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Circadian Rhythm Disruption Exacerbates Autoimmune Uveitis: The Essential Role of PER1 in Treg Cell Metabolic Support for Stability and Function.

Adv Sci (Weinh)

January 2025

State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China.

Circadian rhythm plays a critical role in the progression of autoimmune diseases. While our previous study demonstrated the therapeutic effects of melatonin in experimental autoimmune uveitis, the involvement of circadian rhythm remained unclear. Using a light-induced circadian rhythm disruption model, we showed that disrupted circadian rhythms exacerbate autoimmune uveitis by impairing the stability and function of Treg cells.

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Background And Aims: Atrial fibrillation (AF) or atrial flutter (AFL) after cardiac surgery are common and associated with adverse outcomes. The increased risk related to AF or AFL may extend beyond discharge. This study aims to determine whether photoplethysmography (PPG)-based smartphone monitoring to detect AF or AFL after hospital discharge following cardiac surgery improves AF management.

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Opioid dependence is defined by an aversive withdrawal syndrome upon drug cessation that can motivate continued drug-taking, development of opioid use disorder, and precipitate relapse. An understudied but common opioid withdrawal symptom is disrupted sleep, reported as both insomnia and daytime sleepiness. Despite the prevalence and severity of sleep disturbances during opioid withdrawal, there is a gap in our understanding of their interactions.

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Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions.

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