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Objectives: There is concern that junior doctors are not prepared for their post-graduate attachments in ENT. The aims of this study were to capture the learning priorities of those in the ENT first on-call role and facilitate further educational opportunities to address these needs. Method: Semi-structured interviews were undertaken to explore the learning needs of junior doctors with seven junior and two senior ENT clinicians. Results: The thematic analysis generated three themes: the role of the ENT Junior; the perceived, expressed and prescribed learning needs; and attitudes towards future learning. These themes explored the misalignment between undergraduate training and post-graduate expectations, the lack of competence in ENT practical skills and the need for focused ENT training prior to commencing on-call shifts. Conclusion: All interviewees identified the need for greater experience in practical interventional skills prior to their ENT attachments and expressed interest towards a standardised, bootcamp-style induction with simulated emergency experience. [ABSTRACT FROM AUTHOR]

Socio‐scientific issues (SSI) instruction positions the understanding and practice of science in the context of issues that are informed by science but require reasoning about their societal dimensions to respond to those issues effectively. For this reason, instruction in the context of SSI has been considered the gateway to contemporary visions of scientific literacy. SSI instruction is often framed in line with the Socio‐Scientific Issues Teaching and Learning (SSI‐TL) framework, which is prominent in the literature and well‐used by researchers to frame professional development but potentially less familiar to classroom teachers. Given that teachers are likely familiar with the 5E learning cycle, they might experience an easier transition to developing and facilitating SSI instruction using the SSI‐TL model if framed through a lens of 5E. In this article, we unpack the SSI‐TL model of instruction through a 5E lens, then provide an exemplary prototype of the new SSI‐TL infused 5E instruction in the context of a globally relevant SSI to highlight the overlap between engagement in essential science practices and socio‐scientific reasoning. We hope that teachers become more comfortable developing science literacy by addressing both science and societal dimensions of contemporary SSI by considering the SSI‐TL Framework through a 5E lens. [ABSTRACT FROM AUTHOR]

Introduction: To compare responses to basal insulin glargine 300 U/ml (IGlar‐300) and 100 U/ml (IGlar‐100) in newly defined subphenotypes of type 2 diabetes. Methods: Insulin‐naive participants (n = 858) from the EDITION 3 trial were assigned to subphenotypes 'Mild Age‐Related Diabetes (MARD)', 'Mild Obesity Diabetes (MOD)', 'Severe Insulin Resistant Diabetes (SIRD)' and 'Severe Insulin Deficient Diabetes (SIDD)'. Key variables were analysed at baseline and 26 weeks. Results: Participants were comprised of MOD 56.1% (n = 481), SIDD 22.1% (n = 190), MARD 18.2% (n = 156) and SIRD 3.0% (n = 26). After 26 weeks a similar decrease in glycated haemoglobin (HbA1c) and fasting plasma glucose (FPG) of 16‐19 mmol/mol and 1.4‐1.7 mmol/L, respectively, occurred in MARD and MOD with both insulins. SIDD had the most elevated HbA1c and FPG (80‐83 mmol/mol/11.1‐11.4 mmol/L) and reduction in both HbA1c and FPG was greater with IGlar‐100 than with IGlar‐300 (−18 vs. −15 mmol/mol and −1.6 vs. −1.3 mmol/L, respectively; each p =.03). In SIDD, despite receiving the highest basal insulin doses, HbA1c decline (57‐60 mmol/mol/7.3‐7.6%) was suboptimal at week 26. In MOD and SIDD lower incidences with IGlar‐300 were found for level 1 nocturnal hypoglycaemia [odds ratio (OR) 0.59, 95% confidence intervals (CI) 0.36‐0.97; OR 0.49, 95% CI 0.24‐0.99]. In addition, fewer level 2 hypoglycaemia episodes occurred at any time with IGlar‐300 in SIDD (OR 0.31, 95% CI 0.13‐0.77). Conclusion: Both insulins produce comparable outcomes in type 2 diabetes subphenotypes, but in SIDD, add‐on treatment to basal insulin is required to achieve glycaemic targets. [ABSTRACT FROM AUTHOR]

Background: Management of lateral cystic neck masses with no apparent upper aerodigestive tract primary tumour in adults is controversial. Imaging modalities and fine needle aspiration cytology often struggle to distinguish the presence of malignancy. Method: This study entailed a multicentre retrospective review of all patients with isolated lateral cystic neck masses from 2012 to 2018 in three Welsh health boards, utilising demographic data and first-line investigations (ultrasound scanning and fine needle aspiration cytology) to develop an evidence-based predictive tool for risk of malignancy. Results: It was found that 29.1 per cent of cystic lesions were malignant on final histology. Age, male gender, non-benign ultrasound scan findings and fine needle aspiration cytology were significant risk factors on univariate analysis. The final multivariate analysis predicted a risk of malignancy ranging from 2.1 to 65.0 per cent depending on the covariate pattern. Smoking status was non-significant. Conclusion: A rigorous, risk-stratified approach to the management of these patients should aid the clinician in minimising morbidity and optimising resources. [ABSTRACT FROM AUTHOR]

Transcriptional regulation, involving the complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate in unseen cell types and conditions. Here, we introduce GET, an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types. GET showcases remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovering universal and cell type specific transcription factor interaction networks. We evaluated its performance on prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors. Specifically, we show GET outperforms current models in predicting lentivirus-based massive parallel reporter assay readout with reduced input data. In fetal erythroblasts, we identify distal (>1Mbp) regulatory regions that were missed by previous models. In B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukemia-risk predisposing germline mutation. In sum, we provide a generalizable and accurate model for transcription together with catalogs of gene regulation and transcription factor interactions, all with cell type specificity.

Stroke stands as a major global health issue, causing high death and disability rates and significant social and economic burdens. The effectiveness of existing stroke risk assessment methods is questionable due to their use of inconsistent and varying biomarkers, which may lead to unpredictable risk evaluations. This study introduces an automatic deep learning-based system for predicting stroke risk (both ischemic and hemorrhagic) and estimating the time frame of its occurrence, utilizing a comprehensive set of known retinal biomarkers from fundus images. Our system, tested on the UK Biobank and DRSSW datasets, achieved AUROC scores of 0.83 (95% CI: 0.79-0.85) and 0.93 (95% CI: 0.9-0.95), respectively. These results not only highlight our system's advantage over established benchmarks but also underscore the predictive power of retinal biomarkers in assessing stroke risk and the unique effectiveness of each biomarker. Additionally, the correlation between retinal biomarkers and cardiovascular diseases broadens the potential application of our system, making it a versatile tool for predicting a wide range of cardiovascular conditions., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)