Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent.

Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis.

A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients.

Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD.

Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD.

Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD.

Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up.

Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

To establish optimal gestational weight gain (GWG) in Chinese pregnant women by Chinese-specific BMI categories and compare the new recommendations with the Institute of Medicine (IOM) 2009 guidelines.

Multicentre, prospective cohort study. Unconditional logistic regression analysis was used to evaluate the OR, 95 % CI and the predicted probabilities of adverse pregnancy outcomes. The optimal GWG range was defined as the range that did not exceed a 1 % increase from the lowest predicted probability in each pre-pregnancy BMI group.

From nine cities in mainland China.

A total of 3731 women with singleton pregnancy were recruited from April 2013 to December 2014.

The optimal GWG (ranges) by Chinese-specific BMI was 15·0 (12·8–17·1), 14·2 (12·1–16·4) and 12·6 (10·4–14·9) kg for underweight, normal weight and overweight pregnant women, respectively. Inappropriate GWG was associated with several adverse pregnancy outcomes. Compared with women gaining weight within our proposed recommendations, women with excessive GWG had higher risk for macrosomia, large for gestational age and caesarean section, whereas those with inadequate GWG had higher risk for low birth weight, small for gestational age and preterm delivery. The comparison between our proposed recommendations and IOM 2009 guidelines showed that our recommendations were comparable with the IOM 2009 guidelines and could well predict the risk of several adverse pregnancy outcomes.

Inappropriate GWG was associated with higher risk of several adverse pregnancy outcomes. Optimal GWG recommendations proposed in the present study could be applied to Chinese pregnant women.

The aim of this study was to assess the current status of disease-related knowledge and to analyze the relationship among the general condition, illness perception, and psychological status of patients with coronavirus disease 2019 (COVID-19).

A hospital-based cross-sectional study was conducted on 118 patients using convenience sampling. The general questionnaire, disease-related knowledge questionnaire of COVID-19, Illness Perception Questionnaire (IPQ), and Profile of Mood States (POMS) were used to measure the current status of participants.

The overall average score of the disease-related knowledge of patients with COVID-19 was (79.19 ± 14.25), the self-care situation was positively correlated with knowledge of prevention and control (r = 0.265; P = 0.004) and total score of disease-related knowledge (r = 0.206; P = 0.025); the degree of anxiety was negatively correlated with the knowledge of diagnosis and treatment (r = −0.182; P = 0.049). The score of disease-related knowledge was negatively correlated with negative cognition (volatility, consequences, emotional statements) and negative emotions (tension, fatigue, depression) (P < 0.05); positively correlated with positive cognition (disease coherence) and positive emotion (self-esteem) (P < 0.05).

It was recommended that we should pay more attention to the elderly and low-income groups, and increase the knowledge about diagnosis and treatment of COVID-19 and self-care in the future health education for patients.

The coronavirus disease 2019 (COVID-19) pandemic has led to significant strain on front-line healthcare workers.

In this multicentre study, we compared the psychological outcomes during the COVID-19 pandemic in various countries in the Asia-Pacific region and identified factors associated with adverse psychological outcomes.

From 29 April to 4 June 2020, the study recruited healthcare workers from major healthcare institutions in five countries in the Asia-Pacific region. A self-administrated survey that collected information on prior medical conditions, presence of symptoms, and scores on the Depression Anxiety Stress Scales and the Impact of Events Scale-Revised were used. The prevalence of depression, anxiety, stress and post-traumatic stress disorder (PTSD) relating to COVID-19 was compared, and multivariable logistic regression identified independent factors associated with adverse psychological outcomes within each country.

A total of 1146 participants from India, Indonesia, Singapore, Malaysia and Vietnam were studied. Despite having the lowest volume of cases, Vietnam displayed the highest prevalence of PTSD. In contrast, Singapore reported the highest case volume, but had a lower prevalence of depression and anxiety. In the multivariable analysis, we found that non-medically trained personnel, the presence of physical symptoms and presence of prior medical conditions were independent predictors across the participating countries.

This study highlights that the varied prevalence of psychological adversity among healthcare workers is independent of the burden of COVID-19 cases within each country. Early psychological interventions may be beneficial for the vulnerable groups of healthcare workers with presence of physical symptoms, prior medical conditions and those who are not medically trained.

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.

More than 80% of coronavirus disease 2019 (COVID-19) cases are mild or moderate. In this study, a risk model was developed for predicting rehabilitation duration (the time from hospital admission to discharge) of the mild-moderate COVID-19 cases and was used to conduct refined risk management for different risk populations.

A total of 90 consecutive patients with mild-moderate COVID-19 were enrolled. Large-scale datasets were extracted from clinical practices. Through the multivariable linear regression analysis, the model was based on significant risk factors and was developed for predicting the rehabilitation duration of mild-moderate cases of COVID-19. To assess the local epidemic situation, risk management was conducted by weighing the risk of populations at different risk.

Ten risk factors from 44 high-dimensional clinical datasets were significantly correlated to rehabilitation duration (P < 0.05). Among these factors, 5 risk predictors were incorporated into a risk model. Individual rehabilitation durations were effectively calculated. Weighing the local epidemic situation, threshold probability was classified for low risk, intermediate risk, and high risk. Using this classification, risk management was based on a treatment flowchart tailored for clinical decision-making.

The proposed novel model is a useful tool for individualized risk management of mild-moderate COVID-19 cases, and it may readily facilitate dynamic clinical decision-making for different risk populations.

Psychiatric disorders are a group of complex psychological syndromes with high prevalence. Recent studies observed associations between altered plasma proteins and psychiatric disorders. This study aims to systematically explore the potential genetic relationships between five major psychiatric disorders and more than 3,000 plasma proteins.

The genome-wide association study (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) were driven from the Psychiatric GWAS Consortium. The GWAS datasets of 3,283 human plasma proteins were derived from recently published study, including 3,301 study subjects. Linkage disequilibrium score (LDSC) regression analysis were conducted to evaluate the genetic correlations between psychiatric disorders and each of the 3,283 plasma proteins.

LDSC observed several genetic correlations between plasma proteins and psychiatric disorders, such as ADHD and lysosomal Pro-X carboxypeptidase (p value = 0.015), ASD and extracellular superoxide dismutase (Cu-Zn; p value = 0.023), BD and alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase 6 (p value = 0.007), MDD and trefoil factor 1 (p value = 0.011), and SCZ and insulin-like growth factor-binding protein 6 (p value = 0.011). Additionally, we detected four common plasma proteins showing correlation evidence with both BD and SCZ, such as tumor necrosis factor receptor superfamily member 1B (p value = 0.012 for BD, p value = 0.011 for SCZ).

This study provided an atlas of genetic correlations between psychiatric disorders and plasma proteome, providing novel clues for pathogenetic and biomarkers, therapeutic studies of psychiatric disorders.