Circadian rhythm is a biological endogenous process regulated by the suprachiasmatic nucleus of the hypothalamus, which transmits light signals to peripheral clocks and synchronizes the body with the external environment through balanced expression of circadian rhythm genes. Working the night shift, sleep disorders, and exposure to artificial light can lead to disturbances in circadian rhythm and genetic imbalances. A substantial body of research has demonstrated that circadian rhythm plays a significant role in the treatment of autoimmune diseases and neurodegenerative disorders, with increasing attention being directed toward their impact on oral health. Disturbances in circadian rhythm primarily affect psycho-neuro-immune mechanisms, oxidative stress responses, and oral microflora through pathways such as the hypothalamic-pituitary-adrenal axis (HPA axis), brain and muscle ARNT-like 1 (BMAL1)-brain-derived neurotrophic factor (BDNF) signaling, and BMAL1-nuclear factor kappa-B (NF-κB) interactions. These disruptions may influence the progression of oral diseases. Certain pharmacological agents (e.g., melatonin, vitamin D, nobiletin, and propofol) have been shown to regulate mood disorders, immune function, and sleep-wake cycles by upregulating BMAL1 expression, thus alleviating disturbances in circadian rhythm. In addition, non-pharmacological interventions, such as sleep management strategies, psychotherapy approaches, and light therapy, also modulate these processes through HPA axis regulation. Currently, the specific mechanisms by which circadian rhythm regulates BDNF levels, T cell subsets, and inflammatory signals—thereby influencing both pathogenesis and treatment outcomes for oral diseases—remain unclear. Future research should focus on elucidating these molecular mechanisms as well as identifying therapeutic targets related to circadian rhythm within the oral health context. Further, multidisciplinary collaboration encompassing pharmacy, sleep behavior studies, and psychology will be instrumental in advancing prevention strategies and treatments for oral diseases.

Objective To analyze the relationships of serum microRNA(miR)-15,miR-16,miR-17-5p,and triglyceride-glucose index(TyG)with severity and prognosis of acute ischemic stroke(AIS).Methods A total of 136 AIS patients admitted to the Shijiazhuang Hospital of Traditional Chi-nese Medicine from April 2023 to April 2024 were enrolled.Prognosis and disease severity were as-sessed using modified Rankin Scale(mRS)and National Institutes of Health Stroke Scale(NIHSS),respectively.Based on mRS scores at 3 months post-onset,patients were divided into poor prognosis group(mRS Score＞2,n=42)and good prognosis group(mRS Score ≤ 2,n=94).Serum levels of miR-15,miR-16,miR-17-5p,TyG,and NIHSS scores were compared between two groups.Pearson correlation analysis was used to evaluate associations of these biomarkers with NIHSS scores.Logistic regression was used to identify risk factors for poor prognosis,while receiver operating characteristic(ROC)curves was applied to assess the predictive value of individual and combined biomarkers.Results The poor prognosis group exhibited significantly higher serum miR-15,miR-16,miR-17-5p,TyG,and NIHSS scores than the good prognosis group(t=8.634,13.171,29.018,2.687,26.432;P＜0.05).Positive correlations were observed between these biomarkers and NIHSS scores(r=0.472,0.449,0.492,0.437;P＜0.05).Logistic regression identified miR-15[OR(95％CI):3.526(2.628 to 5.859)],miR-16[1.976(1.226 to 3.017)],miR-17-5p[1.828(1.294 to 3.428)],NIHSS score[1.787(1.105 to 2.896)],TyG[1.886(1.233 to 3.284)],and homo-cysteine[1.906(1.252 to 3.794)]as independent risk factors for poor prognosis(P＜0.05).ROC analysis demonstrated that the combined model(miR-15,miR-16,miR-17-5p,TyG,and NIHSS)achieved superior predictive performance(AUC=0.877;95％CI,0.820 to 0.948)com-pared to individual biomarkers(Z＞2.527,P＜0.05).Conclusion Elevated serum miR-15,miR-16,miR-17-5p,and TyG are closely associated with AIS severity and poor prognosis.The above indicators combined with NIHSS scores can enhance predictive accuracy for unfavorable outcomes.