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Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises.

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Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition emerging in early childhood, characterized by core features such as sociocommunicative deficits and repetitive, rigid behaviors, interests, and activities. In addition to these, disruptive behaviors (DB), including aggression, self-injury, and severe tantrums, are frequently observed in pediatric patients with ASD. The atypical antipsychotics risperidone and aripiprazole, currently the only Food and Drug Administration-approved treatments for severe DB in patients with ASD, often encounter therapeutic failure or intolerance.

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Purpose: To assess trial-level surrogacy value for overall survival (OS) of the pathologic complete response (pCR) and invasive disease-free survival (iDFS) in randomized clinical trials (RCTs) for early breast cancer (BC).

Methods: Individual patient data of neoadjuvant RCTs with available data on pCR, iDFS, and OS were included in the analysis. We used the coefficient of determination from weighted linear regression models to quantify the association between treatment effects on OS and on the surrogate end points.

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Personalized sports training plans are essential for addressing individual athlete needs, but traditional methods often need to integrate diverse data types, limiting adaptability and effectiveness. Existing machine learning (ML) and rule-based approaches cannot dynamically generate context-specific training programs, reducing their applicability in real-world scenarios. This study aims to develop a Generative Adversarial Network (GAN)- based framework to create context-specific training plans by integrating numeric attributes (e.

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Background: Immune checkpoint inhibitors (ICIs) have revolutionised cancer therapy, yet they carry a unique spectrum of immune-related adverse events (irAEs). Given the ageing global population and the underrepresentation of older adults in clinical trials for ICIs, we investigated the occurrence and characteristics of irAEs in older versus younger adults as well as among different age subsets within the older adult population.

Methods: We analysed the U.

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