Results 131 to 140 of about 142,512 (254)

Transitioning to Higher Education: A Case Study of an Individual With Autism

open access: yesPsychology in the Schools, EarlyView.
ABSTRACT Necessary arrangements should be made to enable university students with autism (the term “autism” is used to refer to the entire autism spectrum in this study) to improve their transition to university. The need for support may be higher in countries like Türkiye, where the number of university students with autism is relatively low, as ...
Mahmut Serkan Yazıcı   +2 more
wiley   +1 more source

Objective sleep parameters and diurnal blood pressure in concurrent hypertension and type 2 diabetes

open access: yesSleep Research, EarlyView.
Abstract Background Hypertension is a primary cardiovascular complication in type 2 diabetes associated with increased morbidity and mortality. Ambulatory blood pressure monitoring (ABPM) is essential for capturing circadian BP variation, which is closely influenced by sleep. Methods Twenty patients (63.75 ± 4.44 years old, 40% female, duration of T2D:
Yan Zhao   +6 more
wiley   +1 more source

The Sympathetic Nervous System in Hypertensive Heart Failure with Preserved LVEF. [PDF]

open access: yesJ Clin Med, 2023
Triposkiadis F   +7 more
europepmc   +1 more source

Salivary biomarkers in sleep‐related disorders

open access: yesSleep Research, EarlyView.
Abstract The exploration of salivary biomarkers has emerged as a promising avenue in the diagnosis and management of sleep‐related disorders, such as obstructive sleep apnea (OSA), insomnia, and sleep deprivation. Saliva is a noninvasive biofluid that contains a wealth of biological markers, reflecting both local and systemic physiological changes ...
Chuan Xiang Li   +9 more
wiley   +1 more source

Deep learning for sleep quality assessment: A CNN‐based approach outperforming traditional algorithms in wearable accelerometer data analysis

open access: yesSleep Research, EarlyView.
Abstract Objective This study evaluates and enhances wearable sleep monitoring by comparing two feature extraction methods: traditional activity counts and deep learning‐derived features. By identifying optimal machine learning architectures, we aim to improve sleep stage classification accuracy, providing a robust, noncontact tool for clinical chronic
Lin Zhongyue   +4 more
wiley   +1 more source

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