Depression is an important risk factor for life satisfaction in the elderly, but the underlying mechanism remains to be further explored. Depression is an important risk factor for life satisfaction in the elderly, but the underlying mechanism of depression affecting life satisfaction remains to be further explored. Existing research has found that activity of daily living (ADL) and self-rated health (SRH) are respectively associated with depression and life satisfaction. Based on these findings, activity of daily living may mediate the relationship between depression and life satisfaction; meanwhile, self-rated health may also serve as a key factor linking the two. This study aims to examine the associations among depression, activity of daily living, self-rated health, and life satisfaction, as well as the hypothesized mediating and chain-mediating roles of activity of daily living and self-rated health in this association. To explore the chain mediating effect of activity of daily living and self-rated health on the relationship between depression and life satisfaction in the elderly in China. A cross-sectional design was used in this study. Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS), including 4731 elderly aged 60 years and above (mean age: 68.2 years). Depression was measured using the Center for Epidemiologic Studies Depression Scale (CES-D-10), and the participants were divided into depression group and non-depression group according to the cut-off value. Activity of daily living was assessed by 12 items of daily activities such as dressing, bathing, and cooking. Self-rated health was measured by self-assessment of health status. Life satisfaction was measured by an assessment of overall life satisfaction. SPSS 27.0 was used for data cleaning and descriptive statistics. Model 6 in the PROCESS macro program was used to test the chain mediation effect, and the confidence interval of the indirect effect was estimated by the Bootstrap method (5 000 samples). Among the 4731 participants, 41.5

ABSTRACT Background The growing number of hypoglycaemia risk prediction models for Type 2 diabetes mellitus (T2DM) underscores the need for systematic evaluation of their risk of bias and applicability. This study summarises and critically assesses their characteristics and predictive performance using established guidelines for prediction model development. Methods The review protocol was registered on PROSPERO (CRD420251031980). We searched nine main English and Chinese databases from inception to May 2025. The CHARMS checklist and PROBAST tool were used to assess the risk of bias and applicability. A meta‐analysis of AUC values from models was conducted using MedCalc software. Results We included 25 studies (45 models), with reported AUCs ranging from 0.630 to 0.996. The pooled AUC value of 16 models was 0.815 (95% CI 0.765–0.861), indicating excellent discrimination. 24 (96%) studies were overall at high risk of bias and 22 (88%) studies had low‐risk applicability, primarily due to small sample size, improper handling of missing data, failure to report calibration, screening of predictors by univariate analysis and lack of external validation. Conclusions Current hypoglycaemia prediction models for T2DM show substantial methodological limitations and high bias risk. While machine learning models have advanced rapidly in recent years, their methodology remains opaque and validation is limited. Future research should focus on optimising existing models, enhancing methodological rigour and conducting external validation.

To evaluate the effects of social network-based health education on self-management, self-efficacy and HbA1c of older adults with type 2 diabetes mellitus (T2DM). A convenience sample of 64 elderly T2DM patients with poor glycemic control was randomly divided into two groups. The intervention group received social network-based health education with their nominated social network member for 12 weeks, while the control group received health education alone. The scores of Summary of Diabetes Self-Care Activities (SDSCA), Diabetes Self-Efficacy Scale (DSES), and HbA1c were compared between groups at the baseline and after 12 weeks by using RM-ANOVA. Sixty older adults with T2DM, 30 cases in each group, completed the study. The diet and blood glucose testing dimensions of C-SDSCA had an interaction effect on group-by-time (F were 4.700 and 4.752, respectively, p < 0.05). The mean diet dimension score increased by 1.55 in the intervention group, while 0.76 in the control group, and the score of blood glucose testing dimension increased by 3.5 in the intervention group, while 0.75 in the control group. No significant group-by-time differences were found in C-DSES (F = 1.667, p > 0.05) and HbA1c (F = 0.553, p > 0.05). Social network-based health education effectively promotes self-management in diet and blood glucose testing of the elderly T2DM patients with poor glycemic control. Trial Registration: China Clinical Trial Registration Center (ChiCTR2000038177).

Background: This study aims to explore the relationship between birth interval and prevalence of depression among postmenopausal women with two deliveries in the United States. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) were used, which spanned the years 2005-2018 and is publicly accessible. We utilized weighted multivariable logistic regression analysis, restricted cubic splines (RCS), and subgroup analysis to examine the relationship between the prevalence of depression in postmenopausal women with only two deliveries and the age at first birth (AFB), age at last birth (ALB), and birth interval (the difference between ALB and AFB). Results: A total of 2375 postmenopausal women with only two deliveries were included in the study, and 271 (11.4%) had depression. RCS models showed that AFB and ALB were U-shaped curves associated with the prevalence of depression. Additionally, the birth interval was roughly L-shaped curve correlated with the risk of depression. Conclusions: Both early and late childbearing, as well as short birth intervals, may contribute to mental health challenges in this demographic. These findings suggest that women with both early and late childbearing, as well as those with short birth intervals, may face a higher risk of depression during their postmenopausal years. This underscores the importance of targeted mental health screening and support for these groups.

A micro-expression is a fleeting, delicate and localized facial gesture. It can expose the true feelings that someone is trying to hide and is seen to be a crucial indicator for spotting lies. Because of its possible applications in a variety of sectors, micro-expression research has garnered a lot of attention. The accuracy of micro-expression recognition still needs to be improved, though, because of the brief and weak motions that make up micro-expressions. In recent years, Deep convolution neural methods have depicted a higher degree of efficiency for complex challenge of face detection. Although several attempts were made for micro-expression recognition (MER), the problem is far from being resolved problem which is portrayed by the lowest accuracy rate depicted by the other models. In this study, present a Facial Micro-Expression Detection and Classification using Modified Multimodal Ensemble Learning (FMEDC-MMEL) approach. The major intention of the FMEDC-MMEL technique lies in the proficient identification of MEs that exist in the facial images. As a pre-processing phase, the FMEDC-MMEL technique exploits histogram equalization (HE) approach to improve the contrast level of the image. In the FMEDC-MMEL technique, improved densely connected networks (DenseNet) model is used for learning feature patterns from the pre-processed images. To enhance the proficiency of the improved DenseNet model, stochastic gradient descent (SGD) approach is used for hyperparameter selection process. For facial ME detection, the FMEDC-MMEL technique follows an ensemble of three classifiers namely bi-directional gated recurrent unit (Bi-GRU), long short-term memory (LSTM) and extreme learning machine (ELM). A tailored ensemble learning approach is shown, which combines many machine learning models to improve classification performance and detection accuracy. Sophisticated feature extraction methods are utilized to extract the subtle aspects of micro-expressions, and precision is maintained by optimizations that minimize computing cost. Empirical findings reveal that this methodology notably surpasses conventional techniques, providing enhanced precision and resilience on a variety of complex and demanding datasets. In addition to pushing the boundaries of micro-expression analysis research, the proposed strategy has potential uses in the real world in fields including security, psychology testing, and human-computer interaction.

Objective:This study aimed to translate the Edmonton-33 scale (E-33) into Chinese and evaluate its reliability and validity in patients with head and neck cancer (HNC). Methods:In Phase 1, the E-33 was translated from English to Chinese using the Brislin double-back translation method. Content validity was evaluated by a panel of experts, and a pilot test was conducted with a small sample of HNC patients. In Phase 2, a cohort of 510 patients from Henan and Hubei provinces was recruited. Psychometric properties were assessed through item analysis; and reliability testing (including Cronbach's alpha, test-retest reliability, and split-half reliability), as well as construct validity (using exploratory and confirmatory factor analysis). Results:The item-level content validity index (I-CVI) ranged from 0.833 to 1.000, and the scale-level content validity index (S-CVI/Ave) was 0.965. The Cronbach's alpha, the test-retest reliability coefficient, and the split-half reliability values were 0.922, 0.973, and 0.971, respectively. Four main factors were identified using exploratory factor analysis, explaining 77.07% of the total variance. Confirmatory factor analysis showed good fit indices: χ2/df ​= ​1.626, RMSEA ​= ​0.048, NFI ​= ​0.936, RFI ​= ​0.930, IFI ​= ​0.974, TLI ​= ​0.972, and CFI ​= ​0.974. Conclusions:The Chinese version of the Edmonton-33 scale (CE-33) demonstrated high reliability and validity, suggesting its potential as a valuable self-report tool for assessing functional outcomes in Chinese-speaking HNC patients.

Background:Almost half of stoma caregivers develop anxiety or depression, yet follow-up still centers on patients and offers caregivers little structured support. Social isolation-worsened by the pandemic and likely to grow as colorectal-cancer ostomies rise-appears central to this distress, but its role in caregiver programs has never been tested. We therefore assessed a 12-week multidisciplinary accompaniment program and measured how much reducing isolation improves caregivers' skills and emotional wellbeing. Methodology:A cross-sectional study was conducted with 302 family caregivers of ostomy patients. Participants were divided into an Intervention Group (IG) and a Non-Intervention Group (NIG). Logistic regression models examined associations between demographic and behavioral factors, caregiving outcomes, and social isolation. Mediation analysis was performed to determine the indirect effects of social isolation on caregiving ability and negative emotions. Results:Multidisciplinary accompaniment interventions significantly improved caregiving ability (OR = 2.33, 95% CI: 1.12-3.54), reduced negative emotions (OR = 2.58, 95% CI: 1.13-4.03) and social isolation score (OR = 1.69, 95% CI: 1.09-2.29), with social isolation accounting for 18.7% of the effect on caregiving ability and 15.2% on negative emotions. In addition, significant predictors also included place of residence, marital status, and alcohol consumption. Conclusions:Multidisciplinary accompaniment interventions that address social isolation can enhance caregiving ability and reduce emotional strain in family caregivers of ostomy patients.

[This corrects the article DOI: 10.3389/fpubh.2025.1562186.].

BackgroundDue to aging, the use of antidiabetic drugs, and dietary restrictions following a diagnosis of type 2 diabetes mellitus (T2DM), the social interactions of older adults with T2DM are often limited. As a result, this population experiences a higher incidence of social isolation than the general older adult population. This study aims to analyze the prevalence and influencing factors of social isolation among older adults with T2DM using a structural equation model.Patients and methodsA cross-sectional study was conducted between January and November 2023. A total of 496 older adults with T2DM were recruited from hospitals or community health service centers in Beijing to investigate their social isolation status and related factors. The Lubben Social Network Scale-6, along with related scales, was used for data collection. The effects of different factors on social isolation were determined using a path analysis.ResultsAmong 496 older adults with T2DM, 227 reported social isolation, resulting in a prevalence rate of 45.77%. Activity of daily living, cognitive function, loneliness, exercise management, smoking, social support, and social participation are all directly related to social isolation. Additionally, six factors—activity of daily living, loneliness, depression, diet management, blood glucose monitoring, and social support—were related to social isolation through social participation.ConclusionThe incidence of social isolation among older adults with T2DM is high. For them, activities of daily living, loneliness, and social support are significant factors in their social isolation since they are directly or indirectly related to social isolation. Meanwhile, diabetes self-management, such as diet management, exercise management, blood glucose management, and smoking, is directly or indirectly related to social isolation. For older adults with T2DM, the important intermediary role of social participation between the factors and social isolation should be given due attention.

BackgroundHypoglycemic episodes cause varying degrees of damage in the functional system of elderly inpatients with type 2 diabetes mellitus (T2DM). The purpose of the study is to construct a nomogram prediction model for the risk of hypoglycemia in elderly inpatients with T2DM and to evaluate the predictive performance of the model.MethodsFrom August 2022 to April 2023, 546 elderly inpatients with T2DM were recruited in seven tertiary-level general hospitals in Beijing and Inner Mongolia province, China. Medical history and clinical data of the inpatients were collected with a self-designed questionnaire, with follow up on the occurrence of hypoglycemia within one week. Factors related to the occurrence of hypoglycemia were screened using regularized logistic analysis(r-LR), and a nomogram prediction visual model of hypoglycemia was constructed. AUROC, Hosmer-Lemeshow, and DCA were used to analyze the prediction performance of the model.ResultsThe incidence of hypoglycemia of elderly inpatients with T2DM was 41.21% (225/546). The risk prediction model included 8 predictors as follows(named ADOCHBIU): duration of diabetes (OR=2.276, 95%CI 2.097˜2.469), urinary microalbumin(OR=0.864, 95%CI 0.798˜0.935), oral hypoglycemic agents (OR=1.345, 95%CI 1.243˜1.452), cognitive impairment (OR=1.226, 95%CI 1.178˜1.276), insulin usage (OR=1.002, 95%CI 0.948˜1.060), hypertension (OR=1.113, 95%CI 1.103˜1.124), blood glucose monitoring (OR=1.909, 95%CI 1.791˜2.036), and abdominal circumference (OR=2.998, 95%CI 2.972˜3.024). The AUROC of the prediction model was 0.871, with sensitivity of 0.889 and specificity of 0.737, which indicated that the nomogram model has good discrimination. The Hosmer-Lemeshow was χ2 = 2.147 (P=0.75), which meant that the prediction model is well calibrated. DCA curve is consistently higher than all the positive line and all the negative line, which indicated that the nomogram prediction model has good clinical utility.ConclusionsThe nomogram hypoglycemia prediction model constructed in this study had good prediction effect. It is used for early detection of high-risk individuals with hypoglycemia in elderly inpatients with T2DM, so as to take targeted measures to prevent hypoglycemia.Trial registrationChiCTR2200062277. Registered on 31 July 2022.

Objective To identify the defining attributes, antecedents, consequences and empirical referents to form an operational definition of social isolation in people with type 2 diabetes. Design The Walker and Avant approach. Data source An electronic search of the literature using China National Knowledge Infrastructure (CNKI), Wanfang database, PubMed, Web of Science, CINAHL, and PsycINFO informed the analysis. The search included both quantitative and qualitative studies related to social isolation in people with type 2 diabetes published in Chinese and English. Results Of the 2918 articles identified, 21 ultimately met the inclusion criteria. The analysis identified the defining attributes of social isolation in people with type 2 diabetes as objective and subjective. Antecedents included five aspects: personal characteristics, disease-related physiological factors, and psychological, behavioral, and social factors. Consequences were identified as physiological, psychological, behavioral aspects and quality of life. Conclusions The operational definition of social isolation in people with type 2 diabetes is that due to personal characteristics, disease-related physiological factors, and psychological, behavioral, and social factors, people with type 2 diabetes will have limited social networks and social support, reduced social contact and social involvement, and/or negative feelings of disconnection from the outside world, which lead to adverse physiological, psychological, and behavioral outcomes and poor quality of life. Clinicians can further develop tools to measure social isolation in people with type 2 diabetes and analyze the path of the antecedents to social isolation to investigate the interplay between them in order to develop target interventions.

Background:There is limited evidence, mainly from high-income countries, that digital health interventions improve type 2 diabetes (T2DM) care. Large-scale implementation studies are lacking. Methods:A multifaceted digital health intervention comprising: (1) a self-management application ('app') for patients and lay 'family health promotors' (FHPs); and (2) clinical decision support for primary care doctors was evaluated in an open-label, parallel, cluster randomized controlled trial in 80 communities (serviced by a primary care facility for >1000 residents) in Hebei Province, China. People >40 years with T2DM and a glycated haemoglobin (HbA1c) ≥7% were recruited (∼25/community). After baseline assessment, community clusters were randomly assigned to intervention or control groups (1:1) via a web-based system, stratified by locality (rural/urban). Control arm clusters received usual care without access to the digital health application or family health promoters. The primary outcome was at the participant level defined as the proportion with ≥2 "ABC" risk factor targets achieved (HbA1c < 7.0%, blood pressure < 140/80 mmHg and LDL-cholesterol < 2.6 mmol/L) at 24 months. Findings:A total of 2072 people were recruited from the 80 community clusters (40 urban and 40 rural), with 1872 (90.3%) assessed at 24 months. In the intervention arm, patients used FHPs for support more in rural than urban communities (252 (48.6%) rural vs 92 (21.5%) urban, p < 0.0001). The mean monthly proportion of active app users was 46.4% (SD 7.8%) with no significant difference between urban and rural usage rates. The intervention was associated with improved ABC control rates (339 [35.9%] intervention vs 276 [29.9%] usual care; RR 1.20, 95% CI 1.02-1.40; p = 0.025), with significant heterogeneity by geography (rural 220 [42.6%] vs 158 [31.0%]; urban 119 [27.9%] vs 118 [28.6%]; p = 0.022 for interaction). Risk factor reductions were mainly driven by improved glycaemic control (mean HbA1C difference -0.33%, 95% CI -0.48 to -0.17; p = 0.00025 and mean fasting plasma glucose difference -0.58 mmol, 95% CI -0.89 to -0.27; p = 0.00013). There were no changes in blood pressure and LDL-cholesterol levels. Interpretation:A multifaceted digital health intervention improved T2DM risk factor control rates, particularly in rural communities where there may be stronger relationships between patients and doctors and greater family member support. Funding:National Health and Medical Research CouncilGlobal Alliance for Chronic Diseases (ID 1094712).

Background:Erectile dysfunction (ED) is a prevalent condition that affects middle-aged and older men, impacting their sexual health and overall wellbeing. We aimed to investigate the relationship between social support and ED among this specific population. Methods:Data were collected from the National Health and Nutrition Examination Survey. Social support was assessed through various dimensions, including emotional support, material support, and network support. Multivariate logistic regression was performed to examine the association between social support and ED, and a propensity-score-matched (PSM) analysis was further conducted. Results:Among 1938 middle-aged and older males in the United States, 49.9% had a history of ED. ED was more prevalent in older individuals and those with comorbidities such as hypertension, prostate disease, higher serum creatinine level, and mental problems. Males with lower social support scores had a higher weighted rate of ED (P < 0.001). After adjusting for multiple variables in logistic regression analysis, a higher social support score was associated with a 19% lower likelihood of ED (weighted odds ratio [OR] 0.81, 95% confidence interval [CI] 0.66-0.98, P = 0.032). The association remained consistent after propensity score matching (OR 0.80, 95% CI 0.66-0.98, P = 0.028). Conclusion:Social support appears to be associated with a reduced risk of ED in middle-aged and older men. Further research is needed to better understand this relationship and explore interventions that enhance social support, potentially leading to improved sexual health outcomes.

Hypertensive nephropathy (HTN) is the second leading cause of end-stage renal disease (ESRD) and a chronic inflammatory disease. Persistent hypertension leads to lesions of intrarenal arterioles and arterioles, luminal stenosis, secondary ischemic renal parenchymal damage, and glomerulosclerosis, tubular atrophy, and interstitial fibrosis. Studying the pathogenesis of hypertensive nephropathy is a prerequisite for diagnosis and treatment. The main cause of HTN is poor long-term blood pressure control, but kidney damage is often accompanied by the occurrence of immune inflammation. Some studies have found that the activation of innate immunity, inflammation and acquired immunity is closely related to the pathogenesis of HTN, which can cause damage and dysfunction of target organs. There are more articles on the mechanism of diabetic nephropathy, while there are fewer studies related to immunity in hypertensive nephropathy. This article reviews the mechanisms by which several different immune cells and inflammatory cytokines regulate blood pressure and renal damage in HTN. It mainly focuses on immune cells, cytokines, and chemokines and inhibitors. However, further comprehensive and large-scale studies are needed to determine the role of these markers and provide effective protocols for clinical intervention and treatment.

IntroductionDue to the sexual orientation and HIV diagnosis, young and middle-aged men who have sex with men (MSM) with new HIV-diagnosis may experience more depressive syndromes and face greater psychological stress. The study explored trajectories of depressive symptoms of young and middle-aged MSM within 1 year after new HIV-diagnosis and analyze the related factors.MethodsFrom January 2021 to March 2021, 372 young and middle-aged MSM who were newly diagnosed as HIV-infection were recruited in two hospitals in Beijing. Self-rating Depression Scale was used to measure the participants’ depressive symptom in 1st month, 3rd month, 6th month, 9th month and 12th month after HIV diagnosis. The latent class growth model was used to identify trajectories of the participants’ depressive symptoms. Multinomial logistic regression was used to analyse factors related with the trajectories.ResultsThree hundred and twenty-eight young and middle-aged MSM with new HIV-diagnosis completed the research. Depressive symptom in 328 young and middle-aged MSM was divided into three latent categories: non-depression group (56.4%), chronic-mild depression group (28.1%), and persistent moderate–severe depression group (15.5%). The participants assessed as non-depression (non-depression group) or mild depression (chronic-mild depression group) at the baseline were in a non-depression state or had a downward trend within one-year, and the participants assessed as moderate and severe depression (persistent moderate–severe depression group) at the time of diagnosis were in a depression state continuously within 1-year. Multinomial logistic regression analysis showed that, compared with the non-depression group, monthly income of 5,000 ~ 10,000 RMB (equal to 690 ~ 1,380 USD) was the risk factor for the chronic-mild depression group, and self-rating status being fair/good and self-disclosure of HIV infection were protective factors for the persistent moderate–severe depression group while HIV-related symptoms was the risk factor.ConclusionDepressive symptoms in young and middle-aged MSM is divided into three latent categories. Extra care must be given to young and middle-aged MSM assessed as moderate or severe depression at the time of HIV-diagnosis, especially to those who had poor self-rating health status, did not tell others about their HIV-infection and experienced HIV-related symptoms.

In the last decade, the explosive growth of vision sensors and video content has driven numerous application demands for automating human action detection in space and time. Aside from reliable precision, vast real-world scenarios also mandate continuous and instantaneous processing of actions under limited computational budgets. However, existing studies often rely on heavy operations such as 3D convolution and fine-grained optical flow, therefore are hindered in practical deployment. Aiming strictly at a better mixture of detection accuracy, speed, and complexity for online detection, we customize a cost-effective 2D-CNN-based tubelet detection framework coined Accumulated Micro-Motion Action detector (AMMA). It sparsely extracts and fuses visual-dynamic cues of actions spanning a longer temporal window. To lift reliance on expensive optical flow estimation, AMMA efficiently encodes actions’ short-term dynamics as accumulated micro-motion from RGB frames on-the-fly. On top of AMMA’s motion-aware 2D backbone, we adopt an anchor-free detector to cooperatively model action instances as moving points in the time span. The proposed action detector achieves highly competitive accuracy as state-of-the-arts while substantially reducing model size, computational cost, and processing time (6 million parameters, 1 GMACs, and 100 FPS respectively), making it much more appealing under stringent speed and computational constraints. Codes are available on https://github.com/alphadadajuju/AMMA.

Background Hypoglycemia is one of the most common complications in patients with DN during hemodialysis. The purpose of the study is to construct a clinical automatic calculation to predict risk of hypoglycemia during hemodialysis for patients with diabetic nephropathy. Methods In this cross-sectional study, patients provided information for the questionnaire and received blood glucose tests during hemodialysis. The data were analyzed with logistic regression and then an automated calculator for risk prediction was constructed based on the results. From May to November 2022, 207 hemodialysis patients with diabetes nephropathy were recruited. Patients were recruited at blood purifying facilities at two hospitals in Beijing and Inner Mongolia province, China. Hypoglycemia is defined according to the standards of medical care in diabetes issued by ADA (2021). The blood glucose meter was used uniformly for blood glucose tests 15 minutes before the end of hemodialysis or when the patient did not feel well during hemodialysis. Results The incidence of hypoglycemia during hemodialysis was 50.2% (104/207). The risk prediction model included 6 predictors, and was constructed as follows: Logit ( P ) = 1.505×hemodialysis duration 8~15 years ( OR = 4.506, 3 points) + 1.616×hemodialysis duration 16~21 years ( OR = 5.032, 3 points) + 1.504×having hypotension during last hemodialysis ( OR = 4.501, 3 points) + 0.788×having hyperglycemia during the latest hemodialysis night ( OR = 2.199, 2 points) + 0.91×disturbance of potassium metabolism ( OR = 2.484, 2 points) + 2.636×serum albumin<35 g/L ( OR = 13.963, 5 points)-4.314. The AUC of the prediction model was 0.866, with Matthews correlation coefficient (MCC) of 0.633, and Hosmer-Lemeshow χ 2 of 4.447( P = 0.815). The automatic calculation has a total of 18 points and four risk levels. Conclusions The incidence of hypoglycemia during hemodialysis is high in patients with DN. The risk prediction model in this study had a good prediction outcome. The hypoglycemia prediction automatic calculation that was developed using this model can be used to predict the risk of hypoglycemia in DN patients during hemodialysis and also help identify those with a high risk of hypoglycemia during hemodialysis.

To explore the status quo of self‐management among young adults with type 2 diabetes mellitus (T2DM) and the determinants of self‐management under the guidance of social cognitive theory.

MSM是我国艾滋病防治的重点人群之一.感染HIV的MSM因其同时面临着HIV感染和性向的双重压力,更易产生心理问题,其中抑郁是最为常见的症状之一,尤其是中青年阶段.因此,本文旨在对中青年HIV感染MSM的抑郁现状及影响因素进行总结,以期为相关部门预防中青年HIV感染男男性行为者抑郁的发生提供一定参考.