The mental health risk factors for primary healthcare workers (PHWs) following the Coronavirus Disease 2019 pandemic and the differences by urbanicity remain unclear. In this study, we aimed to identify key factors of anxiety and depression among PHWs in urban and rural settings in China.

This cross-sectional study was conducted in all 31 provinces in mainland China, between 1 May and 31 October 2022. A total of 3,769 PHWs, including family physicians, nurses, public health professionals, pharmacists, and other medical staff, were recruited from 44 urban community health service centers and 27 rural township hospitals. The Bayesian Additive Regression Tree model was employed to identify risk factors of anxiety and depression.

Among 3,769 PHWs, 1,006 (26.7%) worked in urban areas and 2,763 (73.3%) in rural areas. Occupational satisfaction significantly influenced anxiety in both urban and rural practitioners. For urban PHWs, living with family (odds ratio (OR): 0.42, 95% confidence interval (CI): 0.28–0.62) and self-rated health (fair: OR: 0.31, 95% CI: 0.23–0.42; good: OR: 0.13, 95% CI: 0.09–0.20) were key factors of anxiety. For rural PHWs, after-work exercise (rarely: OR: 0.28, 95% CI: 0.11–0.76; frequently: OR: 0.15, 95% CI: 0.05–0.44) played a critical role. Depression was associated with after-work exercise, self-rated health, and occupational satisfaction for all PHWs. Additionally, living with family (OR: 0.51, 95% CI: 0.34–0.75) and organizational support satisfaction (satisfied: OR: 0.28, 95% CI: 0.19–0.42) were significant for urban practitioners.

Risk factors such as occupational satisfaction, health, and family relations significantly influence PHW mental health in China, with notable differences by urbanicity. Tailored mental health interventions are recommended to address urban–rural disparities.

Growing studies have reported an elevated risk of violence in patients with depression, yet the neurobiological underpinnings remain poorly understood. The present study explored the resting-state electroencephalogram (EEG) features in major depressive disorder (MDD) patients with violent offenses to identify potential neurological markers for violence prediction and intervention.

Twenty-nine MDD patients who committed violent offenses (violent depression [VD] group), 27 MDD patients without violent behaviors (nonviolent depression [NVD] group), and 25 healthy controls (HCs) were included. Resting-state EEGs were recorded for at least 5 min. EEG microstates, functional connectivity (FC), and graph theory metrics were analyzed and compared between groups.

First, the VD group had increased microstate A, more microstates A-B transition, but lower microstates B-D and C-D transition. Second, the VD group exhibited two enhanced functional brain networks compared to NVD and HCs, and three weakened functional brain networks compared to HCs, which were primarily distributed in the frontal and frontoparietal networks. Third, the VD group specifically exhibited reduced nodal efficiency (aNe) in the superior parietal lobe and increased aNe in the middle occipital gyrus.

MDD patients with violent offenses exhibited alterations in EEG microstates, FCs in the frontal lobe and frontoparietal network, and disrupted aNe in specific parietal and occipital lobes. These alternations are closely associated with deficits in emotional regulation, executive function, and inhibitory control, which may subserve as potential neurobiomarkers for violence risk assessment in patients with depression.

This study aims to systematically identify and summarize the key characteristics of Mini-Health Technology Assessment (Mini-HTA) and assess the completeness of its basic reporting information, providing a theoretical foundation for developing future reporting guidelines..

A comprehensive search for Mini-HTAs was performed using CNKI, Wanfang Data, VIP, CBM, PubMed, Embase, Web of Science, HTA database, and major HTA-related websites from inception until February 2024. The completeness of basic information reporting in Mini-HTAs was assessed using the INAHTA checklist. The key characteristics of the included Mini-HTAs were summarized descriptively. Microsoft Excel 2019 was used to analyze and visually present the data.

A total of 21 Mini-HTA reports were included, with the highest number published in 2021 (5 reports, 23.8 percent). China contributed the most reports (18 reports, 85.7 percent). The most common assessment purpose was technology comparison (15 reports, 71.4 percent), with general hospitals being the predominant assessment setting (17 reports, 80.9 percent), and drugs being the most frequently assessed technology type (14 reports, 66.7 percent). The INAHTA checklist evaluation identified notable deficiencies in reporting key methodological aspects, including participant roles, conflict of interest statements, data sources, literature search strategies, and methods for data assessment and analysis.

Mini-HTAs have significantly increased in China since 2020, mainly in technology comparison, drug evaluation, and general hospitals. However, gaps remain in reporting key aspects, such as participant roles, conflict of interest, and data sources. Future efforts should focus on refining reporting guidelines to improve consistency and address these reporting deficiencies in Mini-HTA.

Although crisis events have become increasingly frequent in recent years, few studies have examined the changes in employees’ work productivity across different stages of a crisis. To advance theory and research on crisis, we investigated the temporal patterns of employees’ work productivity before, during, and after a crisis event. Drawing on the Conservation of Resources Theory, we proposed that employees’ work productivity undergoes a substantial decline during a crisis, which will gradually slow down over time. We further examined the moderating roles of leader–member communication frequency and organizational tenure, positing these factors as critical in shaping productivity trajectories during crisis adaptation. We analyzed data from 342 team members and 69 team leaders within a high-tech off-campus tutoring company, and our findings substantiated the hypothesized productivity change patterns and boundary conditions. To complement the quantitative analysis, we conducted a qualitative study to unveil the underlying psychological mechanisms driving these changes. Our research contributes to the crisis management literature and offers insights into managing employee productivity during times of crisis.

With the increased prevalence of major depressive episodes with mixed features specifier (MDE-MFS), the pharmacological treatment for MDE-MFS has attracted great clinical attention. This study aimed to investigate the efficacy and safety of medication use for MDE-MFS.

Commonly used databases were searched for the meta-analysis. Primary efficacy outcomes included response rate and the change in the Young Mania Rating Scale scores; the primary safety outcome was the rate of treatment-emergent hypomania/mania. Effects were expressed as relative risk (RR) or standardized mean difference (SMD).

In patients with MDE-MFS, antipsychotics significantly improved depressive (RR = 1.46 [95% CI: 1.31, 1.61]) and manic (SMD = −0.35 [95% CI: −0.53, −0.17]) symptoms without increasing the risk of manic switch (RR = 0.91 [95% CI: 0.53, 1.55]). However, subgroup analysis of bipolar disorder (BD) patients with MDE-MFS indicated that antipsychotics had limited effects on manic symptoms. Mood stabilizers, especially valproate, demonstrated significant effects in BD patients with MDE-MFS by relieving depressive and manic symptoms. For MDE-MFS in patients with major depressive disorder, trazodone has shown potential effectiveness in retrospective studies, while the effectiveness of antidepressants on BD patients with MDE-MFS lacked evidence.

While antipsychotics are first options for MDE-MFS, their effect on manic symptoms in BD patients with MDE-MFS is still unclear. Mood stabilizers may also be considered, and the use of antidepressants remains a topic of controversy. Since our findings are mostly based on post-hoc analyses, the evidence remains preliminary, highlighting the need for further research to produce more conclusive evidence.

Antimicrobial resistance (AMR) is a global health crisis exacerbated by policies like China’s Volume-Based Procurement (VBP), which may inadvertently increase antimicrobial overuse. This study evaluates a clinical pharmacist-led Antimicrobial Stewardship (AMS) program with prospective audit for special-restricted antimicrobials under VBP.

A retrospective quasi-experimental interrupted time-series analysis compared pre-intervention (2022) and post-intervention (2023–2024) data at Tongji Hospital, a tertiary hospital in Wuhan, China. Key metrics included Antimicrobial Use Density (AUD), prescription rationality, antimicrobial costs, and multidrug-resistant infection rates.

The intervention significantly improved prescription appropriateness for special-restricted antimicrobials (80.24% vs. 93.83%, P < 0.005) and reduced AUD (47.87 vs. 34.25, P < 0.001). Total antimicrobial costs decreased by 41.26%, with a reduction in the incidence of multidrug-resistant infections from 0.084% to 0.062% (P < 0.05). Carbapenem use correlated with CRKP isolation rates (R = 0.62, P < 0.05). Clinical pharmacists rejected 10.24% of prescriptions, all accepted by physicians.

Pharmacist-led prospective audits optimize antimicrobial use under VBP, mitigate resistance risks, and reduce costs, while acknowledging that concurrent infection control measures may have contributed to these trends. This model may inform similar interventions in other institutions, particularly those in resource-limited settings.

This study aimed to explore clinical characteristics and treatment efficacy in patients with posterior canal benign paroxysmal positional vertigo and different sleep qualities.

Patients with posterior canal benign paroxysmal positional vertigo were divided into high and low sleep quality groups based on Pittsburgh Sleep Quality Index scores.

No significant baseline differences existed between low (n = 53) and high (n = 39) sleep quality groups. However, the proportion of cupulolithiasis was higher in the low sleep quality group (60.38 per cent vs. 35.90 per cent; p < 0.05). Additionally, the low sleep quality group had a longer median duration of upbeat nystagmus during the Dix-Hallpike test (63.50 seconds vs. 26.80 seconds; p < 0.05) and a lower cured rate in initial repositioning (9.43 per cent vs. 56.41 per cent) compared to high sleep quality group. Repositioning therapy significantly improved depressive and anxiety symptoms in all patients with posterior canal benign paroxysmal positional vertigo, with a more pronounced improvement in depressive symptoms in the low sleep quality group.

Poor sleep quality is associated with higher cupulolithiasis prevalence and treatment resistance, with residual symptoms mainly affecting social functioning.

MicroRNAs (miRNAs) alterations in patients with bipolar disorder (BD) are pivotal to the disease’s pathogenesis. Since obtaining brain tissue is challenging, most research has shifted to analyzing miRNAs in peripheral blood. One innovative solution is sequencing miRNAs in plasma extracellular vesicles (EVs), particularly those neural-derived EVs emanating from the brain.

We isolated plasma neural-derived EVs from 85 patients with BD and 39 healthy controls (HC) using biotinylated antibodies targeting a neural tissue marker, followed by miRNA sequencing and expression analysis. Furthermore, we conducted bioinformatic analyses and functional experiments to delve deeper into the underlying pathological mechanisms of BD.

Out of the 2,656 neural-derived miRNAs in EVs identified, 14 were differentially expressed between BD patients and HC. Moreover, the target genes of miR-143-3p displayed distinct expression patterns in the prefrontal cortex of BD patients versus HC, as sourced from the PsychENCODE database. The functional experiments demonstrated that the abnormal expression of miR-143-3p promoted the proliferation and activation of microglia and upregulated the expression of proinflammatory factors, including IL-1β, IL-6, and NLRP3. Through weighted gene co-expression network analysis, a module linking to the clinical symptoms of BD patients was discerned. Enrichment analyses unveiled these miRNAs’ role in modulating the axon guidance, the Ras signaling pathway, and ErbB signaling pathway.

Our findings provide the first evidence of dysregulated plasma miRNAs within neural-derived EVs in BD patients and suggest that neural-derived EVs might be involved in the pathophysiology of BD through related biological pathways, such as neurogenesis and neuroinflammation.

Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network.

We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit.

The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility.

This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.

Social determinants of health (SDHs) exert a significant influence on various health outcomes and disparities. This study aimed to explore the associations between combined SDHs and mortality, as well as adverse health outcomes among adults with depression.

The research included 48,897 participants with depression from the UK Biobank and 7,771 from the US National Health and Nutrition Examination Survey (NHANES). By calculating combined SDH scores based on 14 SDHs in the UK Biobank and 9 in the US NHANES, participants were categorized into favourable, medium and unfavourable SDH groups through tertiles. Cox regression models were used to evaluate the impact of combined SDHs on mortality (all-cause, cardiovascular disease [CVD] and cancer) in both cohorts, as well as incidences of CVD, cancer and dementia in the UK Biobank.

In the fully adjusted models, compared to the favourable SDH group, the hazard ratios for all-cause mortality were 1.81 (95% CI: 1.60–2.04) in the unfavourable SDH group in the UK Biobank cohort; 1.61 (95% CI: 1.31–1.98) in the medium SDH group and 2.19 (95% CI: 1.78–2.68) in the unfavourable SDH group in the US NHANES cohort. Moreover, higher levels of unfavourable SDHs were associated with increased mortality risk from CVD and cancer. Regarding disease incidence, they were significantly linked to higher incidences of CVD and dementia but not cancer in the UK Biobank.

Combined unfavourable SDHs were associated with elevated risks of mortality and adverse health outcomes among adults with depression, which suggested that assessing the combined impact of SDHs could serve as a key strategy in preventing and managing depression, ultimately helping to reduce the burden of disease.

A retrospective analysis of paediatric infective endocarditis characterised causative pathogens, antimicrobial susceptibility patterns, and treatment outcomes to guide clinical decision-making.

The data of patients who received infective endocarditis between 2016 and 2023 were retrospectively collected from the medical records database. The clinical characteristics, treatment plans, and pharmaceutical monitoring characteristics were analysed and summarised.

A total of 12 paediatric infective endocarditis cases were identified. Bacterial isolates included 27 Gram-positive and 1 Gram-negative strains. The most common pathogen was Staphylococcus aureus (n = 13), all methicillin-resistant Staphylococcus aureus (MRSA), followed by Abiotrophia defectiva (n = 6), Streptococcus mitis (n = 5), Streptococcus sanguinis (n = 2), Bacillus cereus (n = 1), and Klebsiella oxytoca (n = 1). Antimicrobial therapy primarily involved linezolid, vancomycin, and cephalosporin/enzyme inhibitor combinations. Cardiac glycosides were used in 10 cases, and all patients received phosphocreatine to support myocardial energy metabolism. Therapeutic drug monitoring for vancomycin was performed in 25% of cases, while no therapeutic drug monitoring was conducted for meropenem or linezolid.

All the causative organisms were predominantly Gram-positive cocci, with MRSA accounting for the largest proportion; different streptococci varied considerably in terms of drug resistance. The antimicrobial drugs used were predominantly linezolid and glycopeptides. The rate of blood concentration monitoring was low.