This review examines the mechanisms of anxiety and depression in menopausal syndrome from an integrated physiological to psychological perspective. Fluctuations in estrogen and progesterone levels during menopause affect neurotransmitter systems (including serotonin, norepinephrine, and dopamine), hypothalamic-pituitary-adrenal axis function, inflammatory processes, and neurotrophic factor expression, collectively diminishing the resilience of emotional regulation neural circuits. Simultaneously, vasomotor symptoms (such as hot flashes and night sweats), sleep disruption, genetic susceptibility, and epigenetic modifications interact with mood disorders, while psychosocial factors (such as midlife stressors and role transitions) and cognitive factors (including negative schemas about aging, attentional bias toward threats, and difficulties in emotional regulation) further shape women's experiences of menopausal changes. Clinical practice should adopt a biopsychosocial model, employing personalized multimodal approaches through hormone therapy, antidepressants, psychotherapy, and lifestyle adjustments, while future research should focus on developing biomarkers, utilizing advanced technologies, and developing targeted interventions to support women's psychological wellbeing during menopause.
Background: Post-stroke depression (PSD), a condition commonly developed in patients with chronic stroke, impairs both functional rehabilitation and daily living.
Aim: To comprehensively analyze PSD contributors in chronic phase stroke and construct a precise nomogram.
Methods: Two hundred patients with chronic stroke admitted in over 7 years (January 2017 to January 2024), were enrolled and categorized into the PSD group (n = 96) and the non-PSD (NPSD) group (n = 104). Demographic characteristics, clinicopathological data, and biochemical indicators were collected and analyzed by univariate analysis. Significant predictors identified in the univariate analysis were subsequently incorporated into a binary logistic regression model to assess their independent effects on PSD risk. The discriminative ability/calibration of the developed PSD prediction nomogram was assessed.
Results: Compared with the NPSD group, the PSD group included a higher proportion of patients aged ≥ 60 years, divorced/widowed, with an education level below senior high school, presenting with ≥ 2 comorbidities, exhibiting severe neurological impairment, and having multiple lesions. Additionally, the PSD group showed significantly higher neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) than the NPSD group. After assigning values to significant predictors, multivariate analysis indicated that educational level (P = 0.046), NLR (P < 0.001), and PLR (P < 0.001) were independently associated with PSD in patients with chronic stroke. The developed nomogram exhibited favorable discrimination performance. The nomogram's calibration remained accurate for high-risk stratification but displayed modest inconsistencies in low- and middle-risk categories.
Conclusion: Education level, NLR, and PLR independently contribute to PSD in patients with chronic stroke. The constructed nomogram effectively predicts PSD risk within the range of 0.10-0.90, presenting a valuable tool for clinical monitoring and risk assessment of PSD in patients with chronic stroke.
Patients with chronic kidney disease (CKD), particularly those undergoing maintenance hemodialysis (MHD), often experience mental health issues. Recent studies have indicated a significantly elevated prevalence of anxiety and depression coupled with reduced resilience in this population. The complex interaction between these three factors is exacerbated by multiple negative influences. This study reviews recent advances in research on the relationship between mental health impairment, quality of life (QoL), and prognosis in patients with CKD. The findings revealed that prolonged MHD dependence, economic burden of healthcare, and other psychological factors diminish patients' resilience and intensify negative emotions (e.g., anxiety and depression). The interactions between negative emotions and physiology create a vicious cycle, deteriorating QoL and clinical outcomes. We propose that patients with long-term MHD-dependent CKD should be classified as a high-risk group for mental health impairments that need regular psychological screening and personalized psycho-medical interventions. This integrated management model may help improve negative emotions and disrupt the bidirectional psychophysiological interplay, offering a novel clinical pathway for improving the QoL and prognosis.
Background: Gamma-aminobutyric acid type A receptor has long been acknowledged as a key target in the pathophysiology of epilepsy. The GABRA1 and GABRG2 genes encode the α1 and γ2 subunits of the gamma-aminobutyric acid type A receptor, a key protein implicated in the development of epilepsy. However, the specific association of the GABRA1 IVS11+15 A>G rs2279020 and GABRG2 G3145A rs211013 polymorphisms with antiepileptic drug resistance has been elucidated in only a limited number of investigations.
Aim: To elucidate the association between GABRA1 IVS11+15 A>G rs2279020 and GABRG2 G3145A rs211013 gene mutations and drug resistance in epilepsy patients.
Methods: A total of 100 epilepsy patients (50 drug responsive and 50 drug resistant subjects) were recruited and rs2279020 - and rs211013 - polymorphism analyzed by restriction fragment length polymorphism - polymerase chain reaction technique.
Results: For GABRA1 rs2279020 polymorphism, AG genotype exhibited risk association with an odds ratio of 0.966 (95% confidence interval = 0.346-2.698) with P value = 0.948; however, this association did not achieve statistical significance (P = 0.948). Additionally, a higher risk association was identified with the GG genotype, with an odds ratio of 1.808 (P = 0.382). GABRG2 rs211013 polymorphism revealed no significant association with drug resistance.
Conclusion: The GABRA1 rs2279020 genetic variation is associated with an increased risk for the AG and GG variants, although this association was not statistically significant. Limited investigations have explored the relevance of genetic variations in epilepsy and drug resistance. Longitudinal research is needed to better understand their significance in epilepsy management and to optimize therapeutic strategies.
Background: Cervical spondylosis (CS) frequently co-occurs with generalized anxiety disorder (GAD), presenting a complex clinical challenge. Managing CS-related pain in patients with GAD is particularly challenging because of the bidirectional relationship between pain and anxiety, necessitating integrated treatment strategies.
Aim: To evaluate the efficacy of electroacupuncture (EA) in treating CS-related pain and anxiety in patients with GAD.
Methods: This retrospective cohort study analyzed data from 83 patients with CS-related pain and GAD who received EA treatment over 2-year period. Pain intensity was assessed using the visual analog scale, and anxiety symptoms were measured using the Hamilton Anxiety Rating Scale. Additionally, neuroinflammatory markers, including interleukin-6, tumor necrosis factor-alpha, and high-sensitivity C-reactive protein, were examined. Outcomes were evaluated at baseline, after 4 weeks, and after 8 weeks of treatment.
Results: EA treatment significantly reduced CS-related pain (mean visual analog scale reduction: 3.24 ± 1.18; P < 0.001) and improved anxiety symptoms (mean Hamilton Anxiety Rating Scale reduction: 7.83 ± 2.65; P < 0.001) after 8 weeks of treatment. Neuroinflammatory markers also showed significant reductions, with interleukin-6 and tumor necrosis factor-alpha levels decreasing by 32.7% and 28.5%, respectively (P < 0.01). Pain reduction was significantly correlated with improvements in anxiety symptoms (r = 0.68; P < 0.001) and a decrease in inflammatory markers (r = 0.54; P < 0.01).
Conclusion: EA demonstrates significant efficacy in reducing CS-related pain in patients with comorbid GAD, along with concurrent improvements in anxiety symptoms and neuroinflammatory profiles. These findings suggest that EA may offer a valuable integrative approach for managing this complex clinical presentation, potentially addressing both pain and anxiety through the modulation of neuroinflammatory pathways.
Background: Glaucoma, a condition frequently linked to severe depression, anxiety, and sleep disturbances, affects treatment adherence while potentially compromising effectiveness.
Aim: To explore illness uncertainty (IU), anxiety, and depressive symptoms in primary glaucoma and to discuss underlying triggers.
Methods: We recruited 120 primary glaucoma cases between January 2022 and November 2023. The Mishel Uncertainty in Illness Scale (MUIS) and the Hospital Anxiety and Depression Scale (HADS) [include HADS-anxiety subscale (HADS-A) and HADS-depression subscale (HADS-D)] subscales, were used to assess IU and emotional distress (anxiety/depression), respectively. The MUIS-HADS subscale interrelationships were determined by Pearson correlation. IU-associated determinants were identified using univariate and binary logistic regression analyses.
Results: The cohort showed a mean MUIS score of 79.73 ± 8.97, corresponding to a moderately high IU level. The HADS-A and HADS-D scores averaged 6.57 ± 3.89 and 7.08 ± 5.05 points, respectively, with 15.00% of participants showing anxiety symptoms and 24.17% exhibiting depressive signs. Significant positive connections were observed between MUIS and both HADS-A (r = 0.359, P < 0.001) and HADS-D (r = 0.426, P < 0.001). Univariate analysis revealed that disease duration, insomnia, monthly household income per capita, and the presence of comorbid chronic conditions were significantly associated with anxiety or depression. Multivariate analysis identified insomnia as a risk factor and higher monthly household income as a protective factor.
Conclusion: Patients with primary glaucoma experience moderate IU levels, generally low anxiety, and mild depression. Specifically, the anxiety and depression risks were 15.00% and 24.17%, respectively. A significant positive correlation existed between IU and anxiety/depression in these patients. Additionally, insomnia or lower monthly household income elevated anxiety/depression risks, enabling reliable anxiety/depression risk categorization among patients.
Background: Post-thyroidectomy patients frequently experience depressive symptoms triggered by surgical trauma, fluctuating thyroid hormone levels, and the psychological burden of long-term surveillance; however, large-scale multivariable-adjusted risk-factor data remain scarce.
Aim: To determine the prevalence and predictors of postoperative depression, and propose tailored mitigation strategies.
Methods: We enrolled 108 consecutive patients who underwent thyroidectomies at The First Affiliated Hospital of Hebei North University between January 2023 and January 2025. Depression was assessed using the Self-Rating Depression Scale (SDS), while coping styles and social support were evaluated using the Medical Coping Modes Questionnaire and Perceived Social Support Scale. Logistic regression was used to identify independent risk and protective factors.
Results: The mean SDS score was 52.58 ± 10.20; 62 patients (57.4%) met the criteria for depression (mild 32.4%, moderate 15.7%, severe 9.3%). Univariate analyses revealed higher depression rates among patients aged ≥ 60 years, those with ≤ high-school education, monthly family income < 3000 yuan, 131I therapy, and avoidance/surrender coping; and lower social support (P < 0.05). Multivariate regression showed income < 3000 yuan (OR = 5.26, 95%CI: 1.89-14.60), 131I therapy (OR = 5.70, 95%CI: 1.91-17.01), and avoidance/surrender coping (OR = 4.77, 95%CI: 1.51-15.11) as independent risk factors, whereas higher social support was protective (OR = 0.22, 95%CI: 0.09-0.54).
Conclusion: Depression is common after thyroidectomy, and is driven by socioeconomic, treatment-related, and psychosocial factors. Targeted interventions that address coping skills and bolster social support should be integrated into postoperative care.
Psychiatric disorders constitute a complex health issue, primarily manifesting as significant disturbances in cognition, emotional regulation, and behavior. However, due to limited resources within health care systems, only a minority of patients can access effective treatment and care services, highlighting an urgent need for improvement. large language models (LLMs), with their natural language understanding and generation capabilities, are gradually penetrating the entire process of psychiatric diagnosis and treatment, including outpatient reception, diagnosis and therapy, clinical nursing, medication safety, and prognosis follow-up. They hold promise for improving the current severe shortage of health system resources and promoting equal access to mental health care. This article reviews the application scenarios and research progress of LLMs. It explores optimization methods for LLMs in psychiatry. Based on the research findings, we propose a clinical LLM for mental health using the Mixture of Experts framework to improve the accuracy of psychiatric diagnosis and therapeutic interventions.
This letter provides a critical appraisal of the comprehensive meta-analysis by Hou et al, which synthesizes the incidence and risk factors for postoperative delirium (POD) in organ transplant recipients. Their work establishes a pooled POD incidence of 20%, with significant variability across organ types (lung 34%, liver 22%, kidney 6%), and identifies key risk factors including primary graft dysfunction, hepatic encephalopathy, and high model for end-stage liver disease/acute physiology and chronic health evaluation II scores. This commentary acknowledges the study's strength in providing a robust, trans-organ synthesis of current evidence. However, it critically discusses the substantial heterogeneity, the counterintuitive non-significance of age as a risk factor, and the unavoidable limitation of unmeasured confounders inherent in meta-analyses, such as preoperative cognitive/psychiatric status and anesthetic protocols. While the findings provide an essential evidence base for risk stratification and prevention, this letter argues that the high heterogeneity underscores the need for organ-specific analysis and calls for large-scale, prospective studies with standardized protocols to translate these findings into reliable clinical prediction tools and targeted interventions.
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