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Stress can have beneficial and harmful effects on learning. Due to their individual characteristics and past experiences, different individuals respond differently to similar stressful situations. Beck and colleagues' article entitled "Can Stress Be Good for Learning? Pediatric Resident Perspectives on the Beneficial Influence of Stress on Learning and the Role of the Supervisor" discusses trainee perspectives on supervisor behaviors that can shape better learning experiences. In this Invited Commentary, the authors not only discuss the role of stress in learning, including positive effects as highlighted by Beck et al, but also emphasize the importance of creating an optimal learning environment that incorporates stressful experiences. The authors share insights on how supervisors can model a growth mindset and create a psychologically safe environment in view of ongoing stressors that helps build a trusting and mutually respectful relationship with their learners. This approach can foster an environment that is conducive to trainee's individual learning styles and maximizes their learning.
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http://dx.doi.org/10.1097/ACM.0000000000006220 | DOI Listing |
Driven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFAm J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFJ Agric Food Chem
September 2025
Department of Food Science and Engineering, Ningbo University, Ningbo 315211, P.R. China.
Sleep deprivation (SD) is a major contributor to cognitive impairment, often accompanied by central neuroinflammation and gut microbiota dysbiosis. The tryptophan (TRP) pathway, activated via indoleamine 2,3-dioxygenase (IDO), serves as a critical link between immune activation and neuronal damage. Umbelliferone (UMB), a naturally occurring coumarin compound, possesses anti-inflammatory, antioxidant, and microbiota-modulating properties.
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