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Skin cancer is a critical global health issue, with millions of non-melanoma and melanoma cases diagnosed annually. Early detection is essential to improving patient outcomes, yet traditional deep learning models for skin cancer classification are often limited by the need for large, annotated datasets and extensive computational resources. The aim of this study is to address these limitations by proposing an efficient skin cancer classification framework that integrates active learning (AL) with particle swarm optimization (PSO). The AL framework selectively identifies the most informative unlabeled instances for expert annotation, minimizing labeling costs while optimizing classifier performance. PSO, a nature-inspired metaheuristic algorithm, enhances the selection process within AL, ensuring the most relevant data points are chosen. This method was applied to train multiple Convolutional Neural Network (CNN) models on the HAM10000 skin lesion dataset. Experimental results demonstrate that the proposed AL-PSO approach significantly improves classification accuracy, with the Least Confidence strategy achieving approximately 89.4% accuracy while using only 40% of the labeled training data. This represents a substantial improvement over traditional approaches in terms of both accuracy and efficiency. The findings indicate that the integration of AL and PSO can accelerate the adoption of AI in clinical settings for skin cancer detection. The code for this study is publicly available at ( https://github.com/Sayantani-31/AL-PSO )., Competing Interests: Declarations Ethical Approval Not applicable. Informed Consent Not applicable. Competing Interest The authors have no competing interests to declare that are relevant to the content of this article., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

The effective Hamiltonians have been widely applied to simulate the phase transitions in polarizable materials, with coefficients obtained by fitting to accurate first-principles calculations. However, it is tedious to generate distorted structures with symmetry constraints, in particular when high-ordered terms are considered. In this work, we implement and apply a Bayesian optimization-based approach to sample potential energy surfaces, automating the effective Hamiltonian construction by selecting distorted structures via active learning. Taking BaTiO 3 (BTO) as an example, we demonstrate that the effective Hamiltonian can be obtained using fewer than 30 distorted structures. Follow-up Monte Carlo simulations can reproduce the structural phase transition temperatures of BTO, comparable to experimental values with an error<10%. Our approach can be straightforwardly applied on other polarizable materials and paves the way for quantitative atomistic modelling of diffusionless phase transitions., (Creative Commons Attribution license.)

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.)

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.)

Objective: To assess students' performance and perspective on the role of spirituality in pharmacy practice utilizing a blended learning approach., Methods: Students viewed online pre-class videos on general spirituality and five major religions in the United States. Next, students attended an interactive lecture followed by a simulated counseling session. Lastly, students completed an anonymous, 38-question, electronic perspective survey. The items were grouped into scales results and compared using paired-sample t-tests. Effects sizes were measured by Cohen's d. Additionally, students could opt to complete a reflection assignment on this activity as part of a concurrent course. A content analysis qualitative approach was utilized to assess these reflections., Results: All students (n=125) completed the counseling session, and 113 (90.4%) completed the survey. The mean score on the counseling session rubric was 94.8%, demonstrating students' skills in recognizing spiritual concerns utilizing simulated patients. All perspective scale scores indicated a statistically significant change from pre to post, with the greatest change demonstrated in empathic perspective-taking (medium effect), followed by anxiety and lack of multicultural self-efficacy, religious/spiritual openness, and desire to learn (small effects). Students agreed that the instruction helped prepare them as a pharmacist (mean=5.41) and it was valuable hearing from people with authentic experiences (mean=5.40). Major learning themes included individualizing care, respect for beliefs, learning about diverse backgrounds, and prohibited medications., Conclusions: Students performed well utilizing a blended learning approach on counseling patients regarding medication-related spiritual needs. In addition, the students perceived an increase in empathy while addressing spiritual considerations in patient care., 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 Inc.)

Obtaining a fine approximation of a black-box function is important for understanding and evaluating innovative materials. Active learning aims to improve the approximation of black-box functions with fewer training data. In this study, we investigate whether active learning based on uncertainty sampling enables the efficient approximation of black-box functions in regression tasks using various material databases. In cases where the inputs are provided uniformly and defined in a relatively low-dimensional space, the liquidus surfaces of the ternary systems are the focus. The results show that uncertainty-based active learning can produce a better black-box function with higher prediction accuracy than that by random sampling. Furthermore, in cases in which the inputs are distributed discretely and unbalanced in a high-dimensional feature space, datasets extracted from materials databases for inorganic materials, small molecules, and polymers are addressed, and uncertainty-based active learning is occasionally inefficient. Based on the dependency on the material descriptors, active learning tends to produce a better black-box functions than random sampling when the dimensions of the descriptor are small. The results indicate that active learning is occasionally inefficient in obtaining a better black-box function in materials science., (© 2024. The Author(s).)

Learning in undergraduate immunology requires students to be able to retain knowledge, to apply information to new contexts, and to self-assess their understanding of concepts. These core competencies strengthen students' critical thinking and metacognitive skills which, in turn, enhances their ability to identify knowledge gaps and strategies to support future learning. Retrieval practice and feedback-driven metacognition are evidence-based teaching strategies that have been demonstrated to enhance knowledge retention and metacognition in a range of academic disciplines and levels of education, although their implementation and impact on learning in undergraduate immunology remain largely unexplored. To this end, I designed a modular "practice session" activity for a 12-week, upper-level, undergraduate immunology course that incorporates periodic retrieval practice and feedback-driven metacognition to support students' knowledge retention, application of information, and metacognitive skills. Near the end of the course, a survey was conducted to assess student perceptions on whether the activity supported learning and metacognition in immunology. Instructional resources are provided to facilitate easy adaptation of this modular activity to courses of diverse science disciplines and levels of study in higher education.

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.)

[Purpose] This study examined the effectiveness of active learning-based transfer movement training provided to caregivers by physical therapists. [Participants and Methods] This study enrolled 29 participants (age: 47.0 ± 10.1 years, 22 female participants) working at a residential care home. The participants were shown a video in which caregivers experienced difficulty in transferring a patient; this was followed by group discussions. To verify the effectiveness of this educational intervention, a questionnaire including six items related to reducing the burden on caregivers, daily living care, and rehabilitation was administered to the participants at three time points (before training, three days after training, and one month after training). Multiple comparisons were performed, and the effect size (r) was calculated. [Results] Significant differences were observed between the responses obtained before training and three days after training and between the responses obtained before training and one month after training for all six items. The effect sizes (r) after three days and one month of training were above 0.6 for all six items. [Conclusion] The training improved the assessment skills of caregivers, and its effects persisted after one month, suggesting the effectiveness of active learning-based transfer movement training., Competing Interests: The authors declare no conflict of interest., (2024©by the Society of Physical Therapy Science. Published by IPEC Inc.)

Despite many accessible AI models that have been developed, it is an open challenge to fully exploit interpretable insights to enable effective materials design and develop materials with desired properties for target applications. Here, we introduce an interpretable surrogate learning framework that can actively design and generate electronic materials (EMGen), akin to producing updated materials with requirements by screening all possible elements and fractions. Taking the materials system with required band gaps as a case study, EMGen exhibits a benchmarking predictive error and a running time of 1.7 min for designing and producing a structure with a desired band gap. Using EMGen, we establish a large hybrid functional band gap database, and more uplifting is that the proposed EMGen effectively designs Ga x O y with a wide band gap (>5.0 eV) for deep ultraviolet (DUV) optoelectronic devices, enabling a breakthrough extension of the applicability of Ga x O y films in photodetectors to DUV light below 240 nm. The augmented band gap also helps improve the breakdown voltage and the heat resilience performance of the amorphous Ga x O y film, thereby achieving considerable potential within the realm of power electronics applications. The proposed EMGen, as a specialized, interpretable AI model for the generation of electronic materials, is demonstrated to be an essential tool for on-demand semiconductor materials design.

Recent advances in machine learning and deep learning have presented new opportunities for learning to localize the origin of ventricular activation from 12-lead electrocardiograms (ECGs), an important step in guiding ablation therapies for ventricular tachycardia. Passively learning from population data is faced with challenges due to significant variations among subjects, and building a patient-specific model raises the open question of where to select pace-mapping data for training. This work introduces BOATMAP, a novel active learning approach designed to provide clinicians with interpretable guidance that progressively assists in locating the origin of ventricular activation from 12-lead ECGs. BOATMAP inverts the input-output relationship in traditional machine learning solutions to this problem and learns the similarity between a target ECG and a paced ECG as a function of the pacing site coordinates. Using Gaussian processes (GP) as a surrogate model, BOATMAP iteratively refines the estimated similarity landscape while providing suggestions to clinicians regarding the next optimal pacing site. Furthermore, it can incorporate constraints to avoid suggesting pacing in non-viable regions such as the core of the myocardial scar. Tested in a realistic simulation environment in various heart geometries and tissue properties, BOATMAP demonstrated the ability to accurately localize the origin of activation, achieving an average localization accuracy of 3.9±3.6mm with only 8.0±4.0 pacing sites. BOATMAP offers real-time interpretable guidance for accurate localization and enhancing clinical decision-making., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Systematic Review (SR) are foundational to influencing policies and decision-making in healthcare and beyond. SRs thoroughly synthesise primary research on a specific topic while maintaining reproducibility and transparency. However, the rigorous nature of SRs introduces two main challenges: significant time involved and the continuously growing literature, resulting in potential data omission, making most SRs become outmoded even before they are published. As a solution, AI techniques have been leveraged to simplify the SR process, especially the abstract screening phase. Active learning (AL) has emerged as a preferred method among these AI techniques, allowing interactive learning through human input. Several AL software have been proposed for abstract screening. Despite its prowess, how the various parameters involved in AL influence the software's efficacy is still unclear. This research seeks to demystify this by exploring how different AL strategies, such as initial training set, query strategies etc. impact SR automation. Experimental evaluations were conducted on five complex medical SR datasets, and the GLM model was used to interpret the findings statistically. Some AL variables, such as the feature extractor, initial training size, and classifiers, showed notable observations and practical conclusions were drawn within the context of SR and beyond where AL is deployed., 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 The Authors. Published by Elsevier B.V. All rights reserved.)

The effectiveness of incorporating an audience response system (ARS) in improving the learning environment and student performance was assessed in a didactic biomedical science course at a large US dental school. Instructors and students were surveyed for their experiences of using Top Hat-based ARS in a multi-disciplinary biomedical science course. Average exam scores and grade distribution for challenging sections on immunology and developmental biology were compared before and after incorporating ARS. Pearson's chi-squared test, likelihood ratio test, Student's t-test and Fisher's exact test using beta regression model were used to assess statistical significance (p < 0.05). Student survey results with 78% (82/105) response rate, indicated that incorporating ARS improved student engagement, reinforced lecture material, and prepared them better for exams (p < 0.0001). Sixty seven percent of student respondents recommended continued integration of ARS questions in lectures (p < 0.0001). Although faculty survey data (n = 5, 100% response rate) were not statistically significant, the majority of faculty agreed that real-time feedback through ARS allowed them to appropriately pace lecture delivery and restructure forthcoming material. The average exam scores and grade distribution for challenging sections showed modest improvement after incorporation of ARS (p < 0.05). There was no significant correlation between final course grades and ARS grades for participation or correctness. Incorporating the ARS in a multi-disciplinary biomedical science course is perceived by faculty and students as an effective instructional approach that improves the learning environment, teaching strategies, and student performance., (© 2024 International Union of Biochemistry and Molecular Biology.)

Objectives: Active learning (AL) has rarely integrated diversity-based and uncertainty-based strategies into a dynamic sampling framework for clinical named entity recognition (NER). Machine-assisted annotation is becoming popular for creating gold-standard labels. This study investigated the effectiveness of dynamic AL strategies under simulated machine-assisted annotation scenarios for clinical NER., Materials and Methods: We proposed 3 new AL strategies: a diversity-based strategy (CLUSTER) based on Sentence-BERT and 2 dynamic strategies (CLC and CNBSE) capable of switching from diversity-based to uncertainty-based strategies. Using BioClinicalBERT as the foundational NER model, we conducted simulation experiments on 3 medication-related clinical NER datasets independently: i2b2 2009, n2c2 2018 (Track 2), and MADE 1.0. We compared the proposed strategies with uncertainty-based (LC and NBSE) and passive-learning (RANDOM) strategies. Performance was primarily measured by the number of edits made by the annotators to achieve a desired target effectiveness evaluated on independent test sets., Results: When aiming for 98% overall target effectiveness, on average, CLUSTER required the fewest edits. When aiming for 99% overall target effectiveness, CNBSE required 20.4% fewer edits than NBSE did. CLUSTER and RANDOM could not achieve such a high target under the pool-based simulation experiment. For high-difficulty entities, CNBSE required 22.5% fewer edits than NBSE to achieve 99% target effectiveness, whereas neither CLUSTER nor RANDOM achieved 93% target effectiveness., Discussion and Conclusion: When the target effectiveness was set high, the proposed dynamic strategy CNBSE exhibited both strong learning capabilities and low annotation costs in machine-assisted annotation. CLUSTER required the fewest edits when the target effectiveness was set low., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Gamification is emerging as an active learning innovation in medical education to enhance student engagement and promote life-long learning in a unique and collaborative environment. Clinical enzymology in biochemistry is one of the core topics in the medical curriculum. However, students face challenges in comprehension and retention of information. Hence, CARd & Board GAmes in Medical Education (CARBGAME) was introduced and evaluated for its effectiveness in enhancing learning, application, and retention of knowledge in clinical enzymology via gamification context. This mixed-method study involved 150 first-year undergraduate medical students. Before the game, students completed a pre-test in clinical enzymology. Later they were divided into 25 small groups to compete in the board game designed for enzymology in biochemistry. The students took turns throwing the dice and answering the questions on the game board to continue moving forward. The first team to reach 100 and solve the case-based question was deemed the winner. Following the board game, the students took up the post-test to compare the educational impact of the innovation. Also, the subsequent internal assessment scores were compared with previous batch who were not implemented with this intervention. Then students evaluated the effectiveness of CARBGAME-Clinical Enzymology using a 32-item questionnaire on 5-point Likert scale. The feedback obtained on a 10-point rating scale and for qualitative analysis, students' and faculty perceptions were recorded in small groups. CARBGAME received overwhelmingly positive feedback from both students and faculty. It was perceived well by students for being fun, relevant, consistent, motivating, collaborative, and promoting experiential learning. The game's low-stakes approach, effective feedback, and sense of accomplishment were highly appreciated, making it a valuable tool for education. A significant improvement in knowledge was recorded, from a mean score of 8.37 ± 1.126 on a 20-point scoring scale before the game to 16.53 ± 1.219 after with a p-value of 0.0001. The comparison of the internal assessment scores between the intervention and non-intervention group of students also showed a significant improvement among those implemented with CARBGAME (p < 0.0001). The CARBGAME innovation has achieved the intended outcome of promoting active learning and enhanced performance in clinical enzymology. Highly positive responses from faculty and students also indicate the exigent need to introduce innovative components like games into curricula to achieve student engagement and promote a meaningful learning experience., (© 2024 International Union of Biochemistry and Molecular Biology.)

Antimicrobial resistance poses one of the most significant medical challenges for humanity. The current burden is overwhelming and is projected to escalate rapidly, with predictions for 2050 indicating 10 million deaths per year due to antibiotic-resistant microorganisms. Enhancing public awareness and education on this topic is crucial in efforts to mitigate this issue. In our study, we translated an existing questionnaire on antimicrobial resistance into Portuguese, validated it, and applied it between December 2020 and March 2021 to a group of Portuguese students (n = 112) and science teachers (n = 95). A majority of the students surveyed (65.1%) incorrectly believed that antibiotics could treat colds/flus. As anticipated, the teachers outperformed the students in the questionnaire. However, difficulties with this topic were evident in both groups. Most notably, the misconception that the human body becomes resistant to antibiotics was prevalent among most participants (77.0% of students and 68.4% of teachers). Consistent with previous studies in other populations and geographic locations, our research reveals a worrying lack of knowledge about antimicrobial resistance among Portuguese students and science teachers. Consequently, it is deemed urgent to implement effective measures to raise awareness and educate on this topic., (© 2024 International Union of Biochemistry and Molecular Biology.)

The effective implementation of evidence-based teaching (EBT) in large college courses benefits from the successful use of instructional teams. An instructional team's feedback allows instructors to act based on evidence of student learning, addressing students' needs. This feedback may be particularly important for novice instructors or experienced instructors teaching a class for the first time. This study sought to characterize the nature of an instructional team's feedback as well as its influence on the decisions and actions of a seasoned instructor teaching a new class. Instructional team members provided feedback in the form of anticipations, noticings, and suggestions. Anticipations and suggestions seemed to have the largest impact on the instructor's decisions and actions, while noticings, despite providing insights into student thinking, had a smaller effect. Our findings indicate that an instructional team can provide valuable feedback to instructors when team members have an opportunity to meaningfully participate in the planning and teaching processes., (© 2024 International Union of Biochemistry and Molecular Biology.)

We created a 2-week, dual-module summer course introducing high school students to environmental toxicology by teaching them quantitative polymerase chain reaction (qPCR) as a way to quantify gene expression of chemical defense proteins in response to exposure to environmental pollutants. During the course, students are guided through the various stages of a successful qPCR experiment: in silico primer design and quality control, total RNA extraction and isolation, cDNA conversion, primer test PCR, and evaluation of results via agarose gel electrophoresis or UV/Vis spectra. The course combines lectures, discussions, and demonstrations with dry and wet laboratory sections to give students a thorough understanding of the scope, utility, and chemical principles of qPCR. At the end of the course, the students are taught how to analyze qPCR data and are encouraged to discuss their findings with other classmates to evaluate their hypotheses and assess possible sources of error. This course was designed to be easily adaptable to multiple test species, chemical exposures, and genes of interest. To explore both terrestrial and aquatic toxicology, the students use honey bees (Apis mellifera) and mosquitofish (Gambusia affinis) as test organisms, as well as ABC-type efflux transporters, antioxidant enzymes, and cytochrome P450 enzymes as endpoints for assessing gene expression. We share this course setup and applied protocols to encourage others to design and offer similar courses that give high school students a hands-on introduction to a broad swath of environmental toxicology research and an opportunity to develop scientific skills necessary for university-level research., (© 2024 The Author(s). Biochemistry and Molecular Biology Education published by Wiley Periodicals LLC on behalf of International Union of Biochemistry and Molecular Biology.)

Objective: To compare the efficacy of Jigsaw and Inquiry-Based Learning (IBL) methods in promoting effective learning outcomes within diverse dental educational settings., Methods: A total of 188 dental undergraduate students were recruited for the study and divided into Batch A (n = 96) and Batch B (n = 92). Each batch underwent two teaching sessions, one using the Jigsaw method and the other using IBL. A structured questionnaire using five-point Likert scales assessed students' preferences between the two methods. Pre- and post-intervention tests were conducted to measure knowledge acquisition. Statistical analysis was performed using Student's t-test with STATA version 17, with a p-value of < 0.05 considered significant., Results: Both Jigsaw and IBL methods significantly improved students' knowledge, with mean scores showing a significant difference before and after interventions (p < 0.001). IBL was more effective in comprehensive topic coverage (99.46%) and enhancing subject understanding. Both methods yielded comparable outcomes in terms of improving communication skills. The Jigsaw method was simpler and more enjoyable, encouraging active participation and enhancing communication skills. IBL promoted critical thinking, research skills, and deeper comprehension of concepts., Conclusion: Jigsaw and IBL methods are both effective in enhancing dental education. The choice between these methods depends on the specific learning objectives, the complexity of the topic, and the preferences of the dental educator and students., (© 2024 The Author(s). Journal of Dental Education published by Wiley Periodicals LLC on behalf of American Dental Education Association.)

Purpose: Manual annotations for training deep learning models in auto-segmentation are time-intensive. This study introduces a hybrid representation-enhanced sampling strategy that integrates both density and diversity criteria within an uncertainty-based Bayesian active learning (BAL) framework to reduce annotation efforts by selecting the most informative training samples., Methods: The experiments are performed on two lower extremity datasets of MRI and CT images, focusing on the segmentation of the femur, pelvis, sacrum, quadriceps femoris, hamstrings, adductors, sartorius, and iliopsoas, utilizing a U-net-based BAL framework. Our method selects uncertain samples with high density and diversity for manual revision, optimizing for maximal similarity to unlabeled instances and minimal similarity to existing training data. We assess the accuracy and efficiency using dice and a proposed metric called reduced annotation cost (RAC), respectively. We further evaluate the impact of various acquisition rules on BAL performance and design an ablation study for effectiveness estimation., Results: In MRI and CT datasets, our method was superior or comparable to existing ones, achieving a 0.8% dice and 1.0% RAC increase in CT (statistically significant), and a 0.8% dice and 1.1% RAC increase in MRI (not statistically significant) in volume-wise acquisition. Our ablation study indicates that combining density and diversity criteria enhances the efficiency of BAL in musculoskeletal segmentation compared to using either criterion alone., Conclusion: Our sampling method is proven efficient in reducing annotation costs in image segmentation tasks. The combination of the proposed method and our BAL framework provides a semi-automatic way for efficient annotation of medical image datasets., (© 2024. CARS.)

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., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Metal-organic frameworks (MOFs) are versatile nanoporous materials for a wide variety of important applications. Recently, a handful of MOFs have been explored for the storage of toxic fluorinated gases (Keasler et al. Science, 1455), yet the potential of a great number of MOFs for such an environmentally sustainable application has not been thoroughly investigated. In this work, we apply active learning (AL) to accelerate the discovery of hypothetical MOFs (hMOFs) that can efficiently store a specific fluorinated gas, namely, vinylidene fluoride (VDF). First, a force field was developed for VDF and utilized to predict the working capacities ( 2023, 381, 1455), yet the potential of a great number of MOFs for such an environmentally sustainable application has not been thoroughly investigated. In this work, we apply active learning (AL) to accelerate the discovery of hypothetical MOFs (hMOFs) that can efficiently store a specific fluorinated gas, namely, vinylidene fluoride (VDF). First, a force field was developed for VDF and utilized to predict the working capacities ( Δ N ) of VDF in an initial data set of 4502 MOFs from the computation-ready experimental MOF (CoRE-MOF) database that successfully underwent featurization and grand-canonical Monte Carlo simulations. Next, the initial data set was diversified by Greedy sampling in an unexplored sample space of 119,387 hMOFs from the ab initio REPEAT charge MOF (ARC-MOF) database. A budget of 10,000 samples (i.e., <10% of total ARC-MOFs) was selected to train a random forest model. Then, Δ N in the unlabeled ARC-MOFs were predicted and top-performing ones were validated by simulations. Integrating with the stability requirement, mechanically stable ARC-MOFs were finally identified, along with high Δ N . Furthermore, by Pareto-Frontier analysis, we revealed that long linear linkers can enhance Δ N , while bulkier multiphenyl linkers or interpenetrated frameworks improve mechanical strength. From this work, we efficiently discover top-performing MOFs for VDF storage by AL and also demonstrate the importance of integrating stability to identify stable promising MOFs for a practical application.

MXenes possess a wide range of materials properties owing to their compositional and stoichiometric diversities, facilitating their utilization in various technological applications such as electrodes, catalysts, and supercapacitors. To explore their applicability, identification of thermodynamically stable and synthesizable MXenes should precede. The energy above the convex hull ( E ) calculated using the density functional theory (DFT) is a powerful scale to probe the thermodynamic stability. However, the high calculation cost of DFT limits the search space of unknown chemistry. To address this challenge, this study proposes an active learning (AL) framework consisting of a surrogate model and utility function for expeditious identification of thermodynamically stable MXenes in the extensive chemical space of 23,857 MXenes with compositional and stoichiometric diversity. Exploiting the fast inference speed and the capability of the AL framework to accurately identify stable MXenes, only 480 DFT calculations were required to identify 126 thermodynamically stable MXenes; among these, the stabilities of 89 MXenes have not been previously reported. In contrast, only two stable MXenes were identified among randomly selected 1693 MXenes, demonstrating the inefficiency of using only DFT calculations in exploring a large chemical space. The AL framework successfully minimized the number of DFT calculations while maximizing that of thermodynamically stable MXenes identified and can contribute to future studies in finding stable MXenes expeditiously.hull ) calculated using the density functional theory (DFT) is a powerful scale to probe the thermodynamic stability. However, the high calculation cost of DFT limits the search space of unknown chemistry. To address this challenge, this study proposes an active learning (AL) framework consisting of a surrogate model and utility function for expeditious identification of thermodynamically stable MXenes in the extensive chemical space of 23,857 MXenes with compositional and stoichiometric diversity. Exploiting the fast inference speed and the capability of the AL framework to accurately identify stable MXenes, only 480 DFT calculations were required to identify 126 thermodynamically stable MXenes; among these, the stabilities of 89 MXenes have not been previously reported. In contrast, only two stable MXenes were identified among randomly selected 1693 MXenes, demonstrating the inefficiency of using only DFT calculations in exploring a large chemical space. The AL framework successfully minimized the number of DFT calculations while maximizing that of thermodynamically stable MXenes identified and can contribute to future studies in finding stable MXenes expeditiously.

Deep neural network (DNN) models have been applied to a wide variety of medical image analysis tasks, often with the successful performance outcomes that match those of medical doctors. However, given that even minor errors in a model can impact patients' life, it is critical that these models are continuously improved. Hence, active learning (AL) has garnered attention as an effective and sustainable strategy for enhancing DNN models for the medical domain. Extant AL research in histopathology has primarily focused on patch datasets derived from whole-slide images (WSIs), a standard form of cancer diagnostic images obtained from a high-resolution scanner. However, this approach has failed to address the selection of WSIs, which can impede the performance improvement of deep learning models and increase the number of WSIs needed to achieve the target performance. This study introduces a WSI-level AL method, termed WSI-informative selection (WISE). WISE is designed to select informative WSIs using a newly formulated WSI-level class distance metric. This method aims to identify diverse and uncertain cases of WSIs, thereby contributing to model performance enhancement. WISE demonstrates state-of-the-art performance across the Colon and Stomach datasets, collected in the real world, as well as the public DigestPath dataset, significantly reducing the required number of WSIs by more than threefold compared to the one-pool dataset setting, which has been dominantly used in the field., 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 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: to analyze which factors are associated with students' engagement and participation in video-based lectures in a flipped classroom environment., Methods: A single-center experience with video-based lectures in undergraduate medical education is described. The activity was applied to the subject of Neurosurgery during two consecutive courses (2021/22 and 2022/23). The videos were available prior to face-to-face classes through the online application Edpuzzle. Information was obtained from the own platform at the end of each course. Multivariable linear regression analyses were performed to assess the association between different variables and the percentage of video viewing, the early dropout rate, and the percentage of audience retention., Results: A total of 109 students registered in Edpuzzle (87.2% of all enrolled students). Fifty-one videos were uploaded each course to cover 11 topics. Mean video viewing rate was 41%. Those videos linked to the earliest classroom lessons showed more percentage of viewing and audience retention than those programmed at the end of the course. With mandatory classroom assistance and homework assignments, the seminar videos were viewed more but retained less audience. Shorter videos were associated with higher viewing and audience adhesion, but the presence of questions embedded throughout the clip did not significantly engage students. No significant difference was observed regarding lesson topics., Conclusions: It is essential to emphasize the importance of designing strategies to initially engage learners since more than half of our students never connected to the clips. Decreasing engagement was associated with the end of the course and video length. Seminar videos were viewed more but retained less audience. Active learning activities such as quizzes embedded throughout the clips did not significantly engage learners., (© 2024. The Author(s).)

There are several main patterns in lung ultrasound (LUS) images, including A-lines, B-lines, consolidation and pleural effusion. LUS images of healthy lungs typically only exhibit A-lines, while other patterns may emerge or coexist in LUS images associated with different lung diseases. The accurate categorization of these primary patterns is pivotal for effective lung disease screening. However, two challenges complicate the classification task: the first is the inherent blurring of feature differences between main patterns due to ultrasound imaging properties; and the second is the potential coexistence of multiple patterns in a single case, with only the most dominant pattern being clinically annotated. To address these challenges, we propose the active learning based on multi-enhanced views (MEVAL) method to achieve more precise pattern classification in LUS. To accentuate feature differences between multiple patterns, we introduce a feature enhancement module by applying vertical linear fitting and k-means clustering. The multi-enhanced views are then employed in parallel with the original images, thus enhancing MEVAL's awareness of feature differences between multiple patterns. To tackle the patterns coexistence issue, we propose an active learning strategy based on confidence sets and misclassified sets. This strategy enables the network to simultaneously recognize multiple patterns by selectively labeling of a small number of images. Our dataset comprises 5075 LUS images, with approximately 4% exhibiting multiple patterns. Experimental results showcase the effectiveness of the proposed method in the classification task, with accuracy of 98.72%, AUC of 0.9989, sensitivity of 98.76%, and specificity of 98.16%, which outperforms than the state-of-the-art deep learning-based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application., 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 Ltd. All rights reserved.)

Introducing students to primary scientific literature is essential for establishing scientific literacy; however, students can feel overwhelmed by the amount of information within a research article. In our virology discussion courses, we address this issue by creating a student-centered and active learning environment. Students present and guide discussions on the rationales, background, methods, results, and conclusions from research literature in class. This approach has been applied in both undergraduate and graduate settings for students studying microbiology, with different expectations and criteria between the two groups. Student evaluations collected from three semesters were positive toward the teaching methods. Students praised the inclusion of "landmark discovery" articles, which examined paradigm-shifting concepts in virology. Undergraduates expressed their increased confidence in comprehending scientific literature and highlighted the impact of group work. In a critique of the course, students suggested more variety in the topics covered and inclusion of recent publications. Overall, our enhanced approach and methods improved student experiences with primary scientific literature and promoted student learning.

This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move-in the ensuing schemes-is to place priors on the selection of models, based upon expected free energy. In this setting, expected free energy reduces to a constrained mutual information, where the constraints inherit from priors over outcomes (i.e., preferred outcomes). The resulting scheme is first used to perform image classification on the MNIST dataset to illustrate the basic idea, and then tested on a more challenging problem of discovering models with dynamics, using a simple sprite-based visual disentanglement paradigm and the Tower of Hanoi (cf., blocks world) problem. In these examples, generative models are constructed autodidactically to recover (i.e., disentangle) the factorial structure of latent states-and their characteristic paths or dynamics., Competing Interests: Declaration of Competing Interest The authors have no disclosures or conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Data-driven methodologies in healthcare necessitate labeled data for effective decision-making. However, medical data, particularly in unstructured formats, such as clinical notes, often lack explicit labels, making manual annotation challenging and tedious., Methods: This paper introduces a novel deep active learning framework designed to facilitate the annotation process for multiclass text classification, specifically using the SOAP (subjective, objective, assessment, plan) framework, a widely recognized medical protocol. Our methodology leverages transformer-based deep learning techniques to automatically annotate clinical notes, significantly easing the manual labor involved and enhancing classification performance. Transformer-based deep learning models, with their ability to capture complex patterns in large datasets, represent a cutting-edge approach for advancing natural language processing tasks., Results: We validate our approach through experiments on a diverse set of clinical notes from publicly available datasets, comprising over 426 documents. Our model demonstrates superior classification accuracy, with an F1 score improvement of 4.8% over existing methods but also provides a practical tool for healthcare professionals, potentially improving clinical documentation practices and patient care., Conclusions: The research underscores the synergy between active learning and advanced deep learning, paving the way for future exploration of automatic text annotation and its implications for clinical informatics. Future studies will aim to integrate multimodal data and large language models to enhance the richness and accuracy of clinical text analysis, opening new pathways for comprehensive healthcare insights., 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) 2024.)

Objectives: To develop and evaluate a multi-task deep-learning (DL) model for automated segment-level coronary artery calcium (CAC) scoring on non-contrast computed tomography (CT) for precise localization and quantification of calcifications in the coronary artery tree., Methods: This study included 1514 patients (mean age, 60.0 ± 10.2 years; 56.0% female) with stable chest pain from 26 centers participating in the multicenter DISCHARGE trial (NCT02400229). The patients were randomly assigned to a training/validation set (1059) and a test set (455). We developed a multi-task neural network for performing the segmentation of calcifications on the segment level as the main task and the segmentation of coronary artery segment regions with weak annotations as an auxiliary task. Model performance was evaluated using (micro-average) sensitivity, specificity, F1-score, and weighted Cohen's κ for segment-level agreement based on the Agatston score and performing interobserver variability analysis., Results: In the test set of 455 patients with 1797 calcifications, the model assigned 73.2% (1316/1797) to the correct coronary artery segment. The model achieved a micro-average sensitivity of 0.732 (95% CI: 0.710-0.754), a micro-average specificity of 0.978 (95% CI: 0.976-0.980), and a micro-average F1-score of 0.717 (95% CI: 0.695-0.739). The segment-level agreement was good with a weighted Cohen's κ of 0.808 (95% CI: 0.790-0.824), which was only slightly lower than the agreement between the first and second observer (0.809 (95% CI: 0.798-0.845))., Conclusion: Automated segment-level CAC scoring using a multi-task neural network approach showed good agreement on the segment level, indicating that DL has the potential for automated coronary artery calcification classification., Critical Relevance Statement: Multi-task deep learning can perform automated coronary calcium scoring on the segment level with good agreement and may contribute to the development of new and improved calcium scoring methods., Key Points: Segment-level coronary artery calcium scoring is a tedious and error-prone task. The proposed multi-task model achieved good agreement with a human observer on the segment level. Deep learning can contribute to the automation of segment-level coronary artery calcium scoring., (© 2024. The Author(s).)

Background: Team-based learning (TBL) and problem-based learning (PBL) are two effective educational approaches used in medical education for increasing knowledge acquisition, learning motivation, and collaborative and communication skills among students. Few studies have been conducted in Saudi Arabia to evaluate medical students' perceptions of these approaches., Aim of the Study: The present study aimed to evaluate the perceptions of current or previous preclinical students at the College of Medicine, Najran University (NU) about the effectiveness of PBL and TBL and to assess students' perceptions regarding the strengths and limitations of both educational approaches and their preferences., Methods: A cross-sectional survey was conducted on 106 preclinical students at NU, Saudi Arabia. The survey questionnaire was validated by a pilot study and designed based on Kirkpatrick's evaluation model. Descriptive statistics was used to analyze the collected data through IBM SPSS Statistics 2.0., Results: 73.6% of participants enjoyed PBL and found it motivating compared to TBL and classic lectures. PBL was reported as more effective for knowledge acquisition (50%), teamwork (62.3%), and high academic performance (58%)., Conclusion: It is concluded that PBL was more favored by the students regarding the learning outcomes. TBL was reported to need more individual preparation and effort and require lecturer involvement, suggesting the need for structured facilitation in this learning approach and potentially indicating the challenges associated with coordinating team-based activities., (© 2024. The Author(s).)

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.)

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.)

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.)

Introduction: The flipped classroom teaching-learning method has been increasingly adopted in higher education to enhance student-centered learning. Despite its growing popularity, limited evidence exists regarding its effectiveness in medical education in Ethiopia. This study aimed to assess the impact of the flipped classroom method on academic performance, student engagement, and satisfaction among undergraduate medical students at Gondar University, College of Medicine and Health Science., Methods: A quasi-experimental study was conducted involving 100 s-year undergraduate medical students, divided into two groups: the flipped classroom group (n = 50) and the traditional lecture-based group (n = 50). The flipped classroom group received online instructional materials before class, while classroom sessions focused on interactive activities. The traditional lecture-based group attended conventional lectures followed by homework assignments. Data were collected using pre- and post-intervention tests to measure academic performance, the Student Engagement Scale to gauge engagement, and a validated questionnaire to assess student satisfaction. Data were analyzed using Statistical Package for the Social Sciences version 25, with descriptive statistics, paired t-tests, independent t-tests, and Chi-square tests employed to interpret the findings., Results: The flipped classroom group demonstrated a significant improvement in academic performance, with mean pre-test and post-test scores of 65.2 ± 8.1 and 78.6 ± 6.9, respectively. In contrast, the traditional lecture-based group had pre-test and post-test mean scores of 62.8 ± 7.5 and 74.2 ± 8.3, respectively. Additionally, the flipped classroom group showed higher levels of student engagement mean scores of 4.5 ± 0.8 and satisfaction mean scores of 4.2 ± 0.7 compared to the traditional lecture-based group, which had engagement mean scores of 3.8 ± 0.6 and satisfaction mean scores of 3.9 ± 0.5. Chi-square tests showed a statistically significant association between the teaching method and both pass/fail rates and participation in activities, but not a significant association with gender distribution., Conclusion: The flipped classroom approach proves to be a superior teaching method, promoting better academic outcomes and greater student engagement and satisfaction. This study adds to the growing body of evidence advocating for the implementation of flipped classrooms in medical education., Trial Registration: Not applicable., (© 2024. The Author(s).)

Shape memory alloys (SMAs) with large latent heat absorbed/released during phase transformation at elevated temperatures benefit their potential application on thermal energy storage (TES) in high temperature environment like power plants, etc. The desired alloys can be designed quickly by searching the vast component space of doped NiTi-based SMAs via data-driven method, while be challenging with the noisy experimental data. A noise-aware active learning strategy is proposed to accelerate the design of SMAs with large latent heat at elevated phase transformation temperatures based on noisy data. The optimal noise level is estimated by minimizing the model error with incorporation of a range of noise levels as noise hyper-parameters into the noise-aware Kriging model. The employment of this strategy leads to the discovery of the alloy with latent heat of -36.08 J g -1 , 9.2% larger than the best value (-33.04 J g -1 ) in the original training dataset within another four experiments. Additionally, the alloy represents high austenite finish temperature (481.71°C) and relatively small hysteresis. This promotes the latent heat TES application of SMAs in high temperature circumstance. It is expected that the noise-aware approach can be convenient for the accelerated materials design via the data-driven method with noisy data., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Electrocatalytic carbon dioxide reduction (CO 2 RR) technology enables the conversion of excessive CO 2 into high-value fuels and chemicals, thereby mitigating atmospheric CO 2 concentrations and addressing energy scarcity. Single-atom alloys (SAAs) possess the potential to enhance the CO 2 RR performance by full utilization of atoms and breaking linear scaling relationships. However, quickly screening high-performance metal portfolios of SAAs remains a formidable challenge. In this study, we proposed an active learning (AL) framework to screen high-performance catalysts for CO 2 RR to yield fuels such as CH 4 and CH 3 OH. After four rounds of AL iterations, the ML model attained optimal prediction performance with the test set R 2 of approximately 0.94 and successful prediction was achieved for the binding free energy of *CHO, *COH, *CO, and *H on 380 catalyst surfaces with an accuracy within 0.20 eV. Subsequent analysis of the SAA catalysts' activity, selectivity, and stability led to the discovery of eight previously unexplored SAA catalysts for CO 2 RR. Notably, the surface activity of Ti@Cu(100), Au@Pt(100), and Ag@Pt(100) shines prominently. Utilizing DFT calculations, we elucidated the complete reaction pathway of the CO 2 RR on the surfaces of these catalysts, confirming their high catalytic activity with limiting potentials of -0.11, -0.34, and -0.46 eV, respectively, which are significantly lower than those of pure Cu catalysts. The results showcase the exceptional predictive prowess of AL, providing a valuable reference for the design of CO 2 RR catalysts.

Veterinary programs traditionally teach gross anatomy by having students perform regional dissections on animal cadavers. Dissection is effective but also costly, time consuming, and intimidating for students. These factors, along with reduced contact hours devoted to gross anatomy, warrant investigation of more time-efficient teaching modalities. We sought to determine whether learning anatomy from instructor-prosected cadavers is a suitable alternative to in-class cadaveric dissections. Veterinary students completed nine units of regional gross anatomy over three courses. For each unit, students were randomly assigned to study the region on instructor-prosected cadavers (i.e., prosection students, n = 25) or perform their own dissection of the region in small groups (i.e., dissection students, n = 25). Prosection students spent on average 18 minutes/week less ( p < .05) in class than dissection students. Despite comparable amounts of time spent studying outside of class each week, prosection students outperformed ( p < .05) dissection students on 56% of the practical unit exams and 44% of the overall unit exams, whereas dissection students outperformed ( p < .05) prosection students on only a single unit exam. Prosection students also performed better ( p < .05) on subsequent quizzes administered to assess knowledge retention. Survey responses indicated that students were more confident in the accuracy of prosections and valued the efficiency they provided. Although they found value in performing dissections and were generally satisfied with the knowledge they gained, many students reported feeling timid toward dissecting, which diminished the experience. Together, these findings demonstrate that expertly prosected cadavers were more time-efficient than in-class cadaveric dissections and were generally more effective for learning gross veterinary anatomy.

Teaching online poses unique challenges for keeping participants engaged. To enhance the effectiveness of virtual childbirth education, this article explores how active learning techniques can guide the design of online prenatal classes. Active learning involves encouraging class participants to process and engage with ideas as the instructor presents them in class, rather than just hearing about concepts through lectures. This article draws from pedagogical literature on active learning and applies its key components to the needs of childbirth instructors in an online setting. Specific recommendations for activities to involve participants and create lasting learning are also provided for synchronous online classes., Competing Interests: The author has no relevant financial interest or affiliations with any commercial interests related to the subjects discussed within this article., (Copyright 2024 Lamaze International.)

Background: History and health humanities instruction offers a framework for professional students to examine the arc and development of their profession as well as develop cultural competencies. Exploring ideas, themes, and health care practices and approaches through historical instruction can show students how culture influences health care and practice, therefore providing a context for further development of cultural competence skills., Case Presentation: This case report describes a collaboration among a dentistry course instructor, a liaison librarian, and curators of a rare book collection. Working together, this team offers an active learning class that examines the historical arc of the dental profession. We aim to have students use primary source materials to examine the experiences, research, and narratives of their profession., Discussion: Using a World Cafe with thematic tables allows students to safely examine rare materials and artifacts and have meaningful conversations about themes that are critical to dentistry's past, present, and future. Students reported that engaging with artifacts and historical materials provided a different way to understand history and enhanced their learning experience. Engaging students in this work builds critical thinking skills that are essential to evidence-based practice., (Copyright © 2024 Nicole Theis-Mahon, AHIP, Anna Opryszko, Ai Miller, Emily Beck, Lois Hendrickson.)

Objective: Many strategies are used in pharmacy education to maintain student engagement through active learning. Gamification, such as the choose-your-own-adventure activity (CYOAA), is one such method. The purpose of this study was to assess improvement in clinical knowledge and retention after the implementation of CYOAA., Methods: Two CYOAAs were designed and implemented within second-year pharmacy courses. One activity was on venous thromboembolism (VTE) and the other on chronic obstructive pulmonary disease (COPD). Students completed a six question prequiz and postquiz for both activities mapped to core learning objectives and four similar but more difficult questions on corresponding exams. After the CYOAAs, the students completed a reflection and perceptions survey., Results: Seventy-five students completed the VTE CYOAA and 77 completed COPD CYOAA. Over the three time points, there was a significant decrease in the mean assessment scores on the VTE activity (68% to 65% to 40%) versus a significant and sustained increase in mean assessment scores on COPD (62% to 83% to 85%). Of the 36 and 33 students who completed the postsurvey, the majority agreed or strongly agreed that they preferred CYOAA to traditional cases and that the activities improved their knowledge, critical thinking skills, and confidence in clinical decision-making., Conclusion: There was a mixed result in the mean assessment scores, with an increase seen with the COPD CYOAA and a decrease with VTE, as well as positive perceptions for VTE and COPD, indicating that this classroom innovation is well-liked and may improve knowledge outcomes., Competing Interests: Declaration of Competing Interest None declared., (Published by Elsevier Inc.)

Learning 2D sectional anatomy facilitates the comprehension of 3D anatomical structures, anatomical relationships, and radiological anatomy. However, the efficacy of technology-enhanced collaborative instructional activities in sectional anatomy remains unclear, especially if theoretical frameworks, namely the Cognitive Theory of Multimedia Learning (CTML), are applied in instructional design. Thus, this study compared the educational impact of distinct 45-min-long technology-enhanced collaborative learning tasks in sectional anatomy. A sample of 115 first-year medical students was randomly divided into three experimental groups that used different supporting technologies to learn the sectional anatomy of the chest: IMAIOS e-learning platform and Microsoft Surface Hub (n = 37); anatomage table (n = 38); anatomage table with CTML-based presets (n = 40). Prelearning and postlearning tests revealed that significant knowledge gains in sectional anatomy were obtained by all groups even though no inter-group differences were found. Moreover, a five-point Likert scale questionnaire showed that the learning session was highly valued by all participants and that users of the anatomage with CTML-based presets reported higher enjoyment than users of the IMAIOS system (mean difference = 0.400; p = 0.037). In addition, students using the IMAIOS system and the anatomage with CTML-based presets provided System Usability Scale (SUS) scores of 67.64 and 67.69, respectively, reaching the benchmark of usability. By contrast, students using the anatomage table without presets awarded a SUS score of 64.14. These results suggest that the integration of multimedia technologies in anatomy teaching and learning should be grounded on CTML principles of instructional design. Otherwise, students' perceptions of ed-tech usability are potentially hindered., (© 2024 The Author(s). Clinical Anatomy published by Wiley Periodicals LLC on behalf of American Association of Clinical Anatomists and British Association of Clinical Anatomists.)

Introduction: For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods., Areas Covered: The authors examine recent examples of VS in drug discovery and discuss prospective hit finding results from the critical assessment of computational hit-finding experiments (CACHE) challenge. The authors also highlight the cost considerations and open-source options for conducting VS and examine chemical space coverage and library selections for VS., Expert Opinion: The advancement of sophisticated VS approaches, including the use of machine learning techniques and increased computer resources as well as the ease of access to synthetically available chemical spaces, and commercial and open-source VS platforms allow for interrogating ultra-large libraries (ULL) of billions of molecules. An impressive number of prospective ULL VS campaigns have generated potent and structurally novel hits across many target classes. Nonetheless, many successful contemporary VS approaches still use considerably smaller focused libraries. This apparent dichotomy illustrates that VS is best conducted in a fit-for-purpose way choosing an appropriate chemical space. Better methods need to be developed to tackle more challenging targets.

Deep learning models have demonstrated great potential in medical imaging but are limited by the expensive, large volume of annotations required. To address this, we compared different active learning strategies by training models on subsets of the most informative images using real-world clinical datasets for brain tumor segmentation and proposing a framework that minimizes the data needed while maintaining performance. Then, 638 multi-institutional brain tumor magnetic resonance imaging scans were used to train three-dimensional U-net models and compare active learning strategies. Uncertainty estimation techniques including Bayesian estimation with dropout, bootstrapping, and margins sampling were compared to random query. Strategies to avoid annotating similar images were also considered. We determined the minimum data necessary to achieve performance equivalent to the model trained on the full dataset (α = 0.05). Bayesian approximation with dropout at training and testing showed results equivalent to that of the full data model (target) with around 30% of the training data needed by random query to achieve target performance (p = 0.018). Annotation redundancy restriction techniques can reduce the training data needed by random query to achieve target performance by 20%. We investigated various active learning strategies to minimize the annotation burden for three-dimensional brain tumor segmentation. Dropout uncertainty estimation achieved target performance with the least annotated data., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

State-of-the-art deep learning models often fail to generalize in the presence of distribution shifts between training (source) data and test (target) data. Domain adaptation methods are designed to address this issue using labeled samples (supervised domain adaptation) or unlabeled samples (unsupervised domain adaptation). Active learning is a method to select informative samples to obtain maximum performance from minimum annotations. Selecting informative target domain samples can improve model performance and robustness, and reduce data demands. This paper proposes a novel pipeline called ALFREDO (Active Learning with FeatuRe disEntangelement and DOmain adaptation) that performs active learning under domain shift. We propose a novel feature disentanglement approach to decompose image features into domain specific and task specific components. Domain specific components refer to those features that provide source specific information, e.g., scanners, vendors or hospitals. Task specific components are discriminative features for classification, segmentation or other tasks. Thereafter we define multiple novel cost functions that identify informative samples under domain shift. We test our proposed method for medical image classification using one histopathology dataset and two chest X-ray datasets. Experiments show our method achieves state-of-the-art results compared to other domain adaptation methods, as well as state of the art active domain adaptation methods., 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.)

Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve a high performance. In general, severity estimation uses training data annotated with discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult in images with ambiguous severity, and the annotation cost is high. In contrast, relative annotation, in which the severity between a pair of images is compared, can avoid quantizing severity and thus makes it easier. We can estimate relative disease severity using a learning-to-rank framework with relative annotations, but relative annotation has the problem of the enormous number of pairs that can be annotated. Therefore, the selection of appropriate pairs is essential for relative annotation. In this paper, we propose a deep Bayesian active learning-to-rank that automatically selects appropriate pairs for relative annotation. Our method preferentially annotates unlabeled pairs with high learning efficiency from the model uncertainty of the samples. We prove the theoretical basis for adapting Bayesian neural networks to pairwise learning-to-rank and demonstrate the efficiency of our method through experiments on endoscopic images of ulcerative colitis on both private and public datasets. We also show that our method achieves a high performance under conditions of significant class imbalance because it automatically selects samples from the minority classes., 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.)

Orthopaedic researchers need new strategies for engaging diverse 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, an orthopaedic research outreach module to teach diverse high school students about orthopaedic research. During the 4-hr module, students completed hands-on activities to learn how biomechanical testing, microcomputed tomography, cell culture, and histology are used in orthopaedic research. Over three years, we recruited 32 high school students from the Greater Philadelphia Area to participate in Learning on a Limb. 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 Learning on a Limb. In addition to teaching students about orthopaedic research, post-survey data demonstrated that participating in Learning on a Limb strongly influenced students' interest in orthopaedic research. Several students acted on this interest by completing summer research experiences in the McKay Orthopaedic Research Laboratory at the University of Pennsylvania. Learning on a Limb 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 Learning on a Limb would synergistically inspire diverse high school students and strengthen community within orthopaedic departments to ultimately enhance orthopaedic research innovations.

An educational escape room is a form of game-based learning that has been shown to improve students' learning experience. A cardiovascular escape room was designed to promote the development of transversal skills and help students retain learned content knowledge in an integrated pharmacology and therapeutic unit in a Pharmacy course., Objective: This study aims to design, pilot, and evaluate the students' self-perceived transversal skills, learning experience and knowledge acquisition from a cardiovascular educational escape room., Methods: The model pharmacy at Curtin University was used as the room that students were challenged to escape. Students were required to acquire a four-digit safe code, with each digit derived from four separate briefcases containing different puzzles and clues. This code enabled students to unlock a lifesaving medication to successfully escape the room and rescue a fictional patient who had collapsed outside of the pharmacy. Each session was followed by a debrief to reflect on the learning experience., Results: Fifty-four students participated in the study. Eight out of twelve groups successfully escaped the room. There was negligible difference in the score of the pre- and post-knowledge assessments. All students strongly agreed/agreed that the escape room was an effective way to review the cardiovascular content covered in the lecture and tutorial and that the activity should be continued for future students., Conclusion: Students perceived the education escape room as a valuable learning exercise to help them retain content knowledge and develop transversal skills in the learning of integrated pharmacology and therapeutic within a Pharmacy course., Competing Interests: Declaration of competing interest The author(s) declare that they have no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

We know little about the mechanisms through which leader-follower dynamics during dyadic play shape infants' language acquisition. We hypothesized that infants' decisions to visually explore a specific object signal focal increases in endogenous attention, and that when caregivers respond to these proactive behaviors by naming the object it boosts infants' word learning. To examine this, we invited caregivers and their 14-mo-old infants to play with novel objects, before testing infants' retention of the novel object-label mappings. Meanwhile, their electroencephalograms were recorded. Results showed that infants' proactive looks toward an object during play associated with greater neural signatures of endogenous attention. Furthermore, when caregivers named objects during these episodes, infants showed greater word learning, but only when caregivers also joined their focus of attention. Our findings support the idea that infants' proactive visual explorations guide their acquisition of a lexicon., Competing Interests: Competing interests statement:The authors declare no competing interest.