In Silico TRials guide optimal stratification of ATrIal FIbrillation patients to Catheter Ablation and pharmacological medicaTION: the i-STRATIFICATION study.

AIMS: Patients with persistent atrial fibrillation (AF) experience 50% recurrence despite pulmonary vein isolation (PVI), and no consensus is established for secondary treatments. The aim of our i-STRATIFICATION study is to provide evidence for stratifying patients with AF recurrence after PVI to optimal pharmacological and ablation therapies, through in silico trials. METHODS AND RESULTS: A cohort of 800 virtual patients, with variability in atrial anatomy, electrophysiology, and tissue structure (low-voltage areas, LVAs), was developed and validated against clinical data from ionic currents to electrocardiogram. Virtual patients presenting AF post-PVI underwent 12 secondary treatments. Sustained AF developed in 522 virtual patients after PVI. Second ablation procedures involving left atrial ablation alone showed 55% efficacy, only succeeding in the small right atria (<60 mL). When additional cavo-tricuspid isthmus ablation was considered, Marshall-PLAN sufficed (66% efficacy) for the small left atria (<90 mL). For the bigger left atria, a more aggressive ablation approach was required, such as anterior mitral line (75% efficacy) or posterior wall isolation plus mitral isthmus ablation (77% efficacy). Virtual patients with LVAs greatly benefited from LVA ablation in the left and right atria (100% efficacy). Conversely, in the absence of LVAs, synergistic ablation and pharmacotherapy could terminate AF. In the absence of ablation, the patient's ionic current substrate modulated the response to antiarrhythmic drugs, being the inward currents critical for optimal stratification to amiodarone or vernakalant. CONCLUSION: In silico trials identify optimal strategies for AF treatment based on virtual patient characteristics, evidencing the power of human modelling and simulation as a clinical assisting tool.

Virtual patients in undergraduate psychiatry education: a systematic review and synthesis.

Virtual patients are increasingly used in undergraduate psychiatry education. This article reports on a systematic review aimed at providing an overview of different approaches in this context, describing their effectiveness, and thematically comparing learning outcomes across different undergraduate programs. The authors searched PubMed, PsycInfo, CINAHL, and Scopus databases for articles published between 2000 and January 2021. Quantitative and qualitative studies that reported on outcomes related to learners' knowledge, skills, and attitudes following an intervention with virtual patients in undergraduate psychiatry education were reviewed. Outcomes were thematically compared, and a narrative synthesis of the different outcomes and effectiveness was provided. Of 7856 records identified, 240 articles were retrieved for full-text review and 46 articles met all inclusion criteria. There were four broad types of virtual patient interventions: case-based presentation (n = 17), interactive virtual patient scenarios (n = 14), standardized virtual patients (n = 10), and virtual patient videogames (n = 5). The thematic analysis revealed that virtual patients in psychiatry education have been used for learners to construe knowledge about symptomatology and psychopathology, develop interpersonal and clinical communicative skills, and to increase self-efficacy and decrease stigmatizing attitudes towards psychiatric patients. In comparison with no intervention, traditional teaching, and text-based interventions, virtual patients were associated with higher learning outcomes. However, the results did not indicate any superiority of virtual patients over non-technological simulation. Virtual patients in psychiatry education offer opportunities for students from different health disciplines to build knowledge, practice skills, and improve their attitudes towards individuals with mental illness. The article discusses methodological shortcomings in the reviewed literature. Future interventions should consider the mediating effects of the quality of the learning environment, psychological safety, and level of authenticity of the simulation.

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data.

The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increase available data (e.g., in low-density regions-i.e., for patients with under-represented characteristics). Generative methods employ a family of solutions for generating synthetic data. The objective of this research is to benchmark numerous state-of-the-art deep-learning generative methods across different scenarios and clinical datasets comprising patient covariates and several pharmacokinetic/pharmacodynamic endpoints. We did this by implementing various probabilistic models aimed at generating synthetic data, such as the Multi-layer Perceptron Conditioning Generative Adversarial Neural Network (MLP cGAN), Time-series Generative Adversarial Networks (TimeGAN), and a more traditional approach like Probabilistic Autoregressive (PAR). We evaluated their performance by calculating discriminative and predictive scores. Furthermore, we conducted comparisons between the distributions of real and synthetic data using Kolmogorov-Smirnov and Chi-square statistical tests, focusing respectively on covariate and output variables of the models. Lastly, we employed pharmacometrics-related metric to enhance interpretation of our results specific to our investigated scenarios. Results indicate that multi-layer perceptron-based conditional generative adversarial networks (MLP cGAN) exhibit the best overall performance for most of the considered metrics. This work highlights the opportunities to employ synthetic data generation in the field of clinical pharmacology for augmentation and sharing of proprietary data across institutions.

Virtual clinical trials via a QSP immuno-oncology model to simulate the response to a conditionally activated PD-L1 targeting antibody in NSCLC.

Recently, immunotherapies for antitumoral response have adopted conditionally activated molecules with the objective of reducing systemic toxicity. Amongst these are conditionally activated antibodies, such as PROBODY® activatable therapeutics (Pb-Tx), engineered to be proteolytically activated by proteases found locally in the tumor microenvironment (TME). These PROBODY® therapeutics molecules have shown potential as PD-L1 checkpoint inhibitors in several cancer types, including both effectiveness and locality of action of the molecule as shown by several clinical trials and imaging studies. Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for triple-negative breast cancer (TNBC), to computationally predict the effectiveness and targeting specificity of a PROBODY® therapeutics drug compared to the non-modified antibody. We begin with the analysis of anti-PD-L1 immunotherapy in non-small cell lung cancer (NSCLC). As a first contribution, we have improved previous virtual patient selection methods using the omics data provided by the iAtlas database portal compared to methods previously published in literature. Furthermore, our results suggest that masking an antibody maintains its efficacy while improving the localization of active therapeutic in the TME. Additionally, we generalize the model by evaluating the dependence of the response to the tumor mutational burden, independently of cancer type, as well as to other key biomarkers, such as CD8/Treg Tcell and M1/M2 macrophage ratio. While our results are obtained from simulations on NSCLC, our findings are generalizable to other cancer types and suggest that an effective and highly selective conditionally activated PROBODY® therapeutics molecule is a feasible option.

Integrating virtual patients into undergraduate health professions curricula: a framework synthesis of stakeholders' opinions based on a systematic literature review.

BACKGROUND: Virtual patients (VPs) are widely used in health professions education. When they are well integrated into curricula, they are considered to be more effective than loosely coupled add-ons. However, it is unclear what constitutes their successful integration. The aim of this study was to identify and synthesise the themes found in the literature that stakeholders perceive as important for successful implementation of VPs in curricula. METHODS: We searched five databases from 2000 to September 25, 2023. We included qualitative, quantitative, mixed-methods and descriptive case studies that defined, identified, explored, or evaluated a set of factors that, in the perception of students, teachers, course directors and researchers, were crucial for VP implementation. We excluded effectiveness studies that did not consider implementation characteristics, and studies that focused on VP design factors. We included English-language full-text reports and excluded conference abstracts, short opinion papers and editorials. Synthesis of results was performed using the framework synthesis method with Kern's six-step model as the initial framework. We appraised the quality of the studies using the QuADS tool. RESULTS: Our search yielded a total of 4808 items, from which 21 studies met the inclusion criteria. We identified 14 themes that formed an integration framework. The themes were: goal in the curriculum; phase of the curriculum when to implement VPs; effective use of resources; VP alignment with curricular learning objectives; prioritisation of use; relation to other learning modalities; learning activities around VPs; time allocation; group setting; presence mode; VPs orientation for students and faculty; technical infrastructure; quality assurance, maintenance, and sustainability; assessment of VP learning outcomes and learning analytics. We investigated the occurrence of themes across studies to demonstrate the relevance of the framework. The quality of the studies did not influence the coverage of the themes. CONCLUSIONS: The resulting framework can be used to structure plans and discussions around implementation of VPs in curricula. It has already been used to organise the curriculum implementation guidelines of a European project. We expect it will direct further research to deepen our knowledge on individual integration themes.

Virtual patients versus standardized patients for improving clinical reasoning skills in ophthalmology residents. A randomized controlled trial.

BACKGROUND: History taking and clinical reasoning are important skills that require knowledge, cognition and meta-cognition. It is important that a trainee must experience multiple encounters with different patients to practice these skills. However, patient safety is also important, and trainees are not allowed to handle critically ill patients. To address this issue, a randomized controlled trial was conducted to determine the effectiveness of using Virtual Patients (VP) versus Standardized Patients (SP) in acquiring clinical reasoning skills in ophthalmology postgraduate residents. METHODS: Postgraduate residents from two hospitals in Lahore, Pakistan, were randomized to either the VP group or the SP group and were exposed to clinical reasoning exercise via the VP or SP for 30 min after the pretest. This was followed by a posttest. One month after this activity, a follow-up posttest was conducted. The data were collected and analysed using IBM-SPSS version 25. Repeated measures ANOVA was used to track the effect of learning skills over time. RESULTS: The mean age of the residents was 28.5 ± 3 years. The male to female ratio was 1:1.1. For the SP group, the mean scores were 12.6 ± 3.08, 16.39 ± 3.01 and 15.39 ± 2.95, and for the VP group, the mean scores were 12.7 ± 3.84, 16.30 ± 3.19 and 15.65 ± 3.18 for the pretest, posttest and follow-up posttest, respectively (p value < 0.00). However, the difference between the VP and SP groups was not statistically significant (p = 0.896). Moreover, there was no statistically significant difference between the VP and SP groups regarding the retention of clinical reasoning ability. In terms of learning gain, compared with the VP group, the SP group had a score of 51.46% immediately after clinical reasoning exercise as compared to VP group, in which it was 49.1%. After one month, it was 38.01 in SP and 40.12% in VP group. CONCLUSION: VPs can be used for learning clinical reasoning skills in postgraduate ophthalmology residents in a safe environment. These devices can be used repeatedly without any risk to the real patient. Although similarly useful, SP is limited by its nonavailability for repeated exercises.

Clinical virtual simulation: predictors of user acceptance in nursing education.

BACKGROUND: Using virtual patients integrated in simulators expands students' training opportunities in healthcare. However, little is known about the usability perceived by students and the factors/determinants that predict the acceptance and use of clinical virtual simulation in nursing education. OBJECTIVES: To identify the factors/determinants that predict the acceptance and use of clinical virtual simulation in learning in nursing education. METHODS: Observational, cross-sectional, analytical study of the use of clinical virtual simulation in nursing to answer the research question: What factors/determinants predict the acceptance and use of a clinical virtual simulator in nursing education? We used a non-probabilistic sampling, more specifically a convenience sample of nursing degree students. The data were collected through a questionnaire adapted from the Technology Acceptance Model 3. In technology and education, the Technology Acceptance Model is a theoretical model that predicts the acceptance of the use of technology by users. RESULTS: The sample comprised 619 nursing students, who revealed mean values of perceived usefulness (M = 5.34; SD = 1.19), ease of use (M = 4.74; SD = 1.07), and intention to use the CVS (M = 5.21; SD = 1.18), in a Likert scale of seven points (1-the worst and 7 the best possible opinion). This study validated the use of Technology Acceptance Model 3 adapted and tested the related hypotheses, showing that the model explains 62% of perceived utility, 32% of ease of use, and 54% of intention to use the clinical virtual simulation in nursing by nursing students. The adequacy of the model was tested by analysis of the direct effects of the relationships between the internal constructs (PU-BI, ß = 0.11, p = 0.012; PEOU-BI, ß = -0.11, p = 0.002) and the direct relations between some of the constructs internal to the Technology Acceptance Model 3 and the external determinants Relevance for learning and Enjoyability. In the proposed model, the external constructs that best predicted perceived usefulness, ease of use, and behaviour intention to use the clinical virtual simulation in nursing were Relevance for learning and Enjoyability. CONCLUSIONS: These study results allowed us to identify relevance for learning and enjoyability as the main factors/determinants that predict the acceptance and use of clinical virtual simulation in learning in nursing.

Diagnosing virtual patients: the interplay between knowledge and diagnostic activities.

Clinical reasoning theories agree that knowledge and the diagnostic process are associated with diagnostic success. However, the exact contributions of these components of clinical reasoning to diagnostic success remain unclear. This is particularly the case when operationalizing the diagnostic process with diagnostic activities (i.e., teachable practices that generate knowledge). Therefore, we conducted a study investigating to what extent knowledge and diagnostic activities uniquely explain variance in diagnostic success with virtual patients among medical students. The sample consisted of N = 106 medical students in their third to fifth year of university studies in Germany (6-years curriculum). Participants completed professional knowledge tests before diagnosing virtual patients. Diagnostic success with the virtual patients was assessed with diagnostic accuracy as well as a comprehensive diagnostic score to answer the call for more extensive measurement of clinical reasoning outcomes. The three diagnostic activities hypothesis generation, evidence generation, and evidence evaluation were tracked. Professional knowledge predicted performance in terms of the comprehensive diagnostic score and displayed a small association with diagnostic accuracy. Diagnostic activities predicted comprehensive diagnostic score and diagnostic accuracy. Hierarchical regressions showed that the diagnostic activities made a unique contribution to diagnostic success, even when knowledge was taken into account. Our results support the argument that the diagnostic process is more than an embodiment of knowledge and explains variance in diagnostic success over and above knowledge. We discuss possible mechanisms explaining this finding.

Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine.

Big data and AI have enabled digital simulation for prediction of future health states or behaviors of specific individuals, populations or humans in general. "Digital simulacra" use multimodal datasets to develop computational models that are virtual representations of people or groups, generating predictions of how systems evolve and react to interventions over time. These include digital twins and virtual patients for in silico clinical trials, both of which seek to transform research and health care by speeding innovation and bridging the epistemic gap between population-based research findings and their application to the individual. Nevertheless, digital simulacra mark a major milestone on a trajectory to embrace the epistemic culture of data science and a potential abandonment of medical epistemological concepts of causality and representation. In doing so, "data first" approaches potentially shift moral attention from actual patients and principles, such as equity, to simulated patients and patient data.

Socioeconomic Level and the Relationship in Glycemic Behavior in the Mexican Population. A Nutritional Alternative Focused on Vulnerable Populations.

In this study, numerical approximations were generated to analyze the behavior of glycemic variations in the non-diabetic population of the Mexican republic. The main objective of this work is to obtain an overview of the glycemic variations in the non-diabetic population from different socioeconomic statuses in Mexico (Medium-high, medium, and low). Thus, evaluating the effect on the glucose level under a healthy diet considering the socioeconomic capabilities of the population. Through the national health and nutrition survey of Mexico 2020 and the Mexican food base, 1420 virtual patients were proposed (522 low status, 485 medium status and 413 Medium-High status) focused on simulating the glycemic behavior in each of the survey participants. Considering that the average food expenditure of the Mexican population is $107.00 MXN, and the cost of a healthy diet is $66.50 MXN, the economic sustainability of the Mexican population to adopt a healthy diet is revealed. The particularity of this work is focused on obtaining diverse data that are difficult to access in the development of population analyses. Such is the case of the approach proposed for different socioeconomic statuses. In this way, the proposed methodology provides a framework for complementary research contributions to the subject.

Twine virtual patient games as an online resource for undergraduate diabetes acute care education.

BACKGROUND: Virtual patients provide a safe way to simulate authentic clinical practice. Twine is an open-source software that can be used to create intricate virtual patient games, including elements like non-linear free text history taking and time-related changes to the game's narrative. We evaluated the incorporation of Twine virtual patient games into a diabetes acute care online learning package for undergraduate medical students at the University of Glassgow, Scotland. METHODS: Three games were developed using Twine, Wacom Intuous Pro, Autodesk SketchBook, Camtasia Studio, and simulated patients. Online material included three VP games, eight microlectures, and a single best answer multiple choice question quiz. The games were evaluated at Kirkpatrick Level 1 with an acceptability and usability questionnaire. The entire online package was evaluated at Kirkpatrick Level 2 with pre- and post-course multiple choice and confidence questions, with statistical analysis performed using paired t-tests. RESULTS: 122 of approximately 270 eligible students provided information on resource utilisation, with 96% of these students using at least one online resource. 68% of students who returned surveys used at least one VP game. 73 students provided feedback on the VP games they had played, with the majority of median responses being "agree" on positive usability and acceptability statements. The online resources were associated with a mean multiple choice score increase from 4.37 out of 10 to 7.96 out of 10 (p < 0.0001, 95% CI + 2.99 to + 4.20, n = 52) and a mean total confidence score increase from 4.86 out of 10 to 6.70 out of 10 (p < 0.0001, 95% CI + 1.37 to + 2.30, n = 48). CONCLUSIONS: Our VP games were well-received by students and promoted engagement with online material. The package of online material led to statistically significant increases in confidence and knowledge in diabetes acute care outcomes. A blueprint with supporting instructions has now been created to facilitate rapid creation of further games using Twine software.

"It kinda helped us to be there": students' perspectives on the use of virtual patient software in psychiatry posting.

BACKGROUND: At the Faculty of Medicine of the National University of Malaysia, a virtual patient software program, DxR Clinician, was utilised for the teaching of neurocognitive disorder topics during the psychiatry posting of undergraduate medical students in a modified team-based learning (TBL) module. This study aimed to explore medical students' learning experiences with virtual patient. METHODS: Ten students who previously underwent the learning module were recruited through purposive sampling. The inclusion criteria were: (a) Fourth-year medical students; and (b) Completed psychiatry posting with the new module. Students who dropped out or were unable to participate in data collection were excluded. Two online focus group discussions (FGDs) with five participants each were conducted by an independent facilitator, guided by a questioning route. The data were transcribed verbatim and coded using the thematic analysis approach to identify themes. RESULTS: Three main themes of their learning experience were identified: (1) fulfilment of the desired pedagogy (2), realism of the clinical case, and (3) ease of use related to technical settings. The pedagogy theme was further divided into the following subthemes: level of entry for students, flexibility of presentation of content, provision of learning guidance, collaboration with peers, provision of feedback, and assessment of performance. The realism theme had two subthemes: how much the virtual patient experience mimicked an actual patient and how much the case scenario reflected real conditions in the Malaysian context. The technical setting theme entailed two subthemes: access to the software and appearance of the user interface. The study findings are considered in the light of learning formats, pedagogical and learning theories, and technological frameworks. CONCLUSIONS: The findings shed light on both positive and negative aspects of using virtual patients for medical students' psychiatry posting, which opens room for further improvement of their usage in undergraduate psychiatry education.

A simulator with realistic and challenging scenarios for virtual T1D patients undergoing CSII and MDI therapy.

In silico simulations have become essential for the development of diabetes treatments. However, currently available simulators are not challenging enough and often suffer from limitations in insulin and meal absorption variability, which is unable to realistically reflect the dynamics of people with type 1 diabetes (T1D). Additionally, T1D simulators are mainly designed for the testing of continuous subcutaneous insulin infusion (CSII) therapies. In this work, a simulator is presented that includes a generated virtual patient (VP) cohort and both fast- and long-acting Glargine-100 U/ml (Gla-100), Glargine-300 U/ml (Gla-300), and Degludec-100 U/ml (Deg-100) insulin models. Therefore, in addition to CSII therapies, multiple daily injections (MDI) therapies can also be tested. The Hovorka model and its published parameter probability distributions were used to generate cohorts of VPs that represent a T1D population. Valid patients are filtered through restrictions that guarantee that they are physiologically acceptable. To obtain more realistic scenarios, basal insulin profile patterns from the literature have been used to identify variability in insulin sensitivity. A library of mixed meals identified from real data has also been included. This work presents and validates a methodology for the creation of realistic VP cohorts that include physiological variability and a simulator that includes challenging and realistic scenarios for in silico testing. A cohort of 47 VPs has been generated and in silico simulations of both CSII and MDI therapies were performed in open-loop. The simulation outcome metrics were contrasted with literature results.

Application of different approaches to generate virtual patient populations for the quantitative systems pharmacology model of erythropoiesis.

In a standard situation, a quantitative systems pharmacology model describes a "reference patient," and the model parameters are fixed values allowing only the mean values to be described. However, the results of clinical trials include a description of variability in patients' responses to a drug, which is typically expressed in terms of conventional statistical parameters, such as standard deviations (SDs) from mean values. Therefore, in this study, we propose and compare four different approaches: (1) Monte Carlo Markov Chain (MCMC); (2) model fitting to Monte Carlo sample; (3) population of clones; (4) stochastically bounded selection to generate virtual patient populations based on experimentally measured mean data and SDs. We applied these approaches to generate virtual patient populations in the QSP model of erythropoiesis. According to the results of our research, stochastically bounded selection showed slightly better results than the other three methods as it allowed the description of any number of patients from clinical trials and could be applied in the case of complex models with a large number of variable parameters.

Understanding virtual patients efficiently and rigorously by combining machine learning with dynamical modelling.

Individual biological organisms are characterized by daunting heterogeneity, which precludes describing or understanding populations of 'patients' with a single mathematical model. Recently, the field of quantitative systems pharmacology (QSP) has adopted the notion of virtual patients (VPs) to cope with this challenge. A typical population of VPs represents the behavior of a heterogeneous patient population with a distribution of parameter values over a mathematical model of fixed structure. Though this notion of VPs is a powerful tool to describe patients' heterogeneity, the analysis and understanding of these VPs present new challenges to systems pharmacologists. Here, using a model of the hypothalamic-pituitary-adrenal axis, we show that an integrated pipeline that combines machine learning (ML) and bifurcation analysis can be used to effectively and efficiently analyse the behaviors observed in populations of VPs. Compared with local sensitivity analyses, ML allows us to capture and analyse the contributions of simultaneous changes of multiple model parameters. Following up with bifurcation analysis, we are able to provide rigorous mechanistic insight regarding the influences of ML-identified parameters on the dynamical system's behaviors. In this work, we illustrate the utility of this pipeline and suggest that its wider adoption will facilitate the use of VPs in the practice of systems pharmacology.

A scoping review: virtual patients for communication skills in medical undergraduates.

BACKGROUND: Communication is an essential competence for medical students. Virtual patients (VP), computerized educational tools where users take the role of doctor, are increasingly used. Despite the wide range of VP utilization, evidence-based practical guidance on supporting development of communication skills for medical students remains unclear. We focused this scoping review on VP affordance for student learning especially important in the current environment of constrained patient access. METHODS: This scoping review followed Arksey & O'Malley's methodology. We tested and used a search strategy involving six databases, resulting in 5,262 citations. Two reviewers independently screened titles, full texts (n= 158) and finally performed data extraction on fifty-five included articles. To support consideration of educational affordance the authors employed a pragmatic framework (derived from activity theory) to map included studies on VP structure, curricular alignment, mediation of VP activity, and socio-cultural context. RESULTS: Findings suggest that not only the VP itself, but also its contextualization and associated curricular activities influence outcomes. The VP was trialled in the highest proportion of papers as a one-off intervention (19 studies), for an average duration of 44.9 minutes (range 10-120min), mainly in senior medical students (n=23), notably the largest group of studies did not have VP activities with explicit curricular integration (47%). There was relatively little repeated practice, low levels of feedback, self-reflection, and assessment. Students viewed VPs overall, citing authenticity and ease of use as important features. Resource implications are often omitted, and costings would facilitate a more complete understanding of implications of VP use. CONCLUSION: Students should be provided with maximal opportunity to draw out the VPs' full potential through repeated practice, without time-constraint and with curricular alignment. Feedback delivery enabling reflection and mastery is also key. The authors recommend educators to explicitly balance computerized authenticity with instructional design integrated within the curriculum.

Flexible interprofessional student encounters based on virtual patients: a contribution to an interprofessional strategy.

It is challenging to organize interprofessional activities in terms of coordinating students' various schedules. These challenges can be overcome by providing flexible online opportunities based on virtual patients (VPs). This study set out to study feasibility of using a blended approach based on virtual patients and a flexible interprofessional student encounter. The encounter was arranged in pairs or triads between nursing and medical students from two separate courses. Data were gathered through a questionnaire and followed up with group interviews. Reflective texts from the interprofessional encounters were analyzed in relation to descriptions of interprofessional competence. The great majority (86%) chose to meet online due to its flexibility. The participants gained an understanding of the other profession's roles and competences and a holistic patient awareness. Given its flexible and scalable opportunities, the blended online virtual patient approach provides a valuable contribution to an interprofessional programme.

A mechanistic systems pharmacology modeling platform to investigate the effect of PD-L1 expression heterogeneity and dynamics on the efficacy of PD-1 and PD-L1 blocking antibodies in cancer.

Tumors have developed multitude of ways to evade immune response and suppress cytotoxic T cells. Programed cell death protein 1 (PD-1) and programed cell death ligand 1 (PD-L1) are immune checkpoints that when activated, rapidly inactivate the cytolytic activity of T cells. Expression heterogeneity of PD-L1 and the surface receptor dynamics of both PD-1 and PD-L1 may be important parameters in modulating the immune response. PD-L1 is expressed on both tumor and non-tumor immune cells and this differential expression reflects different aspects of anti-tumor immunity. Here, we developed a mechanistic computational model to investigate the role of PD-1 and PD-L1 dynamics in modulating the efficacy of PD-1 and PD-L1 blocking antibodies. Our model incorporates immunological synapse restricted interaction of PD-1 and PD-L1, basal parameters for receptor dynamics, and T cell interaction with tumor and non-tumor immune cells. Simulations predict the existence of a threshold in PD-1 expression above which there is no efficacy for both anti-PD-1 and anti-PD-L1. Model also predicts that anti-tumor response is more sensitive to PD-L1 expression on non-tumor immune cells than tumor cells. New combination strategies are suggested that may enhance efficacy in resistant cases such as combining anti-PD-1 with a low dose of anti-PD-L1 or with inhibitors of PD-L1 recycling and synthesis. Another combination strategy suggested by the model is the combination of anti-PD-1 and anti-PD-L1 with enhancers of PD-L1 degradation rate. Virtual patients are then generated to test specific biomarkers of response. Intriguing predictions that emerge from the virtual patient simulations are that PD-1 blocking antibody results in higher response rate than PD-L1 blockade and that PD-L1 expression density on non-tumor immune cells rather than tumor cells is a predictor of response.

Influence of patient immigrant status on physician trainee diabetes treatment decisions: a virtual patient experimental study.

To determine the effect of patient immigrant status on physician trainees' diabetes treatment decisions. Participants were 140 physician trainees ('providers'). Providers viewed videos and vignettes of virtual patients differing in immigrant status (born in Mexico or U.S.; other characteristics held constant). Analyses were completed at the group and individual levels. Providers were less likely to refer foreign-born (vs. U.S.-born) patients to endocrinology. Individual-level results showed an almost even split between treatment ratings for foreign-born vs. U.S.-born patients for three decisions (take no action, add oral hypoglycemic agent, add/switch to insulin), explaining why group-level differences for these ratings did not emerge (i.e., they were cancelled out). Physician trainees are less likely to refer foreign-born patients to endocrinology. Half of individual-level decisions were influenced by patient immigrant status, but group-level analyses mask these differences. Systematic treatment differences based on non-relevant factors could lead to adverse outcomes for immigrants.

Blended Learning Compared to Traditional Learning in Medical Education: Systematic Review and Meta-Analysis.

BACKGROUND: Blended learning, which combines face-to-face learning and e-learning, has grown rapidly to be commonly used in education. Nevertheless, the effectiveness of this learning approach has not been completely quantitatively synthesized and evaluated using knowledge outcomes in health education. OBJECTIVE: The aim of this study was to assess the effectiveness of blended learning compared to that of traditional learning in health education. METHODS: We performed a systematic review of blended learning in health education in MEDLINE from January 1990 to July 2019. We independently selected studies, extracted data, assessed risk of bias, and compared overall blended learning versus traditional learning, offline blended learning versus traditional learning, online blended learning versus traditional learning, digital blended learning versus traditional learning, computer-aided instruction blended learning versus traditional learning, and virtual patient blended learning versus traditional learning. All pooled analyses were based on random-effect models, and the I2 statistic was used to quantify heterogeneity across studies. RESULTS: A total of 56 studies (N=9943 participants) assessing several types of learning support in blended learning met our inclusion criteria; 3 studies investigated offline support, 7 studies investigated digital support, 34 studies investigated online support, 8 studies investigated computer-assisted instruction support, and 5 studies used virtual patient support for blended learning. The pooled analysis comparing all blended learning to traditional learning showed significantly better knowledge outcomes for blended learning (standardized mean difference 1.07, 95% CI 0.85 to 1.28, I2=94.3%). Similar results were observed for online (standardized mean difference 0.73, 95% CI 0.60 to 0.86, I2=94.9%), computer-assisted instruction (standardized mean difference 1.13, 95% CI 0.47 to 1.79, I2=78.0%), and virtual patient (standardized mean difference 0.62, 95% CI 0.18 to 1.06, I2=78.4%) learning support, but results for offline learning support (standardized mean difference 0.08, 95% CI -0.63 to 0.79, I2=87.9%) and digital learning support (standardized mean difference 0.04, 95% CI -0.45 to 0.52, I2=93.4%) were not significant. CONCLUSIONS: From this review, blended learning demonstrated consistently better effects on knowledge outcomes when compared with traditional learning in health education. Further studies are needed to confirm these results and to explore the utility of different design variants of blended learning.