BackgroundSleep apnea–hypopnea syndrome (SAHS) has high prevalence and cardiovascular/cerebrovascular comorbidities. Polysomnography (PSG) is the diagnostic gold standard but limited by complexity, cost, and accessibility; smart wearables are convenient but less accurate. This study validated an AI-based snoring-sound model and compared its diagnostic performance with a wearable, using PSG as reference.MethodsAdults with suspected SAHS (n = 134) were included and underwent overnight PSG, with simultaneous snoring sound and wearable signal recording. The snoring algorithm used short-time Fourier transform (STFT) spectrograms and a multiscale encoder–attention–decoder neural network for classification. Model performance was assessed using correlation analysis, intraclass correlation coefficient (ICC), Bland–Altman plots, and receiver operating characteristic (ROC) analyses. Accuracy, sensitivity, specificity, and area under the curve (AUC) values were computed for both models.ResultsDisease severity was associated with variation in BMI (p < 0.001) and decreasing oxygen saturation (p < 0.001). The snoring model showed stronger correlation and higher agreement with PSG-derived AHI (r = 0.79, ICC = 0.753) than the wearable (r = 0.68). Although the snoring model yielded consistently higher AUCs, particularly in mild SAHS (AUC = 0.83 vs. 0.72, p = 0.07), the differences between methods were not statistically significant (all p > 0.05). Both models achieved excellent discriminative performance in severe SAHS (AUC ≥ 0.90).ConclusionThe AI-driven snoring-sound model demonstrated comparable overall performance and numerically better performance in certain settings, particularly in identifying mild SAHS.

Background and AimsThe comorbid state of obstructive sleep apnea (OSA) and coronary artery disease (CAD) has emerged as a significant public health challenge. However, the underlying metabolic regulation and molecular biomarkers linking to the CAD and OSA comorbidity (CADOSA) remain poorly understood. This study aimed to identify non-invasive biomarkers for the CADOSA comorbidity.MethodsWe randomly recruited a total of 143 participants from a hospital-based setting, comprising four groups: healthy controls (n=38), patients with OSA-only (n=37), CAD-only (n=36), and comorbid CADOSA (n=32). Participants were predominantly male (69.2%), with a mean age of 59.44 years (SD 12.25) and 24.77±3.94 BMI index. Comprehensive clinical evaluation and blood metabolomic profiling were conducted. We integrated differential expression analysis, functional enrichment, machine learning, receiver operating characteristic (ROC) curve, and matched transcriptomes association analysesto uncover key metabolites and potential molecular regulatory mechanisms associated with CADOSA.ResultsBaseline clinical data revealed that CADOSA comorbidity significantly exacerbates more severe cardiac dysfunction and sleep disordered breathing than two unique disease stages, despite no significant differences in age or BMI among the groups. Common disordered metabolites in patients with OSA-only or CAD-only were primarily associated with fatty acid, steroid, amino acid, and energy metabolism. While OSA-specific metabolic pathways included ascorbate and aldarate metabolism, alpha-linolenic acid metabolism, and primary bile acid biosynthesis. In contract, CAD specific pathways consisted of arginine biosynthesis, sphingolipid metabolism, pyruvate metabolism, and glycerophospholipid metabolism. Specifically, CADOSA patients exhibited unique metabolic pathways involving unsaturated fatty acid biosynthesis. Machine learning analysis identified the newly discovered and top-ranking potential metabolomic biomarkers for each condition: upregulated D-glucuronolactone for OSA, increased 12-hydroxyoctadecanoic acid for CAD, and downregulated tryptophan for CADOSA. Matched transcriptome association analysis revealed highly expressed and strongly correlated metabolite-gene pairs in the CADOSA comorbidity, including tryptophan-DMXL2, (C12-AE1S)-ZDHHC11, and IBMX-FCAR, which acting as potential up- and down-stream regulatory relationships underlying progress of CADOSA.ConclusionThis study elucidates the distinct clinical features and metabolic profiles of OSA, CAD, and comorbid CADOSA. These newly identified metabolic biomarkers and associative gene-metabolite pairs highlight a distinct comorbidity-specific metabolic and transcriptional architectures, providing novel insights into the development of molecular biomarkers and therapeutic targets for CADOSA.

BackgroundSleep plays a critical role in neonatal brain maturation and behavioral regulation, and early-life sleep disturbances have been widely studied in relation to neurodevelopmental vulnerability, particularly among preterm and critically ill infants. Although research on neonatal and early developmental sleep has demonstrated steady growth over the past two decades, the global structure of this literature, its evolving research frontiers, and patterns of scholarly collaboration have not been systematically mapped.MethodsRelevant publications were retrieved from the Web of Science(WOS) Core Collection (2000–2025). Using multiple complementary bibliometric tools, including CiteSpace, VOSviewer, and the bibliometrix R package, we analyzed publication trends, contributing countries and institutions, authorship and co-citation networks, journal distribution, keyword co-occurrence, and emerging thematic clusters.ResultsA total of 530 articles were identified, involving 55 countries and regions, 438 institutions, and 636 authors. The United States contributed 30.75% of all publications and demonstrated the highest citation impact. Véronique Bach was the most productive author, whereas Mark S. Scher was the most frequently co-cited, reflecting sustained methodological influence. Monash University emerged as the leading institution, and Sleep Medicine published the highest number of articles. Keyword and temporal analyses revealed an early emphasis on vulnerable neonatal populations and sleep maturation, followed by a progressive shift toward computational analytics, prenatal influences, autonomic regulation, and outcome-oriented research themes.ConclusionNeonatal sleep research has evolved from predominantly descriptive studies toward increasingly multidisciplinary and methodologically sophisticated investigations. Bibliometric mapping highlights growing emphasis on advanced analytical approaches, integrative physiological framing, and outcome-oriented perspectives, reflecting shifts in research focus rather than direct mechanistic validation. This structured overview delineates dominant themes, emerging frontiers, and persistent knowledge gaps, and may inform the future development of neonatal sleep research.

BackgroundInsomnia may lead to or be co-morbid with mental disorders. However, available treatments have significant side effects. Time-acupoints-space acupuncture, eight methods of intelligent turtle (ATAS-EMIT), as a low-hazard insomnia treatment, has been focused on.MethodsThirty-six rats were randomly divided into 4 groups: normal (Control), insomnia (Model), ATAS-EMIT treatment (Treatment), and sham acupuncture treatment (Sham). Behavioral experiments were used to assess the treatment of ATAS-EMIT for insomnia. ELISA, Western blot, and immunofluorescence staining were used to detect the expression of 5-HT, AC, cAMP, 5HT1A receptor (5HT1AR), and 5HT2A receptor (5HT2AR) in rat hippocampus. Transcriptome sequencing was used to explore more therapeutic mechanisms.ResultsATAS-EMIT treatment significantly reduced anxiety-like behaviors and activities in PCPA insomniac rats in the open field test. Additionally, ATAS-EMIT significantly shortened the sleep latency period in insomnia-prone rats while prolonging both sleep duration and hanging rest time. These behavioral studies suggest that ATAS-EMIT is a treatment for insomnia. The insomnia-related indicators showed that ATAS-EMIT significantly activated the 5-HT/AC/cAMP pathway, promoted the expression of the 5HT1AR, and inhibited the expression of the 5HT2AR for therapeutic purposes. Subsequent transcriptome sequencing revealed that ATAS-EMIT-treated DEGs were enriched for multiple insomnia-related functions such as phototransduction, stimulus-response, and tryptophan metabolism. Six key genes, Cngb1, Cabp4, Sag, Tyr, Trpm1, and Adipoq, were screened and validated.ConclusionATAS-EMIT significantly improved insomnia symptoms in PCPA insomniac rats, and activation of the 5-HT/AC/cAMP pathway was involved. Various mechanisms, such as phototransduction, tryptophan metabolism, and reduction of stimulation, contributed to the therapeutic effects of ATAS-EMIT.

BackgroundObstructive sleep apnea is a common condition worldwide, and prostate cancer is the most prevalent cancer among men. However, the link between OSA and prostate cancer remains unclear. This study aims to explore the relationship between OSA and prostate cancer.MethodsInitially, a multivariable regression analysis was conducted to control for potential confounders and assess the impact of OSA on the risk of developing prostate cancer. Subsequently, age-stratified analyses were performed to further investigate the relationship between OSA and prostate cancer across different age groups. Finally, Mendelian randomization was employed to evaluate the causal relationship between OSA and prostate cancer risk.ResultsThe multivariable regression analysis of the NHANES data showed no significant association between OSA and prostate cancer after controlling for age, smoking habits, hypertension, diabetes, cardiovascular diseases, stroke, alcohol consumption, body mass index (OR = 0.83; 95% CI: 0.46–1.48; p = 0.49). However, age-stratified analysis revealed a significant negative correlation between OSA and prostate cancer in the younger population (<60 years) (OR = 0.07; 95% CI: 0.01–0.74; p = 0.03), whereas no significant association was found in the elderly population (≥60 years) (OR = 1.01; 95% CI: 0.69–1.49; p = 0.96). Finally, our Mendelian randomization results did not find a causal relationship between OSA and prostate cancer (OR = 0.992; 95% CI: 0.876–1.124; p = 0.906).ConclusionThe findings indicate that while there is no significant association between OSA and prostate cancer in the overall analysis, a significant negative correlation exists in the younger population. The lack of significant association in the Mendelian randomization analysis may be due to the inability to perform age stratification. Further prospective studies and mechanistic research are needed to better understand the biological mechanisms underlying this association.

PurposeInsomnia places significant physical and psychological burdens on female cancer patients undergoing chemotherapy, affecting their quality of life. This study aimed to investigate the prevalence of insomnia and its associated factors in female outpatients receiving chemotherapy.Patients and MethodsA cross-sectional study was conducted with female cancer patients receiving chemotherapy. The questionnaire included items assessing sociodemographic and clinical characteristics. Insomnia was measured using the Insomnia Severity Index.ResultsA total of 206 female patients undergoing chemotherapy were included, with a mean age of 56.1 years (SD ± 11.7). The most common cancer types were breast (57.3%), gastrointestinal (22.8%), and gynecological malignancies (19.9%). Based on the Insomnia Severity Index (ISI), 34.0% of participants had subclinical insomnia and 17.0% had clinical insomnia. Increasing age was significantly associated with lower insomnia severity (aOR: 0.971; 95% CI: 0.945–0.998; p = 0.038). Among gynecological cancer patients, insomnia was more prevalent in those receiving treatment for metastatic disease (76.2% vs 35.0%). Psychiatric conditions (depression and/or anxiety) requiring medication and the presence of pain were both significantly associated with higher rates of insomnia (p < 0.001 for both).ConclusionInsomnia was highly prevalent among female cancer patients undergoing chemotherapy. Younger age, presence of pain, psychiatric comorbidities (particularly depression and/or anxiety), and metastatic disease status emerged as significant correlates. Considering the relationship between insomnia and physical and psychological distress, it is anticipated that regular screening and treatment approaches for insomnia will contribute to the holistic cancer care process by improving patient quality of life.

ObjectiveThis study aims to explore the mediating role of social support among the elderly in the relationship between psychological resilience and sleep quality.MethodsFrom December 2024 to March 2025, researchers conducted a questionnaire survey among elderly individuals aged ≥65 years in Shaanxi Province and Shanghai Municipality. The Connor-Davidson Resilience Scale, Perceived Social Support Scale, and Pittsburgh Sleep Quality Index (PSQI) were used to assess the psychological resilience, social support, and sleep quality of the elderly. Structural equation modeling was employed to explore the relationships among the variables.ResultsThis study included a total of 359 elderly participants, with an average sleep quality score of 12.69 (standard deviation = 4.15), indicating poor sleep quality. Psychological resilience was significantly associated with sleep quality in the elderly (r = −0.781, p < 0.001). In the model constructed in this study, social support was considered a partial mediating factor in the relationship between psychological resilience and sleep quality among the elderly, with the mediating effect accounting for 33.7% of the total effect (indirect effect β = −0.070, 95% CI = −0.108 to −0.025, p = 0.006).ConclusionIn the elderly population, psychological resilience and sleep quality are significantly associated, with social support acting as a mediator in this relationship. The above findings provide scientific basis for the formulation of intervention strategies.

BackgroundCarotid atherosclerosis (CAS) is a critical cardiovascular complication in elderly patients with obstructive sleep apnea (OSA). Current risk assessment tools inadequately capture OSA-specific pathophysiological mechanisms for CAS prediction in this high-risk population.MethodsThis multicenter retrospective study included 1196 elderly patients (≥60 years) with polysomnography-confirmed OSA from six tertiary hospitals in China. CAS was diagnosed by carotid ultrasound. LASSO (Least Absolute Shrinkage and Selection Operator) regression identified optimal predictive features from 18 candidate variables. Four machine learning algorithms were developed and validated using 5-fold cross-validation. Model interpretability was achieved through SHapley Additive exPlanations (SHAP) analysis.ResultsAmong participants, 273 (22.8%) had CAS. LASSO regression selected eight optimal features. XGBoost achieved the best performance with test AUC of 0.854, accuracy of 79.8%, sensitivity of 81.2%, and specificity of 78.5%. SHAP analysis revealed systolic blood pressure (importance: 0.3148) and percentage of sleep time with oxygen saturation <90% (T90, importance: 0.2660) as the most influential predictors, surpassing traditional apnea-hypopnea index. Other significant predictors included alcohol consumption, mean oxygen saturation, body mass index, age, platelet count, and smoking status.ConclusionThis study developed the first machine learning-based CAS prediction model for elderly OSA patients, achieving clinically relevant performance (AUC=0.854). The prominence of T90 over conventional apnea-hypopnea index suggests nocturnal hypoxemic burden is more important than respiratory event frequency for cardiovascular risk stratification in this population.

Chronic subjective tinnitus (CST) is frequently accompanied by anxiety and sleep disturbances, together forming a self-reinforcing cycle that significantly impairs patients’ quality of life and complicates clinical management. This narrative review aims to elucidate the shared pathophysiological mechanisms underlying these interrelated conditions and to evaluate current multidisciplinary therapeutic strategies. Relevant literature was identified through a targeted search of PubMed, Scopus, and Web of Science databases, focusing on recent clinical and translational studies addressing the neurobiology and treatment of CST, anxiety, and sleep disorders. Emerging evidence highlights the involvement of maladaptive auditory-limbic network connectivity, neurotransmitter imbalances (eg, GABA, glutamate, serotonin), dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis, autonomic dysfunction, and disruption of circadian and sleep–wake regulatory pathways in the persistence and mutual reinforcement of these symptoms. Multimodal interventions—including cognitive behavioral therapy, sound therapy, pharmacotherapy, neuromodulation techniques, and targeted sleep interventions—have demonstrated synergistic benefits, particularly when tailored to individual neurobiological profiles. From a clinical perspective, recognizing the bidirectional interactions among tinnitus, affective dysregulation, and sleep impairment is essential for developing effective, integrative treatment frameworks. This review underscores the necessity of personalized, mechanism-informed therapeutic approaches and outlines key directions for future research.