framework can decrease the amount of annotated data and preserve patient privacy while maintaining the performance of FL. To our knowledge, this federated AL framework applied to medical images has not been previously reported. We validated our framework on real-world dermoscopic datasets. Using only 50% of samples, our framework was able to achieve state-of-the-art performance on a skin-lesion classification task. Our framework performed better than several state-of-the-art AL methods under FL and achieved comparable performance with full-data FL., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Undergraduate courses in immunology are content-heavy and combined with a new, complex vocabulary, can be an overwhelming subject for students. In-class active learning approaches have been found to improve understanding of difficult concepts in science, technology, engineering and mathematics (STEM) disciplines; however, many undergraduate courses maintain a high dependence on lecture-style teaching because of time constraints, content demands and student resistance. We designed an online, out-of-class activity, the "Life and Death of a T cell", to complement a lecture on a complex immunological concept, T-cell development. Inspired by the "Choose Your Own Adventure" children's books, a fictional narrative was created in which students assume the role of a cell with a dream of becoming a helper T cell. Decision-making scenarios then prompt students to draw on their knowledge from the lecture to successfully navigate the steps of T-cell development. The activity was built on two platforms, Google Forms and H5P (HTML 5 Package), both of which are readily accessible and allow the inclusion of branching logic and the creation of a decision tree-based activity. An anonymous survey revealed that students found this interactive approach enjoyable, and their perceived understanding of the content significantly increased. Students appreciated the inclusion of a novel learning resource, with requests for similar activities to be developed for other immunological concepts. In conclusion, we developed a narrative-based, decision-making activity to complement a lecture on T-cell development. As an out-of-class activity, this style of learning approach can potentially capitalize on the benefits of active learning, while also overcoming barriers of student resistance., (© 2024 the Australian and New Zealand Society for Immunology, Inc.)

Background: Educational research highlights active approaches to learning are more effective in knowledge retention and problem-solving. It has long been acknowledged that adapting to more active ways of learning form part of the challenge for new university students as the pedagogical distance between the didactical approach largely followed by secondary school systems the world over differs quite significantly from the often more student-led, critical approach taken by universities. University students encounter various learning challenges, particularly during the transition from secondary school to university. Poor adaptation and low performance in the first year of tertiary education can lead to higher failure rates and potential withdrawal from study programmes. Adopting active learning strategies early in this transition phase is crucial for supporting students' adaptation and success. Gaining student engagement with active learning can be a significant challenge when there is an expectation to participate in a discussion or voice an opinion. Case-based learning (CBL), with its scaffolded form of learning, is an approach that could provide the support needed to help multicultural learners adapt to their new learning environment in a non-threatening classroom-based setting. The research question in this study was: what features of CBL support active learning?, Methods: Data was collected using Structured Group Feedback Sessions (SGFS) from 36 students from 12 different countries. Students were placed in eight Structured Group Feedback sessions, a method that facilitates structured discussions and is effect in curriculum evaluation and feedback. The Experience Based Learning model was used as the conceptual framework to guide the analysis, which was completed using the framework analysis method., Results: Themes were derived from the Experience Based Learning model: affective, pedagogical, and organisational and analysed according to the research question. We found CBL can be used to facilitate active learning with all students at a multicultural medical university. We identified six learning points to highlight features of CBL that support active learning: CBL increased contact with peers and facilitated student bonding; students need to feel psychologically safe to participate; prior learning can enhance confidence to participate; facilitators need to be aware of their role, know about psychological safety, and manage student participation including the dominant voice; some students have a lower tolerance of uncertainty and need additional clarity at the end either via the facilitator or additional notes that provide the key learning points to take away; students became more engaged when a case is aligned to a real patient case giving it authenticity., Conclusions: This study explores how CBL can support active learning in a multicultural medical school. We identified that CBL did facilitate active learning and students engaged with it and enjoyed it. We identified six learning points to support others going forward., Competing Interests: Declarations. Ethics approval and consent to participate: Written informed consent was obtained from each student, who were informed of their right to withdraw from the study at any time without any adverse consequences. This study was approved (application No. 212624603) by the Research Ethics committee of RCSI University of Medicine and Health Sciences. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Large group collaborative teaching approaches are rapidly gaining popularity in undergraduate medical education. The case-based collaborative Learning (CBCL) pedagogy was instituted for pre-clerkship teaching at Harvard Medical School in 2015 with subsequent implementation at other medical schools. CBCL emphasizes inductive reasoning, integrates basic and clinical sciences, stimulates curiosity, and fosters teamwork. Given the ongoing educational evolution, guidance on designing and facilitating collaborative learning sessions, such as CBCL may benefit faculty in their instructional design efforts. This perspective article describes strategies to create effective collaborative sessions using CBCL as an example. We reviewed the literature and summarized ten years of experience in CBCL teaching through the lens of contemporary theories of teaching and learning. The recommendations are organized into three main domains: Instructional Design, Facilitation, and Professional Transformation, each aligned with the theoretical principles of CBCL. The recommendations provide a conceptual model to assist faculty in designing engaging and effective class materials and support students' professional transformation during collaborative learning sessions., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2025.)

Background: In the world of academia, traditional lecturing has been the most common pedagogical approach for centuries. However, it can create an environment for students to be passive learners in the classroom. Alternatively, active learning is a pedagogical approach intended to encourage students to engage with content in manners which have been associated with improved exam performance, final course grades, clinical reasoning skills, and critical thinking skills. Given that perfusion is inherently a profession where didactic information must be applied in practice, the implementation of active learning in didactic courses is worth considering. Although active learning is practiced in perfusion education through participation in simulations and clinical rotations, there are no easily identifiable active learning resources available to supplement didactic perfusion courses. In response, written active learning activities were specifically designed for didactic perfusion courses., Methods: The activities were designed based on the format for guided inquiry worksheets, the incorporation of the learning cycle, and the implementation of process oriented guided inquiry learning (POGIL) strategies. Key concepts were identified, learning objectives were specified, and models (figures or tables of data) were created as targets of engagement and analysis. Following each model, questions and prompts are given to guide students through identifying, understanding, and exploring the concepts incorporated in the activity as well as opportunities to apply the newly gained knowledge and understanding., Results: Ten active learning activities have been developed based on the methods summarized above., Conclusion: While the purposes of this paper are to inform the perfusion community of active learning and to share guidelines on how to create active learning activities for didactic perfusion courses, the goals of the activities are to provide examples to perfusion educators and to guide students in understanding basic perfusion concepts in a clinically relevant manner. It is recommended that perfusion students complete these activities alongside their didactic perfusion courses., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background and Aims: The primary teaching approach known as "traditional lecture" has drawbacks, including being dull and reducing student participation, which has made students feel negatively about it. It seems that by implementing certain changes, active learning techniques like the "Audience Response System" could alter students' perceptions of lectures. The purpose of this study is to find out how employing "ARS" throughout a course has affected nursing students' perceptions of traditional lectures., Methods: In present research, 44 nursing students participated in quasi-experimental study (Pretest-Posttest Design). A 5-item, 5-point Likert questionnaire and a pre-test question regarding the degree of participation during lecture were used to gauge the students' attitudes towards lectures. Following that, a six-session pharmacology course including "Lectures + ARS" was held. Following the completion of the course, the original questionnaire, a posttest question regarding attitudes, and four 3-point Likert items regarding students' opinions regarding the use of ARS going forward were used to assess changes in the students' attitudes towards lectures. SPSS Ver. 26 was used for the data analysis. p  < 0.05 was taken into consideration as the significance threshold., Results: In comparison to before the course, the students' attitudes towards lectures had changed in a positive and significant way, specifically in items such as learning quality improvement, decreasing boredom, increasing participation and class activity. However, in the domain of "decreasing the use of lecture," the ARS had failed to change the students' attitudes. 79.6% of students stated that the ARS had changed their attitudes towards lectures. More than 90% of students agreed with continued use of ARS in the future., Conclusion: Lecturing could transform from a passive position to an active position by implementing ARS. Lecture + ARS could lead to improvement in students' class participation and activity, both of which are essential factors of an optimal education., Competing Interests: All authors have read and approved the final version of the manuscript. FARZAD ZAREIE had full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis., (© 2025 The Author(s). Health Science Reports published by Wiley Periodicals LLC.)

The soil ecosystem has been severely damaged because of the increasingly severe environmental problems caused by excessive application of phosphorus (P) fertilizer, which seriously hinders soil fertility restoration and sustainable farmland development. Shoot P uptake (SPU) is an important parameter for monitoring crop growth and health and for improving field nutrition management and fertilization strategies. Achieving on-site measurement of large-scale data is difficult, and effective nondestructive prediction methods are lacking. Improving spatiotemporal SPU estimation at the regional scale still poses challenges. In this study, we proposed a combination prediction model based on some representative samples. Furthermore, using the experimental area of Henan Province, as an example, we explored the potential of the hyperspectral prediction of maize SPU at the canopy scale. The combination model comprises predicted P uptake by maize leaves, stems, and grains. Results show that (1) the prediction accuracy of the combined prediction model has been greatly improved compared with simple empirical prediction models, with accuracy test results of R = 0.87, root mean square error = 2.39 kg/ha, and relative percentage difference = 2.71. (2) In performance tests with different sample sizes, two-dimensional correlation spectroscopy i.e., first-order differentially enhanced two-dimensional correlation spectroscopy (1Der-2DCOS) and two-trace 2DCOS of enhanced filling and milk stages (filling-milk-2T2DCOS)) can effectively and robustly extract spectral trait relationships, with good robustness, and can achieve efficient prediction based on small samples. (3) The hybrid model constrained by the Newton-Raphson-based optimizer's active learning method can effectively filter localized simulation data and achieve localization of simulation data in different regions when solving practical problems, improving the hybrid model's prediction accuracy. The practice has shown that with a small number of representative samples, this method can fully utilize remote sensing technology to predict SPU, providing an evaluation tool for the sustainable use of agricultural P. Therefore, this method has good application prospects and is expected to become an important means of monitoring global soil P surplus, promoting sustainable agricultural development.2 = 0.87, root mean square error = 2.39 kg/ha, and relative percentage difference = 2.71. (2) In performance tests with different sample sizes, two-dimensional correlation spectroscopy i.e., first-order differentially enhanced two-dimensional correlation spectroscopy (1Der-2DCOS) and two-trace 2DCOS of enhanced filling and milk stages (filling-milk-2T2DCOS)) can effectively and robustly extract spectral trait relationships, with good robustness, and can achieve efficient prediction based on small samples. (3) The hybrid model constrained by the Newton-Raphson-based optimizer's active learning method can effectively filter localized simulation data and achieve localization of simulation data in different regions when solving practical problems, improving the hybrid model's prediction accuracy. The practice has shown that with a small number of representative samples, this method can fully utilize remote sensing technology to predict SPU, providing an evaluation tool for the sustainable use of agricultural P. Therefore, this method has good application prospects and is expected to become an important means of monitoring global soil P surplus, promoting sustainable agricultural development., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2025 Wang, Zhang, Liu, Li, Guo, Cao and Zhang.)

Background: Computer-assisted learning (CAL) has the potential to enhance learning outcomes and satisfaction. However, there are limited reports in the literature that describe or evaluate the implementation of this method to promote competency-based learning in removable partial denture (RPD) design. Therefore, this study aimed to: (1) compare the effectiveness of different learning methods using a 3D software-aided RPD design program, (2) evaluate the learning outcomes associated with these different methods following active learning, and (3) assess students' satisfaction., Methods: Forty undergraduate dentistry students were divided into four groups of ten students. Each group was assigned different learning methods for a period of 28 days using the CMU-3D RPD Design program. The learning methods included lecture-based learning (LBL), case-based learning (CBL), a combination of LBL and CBL, and a control group that did not engage in LBL or CBL. Following this period, participants in all four groups engaged in active learning activities related to RPD design. Learning outcomes were measured three times: prior to the experiment, after using the software, and after participating in active learning activities., Results: Analysis of pre- and post-training assessments using multiple-choice questions revealed that dental students in the combined LBL and CBL group demonstrated statistically significant improvements in test scores compared to baseline (p < 0.05). Notably, there were marked enhancements in comprehension, application, and analysis skills. Outcomes following active learning were significantly improved in groups with a foundation in lecture-based learning. Furthermore, all groups reported high levels of satisfaction (over 90%) after utilizing the software and participating in active learning activities., Conclusions: Computer-assisted learning, combined with active learning strategies, improves learners' outcomes and satisfaction. Learning RPD design through computer-assisted methods positively influenced learning outcomes, particularly when both lecture-based and case-based learning approaches were used. Active learning strategies resulted in more substantial improvements when applied to students with a strong foundation in lecture-based learning., Competing Interests: Declarations. Ethics approval and consent to participate: The study was conducted in accordance with the Declaration of Helsinki and approved by the Human Experimentation Committee of Faculty of Dentistry Chiang Mai University, Thailand (9/2023, February 22, 2023). Informed consent was obtained from all subjects involved in the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

With the increasing integration of machine learning into IoT devices, managing energy consumption and data transmission has become a critical challenge. Many IoT applications depend on complex computations performed on server-side infrastructure, necessitating efficient methods to reduce unnecessary data transmission. One promising solution involves deploying compact machine learning models near sensors, enabling intelligent identification and transmission of only relevant data frames. However, existing near-sensor models lack adaptability, as they require extensive pre-training and are often rigidly configured prior to deployment. This paper proposes a novel framework that fuses online learning, active learning, and knowledge distillation to enable adaptive, resource-efficient near-sensor intelligence. Our approach allows near-sensor models to dynamically fine-tune their parameters post-deployment using online learning, eliminating the need for extensive pre-labeling and training. Through a sequential training and execution process, the framework achieves continuous adaptability without prior knowledge of the deployment environment. To enhance performance while preserving model efficiency, we integrate knowledge distillation, enabling the transfer of critical insights from a larger teacher model to a compact student model. Additionally, active learning reduces the required training data while maintaining competitive performance. We validated our framework on both benchmark data from the MS COCO dataset and in a simulated IoT environment. The results demonstrate significant improvements in energy efficiency and data transmission optimization, highlighting the practical applicability of our method in real-world IoT scenarios., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2025 Rezvani, Huang, Chen, Ni and Imani.)

Background: In contemporary medical education, it is essential to raise student involvement and active participation in the learning process. By contrasting small-group peer learning modules with teacher-led conventional tutorial sessions, we aim to provide insights into their respective influences on learning outcomes and the overall learning experience among 150 first-year medical students., Methods: Each group consisted of 50 students. These students were further divided into two groups and a pretest was administered on the day of the session. One group engaged in conventional tutorials, while the other participated in a peer learning module. Post-tests and session feedback were provided after each session., Results: Results from the posttest revealed advancement in both learning approaches compared to the pretest. Compared to tutorials, the level of progress was much higher following peer learning with a p-value of < 0.05. Participants felt that while the tutorials helped them cover the full subject and saved time, they occasionally got monotonous and there was little active engagement. Students who participated in the peer learning method said that while interaction aided in a better learning experience, improved communication skills, and had more active participation, there was less time for discussion and some group members were reticent and ineffective in explaining the concepts., Conclusions: The peer learning module is thought to be superior to conventional tutorial classes since it promises active involvement from all students, promotes greater learning, and aids in skill improvement, thus assisting students to help each other in gaining insight into the process of active learning., Competing Interests: Declarations. Ethical approval and consent to participate: We confirm that our study was conducted in full compliance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval for the study was obtained from the Institutional Ethics Committee of PES Institute of Medical Sciences and Research, Kuppam (PESIMSR/IHEC/42), and all participants provided informed consent prior to their inclusion in the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Deep learning is a promising strategy for modeling cis-regulatory elements. However, models trained on genomic sequences often fail to explain why the same transcription factor can activate or repress transcription in different contexts. To address this limitation, we developed an active learning approach to train models that distinguish between enhancers and silencers composed of binding sites for the photoreceptor transcription factor cone-rod homeobox (CRX). After training the model on nearly all bound CRX sites from the genome, we coupled synthetic biology with uncertainty sampling to generate additional rounds of informative training data. This allowed us to iteratively train models on data from multiple rounds of massively parallel reporter assays. The ability of the resulting models to discriminate between CRX sites with identical sequence but opposite functions establishes active learning as an effective strategy to train models of regulatory DNA. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests B.A.C. is on the scientific advisory board of Patch Biosciences., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: Active learning can significantly decrease the labeling cost of deep learning workflows by prioritizing the limited labeling budget to high-impact data points that have the highest positive impact on model accuracy. Active learning is especially useful for semantic segmentation tasks where we can selectively label only a few high-impact regions within these high-impact images. Most established regional active learning algorithms deploy a static-budget querying strategy where a fixed percentage of regions are queried in each image. A static budget could result in over- or under-labeling images as the number of high-impact regions in each image can vary., Methods: In this paper, we present a novel dynamic-budget superpixel querying strategy that can query the optimal numbers of high-uncertainty superpixels in an image to improve the querying efficiency of regional active learning algorithms designed for semantic segmentation., Results: For two distinct datasets, we show that by allowing a dynamic budget for each image, the active learning algorithm is more effective compared to static-budget querying at the same low total labeling budget. We investigate both low- and high-budget scenarios and the impact of superpixel size on our dynamic active learning scheme. In a low-budget scenario, our dynamic-budget querying outperforms static-budget querying by 5.6% mIoU on a specialized agriculture field image dataset and 2.4% mIoU on Cityscapes., Discussion: The presented dynamic-budget querying strategy is simple, effective, and can be easily adapted to other regional active learning algorithms to further improve the data efficiency of semantic segmentation tasks., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2025 Wang and Stavness.)

Active learning on graphs (ALG) has emerged as a compelling research field due to its capacity to address the challenge of label scarcity. Existing ALG methods incorporate diversity into their query strategies to maximize the gains from node sampling, improving robustness and reducing redundancy in graph learning. However, they often overlook the complex entanglement of latent factors inherent in graph-structured data. This oversight can lead to a sampling process that fails to ensure diversity at a finer-grained level, thereby missing the opportunity to sample more valuable nodes. To this end, we propose a novel approach, Disentangled Active Learning on Graphs (DALG). In this work, we first design the Disenconv-AL layer to learn disentangled feature embedding, then construct the influence graph for each node and create a dedicated "memory list" to store the resultant influence weights. On this basis, our approach aims to make the model not excessively focus on a few latent factors during the sampling phase. Specifically, we prioritize addressing latent factors with the most significant impact on the sampled node in the previous round, thereby ensuring that current sampling can better focus on other latent factors. Compared with existing methodologies, our approach pioneers reach diversity from the latent factor that drives the formation of graph data at a finer-grained level, thereby enabling further improvements in the benefits delivered with a limited labeling budget. Extensive experiments across eight public datasets show that DALG surpasses state-of-the-art graph active learning methods, achieving an improvement of up to approximately 15% in both Micro-F1 and Macro-F1., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025. Published by Elsevier Ltd.)

Responsible conduct of research and ethics are key components of microbiology and are carefully considered at all steps from project conception, design, data analysis to publication, and post-publication use of samples. Yet stand-alone courses covering these topics are not always core components of undergraduate biomedical education curricula. Here, we describe a classroom activity for undergraduate students designed to promote increased understanding of ethics and experimental design in biomedical research.

Accurate prediction of physicochemical properties, such as electronic energy, enthalpy, free energy, and average vibrational frequencies, is critical for optimizing lithium-ion battery (LIB) performance. Traditional methods like density functional theory (DFT) are computationally expensive and inefficient for large-scale screening. In this study, we apply active learning on top of graph neural networks (GNNs) to efficiently predict these properties. By focusing on uncertain data points, active learning reduces training data size while maintaining high accuracy. Applied to the LIBE and MPcules datasets, the model achieved an R-squared (R 2 ) values of 0.9977 with a mean absolute error (MAE) of 9.66 Ha for electronic energy and an R 2 values of 0.957 with an MAE of 13.94 cm -1 for average vibrational frequencies. SHapley Additive exPlanations (SHAP) provided insights into key features influencing predictions, such as atomic number and spin multiplicity. This approach enhances both predictive accuracy and model interpretability, offering a scalable solution for LIB electrolyte discovery., (© 2024 Wiley Periodicals LLC.)

The increasing prevalence of diabetes mellitus worldwide necessitates that medical undergraduates acquire a deep understanding of the disease to ensure accurate diagnosis and effective management. Traditional teaching methods, while foundational, often lack the interactive elements that enhance student engagement and knowledge retention. This study aimed to evaluate the effectiveness of a novel educational board game, "Diabe-teach," in enhancing knowledge retention among medical students compared with conventional self-study methods. A randomized controlled design involving 56 preclinical medical students at Dubai Medical College for Girls was used. The participants were randomly assigned to either the game-based learning group or the control group, with both groups receiving identical instructional materials on diabetes. Knowledge retention was assessed via pre- and post-test scores, whereas student engagement and perceptions were measured via a feedback survey. The results indicated that while both groups showed significant improvement in post-test scores (p < .001), the game-based group demonstrated a notably greater frequency of correct responses and a greater tendency to recognize knowledge gaps, as evidenced by the increased "I do not know" responses compared with incorrect guesses in the control group. Feedback from the game-based learning group highlighted enhanced confidence, improved communication skills, and a preference for the gamified approach over traditional methods. The findings suggest that the "Diabe-teach" board game is an effective tool for teaching complex medical concepts, fostering both knowledge retention and critical self-assessment among students. These results contribute to the growing body of literature on game-based learning, supporting its potential as a valuable adjunct to traditional medical education methods. Further research could explore the application of this approach across other medical disciplines., Competing Interests: Declarations. Ethics approval and consent to participate: The Dubai Medical College for Girls Institutional Review Board approved this study (Ref No: IBR2022-23-F-17). Informed consent was obtained from all participants in the study and conformed to the principles outlined in the 1964 Declaration of Helsinki. Consent for publication: Yes. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

The Fontan procedure is the definitive palliation for pediatric patients born with single ventricles. Surgical planning for the Fontan procedure has emerged as a promising vehicle toward optimizing outcomes, where pre-operative measurements are used prospectively as post-operative boundary conditions for simulation. Nevertheless, actual post-operative measurements can be very different from pre-operative states, which raises questions for the accuracy of surgical planning. The goal of this study is to apply machine leaning techniques to describing pre-operative and post-operative vena caval flow conditions in Fontan patients in order to develop predictions of post-operative boundary conditions to be used in surgical planning. Based on a virtual cohort synthesized by lumped-parameter models, we proposed a novel diversity-aware generative adversarial active learning framework to successfully train predictive deep neural networks on very limited amount of cases that are generally faced by cardiovascular studies. Results of 14 groups of experiments uniquely combining different data query strategies, metrics, and data augmentation options with generative adversarial networks demonstrated that the highest overall prediction accuracy and coefficient of determination were exhibited by the proposed method. This framework serves as a first step toward deep learning for cardiovascular flow prediction/regression with reduced labeling requirements and augmented learning space., Competing Interests: Declarations. Conflict of interest: The authors have no conflict of interest to disclose with the research presented here., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

This paper evaluates an innovative student-led nursing leadership symposium at a Southern California university, specifically designed to bridge educational and practical leadership gaps in nursing with a focus on students from underrepresented groups. Integrating Quality and Safety Education for Nurses (QSEN) competencies, the symposium encouraged active participation, self-reflection, and teamwork. It featured interactive panel discussions with nurse leaders and collaborative learning opportunities aimed at developing leadership knowledge, skills, and action. Participant feedback underscored significant personal and professional growth, particularly in communication, delegation, and collaboration skills. This initiative contributes significantly to the discourse on diversifying nursing leadership and provides a replicable model for leadership development in nursing education programs, emphasizing the symposium's pivotal role in enhancing inclusivity and equipping future nurse leaders from diverse backgrounds., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Published by Elsevier Inc.)

This case report describes a 45-min active learning lesson plan that engages 4th-5th and 6th-8th grade school students in spatial reasoning through a review of medical imaging. The lesson plan reviews different planar orientations and cross-sections of computed tomography (CT) images of familiar objects. The lesson is designed to introduce students to the idea that scientists are key contributors to healthcare, including in medical imaging technologies that facilitate the visualization of internal structures of patients without invasive procedures. The lesson demonstrates the three standard anatomical planes, axial, sagittal and coronal, by guiding students through CT image datasets of various objects. Students then are led in an interactive "dissection" of fruit to compare internal structures with medical images. The lesson plan aligns with key aspects of Next Generation Science Standards and aims to spark interest in the field of medical physics among a young student population through an introduction to imaging technologies. Worksheets and imaging datasets are included as supplementary materials to facilitate interested physicists adapting this work for educational purposes in their own communities, with minimal repeated effort., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

Background: Ungrading is an alternative assessment approach that focuses on feedback provided to students in place of grades. Giving feedback as opposed to grades has been shown to increase intrinsic learner motivation, promote deeper learning, and decrease stress and anxiety., Educational Activity: Four pharmacy faculty implemented ungrading practices into required pharmacy courses, including two pharmacy practice lab courses and two didactic courses. Specific strategies such as formative feedback, peer assessment, self-reflection, and small and large group discussions were incorporated to achieve the learning objectives for each course. This work provides details about the assignments and assessments used in each course., Critical Analysis of Educational Activity: Anonymous surveys were administered to faculty members and students at a Midwest School of Pharmacy. Faculty members expressed varying levels of satisfaction and stress related to grading assessments. They also shared their opinions on teaching courses without traditional grades. The student survey examined factors such as grade point average (GPA), study time, and enjoyment of the pharmacy program. It also captured general opinions about grades. Based on survey results the implementation of ungrading practices helped students feel more prepared for advanced pharmacy practice experiences (APPEs). The findings contribute to the understanding of alternative grading approaches and their potential impact on student learning, well-being, and motivation. Rethinking assessment of student work may be a valid option for pharmacy schools, particularly in the setting of increased focus on student wellness and the educator's goal of increasing learning for all students., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: Choose Your Own Adventure (CYOA) educational activities can be utilized in the classroom to simulate patient cases. The objective of this study was to evaluate changes in pharmacy student knowledge and confidence in hypertension, heart failure, and insulin management skills after completion of CYOA activities., Methods: Three CYOA activities were developed for 3 separate lectures in 2 pharmacy electives on topics of hypertension, heart failure, and insulin management. Two different online platforms were used for the activities. Before and after each activity, instructor-created surveys were administered to students to assess knowledge, confidence, and satisfaction., Results: Overall, 110 potential respondents completed at least 1 of the 3 activities between 2021 and 2023. Average knowledge and confidence improved for each disease-state individually and when combined as a whole. Satisfaction was high (>4.5/5) for all activities and no differences were seen between activity platforms., Conclusion: The CYOA activities can be successfully implemented in Doctor of Pharmacy elective classrooms using a digital platform for a variety of disease states and skills. Knowledge and confidence were improved in each individual topic and overall. Students enjoyed this activity regardless of the platform. Validated surveys and follow-up assessments would be beneficial in the future to determine if knowledge gains are sustainable., Competing Interests: Declaration of Competing Interest The authors have no financial disclosures or conflicts of interest. The authors did receive an internal grant from the University of Texas Faculty Innovation Center to fund the use of advanced features of Typeform., (Copyright © 2024 American Association of Colleges of Pharmacy. Published by Elsevier Inc. All rights reserved.)

Objective: The objectives of this study are to describe 1) the implementation of and 2) student performance and perceptions during an innovative classroom activity., Methods: The Bootcamp was designed as an exercise in productive failure for second-year PharmD students enrolled in a community pharmacy elective course. A prescription verification exercise was introduced; however, students were intermittently interrupted with common community pharmacist tasks. To simulate these tasks (e.g.: receiving a phone call, counseling a patient, administering a vaccine, checking controlled prescription monitoring database etc.), students were directed by the instructor to perform other physical activities (e.g. walking around the classroom, doing jumping jacks, sending an email, etc.). After the activity, accuracy was assessed and each student uploaded a video reflection which were analyzed with content analysis., Results: The activity was delivered in person in 2021 and virtually in 2022 during a two-hour class period. Of the 63 students, 86 % fully completed all 12 prescription verifications in the allotted time; however, the average accuracy was 53 %. Content analysis of video reflections generated three themes: unique and fun activity, importance of attention to detail, and enhanced understanding of pharmacy workflow from the pharmacist's perspective., Conclusions: The Community Pharmacy Bootcamp was a fun and engaging way to expose students to the realities of daily practice in the community pharmacy setting. This activity was effective in helping students discover the need for development of further skills in addition to clinical expertise. This novel activity may be adapted to other environments including integration into required coursework., Competing Interests: Declaration of competing interest The authors have no funding or conflicts of interest to disclose., (Published by Elsevier Inc.)

During the early stages of drug design, identifying compounds with suitable bioactivities is crucial. Given the vast array of potential drug databases, it's feasible to assay only a limited subset of candidates. The optimal method for selecting the candidates, aiming to minimize the overall number of assays, involves an active learning (AL) approach. In this work, we benchmarked a range of AL strategies with two main objectives: (1) to identify a strategy that ensures high model performance and (2) to select molecules with desired properties using minimal assays. To evaluate the different AL strategies, we employed the simulated AL workflow based on "virtual" experiments. These experiments leveraged ChEMBL datasets, which come with known biological activity values for the molecules. Furthermore, for classification tasks, we proposed the hybrid selection strategy that unified both exploration and exploitation AL strategies into a single acquisition function, defined by parameters n and c. We have also shown that popular minimal margin and maximal variance selection approaches for exploration selection correspond to minimization of the hybrid acquisition function with n=1 and 2 respectively. The balance between the exploration and exploitation strategies can be adjusted using a coefficient (c), making the optimal strategy selection straightforward. The primary strength of the hybrid selection method lies in its adaptability; it offers the flexibility to adjust the criteria for molecule selection based on the specific task by modifying the value of the contribution coefficient. Our analysis revealed that, in regression tasks, AL strategies didn't succeed at ensuring high model performance, however, they were successful in selecting molecules with desired properties using minimal number of tests. In analogous experiments in classification tasks, exploration strategy and the hybrid selection function with a constant c<1 (for n=1) and c≤0.2 (for n=2) were effective in achieving the goal of constructing a high-performance predictive model using minimal data. When searching for molecules with desired properties, exploitation, and the hybrid function with c≥1 (n=1) and c≥0.7 (n=2) demonstrated efficiency identifying molecules in fewer iterations compared to random selection method. Notably, when the hybrid function was set to an intermediate coefficient value (c=0.7), it successfully addressed both tasks simultaneously., (© 2025 Wiley-VCH GmbH.)

Objectives: To explore the effectiveness of two educational strategies for teaching empathy to medical students, focusing on their skills and on the theoretical knowledge and self-perception of empathy by patients, professors, and observers., Methods: This is a randomized controlled study. Clerkship students were divided into two groups: an active group - Role Play and Reflection Group(RRG), and a more traditional group - Lecture Presentation Group(LPG). They were assessed pre- and post-intervention using the Jefferson Empathy Scale(JSPE), ESWIM Empathy Scale, CARE, empathic skills through simulated patient encounters, and a theoretical knowledge test., Results: A total of 169 students were included. The RRG had higher scores (d=0.81) and greater gains (d=0.82) in empathic skills than the LPG. However, there were no significant differences in knowledge or other scales. Upon assessing skill acquisition (Post-Pre gains), there was a significant increase in both groups for all outcomes, except for JSPE in the LPG group and ESWIM for both groups., Conclusion: Medical empathy can be taught, and active learning methodologies can lead to better outcomes, especially on the ability to provide empathic care., Practice Implications: These findings reinforce existing literature and helps medical schools make informed decisions regarding their teaching methodologies., Trial Registry: https://ensaiosclinicos.gov.br/rg/RBR-8rwx89c., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Team-based learning (TBL) is an active learning instructional strategy shown to improve student learning in large-enrollment courses. Although early implementations of TBL proved generally effective in an undergraduate exercise physiology course that delivered an online individual readiness assurance test (iRAT) before class, the instructor reported student dissatisfaction with the use of identical questions in the team readiness assurance test (tRAT) in class. This study sought to improve the student experience in this course by including different but related question sets for the online iRAT and in-class tRAT. Two sections of an upper-level undergraduate exercise physiology course received both the traditional and modified tRAT, alternating approaches with each course unit. This crossover research design exposed more students to the proposed modification and provided more student perspectives than would be collected through other research designs. An independent-sample t test indicated that the modified TBL format made no difference on performance on course unit exams ( P > 0.05). However, student survey qualitative data revealed that 69% of students preferred the modified tRAT method to the traditional form. Student responses on the benefits of the modification, represented here as major themes in the analysis, included better content interaction, use of higher-order thinking, and more effective social experience with teams. Although this study showed that different sets of questions for the individual and team quizzes improved the experience but not performance for the majority of students, some students suggested changes to the modification that could further improve the student experience with TBL. NEW & NOTEWORTHY In an exercise physiology course using an already-modified team-based learning approach, this crossover-designed pedagogy trial investigated the learning impact and student experience of introducing different (but related) questions in the in-class team readiness assurance test (tRAT) compared to those given in the individual readiness assurance test (iRAT) that students completed online before class. This approach may be of interest for instructors experimenting with partially flipped classroom designs in a team-based context.

The implementation of active learning methods poses challenges for both instructors and students. Despite institutional support, some educators may encounter difficulties in effectively incorporating this methodology into their teaching practices. We hypothesized that one contributing factor could be the misguided self-perception regarding their class methodology. The objective of this study was to assess whether instructors and students can accurately recognize the application of active learning methods during classes and to identify the characteristics of these classes that influence the satisfaction of both educators and learners. A cross-sectional observational study was conducted during the emergency remote teaching caused by the COVID-19 pandemic. We examined 58 class sessions from a medical school program committed to active learning methodologies using an adapted version of the PORTAAL tool and administered postclass surveys to both instructors and students. Students, but not instructors, were able to accurately recognize the use of active learning methods. Additionally, class satisfaction reported by both instructors and students positively correlated with their self-perceived use of active learning methods in the class, regardless of whether the perception was more accurate, as seen in the students' perceptions, or more inaccurate, as observed in instructors' misperceptions. NEW & NOTEWORTHY This study assesses the recognition and satisfaction of active learning methods among instructors and students during emergency remote teaching due to COVID-19. We examined 58 medical school sessions using the PORTAAL tool and postclass surveys. Findings show students accurately identified active learning, while instructors did not. Satisfaction correlated with perceived active learning use, regardless of accuracy, highlighting the importance of aligning self-perception with actual teaching practices to enhance educational outcomes.

Background: The development of socioemotional skills in the education of nursing students is crucial for improving the quality of care, patient safety, and job satisfaction. However, it is often observed that educational programs do not include sufficient training in these skills, which motivates the implementation of specific programs., Aim: To evaluate the effectiveness of a socioemotional skills training program for nursing students., Design: A convergent mixed method design., Settings and Participants: The study was conducted in a public nursing school the University of Cantabria. In total, 116 s-year students participated in the quantitative part of the study, of which 44 also participated in the qualitative study., Intervention: The students attended a 21-h training program in socio-emotional skills through active learning methodologies, such as flipped classroom, reflective dialogue, and simulation., Methods: The study consisted of two parts. First, a single-group quasi-experimental design with pretest and posttest was used. The students completed the Communication Skills Questionnaire (HABICOM). The second part consisted of a descriptive-exploratory qualitative study, in which the participants were distributed into six focus groups for in-depth analysis of their experiences and perceptions., Results: The quantitative analysis indicated significant improvements in socioemotional skills post-training, particularly in non-verbal communication and information transmission. Notable improvements were observed among younger female students and those with less prior experience in socioemotional training. The qualitative findings highlighted that students valued the realism and dynamic nature of the training, CONCLUSIONS: The training program effectively enhanced socioemotional skills, with significant gains noted in several key areas. The active learning strategies were particularly beneficial. Recommendations include extending program duration, incorporating more real-life cases, and increasing simulation opportunities. These insights are crucial for refining nursing education to better prepare students for the complexities of professional practice., Competing Interests: Declaration of competing interest All authors have participated and collaborated in the preparation of this manuscript and have declared that they have no conflicts of interest., (Copyright © 2025 Elsevier Ltd. All rights reserved.)