The expansion of soybean (Glycine max (L.) Merr.) cultivation into Western Canada is constrained by distinct abiotic stressors, including shorter growing seasons and drought. Heat shock proteins (HSPs) are critical regulators of stress adaptation; however, characterizing their expression in field conditions is often confounded by significant annual environmental variability. In this study, we integrated differential expression analysis, consensus Weighted Gene Co-expression Network Analysis (WGCNA), and rigorous protein-protein interactomic modeling across 4 years of field data (2018-2021) involving 10 genotypes grown in Eastern and Western Canada. By filtering temporal noise, we identified a stable, region-specific module that is computationally predicted to resolve into a highly coordinated physical chaperone complex. Subcellular characterization of this network revealed a robust core of chaperones targeting mitochondria and chloroplasts. Crucially, high-confidence predictions identified a physical interaction between a mitochondrial HSP60 hub and a nuclear domains rearranged methyltransferase, suggesting this chaperone complex potentially bridges localized organelle defense with systemic epigenetic regulation to safeguard metabolic capacity. These findings delineate a computationally modeled, geography-dependent stress response machine and provide ideal genetic targets for breeding programs aiming to improve soybean resilience in northern latitudes.

Cardiovascular diseases (CVDs) are one of the leading causes of mortality worldwide, with myocardial infarction (MI) resulting in irreversible loss of cardiomyocytes and progressive cardiac dysfunction. Clinical interventions primarily restore perfusion and alleviate symptoms yet fail to regenerate the damaged myocardium. Stem cell-based therapeutics have emerged as effective and promising regenerative strategies, largely mediated through paracrine signaling, immunomodulation, and cellular replacement. However, poor cell survival, inadequate engraftment, and functional immaturity of stem cell-derived cardiomyocytes limits their clinical efficacy. Advances in nanotechnology and multi-omics provide powerful tools to address these challenges. Nanomaterials such as gold nanoparticles, graphene derivatives, and MXenes enhance stem cell retention, survival, and electromechanical integration by mimicking the native ECM and enabling targeted delivery of therapeutic molecules. Concurrently, transcriptomic and proteomic profiling offer insights into mechanisms of stem cell-mediated cardiac repair and molecular pathways governing their behavior. Stem cell-based clinical trials demonstrate modest therapeutic benefits, emphasizing the need for integrative, precision-driven strategies. The convergence of stem cell biology, nanomaterial engineering, and multi-omics technologies represents transformative approaches to overcoming limitations and advancing cardiovascular regeneration. This integrative framework holds potential to enable data-driven, personalized therapies to improve functional recovery and long-term outcomes in patients with CVDs.

ObjectivesThe study aimed to evaluate the reliability and validity of a Competency Scale for Nurses Supporting the Lives of Older Adults in Japanese Community-Based Integrated Care.MethodsThis cross-sectional study used a preliminary 36-item scale administered to 296 nurses working in community-based integrated care. Construct validity was examined using exploratory and confirmatory factor analyses. Criterion-related validity was assessed by examining correlations with existing scales. Reliability was evaluated using Cronbach's α and test-retest reliability.ResultsA total of 285 valid responses were obtained. Item analysis excluded 15 items, leaving 21 items. Exploratory factor analysis identified three factors comprising 12 items. Confirmatory factor analysis demonstrated acceptable model fit (goodness-of-fit index [GFI] = 0.931, adjusted GFI = 0.886, comparative fit index = 0.969, root mean square error of approximation = 0.054). Correlations with external scales ranged from r = 0.252 to 0.750. The overall Cronbach's α was 0.852, and test-retest reliability was confirmed (r = 0.814, p < 0.01).ConclusionThe findings support the developed scale's reliability and validity. This scale may be useful for assessing nurses' competencies in community-based integrated care and identifying areas for professional development.

Concentration-QTc analysis from Phase 1 studies is increasingly used to evaluate QTc prolongation risk as an alternative to thorough QT (TQT) studies. According to International Conference on Harmonization E14 documents, such analyses should include exposures at twice the highest clinically relevant exposure instead of a positive control to establish electrocardiogram (ECG) assay sensitivity; however, achieving this may be challenging when the highest dose in Phase 1 studies is restricted for various reasons. This study evaluated the QTc prolongation risk of forvisirvat via concentration-QTc analysis and demonstrated ECG assay sensitivity via the well-described meal effect. A double-blind, randomized, placebo-controlled, single ascending dose study evaluated the effect of forvisirvat on ECG parameters following single oral doses up to 80 mg in 32 healthy adults. Time-matched plasma forvisirvat concentrations and 12-lead ECG data were collected. The prespecified linear mixed-effects model assessed the impact of forvisirvat on baseline- and placebo-adjusted QT interval corrected by the Fridericia method (ΔΔQTcF). ECG assay sensitivity was established by prospectively evaluating the postprandial QTcF shortening. The two-sided 90% confidence interval (CI) for the model-predicted ΔΔQTcF was below 10 ms up to the maximum observed plasma concentration of 703 ng/mL. Meal-induced QTcF shortening was -6.38 ms (95% CI, -8.51 to -4.26) at 4 h after the standardized lunch, with the two-sided 95% CI below 0. Forvisirvat showed no QTcF prolongation, and ECG assay sensitivity was confirmed via meal-induced QTcF shortening. These results supported regulatory acceptance of a TQT study waiver by the Japanese Pharmaceuticals and Medical Devices Agency.

Three-dimensional biological morphologies encode functional and physiological state, yet their directional, orientational, and topological properties are rarely captured by morphometric tools in bioimage analysis. Minkowski tensors encode surface curvature and directionality for arbitrary topologies; their eigensystems directly quantify elongation axes and anisotropy. A C++ implementation, karambola, computes Minkowski tensors for triangulated surfaces but is inaccessible within Python-based bioimage workflows.

Differentiable programming has emerged as an important tool for gradient-based inverse problems and end-to-end hybrid physics-machine learning in computational fluid dynamics. Existing differentiable CFD platforms predominantly rely on structured Cartesian grids, limiting their direct application to geometrically complex domains where body-conforming unstructured discretizations are essential. We present DiFVM, the first GPU-native, end-to-end differentiable finite-volume (FV) CFD solver for incompressible flow on unstructured polyhedral meshes. The central methodological contribution is a graph-based computational reformulation of the complete unstructured FV/PISO solution procedure: by expressing FV operators as static scatter/gather primitives on the mesh connectivity graph, DiFVM transforms irregular unstructured connectivity into a GPU-compatible computational representation. All operations are implemented in JAX/XLA, providing just-in-time compilation, operator fusion, and automatic differentiation through the complete simulation pipeline. Differentiable Windkessel outlet boundary conditions are provided for cardiovascular applications, and DiFVM accepts standard OpenFOAM case directories without modification for seamless adoption in existing workflows. Forward accuracy is assessed through analytical and canonical benchmark problems, comparisons with published reference data, and independent solver comparisons, while end-to-end differentiability is demonstrated through proof-of-concept inverse modeling examples. DiFVM provides a differentiable CFD framework for gradient-based optimization, inverse modeling and future physics-integrated machine learning on complex unstructured geometries.

Background This study aimed to analyze objective questions used to evaluate Phase B residents in the Basics of Medical Education course in Bangladesh, focusing on standardizing test questions and identifying areas for improvement based on Difficulty Index (DIF I) and Discrimination Index (DI). Methodology In November 2022, 148 Phase B residents were examined by an internal assessment on the Basics of Medical Education. The assessment comprised 50 objective questions, including true/false (T/F) statements and fill-in-the-gap exercises. Each correct answer received one mark. Following the assessment, item analysis was performed for validation, with data analysis conducted using Microsoft Excel (Microsoft Corp., Redmond, WA). Results Out of 30 T/F statements, 40% were considered good/acceptable, with a mean DIF I of 76.17 and a standard deviation (SD) of 20.42; 60% were considered too easy. The overall mean (SD) DI for all T/F statements was 0.18 (0.13), with half displaying acceptable to excellent discriminatory capacity. For the 20 fill-in-the-blank statements, 40% were considered acceptable, with a mean (SD) DIF I of 64.80 (20.70) and a DI of 0.32 (0.18), indicating that 75% of these items had a good to excellent quality level. Conclusion Fill-in-the-blank statements demonstrated exceptional discriminatory capabilities, but half of the T/F statements require significant refinement to enhance their quality.

Ambient artificial intelligence scribes (ambient scribes) are increasingly used across healthcare systems to support clinical documentation. Implementation strategies and associated research have been mainly concerned with the ability to accurately and efficiently produce clinical notes. Some concerns have been raised, such as potential hallucinations or omissions that require editorial work of clinicians, but this research has not moved beyond the technical performance of ambient scribes. We sought to synthesise emerging evidence and perspectives on ambient scribes from a sociotechnical perspective, focusing on how these systems interact with existing clinical work practices and organisational processes. We searched Embase, PubMed and Scopus to identify relevant literature and included 27 articles. We used a sociotechnical lens to synthesise research and map core themes against implementation domains from the Non-Adoption, Abandonment, Scale-Up, Spread and Sustainability (NASSS) framework. We identified few empirical studies examining the risks of ambient scribes, and even fewer investigating their implementation and adoption in practice. Existing research has focused predominantly on their potential to reduce administrative burden and improve efficiency. However, ambient scribes do more than automate note-taking. They actively shape clinical workflows and patient-clinician interactions and change the nature and function of clinical documentation. This suggests that there has been a de facto acceptance by policymakers and practitioners of the value proposition without critically examining the underlying assumptions and broader consequences. Ambient scribes must be understood as complex interventions rather than just productivity tools. There is therefore an urgent need for contextual validation and longitudinal evaluation.