This study assesses the difference in professional attitudes among medical students, both before and after coronavirus disease 2019 (COVID-19), and identifies the determinants closely associated with it, while providing precise and scientific evidence for implementing precision education on such professional attitudes.

A pre-post-like study was conducted among medical students in 31 provinces in mainland China, from March 23, to April 19, 2021.

The proportion of medical students whose professional attitudes were disturbed after the COVID-19 pandemic, was significantly lower than before the COVID-19 pandemic (χ2 = 15.6216; P < 0.0001). Compared with the “undisturbed -undisturbed” group, the “undisturbed-disturbed” group showed that there was a 1.664-fold risk of professional attitudes disturbed as grade increased, 3.269-fold risk when others suggested they choose a medical career rather than their own desire, and 7.557-fold risk for students with COVID-19 in their family, relatives, or friends; while the “disturbed-undisturbed” group showed that students with internship experience for professional attitudes strengthened was 2.933-fold than those without internship experience.

The professional attitudes of medical students have been strengthened during the COVID-19 pandemic. The results provide evidence of the importance of education on professional attitudes among medical students during public health emergencies.

There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression.

In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis.

In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset.

These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of robust infection prevention and control (IPAC) practices to maintain patient and staff safety. However, healthcare workers (HCWs) face many barriers that affect their ability to follow these practices. We identified barriers affecting HCW adherence to IPAC practices during the pandemic in British Columbia, Canada.

Cross-sectional web-based survey.

Acute care, long-term care or assisted living, outpatient, mental health, prehospital care, and home care.

Eligible respondents included direct-care providers and IPAC professionals working in these settings in all health authorities across British Columbia.

We conducted a web-based survey from August to September 2021 to assess respondent knowledge and attitudes toward IPAC within the context of the COVID-19 pandemic. Respondents were asked to rate the extent to which various barriers affected their ability to follow IPAC practices throughout the pandemic and to make suggestions for improvement.

The final analysis included 2,488 responses; 36% of respondents worked in acute care. Overall, perceptions of IPAC practice among non-IPAC professionals were positive. The main self-perceived barriers to adherence included inadequate staffing to cover absences (58%), limited space in staff rooms (57%), multibed rooms (51%), and confusing messages about IPAC practices (51%). Common suggestions for improvement included receiving more support from IPAC leadership and clearer communication about required IPAC practices.

Our findings highlight frontline HCW perspectives regarding priority areas of improvement for IPAC practices. They will inform policy and guideline development to prevent transmission of COVID-19 and future emerging infections.

Evidence suggests a familial coaggregation of major psychiatric disorders, including schizophrenia, bipolar disorder, major depression (MDD), autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). Those disorders are further related to suicide and accidental death. However, whether death by suicide may coaggregate with accidental death and major psychiatric disorders within families remains unclear.

To clarify the familial coaggregation of deaths by suicide with accidental death and five major psychiatric disorders.

Using a database linked to the entire Taiwanese population, 68 214 first-degree relatives of individuals who died by suicide between 2003 and 2017 and 272 856 age- and gender-matched controls were assessed for the risks of death by suicide, accidental death and major psychiatric disorders.

A Poisson regression model showed that the first-degree relatives of individuals who died by suicide were more likely to die by suicide (relative risk RR = 4.61, 95% CI 4.02–5.29) or accident (RR = 1.62, 95% CI 1.43–1.84) or to be diagnosed with schizophrenia (RR = 1.53, 95% CI 1.40–1.66), bipolar disorder (RR = 1.99, 95% CI 1.83–2.16), MDD (RR = 1.98, 95% CI 1.89–2.08) or ADHD (RR = 1.34, 95% CI 1.24–1.44).

Our findings identified a familial coaggregation of death by suicide with accidental death, schizophrenia, major affective disorders and ADHD. Further studies would be required to elucidate the pathological mechanisms underlying this coaggregation.

Hospital-based biobanks are being increasingly considered as a resource for translating polygenic risk scores (PRS) into clinical practice. However, since these biobanks originate from patient populations, there is a possibility of bias in polygenic risk estimation due to overrepresentation of patients with higher frequency of healthcare interactions.

PRS for schizophrenia, bipolar disorder, and depression were calculated using summary statistics from the largest available genomic studies for a sample of 24 153 European ancestry participants in the Mass General Brigham (MGB) Biobank. To correct for selection bias, we fitted logistic regression models with inverse probability (IP) weights, which were estimated using 1839 sociodemographic, clinical, and healthcare utilization features extracted from electronic health records of 1 546 440 non-Hispanic White patients eligible to participate in the Biobank study at their first visit to the MGB-affiliated hospitals.

Case prevalence of bipolar disorder among participants in the top decile of bipolar disorder PRS was 10.0% (95% CI 8.8–11.2%) in the unweighted analysis but only 6.2% (5.0–7.5%) when selection bias was accounted for using IP weights. Similarly, case prevalence of depression among those in the top decile of depression PRS was reduced from 33.5% (31.7–35.4%) to 28.9% (25.8–31.9%) after IP weighting.

Non-random selection of participants into volunteer biobanks may induce clinically relevant selection bias that could impact implementation of PRS in research and clinical settings. As efforts to integrate PRS in medical practice expand, recognition and mitigation of these biases should be considered and may need to be optimized in a context-specific manner.

Mental disorders, including depression, obsessive compulsive disorder (OCD), and schizophrenia, share a common neuropathy of disturbed large-scale coordinated brain maturation. However, high-interindividual heterogeneity hinders the identification of shared and distinct patterns of brain network abnormalities across mental disorders. This study aimed to identify shared and distinct patterns of altered structural covariance across mental disorders.

Subject-level structural covariance aberrance in patients with mental disorders was investigated using individualized differential structural covariance network. This method inferred structural covariance aberrance at the individual level by measuring the degree of structural covariance in patients deviating from matched healthy controls (HCs). T1-weighted anatomical images of 513 participants (105, 98, 190 participants with depression, OCD and schizophrenia, respectively, and 130 age- and sex-matched HCs) were acquired and analyzed.

Patients with mental disorders exhibited notable heterogeneity in terms of altered edges, which were otherwise obscured by group-level analysis. The three disorders shared high difference variability in edges attached to the frontal network and the subcortical-cerebellum network, and they also exhibited disease-specific variability distributions. Despite notable variability, patients with the same disorder shared disease-specific groups of altered edges. Specifically, depression was characterized by altered edges attached to the subcortical-cerebellum network; OCD, by altered edges linking the subcortical-cerebellum and motor networks; and schizophrenia, by altered edges related to the frontal network.

These results have potential implications for understanding heterogeneity and facilitating personalized diagnosis and interventions for mental disorders.

Individuals with gender dysphoria display an incongruence between birth-assigned gender and gender expression. However, there is no existing Chinese measure for gender dysphoria.

This study aims to validate the Utrecht Gender Dysphoria Scale – Gender Spectrum (UGDS-GS) in a Chinese population, and compare the psychometric properties of the UGDS-GS with one frequently used scale for gender dysphoria measurement, the Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults (GIDYQ-AA).

A total of 2646 Chinese participants were recruited. The following information was collected: sociodemographic variables, gender identity, sexual orientation, gender dysphoria measured by the UGDS-GS and the GIDYQ-AA, anxiety, depression and suicide assessment. Principal component analyses and confirmatory factor analysis (CFA) were conducted to test the fitness of the model. Discriminant validity was tested with one-way analysis of variance.

The UGDS-GS showed good psychometric properties, with the GIDYQ-AA demonstrating slightly better psychometric properties than the UGDS-GS. UGDS-GS also showed strong internal consistency (Cronbach's α = 0.89), and good convergent validity and criterion validity. Exploratory factor analysis showed a one-factor structure (Kaiser-Meyer-Olkin test, 0.93; χ2 = 13 342.50; d.f. = 153; P < 0.001). The UGDS-GS was positively associated with anxiety symptoms, depressive symptoms, suicidal ideation, attempted suicide and self-harm. We also found the results were robust in different samples.

The validated UGDS-GS can significantly stimulate and promote gender dysphoria assessment in Chinese populations, allowing for assessment in a more diverse subset of gender minorities.