Factors Affecting Specialty Training Preference Among UK Medical Students (FAST): Protocol for a National Cross-Sectional Survey.

BACKGROUND: The UK medical education system faces a complex landscape of specialty training choices and heightened competition. The Factors Affecting Specialty Training Preference Among UK Medical Students (FAST) study addresses the need to understand the factors influencing UK medical students' specialty choices, against a backdrop of increasing challenges in health care workforce planning. OBJECTIVE: The primary objectives of the FAST study are to explore UK medical students' preferred specialties and the factors that influence these choices. Secondary objectives are to evaluate students' confidence in securing their chosen specialty, to understand how demographic and academic backgrounds affect their decisions, and to examine how specialty preferences and confidence levels vary across different UK medical schools. METHODS: A cross-sectional survey design will be used to collect data from UK medical students. The survey, comprising 17 questions, uses Likert scales, multiple-choice formats, and free-text entry to capture nuanced insights into specialty choice factors. The methodology, adapted from the Ascertaining the Career Intentions of UK Medical Students (AIMS) study, incorporates adjustments based on literature review, clinical staff feedback, and pilot group insights. This approach ensures comprehensive and nondirective questioning. Data analysis will include descriptive statistics to establish basic patterns, ANOVA for group comparisons, logistic regression for outcome modeling, and discrete choice models for specialty preference analysis. RESULTS: The study was launched nationally on December 4, 2023. Data collection is anticipated to end on March 1, 2024, with data analysis beginning thereafter. The results are expected to be available later in 2024. CONCLUSIONS: The FAST study represents an important step in understanding the factors influencing UK medical students' career pathways. By integrating diverse student perspectives across year groups and medical schools, this study seeks to provide critical insights into the dynamics of specialty, or residency, selection. The findings are anticipated to inform both policy and educational strategies, aiming to align training opportunities with the evolving needs and aspirations of the future medical workforce. Ultimately, the insights gained may guide initiatives to balance specialty distribution, improve career guidance, and improve overall student satisfaction within the National Health Service, contributing to a more stable and effective health care system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55155.

The role of medical schools in UK students' career intentions: findings from the AIMS study.

OBJECTIVES: To investigate differences in students' career intentions between UK medical schools. DESIGN: Cross-sectional, mixed-methods online survey. SETTING: The primary study included all 44 UK medical schools, with this analysis comprising 42 medical schools. PARTICIPANTS: Ten thousand four hundred eighty-six UK medical students. MAIN OUTCOME MEASURES: Career intentions of medical students, focusing on differences between medical schools. Secondary outcomes included variation in medical students' satisfaction with a prospective career in the NHS, by medical school. RESULTS: 2.89% of students intended to leave medicine altogether, with Cambridge Medical School having the highest proportion of such respondents. 32.35% of respondents planned to emigrate for practice, with Ulster medical students being the most likely. Of those intending to emigrate, the University of Central Lancashire saw the highest proportion stating no intentions to return. Cardiff Medical School had the greatest percentage of students intending to assume non-training clinical posts after completing FY2. 35.23% of participating medical students intended to leave the NHS within 2 years of graduating, with Brighton and Sussex holding the highest proportion of these respondents. Only 17.26% were satisfied with the prospect of working in the NHS, with considerable variation nationally; Barts and the London medical students had the highest rates of dissatisfaction. CONCLUSIONS: This study reveals variability in students' career sentiment across UK medical schools, emphasising the need for attention to factors influencing these trends. A concerning proportion of students intend to exit the NHS within 2 years of graduating, with substantial variation between institutions. Students' intentions may be shaped by various factors, including curriculum focus and recruitment practices. It is imperative to re-evaluate these aspects within medical schools, whilst considering the wider national context, to improve student perceptions towards an NHS career. Future research should target underlying causes for these disparities to facilitate improvements to career satisfaction and retention.

Three-dimensional topology-based analysis segments volumetric and spatiotemporal fluorescence microscopy.

Image analysis techniques provide objective and reproducible statistics for interpreting microscopy data. At higher dimensions, three-dimensional (3D) volumetric and spatiotemporal data highlight additional properties and behaviors beyond the static 2D focal plane. However, increased dimensionality carries increased complexity, and existing techniques for general segmentation of 3D data are either primitive, or highly specialized to specific biological structures. Borrowing from the principles of 2D topological data analysis (TDA), we formulate a 3D segmentation algorithm that implements persistent homology to identify variations in image intensity. From this, we derive two separate variants applicable to spatial and spatiotemporal data, respectively. We demonstrate that this analysis yields both sensitive and specific results on simulated data and can distinguish prominent biological structures in fluorescence microscopy images, regardless of their shape. Furthermore, we highlight the efficacy of temporal TDA in tracking cell lineage and the frequency of cell and organelle replication.

Parallel, Continuous Monitoring and Quantification of Programmed Cell Death in Plant Tissue.

Accurate quantification of hypersensitive response (HR) programmed cell death is imperative for understanding plant defense mechanisms and developing disease-resistant crop varieties. Here, a phenotyping platform for rapid, continuous-time, and quantitative assessment of HR is demonstrated: Parallel Automated Spectroscopy Tool for Electrolyte Leakage (PASTEL). Compared to traditional HR assays, PASTEL significantly improves temporal resolution and has high sensitivity, facilitating detection of microscopic levels of cell death. Validation is performed by transiently expressing the effector protein AVRblb2 in transgenic Nicotiana benthamiana (expressing the corresponding resistance protein Rpi-blb2) to reliably induce HR. Detection of cell death is achieved at microscopic intensities, where leaf tissue appears healthy to the naked eye one week after infiltration. PASTEL produces large amounts of frequency domain impedance data captured continuously. This data is used to develop supervised machine-learning (ML) models for classification of HR. Input data (inclusive of the entire tested concentration range) is classified as HR-positive or negative with 84.1% mean accuracy (F1 score = 0.75) at 1 h and with 87.8% mean accuracy (F1 score = 0.81) at 22 h. With PASTEL and the ML models produced in this work, it is possible to phenotype disease resistance in plants in hours instead of days to weeks.