Objective To analyze the cognitive function and sleep quality of the elderly in Shanghai community, and explore the related influencing factors. Methods A stratified cluster random sampling method was used to select 8 community health centers in Shanghai for a questionnaire survey, including 3 677 elderly individuals who completed the “Comprehensive Health Status Survey of Elderly Residents in Shanghai” from September 2023 to November 2023. Basic information of the elderly was collected, including age, gender, education level, smoking, drinking, mahjong playing behavior, and exercise habits. The Pittsburgh sleep quality index (PSQI) was used to assess the sleep quality of the elderly, subjective cognitive decline (SCD) self-assessment questionnaire and Mini-Mental State Examination (MMSE) were used to evaluate cognitive function, while the Hamilton Anxiety Scale (HAMA) and patient health questionnaire-9 (PHQ-9) were used to assess anxiety and depression levels, and the mini nutritional assessment (MNA) was used to evaluate nutritional status. According to the MMSE scores, the elderly were divided into three groups: no cognitive impairment (MMSE ≥ 27), mild cognitive impairment (MMSE 21-26), and moderate to severe cognitive impairment (MMSE ≤ 20). The general data, lifestyle habits, and scale scores of the three groups were compared. Ordered logistic regression was used to analyze the influencing factors of sleep quality. Results There were statistically significant differences in age, gender, waist circumference, body mass index (BMI), education level, pet ownership, smoking, drinking, mahjong playing behavior, exercise habits, and scale scores among the three groups (P＜0.05). Logistic regression analysis showed that age, waist circumference, gender, drinking habits, mahjong playing behavior, and chronic comorbidities are influencing factors for the PSQI grading in the elderly (P＜0.05). The MMSE score (OR＝1.037, P＝0.001), SCD score (OR＝1.123, P＜0.001), HAMA score (OR＝1.183, P＜0.001), PHQ-9 score (OR＝1.249, P＜0.001) are positive influencing factors for PSQI grading, while the MNA score is a negative influencing factor (OR＝0.960, P＝0.037). Conclusions Advanced age, female gender, low education level, no pet ownership, no mahjong playing behavior, no exercise habits, and poor sleep quality are risk factors for cognitive impairment in the elderly. Advanced age, female gender, no mahjong playing behavior and poor nutritional status are influencing factors for poor sleep quality in the elderly, and severe comorbidities, anxiety, depression, and subjective decline in cognitive function all affect sleep quality.

OBJECTIVE To analyze the components migrating to blood and metabolites of Polygonum cuspidatum in rats with acute gouty arthritis （AGA）. METHODS SD rats were randomly divided into blank group， model group and P. cuspidatum group （10 g/kg， by raw material）， with 6 rats in each group. Except for blank group， AGA model was induced in the remaining groups by injecting potassium oxonate and sodium urate； meanwhile， they were administered corresponding drug solutions or water intragastrically， once a day， for 10 consecutive days. The histopathological morphology of the knee joint tissues in rats was observed；rat serum samples were collected， and the components migrating to blood and metabolites of P. cuspidatum were analyzed by using UPLC-Q-Exactive-Orbitrap-MS. RESULTS Following the intervention with P. cuspidatum， the histopathological morphology of the knee joint synovial tissue in AGA rats showed significant improvement， with reduced inflammatory cell infiltration and hyperplasia， and the preservation of the honeycomb-like structure integrity. In both positive and negative ion modes， a total of 67 chemical components were detected in the serum of rats from P. cuspidatum group， including 25 prototype components and 42 metabolites. The involved compound types encompassed stilbenes， anthraquinones， naphthols， and flavonoids， among others. The metabolic reactions identified included methylation， acetylation， sulfation， and glucuronidation. Notably， compounds such as polydatin， resveratrol and emodin were capable of entering the bloodstream in their prototype forms and undergoing in vivo metabolism. CONCLUSIONS Compounds such as polydatin， resveratrol and emodin are likely to be the active components responsible for the anti-AGA effects of P. cuspidatum.

In modern society, people are increasingly exposed to chronic stress, leading to various mental disorders. However, the activities of brain regions, especially neural firing patterns related to specific behaviors, remain unclear. In this study, we introduce a novel approach, NeuroSync, which integrates open-field behavioral testing with electrophysiological recordings from emotion-related brain regions, specifically the central amygdala and the paraventricular nucleus of the hypothalamus, to explore the mechanisms of negative emotions induced by chronic stress in mice. By applying machine vision techniques, we quantified behaviors in the open field, and signal processing algorithms elucidated the neural underpinnings of the observed behaviors. Synchronizing behavioral and electrophysiological data revealed significant correlations between neural firing patterns and stress-related behaviors, providing insights into real-time brain activity underlying behavioral responses. This research combines deep learning and machine learning to synchronize high-resolution video and electrophysiological data, offering new insights into neural-behavioral dynamics under chronic stress conditions.
Animals ; Mice ; Male ; Emotions/physiology* ; Stress, Psychological/physiopathology* ; Action Potentials/physiology* ; Mice, Inbred C57BL ; Behavior, Animal/physiology* ; Machine Learning ; Amygdala/physiopathology* ; Neurons/physiology* ; Paraventricular Hypothalamic Nucleus/physiopathology* ; Brain/physiology*

Ischemic stroke is the main cause of death and disability in adults, and its incidence is increasing year by year. Obstructive sleep apnea (OSA) is the most common type of sleep-disordered breathing, which can increase the risk of ischemic stroke and affect the outcomes of patients. There is an increasing amount of research on the relationship between OSA and ischemic stroke. This article reviews the bidirectional relationship between OSA and ischemic stroke, the mechanism of OSA causing ischemic stroke, and the diagnosis and treatment of OSA in patients with ischemic stroke.