Objective·To investigate the performance and psychological mechanisms of inhibitory control in patients with alcohol use disorder(AUD)under different emotional contexts,and to examine the influence of emotion regulation difficulties on inhibitory control.Methods·A total of 28 male AUD inpatients(AUD group)and 28 age-and education-matched healthy controls(HC group)were recruited.The emotional Go/Nogo task(angry/neutral facial expressions)was used to evaluate the subjects'behavioral inhibition,and the hierarchical drift-diffusion model(HDDM)was used to quantify the cognitive parameters(drift rate,decision threshold,and non-decision time).The Difficulties in Emotion Regulation Scale(DERS)and Alcohol Use Disorder Identification Test(AUDIT)were used for clinical evaluation.The moderated mediation effects were tested by bootstrap method.Results·The AUD group scored higher than the HC group on the DERS total score and all sub-dimensions(goal-directed behavior,impulse control,strategy access,and emotional clarity),and the difference was statistically significant(all P<0.05).At the behavioral level,compared with the HC group,the AUD group had elevated commission error rates[F(1,54)=8.62,P=0.005]and omission error rates[F(1,54)=4.28,P=0.043],and the reaction time of angry face stimuli was generally prolonged[F(1,54)=12.26,P=0.001].Cognitive modeling showed that the drift rate of the AUD group was significantly lower than that of the HC group[F(1,54)=15.56,P<0.001],indicating impaired information processing efficiency.The moderated mediation model showed that,under the condition of angry face stimuli,the drift rate partially mediated the relationship between group and commission error rate,and the total indirect effect value was 9.564(95%CI 3.874?16.387).Further analysis showed that the conditional indirect effect increased to 10.133(95%CI 3.963?17.927)at high levels of emotion regulation difficulty(one standard deviation above the mean),and to 9.011(95%CI 3.778?14.921)at low levels(one standard deviation below the mean).Conclusion·The deficits in information processing efficiency of AUD patients partly explains the impairment of inhibitory control,and this effect is associated with individual emotion regulation capacity.It is suggested that abnormal processing of social threat information may be an important factor affecting the impairment of inhibitory control in AUD patients,especially in individuals with weak emotion regulation ability.

Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model-calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.
Humans ; Electroencephalography/methods* ; Meditation ; Calibration ; Neural Networks, Computer ; Brain/physiology* ; Rest/physiology* ; Deep Learning ; Signal Processing, Computer-Assisted

Autophagy is a fundamental biological metabolic process involved in immune defense, material metabolism, and homeostasis and closely linked to immune regulation. Systemic lupus erythematosus (SLE) is a widespread connective tissue disorder primarily resulting from immune system imbalance. Due to the immune system's failure to recognize its own substances, it generates autoantibodies that can affect various tissues and organs, leading to diverse clinical manifestations. The pathogenesis and treatment of SLE are currently under extensive investigation. In normal metabolic processes, autophagy engages in both innate and adaptive immunity, regulates the immune response, and is crucial for maintaining normal immune function and the body's internal homeostasis. Research has indicated that SLE patients exhibit immune dysfunction and altered autophagy levels. Modulating autophagy expression can influence immune system functionality and alleviate SLE symptoms. Additionally, autophagy aids in the innate immune response and adaptive immunity by clearing metabolites and regulating the life cycle of immune cells. Studies suggest that drugs targeting autophagy can positively influence the progression of SLE. This article reviews advancements in research regarding the impact of autophagy on the pathogenesis of SLE through the regulation of immune system functions.
Lupus Erythematosus, Systemic/pathology* ; Autophagy/immunology* ; Humans ; Animals ; Immunity, Innate ; Adaptive Immunity ; Disease Progression ; Immune System/immunology*

Objective:It aims to examine the effects of the employee medical insurance outpatient mutual assistance on medical insurance costs for patients with diabetes mellitus,offering insights for optimizing outpatient insurance policies and chronic disease management strategies.Methods:Outpatient cost settlement data of urban employees with diabetes mellitus in Lanzhou from 2022 to 2023 was collected.Univariate analysis and interrupted time-series models were used to compare relevant medical insurance cost indicators before and after the reform.Results:The inpatient data of 765 730 diabetes mellitus patients were included in the study,and male patients account for 62.67％.After the reform,average per-visit pooling fund expenditure,average per-visit individual payment expenditures,average per-visit personal account expenditure,average per-visit eligible expense amount,average per-visit total fund payment,and the average per-visit proportion of basic medical pooling payments were decreased(P＜0.05).Moreover,average per-visit pooling fund expenditure,average per-visit individual account expenditure,average per-visit cash payment,average per-visit eligible expense amount,average per-visit total fund payment,and the average per-visit proportion of basic medical pooling payments showed a notable declining trend post-reform(P＜0.05).In contrast,the per-visit fully out-of-pocket expenditure exhibited no significant change before and after the reform(P＞0.05).Conclusion:The reform of the employee medical insurance outpatient mutual assistance system has alleviated the economic burden of disease for diabetic patients and improved the efficiency of medical insurance fund utilization,but it reduced the proportion of basic medical pooling payments.It is recommended to continuously refine the outpatient medical insurance payment system,strengthen supervision of medical expenses and service quality,and balance patient benefits with fund pressure to enhance chronic disease outpatient benefits.

Mental disorders pose a significant challenge to global public health,profoundly affecting the quality of life of a vast number of individuals and imposing a heavy health burden on society.Nonetheless,there remains a substantial gap between the current societal capacity to provide prevention,diagnosis,and treatment for mental disorders and the existing demand for such services.In recent years,the development and application of artificial intelligence(AI)technologies have provided unprecedented opportunities to enhance mental healthcare services.As one of the fastest-growing fields of AI,generative AI has played a pivotal role in analyzing diverse forms of data,including medical image processing,protein structure prediction,clinical document generation,auxiliary diagnostic discrimination,and clinical decision support.These advancements have significantly strengthened capabilities in clinical diagnosis,data reconstruction,and adjunctive therapeutic interventions.This review highlights the potential applications of generative AI in advancing fundamental psychiatric research,identifying early risk factors for mental disorders,and assisting clinicians in diagnosis and treatment.Additionally,it addresses the challenges and limitations currently facing the application of generative AI to mental healthcare,including biases,privacy breaches,and insufficient interpretability.Finally,the review summarizes strategies to enhance AI's capacity to deliver mental health services,aiming to leverage new technologies to reduce the global burden of mental disorders and improve the quality of life of affected individuals.

Objective·To investigate the performance and psychological mechanisms of inhibitory control in patients with alcohol use disorder(AUD)under different emotional contexts,and to examine the influence of emotion regulation difficulties on inhibitory control.Methods·A total of 28 male AUD inpatients(AUD group)and 28 age-and education-matched healthy controls(HC group)were recruited.The emotional Go/Nogo task(angry/neutral facial expressions)was used to evaluate the subjects'behavioral inhibition,and the hierarchical drift-diffusion model(HDDM)was used to quantify the cognitive parameters(drift rate,decision threshold,and non-decision time).The Difficulties in Emotion Regulation Scale(DERS)and Alcohol Use Disorder Identification Test(AUDIT)were used for clinical evaluation.The moderated mediation effects were tested by bootstrap method.Results·The AUD group scored higher than the HC group on the DERS total score and all sub-dimensions(goal-directed behavior,impulse control,strategy access,and emotional clarity),and the difference was statistically significant(all P<0.05).At the behavioral level,compared with the HC group,the AUD group had elevated commission error rates[F(1,54)=8.62,P=0.005]and omission error rates[F(1,54)=4.28,P=0.043],and the reaction time of angry face stimuli was generally prolonged[F(1,54)=12.26,P=0.001].Cognitive modeling showed that the drift rate of the AUD group was significantly lower than that of the HC group[F(1,54)=15.56,P<0.001],indicating impaired information processing efficiency.The moderated mediation model showed that,under the condition of angry face stimuli,the drift rate partially mediated the relationship between group and commission error rate,and the total indirect effect value was 9.564(95%CI 3.874?16.387).Further analysis showed that the conditional indirect effect increased to 10.133(95%CI 3.963?17.927)at high levels of emotion regulation difficulty(one standard deviation above the mean),and to 9.011(95%CI 3.778?14.921)at low levels(one standard deviation below the mean).Conclusion·The deficits in information processing efficiency of AUD patients partly explains the impairment of inhibitory control,and this effect is associated with individual emotion regulation capacity.It is suggested that abnormal processing of social threat information may be an important factor affecting the impairment of inhibitory control in AUD patients,especially in individuals with weak emotion regulation ability.

Mental disorders pose a significant challenge to global public health,profoundly affecting the quality of life of a vast number of individuals and imposing a heavy health burden on society.Nonetheless,there remains a substantial gap between the current societal capacity to provide prevention,diagnosis,and treatment for mental disorders and the existing demand for such services.In recent years,the development and application of artificial intelligence(AI)technologies have provided unprecedented opportunities to enhance mental healthcare services.As one of the fastest-growing fields of AI,generative AI has played a pivotal role in analyzing diverse forms of data,including medical image processing,protein structure prediction,clinical document generation,auxiliary diagnostic discrimination,and clinical decision support.These advancements have significantly strengthened capabilities in clinical diagnosis,data reconstruction,and adjunctive therapeutic interventions.This review highlights the potential applications of generative AI in advancing fundamental psychiatric research,identifying early risk factors for mental disorders,and assisting clinicians in diagnosis and treatment.Additionally,it addresses the challenges and limitations currently facing the application of generative AI to mental healthcare,including biases,privacy breaches,and insufficient interpretability.Finally,the review summarizes strategies to enhance AI's capacity to deliver mental health services,aiming to leverage new technologies to reduce the global burden of mental disorders and improve the quality of life of affected individuals.

Objective:It aims to examine the effects of the employee medical insurance outpatient mutual assistance on medical insurance costs for patients with diabetes mellitus,offering insights for optimizing outpatient insurance policies and chronic disease management strategies.Methods:Outpatient cost settlement data of urban employees with diabetes mellitus in Lanzhou from 2022 to 2023 was collected.Univariate analysis and interrupted time-series models were used to compare relevant medical insurance cost indicators before and after the reform.Results:The inpatient data of 765 730 diabetes mellitus patients were included in the study,and male patients account for 62.67％.After the reform,average per-visit pooling fund expenditure,average per-visit individual payment expenditures,average per-visit personal account expenditure,average per-visit eligible expense amount,average per-visit total fund payment,and the average per-visit proportion of basic medical pooling payments were decreased(P＜0.05).Moreover,average per-visit pooling fund expenditure,average per-visit individual account expenditure,average per-visit cash payment,average per-visit eligible expense amount,average per-visit total fund payment,and the average per-visit proportion of basic medical pooling payments showed a notable declining trend post-reform(P＜0.05).In contrast,the per-visit fully out-of-pocket expenditure exhibited no significant change before and after the reform(P＞0.05).Conclusion:The reform of the employee medical insurance outpatient mutual assistance system has alleviated the economic burden of disease for diabetic patients and improved the efficiency of medical insurance fund utilization,but it reduced the proportion of basic medical pooling payments.It is recommended to continuously refine the outpatient medical insurance payment system,strengthen supervision of medical expenses and service quality,and balance patient benefits with fund pressure to enhance chronic disease outpatient benefits.

Objective:To investigate the influence of different degrees of facet joint arthropathy on the indirect decompression effect of crenel lumbar interbody fusion (CLIF), and the clinical outcomes of CLIF for the treatment of lumbar spinal stenosis with severe facet joint arthropathy (grade 3).Methods:This study reviewed a total of 269 surgical segments in 156 patients with lumbar spinal stenosis treated with CLIF technique from November 2016 to February 2020. According to preoperative CT images, the facet joint was graded according to Pathria classification. There are 19 segments with grade 0, 156 segments with grade 1, 67 segments with grade 2, and 27 segments with grade 3. Radiographic parameters included disc angle, anterior and posterior disc height, and bilateral intervertebral foramen height on CT, and the midsagittal canal diameter and axial central canal area. In 30 patients with at least one segment of grade 3, the clinical efficacy was assessed using visual analogue scale (VAS) and Oswestry disability index (ODI).Results:The average the anterior and posterior intervertebral space height, intervertebral space angle, height of bilateral intervertebral foramina, spinal canal sagittal diameter and spinal canal area were significantly improved after the operation of grade 3 facet joint degeneration segment compared to preoperation. The preoperative mean spinal canal sagittal diameter and spinal canal area of grade 3 facet joint degeneration segment were significantly less than grade 1 and grade 2. The average change of spinal canal area after grade 3 articular degeneration was significantly less than that of grade 1 and 2, but there was no significant difference with that of grade 0. The posterior decompression rate was 55.56% (15/27) for grade 3, 35.82% (24/67) for grade 2, 16.03% (25/156) for grade 1, and 21.05% (4/19) for grade 0. The posterior decompression rate of grade 3 articular process degeneration was significantly higher than that of other grades ( P<0.001). Severe lateral recess stenosis and 24.24% of severe intervertebral foraminal stenosis were found in 81.48% of grade 3 degenerative segment. The 23 patients were followed up with an average of 21.62±6.52 months, and the average improvement of ODI was 24.10%±11.09%; the average VAS for leg pain and back pain were improved significantly. Conclusion:The degrees of facet joint degeneration do not prevent intervertebral space distraction of CLIF. However, because segments with severe facet joint arthropathy were usually associated with severe spinal canal stenosis, CLIF had a high rate of second-stage posterior decompression in the treatment of lumbar spinal stenosis with severe facet joint arthropathy.

Methamphetamine (MA) has the characteristics of neurotoxicity and strong psychological dependence. At present, there is a lack of effective intervention for MA dependence. Aiming at the brain pathological damage and clinical characteristics of MA dependence, new multi-dimensional evaluation instruments were developed including virtual reality craving evaluation system, cognitive and impulsive damage assessment, and SWOT (strengths, weaknesses, opportunities and threats) analysis. Rehabilitation interventions were also investigated and developed including computerized cognitive addiction therapy, precise neuroregulatory intervention aimed at reducing craving, music therapy, and community-based addiction rehabilitation electronic system. On this basis, On this basis, we propose to establish a whole course and accurate intervention model based on repeatedly measurement-based assesments for MA dependence, or WARM model. The WARM model focuses on the difficulties of MA dependence at different stages and is committed to multi-dimensional rehabilitation of MA patients. The WARM model will further be explored with the assistance of artificial intelligence.