This study investigated the factors influencing the mental health of rural doctors in Hebei Province, to provide a basis for improving the mental health of rural doctors and enhancing the level of primary health care.

The aim of this study was to understand the mental health of rural doctors in Hebei Province, identify the factors that influence it, and propose ways to improve their psychological status and the level of medical service of rural doctors.

Rural doctors from 11 cities in Hebei Province were randomly selected, and their basic characteristics and mental health status were surveyed via a structured questionnaire and the Symptom Checklist-90 (SCL-90). The differences between the SCL-90 scores of rural doctors in Hebei Province and the Chinese population norm, as well as the proportion of doctors with mental health problems, were compared. Logistic regression was used to analyse the factors that affect the mental health of rural doctors.

A total of 2593 valid questionnaires were received. The results of the study revealed several findings: the younger the rural doctors, the greater the incidence of mental health problems (OR = 0.792); female rural doctors were more likely to experience mental health issues than their male counterparts (OR = 0.789); rural doctors with disabilities and chronic diseases faced a significantly greater risk of mental health problems compared to healthy rural doctors (OR = 2.268); rural doctors with longer working hours have a greater incidence of mental health problems; and rural doctors with higher education backgrounds have a higher prevalence of somatization (OR = 1.203).

Rural doctors who are younger, male, have been in medical service longer, have a chronic illness or disability, and have a high degree of education are at greater risk of developing mental health problems. Attention should be given to the mental health of the rural doctor population to improve primary health care services.

Depression has been linked to disruptions in resting-state networks (RSNs). However, inconsistent findings on RSN disruptions, with variations in reported connectivity within and between RSNs, complicate the understanding of the neurobiological mechanisms underlying depression.

A systematic literature search of PubMed and Web of Science identified studies that employed resting-state functional magnetic resonance imaging (fMRI) to explore RSN changes in depression. Studies using seed-based functional connectivity analysis or independent component analysis were included, and coordinate-based meta-analyses were performed to evaluate alterations in RSN connectivity both within and between networks.

A total of 58 studies were included, comprising 2321 patients with depression and 2197 healthy controls. The meta-analysis revealed significant alterations in RSN connectivity, both within and between networks, in patients with depression compared with healthy controls. Specifically, within-network changes included both increased and decreased connectivity in the default mode network (DMN) and increased connectivity in the frontoparietal network (FPN). Between-network findings showed increased DMN–FPN and limbic network (LN)–DMN connectivity, decreased DMN–somatomotor network and LN–FPN connectivity, and varied ventral attention network (VAN)–dorsal attentional network (DAN) connectivity. Additionally, a positive correlation was found between illness duration and increased connectivity between the VAN and DAN.

These findings not only provide a comprehensive characterization of RSN disruptions in depression but also enhance our understanding of the neurobiological mechanisms underlying depression.

COVID-19 carriers experience psychological stresses and mental health issues such as varying degrees of stigma. The Social Impact Scale (SIS) can be used to measure the stigmatisation of COVID-19 carriers who experience such problems.

To evaluate the reliability and validity of the Chinese version of the SIS, and the association between stigma and depression among asymptomatic COVID-19 carriers in Shanghai, China.

A total of 1283 asymptomatic COVID-19 carriers from Shanghai Ruijin Jiahe Fangcang Shelter Hospital were recruited, with a mean age of 39.64 ± 11.14 years (59.6% male). Participants completed questionnaires, including baseline information and psychological measurements, the SIS and Self-Rating Depression Scale. The psychometrics of the SIS and its association with depression were examined through exploratory factor analysis, confirmatory factor analysis and receiver operating characteristic analysis.

The average participant SIS score was 42.66 ± 14.61 (range: 24–96) years. Analyses suggested the model had four factors: social rejection, financial insecurity, internalised shame and social isolation. The model fit statistics of the four-factor SIS were 0.913 for the comparative fit index, 0.902 for the Tucker–Lewis index and 0.088 for root-mean-square error of approximation. Standard estimated factor loadings ranged from 0.509 to 0.836. After controlling for demographic characteristics, the total score of the 23-item SIS predicted depression (odds ratio: 1.087, 95% CI 1.061–1.115; area under the curve: 0.84, 95% CI 0.788–0.892).

The Chinese version of the SIS showed good psychometric properties and can be used to assess the level of perceived stigma experienced by asymptomatic COVID-19 carriers.

Gut microbiome and dietary patterns have been suggested to be associated with depression/anxiety. However, limited effort has been made to explore the effects of possible interactions between diet and microbiome on the risks of depression and anxiety.

Using the latest genome-wide association studies findings in gut microbiome and dietary habits, polygenic risk scores (PRSs) analysis of gut microbiome and dietary habits was conducted in the UK Biobank cohort. Logistic/linear regression models were applied for evaluating the associations for gut microbiome-PRS, dietary habits-PRS, and their interactions with depression/anxiety status and Patient Health Questionnaire (PHQ-9)/Generalized Anxiety Disorder-7 (GAD-7) score by R software.

We observed 51 common diet–gut microbiome interactions shared by both PHQ score and depression status, such as overall beef intake × genus Sporobacter [hurdle binary (HB)] (PPHQ = 7.88 × 10−4, Pdepression status = 5.86 × 10−4); carbohydrate × genus Lactococcus (HB) (PPHQ = 0.0295, Pdepression status = 0.0150). We detected 41 common diet–gut microbiome interactions shared by GAD score and anxiety status, such as sugar × genus Parasutterella (rank normal transformed) (PGAD = 5.15 × 10−3, Panxiety status = 0.0347); tablespoons of raw vegetables per day × family Coriobacteriaceae (HB) (PGAD = 6.02 × 10−4, Panxiety status = 0.0345). Some common significant interactions shared by depression and anxiety were identified, such as overall beef intake × genus Sporobacter (HB).

Our study results expanded our understanding of how to comprehensively consider the relationships for dietary habits–gut microbiome interactions with depression and anxiety.

To assess the association between total alcohol intake, specific alcoholic beverages and sleep quality in a community-based cohort.

A cross-sectional study.

The Kailuan community, China.

Included were 11 905 participants who were free of a history of CVD, cancer, Parkinson’s disease, dementia and head injury in or prior to 2012. Alcohol consumption (amount and frequency intake) and alcoholic beverage type were collected in 2006 (baseline) and 2012. Participants were grouped into non-, light- (women: 0–0·4 serving/d; men: 0–0·9 serving/d), moderate- (women: 0·5–1·0 serving/d; men: 1·0–2·0 servings/d) and heavy- (women: >1·0 servings/d; men: >2·0 servings/d) drinkers. Overall sleep quality was measured in 2012 and included four sleep parameters (insomnia, daytime sleepiness, sleep duration, snoring/obstructive sleep apnoea).

We observed a dose–response association between higher alcohol consumption in 2006 and worse sleep quality in 2012 (Ptrend < 0·001), after adjusting for age, sex, socio-economic status, smoking status, physical activity, obesity, plasma lipid profiles, diabetes and hypertension. A similar association was observed when alcohol consumption in 2012 was used as exposure. Alcohol was associated with higher odds of having short sleep duration (adjusted OR for heavy- v. non-drinkers = 1·31; 95 % CI: 1·09, 1·57) and snoring (adjusted OR for heavy- v. non-drinkers: 1·38; 95 % CI: 1·22, 1·57). Consumption of hard liquor, but not beer or wine, was significantly associated with poor sleep quality.

Higher alcohol consumption was associated with poorer sleep quality and higher odds of having snoring and short sleep duration.

Birth weight influences not only brain development, but also mental health outcomes, including depression, but the underlying mechanism is unclear.

The phenotypic data of 12,872–91,009 participants (59.18–63.38% women) from UK Biobank were included to test the associations between the birth weight, depression, and brain volumes through the linear and logistic regression models. As birth weight is highly heritable, the polygenic risk scores (PRSs) of birth weight were calculated from the UK Biobank cohort (154,539 participants, 56.90% women) to estimate the effect of birth weight-related genetic variation on the development of depression and brain volumes. Finally, the mediation analyses of step approach and mediation analysis were used to estimate the role of brain volumes in the association between birth weight and depression. All analyses were conducted sex stratified to assess sex-specific role in the associations.

We observed associations between birth weight and depression (odds ratio [OR] = 0.968, 95% confidence interval [CI] = 0.957–0.979, p = 2.29 × 10−6). Positive associations were observed between birth weight and brain volumes, such as gray matter (B = 0.131, p = 3.51 × 10−74) and white matter (B = 0.129, p = 1.67 × 10−74). Depression was also associated with brain volume, such as left thalamus (OR = 0.891, 95% CI = 0.850–0.933, p = 4.46 × 10−5) and right thalamus (OR = 0.884, 95% CI = 0.841–0.928, p = 2.67 × 10−5). Additionally, significant mediation effects of brain volume were found for the associations between birth weight and depression through steps approach and mediation analysis, such as gray matter (B = –0.220, p = 0.020) and right thalamus (B = –0.207, p = 0.014).

Our results showed the associations among birth weight, depression, and brain volumes, and the mediation effect of brain volumes also provide evidence for the sex-specific of associations.