Air dispersal of respiratory viruses other than SARS-CoV-2 has not been systematically reported. The incidence and factors associated with air dispersal of respiratory viruses are largely unknown.

We performed air sampling by collecting 72,000 L of air over 6 hours for pediatric and adolescent patients infected with parainfluenza virus 3 (PIF3), respiratory syncytial virus (RSV), rhinovirus, and adenovirus. The patients were singly or 2-patient cohort isolated in airborne infection isolation rooms (AIIRs) from December 3, 2021, to January 26, 2022. The viral load in nasopharyngeal aspirates (NPA) and air samples were measured. Factors associated with air dispersal were investigated and analyzed.

Of 20 singly isolated patients with median age of 30 months (range, 3 months–15 years), 7 (35%) had air dispersal of the viruses compatible with their NPA results. These included 4 (40%) of 10 PIF3-infected patients, 2 (66%) of 3 RSV-infected patients, and 1 (50%) of 2 adenovirus-infected patients. The mean viral load in their room air sample was 1.58×103 copies/mL. Compared with 13 patients (65%) without air dispersal, these 7 patients had a significantly higher mean viral load in their NPA specimens (6.15×107 copies/mL vs 1.61×105 copies/mL; P < .001). Another 14 patients were placed in cohorts as 7 pairs infected with the same virus (PIF3, 2 pairs; RSV, 3 pairs; rhinovirus, 1 pair; and adenovirus, 1 pair) in double-bed AIIRs, all of which had air dispersal. The mean room air viral load in 2-patient cohorts was significantly higher than in rooms of singly isolated patients (1.02×104 copies/mL vs 1.58×103 copies/mL; P = .020).

Air dispersal of common respiratory viruses may have infection prevention and public health implications.

Depression is a debilitating mental disorder that often coexists with anxiety. The genetic mechanisms of depression and anxiety have considerable overlap, and studying depression in non-anxiety samples could help to discover novel gene. We assess the genetic variation of depression in non-anxiety samples, using genome-wide association studies (GWAS) and linkage disequilibrium score regression (LDSC).

The GWAS of depression score and self-reported depression were conducted using the UK Biobank samples, comprising 99,178 non-anxiety participants with anxiety score <5 and 86,503 non-anxiety participants without self-reported anxiety, respectively. Replication analysis was then performed using two large-scale GWAS summary data of depression from Psychiatric Genomics Consortium (PGC). LDSC was finally used to evaluate genetic correlations with 855 health-related traits based on the primary GWAS.

Two genome-wide significant loci for non-anxiety depression were identified: rs139702470 (p = 1.54 × 10−8, OR = 0.29) locate in PIEZO2, and rs6046722 (p = 2.52 × 10−8, OR = 1.09) locate in CFAP61. These associated genes were replicated in two GWAS of depression from PGC, such as rs1040582 (preplication GWAS1 = 0.02, preplication GWAS2 = 2.71 × 10−3) in CFAP61, and rs11661122 (preplication GWAS1 = 8.16 × 10−3, preplication GWAS2 = 8.08 × 10−3) in PIEZO2. LDSC identified 19 traits genetically associated with non-anxiety depression (p < 0.001), such as marital separation/divorce (rg = 0.45, SE = 0.15).

Our findings provide novel clues for understanding of the complex genetic architecture of depression.

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 explore the accuracy of estimated 24-h urinary iodine excretion (24-h UIEest) in assessing iodine nutritional status.

Fasting venous blood, 24-h and spot urine samples were collected during the day. The urinary iodine concentration (UIC) and urinary creatinine concentration (UCrC) were measured, and the urinary iodine-to-creatinine ratio (UI/Cr), 24-h UIEest, and 24-h urinary iodine excretion (24-h UIE) were calculated. At the population level, correlation and consistency between UIC, UI/Cr, 24-h UIEest and 24-h UIE were assessed using correlation analysis and Bland–Altman plots. At the individual level, receiver operating characteristic (ROC) curves were used to analyse the accuracy of the above indicators for evaluating insufficient and excessive iodine intake. The reference interval of 24-h UIEest was established based on percentile values.

Indicator can accurately evaluate individual iodine nutrition during pregnancy remains controversial.

Pregnant women (n 788).

Using 24-h UIE as standard, the correlation coefficients of 24-h UIEest from different periods of the day ranged from 0·409 to 0·531, and the relative average differences ranged from 4·4 % to 10·9 %. For diagnosis of insufficient iodine intake, the area under the ROC curve of 24-h UIEest was 0·754, sensitivity and specificity were 79·6 % and 65·4 %, respectively. For diagnosis of excessive iodine intake, the area of 24-h UIEest was 0·771, sensitivity and specificity were 66·7 % and 82·0 %, respectively. The reference interval of 24-h UIEest was 58·43–597·65 μg.

Twenty-four-hour UIEest can better indicate iodine nutritional status at a relatively large sample size in a given population of pregnant women. It can be used for early screening at the individual level to obtain more lead time for pregnant women.

The current study evaluated the associations between different forms and sources of Fe and breast cancer risk in Southern Chinese women.

Case–control study. We collected data on the consumption of Fe from different forms and food sources by using a validated FFQ. Multivariable logistic regression and restricted cubic spline (RCS) analysis was used to reveal potential associations between Fe intake and breast cancer risk.

A case-control study of women at three major hospitals in Guangzhou, China.

From June 2007 to March 2019, 1591 breast cancer cases and 1622 age-matched controls were recruited.

In quartile analyses, Fe from plants and Fe from white meat intake were inversely associated with breast cancer risk, with OR of 0·65 (95 % CI 0·47, 0·89, Ptrend = 0·006) and 0·76 (95 % CI 0·61, 0·96, Ptrend = 0·014), respectively, comparing the highest with the lowest quartile. No associations were observed between total dietary Fe, heme or non-heme Fe, Fe from meat or red meat and breast cancer risk. RCS analysis demonstrated J-shaped associations between total dietary Fe, non-heme Fe and breast cancer, and reverse L-shaped associations between heme Fe, Fe from meat and Fe from red meat and breast cancer.

Fe from plants and white meat were inversely associated with breast cancer risk. Significant non-linear J-shaped associations were found between total dietary Fe, non-heme Fe and breast cancer risk, and reverse L-shaped associations were found between heme Fe, Fe from meat or red meat and breast cancer risk.

Understanding the patterns of treatment response is critical for the treatment of patients with schizophrenia; one way to achieve this is through using a longitudinal dynamic process study design.

This study aims to explore the response trajectory of antipsychotics and compare the treatment responses of seven different antipsychotics over 6 weeks in patients with schizoprenia (trial registration: Chinese Clinical Trials Registry Identifier: ChiCTR-TRC-10000934).

Data were collected from a multicentre, randomised open-label clinical trial. Patients were evaluated with the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up at weeks 2, 4 and 6. Trajectory groups were classified by the method of k-means cluster modelling for longitudinal data. Trajectory analyses were also employed for the seven antipsychotic groups.

The early treatment response trajectories were classified into a high-trajectory group of better responders and a low-trajectory group of worse responders. The results of trajectory analysis showed differences compared with the classification method characterised by a 50% reduction in PANSS scores at week 6. A total of 349 patients were inconsistently grouped by the two methods, with a significant difference in the composition ratio of treatment response groups using these two methods (χ2 = 43.37, P < 0.001). There was no differential contribution of high- and low trajectories to different drugs (χ2 = 12.52, P = 0.051); olanzapine and risperidone, which had a larger proportion in the >50% reduction at week 6, performed better than aripiprazole, quetiapine, ziprasidone and perphenazine.

The trajectory analysis of treatment response to schizophrenia revealed two distinct trajectories. Comparing the treatment responses to different antipsychotics through longitudinal analysis may offer a new perspective for evaluating antipsychotics.

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.

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.

To evaluate the effects of gestational weight gain (GWG) in the first trimester (GWG-F) and the rate of gestational weight gain in the second trimester (RGWG-S) on gestational diabetes mellitus (GDM), exploring the optimal GWG ranges for the avoidance of GDM in Chinese women.

A population-based prospective study was conducted. Gestational weight was measured regularly in every antenatal visit and assessed by the Institute of Medicine (IOM) criteria (2009). GDM was assessed with the 75-g, 2-h oral glucose tolerance test at 24–28 weeks of gestation. Multivariable logistic regression was performed to assess the effects of GWG-F and RGWG-S on GDM, stratified by pre-pregnancy BMI. In each BMI category, the GWG values corresponding to the lowest prevalence of GDM were defined as the optimal GWG range.

Southwest China.

Pregnant women (n 1910) in 2017.

After adjusting for confounders, GWG-F above IOM recommendations increased the risk of GDM (OR; 95 % CI) among underweight (2·500; 1·106, 5·655), normal-weight (1·396; 1·023, 1·906) and overweight/obese women (3·017; 1·118, 8·138) compared with women within IOM recommendations. No significant difference was observed between RGWG-S and GDM (P > 0·05) after adjusting for GWG-F based on the previous model. The optimal GWG-F ranges for the avoidance of GDM were 0·8–1·2, 0·8–1·2 and 0·35–0·70 kg for underweight, normal-weight and overweight/obese women, respectively.

Excessive GWG in the first trimester, rather than the second trimester, is associated with increased risk of GDM regardless of pre-pregnancy BMI. Obstetricians should provide more pre-emptive guidance in achieving adequate GWG-F.