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.

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 update the incidence of device-associated healthcare-associated infections (DA-HAIs), and to characterize pathogen distribution and carbapenem-resistant Enterobacteriaceae (CRE) detection among ICU patients in Shanghai, China.

Prospective surveillance in 223 ICUs using standardized International Nosocomial Infection Control Consortium methodology (INICC) protocols collected patient-level data on demographics, microbiology, device use, and DA-HAIs. Trends, annual percent change (APC) and average annual percent change (AAPC) were estimated using Joinpoint regression models.

The overall DA-HAIs incidence density in ICUs was 1.67 per 1000 catheter-days for catheter-associated urinary tract infection (CAUTI) (95% CI: 1.62–1.73), 0.59 per 1000 central line-days for central line-associated bloodstream infection (CLABSI) (95% CI: 0.56–0.63), and 4.63 per 1000 ventilator-days for ventilator-associated pneumonia (VAP) (95% CI: 4.51–4.76). Significant reductions were observed in VAP (AAPC: −15.36%; P < 0.001) and CLABSI (AAPC: −11.23%; P < 0.001). Pathogen distributions varied by infection type, with Enterococcus faecium (17.22%) and Klebsiella pneumoniae (16.63%) predominating in CAUTI patients, Klebsiella pneumoniae (26.87%) in CLABSI patients, and Acinetobacter baumannii (37.60%) in VAP patients. The overall CRE detection rate was 33.67% in CAUTI patients, 37.56% in CLABSI patients, and 35.24% in VAP patients.

Although DA-HAI rates showed significant declines, the persistently high CRE prevalence underscores substantial antimicrobial resistance challenges in Chinese ICUs.

Temporal arteritis (TA) is the most common vasculitis over age 50. Untreated, many patients will suffer blindness or stroke. Gold standard diagnosis is achieved by temporal artery biopsy. The aim of this study was to investigate the relevance of small vessel inflammation.

Our dataset was comprised of 72 temporal artery biopsies subjected to a blinded uniform re-examination paired with clinical data including demographics, history, physical examination and laboratory findings. Documented pathology variables included the presence or absence of TA, angiitis of vasa vasorum (AVV) and inflammation of small peri-adventitial vessels (small vessel vasculitis, SVV).

Clinical and pathological variables were subjected to multivariate analysis. In brief, 25% of cases were identified as TA, 20% as isolated AVV, 7% as isolated SVV and 5% as mixed isolated AVV/SVV, while 43% had no inflammation (NI). All cases of TA were accompanied by small vessel inflammation: 95% exhibited AVV with or without SVV, and 5% exhibited SVV alone, demonstrating a strong association between TA and small vessel inflammation. Of the 24 cases with isolated AVV/SVV, 26% received a clinical diagnosis of TA within one year in comparison to 13% of cases that had NI. Furthermore, isolated AVV/SVV was identified in 25% of patients with a high clinical probability for TA, 60% of whom acquired a diagnosis of TA on clinical grounds within one year of follow-up.

Our findings suggest that isolated AVV/SVV identifies a subgroup of patients with a higher risk of harboring or developing TA.

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.

Anhedonia, a transdiagnostic feature common to both Major Depressive Disorder (MDD) and Schizophrenia (SCZ), is characterized by abnormalities in hedonic experience. Previous studies have used machine learning (ML) algorithms without focusing on disorder-specific characteristics to independently classify SCZ and MDD. This study aimed to classify MDD and SCZ using ML models that integrate components of hedonic processing.

We recruited 99 patients with MDD, 100 patients with SCZ, and 113 healthy controls (HC) from four sites. The patient groups were allocated to distinct training and testing datasets. All participants completed a modified Monetary Incentive Delay (MID) task, which yielded features categorized into five hedonic components, two reward consequences, and three reward magnitudes. We employed a stacking ensemble model with SHapley Additive exPlanations (SHAP) values to identify key features distinguishing MDD, SCZ, and HC across binary and multi-class classifications.

The stacking model demonstrated high classification accuracy, with Area Under the Curve (AUC) values of 96.08% (MDD versus HC) and 91.77% (SCZ versus HC) in the main dataset. However, the MDD versus SCZ classification had an AUC of 57.75%. The motivation reward component, loss reward consequence, and high reward magnitude were the most influential features within respective categories for distinguishing both MDD and SCZ from HC (p < 0.001). A refined model using only the top eight features maintained robust performance, achieving AUCs of 96.06% (MDD versus HC) and 95.18% (SCZ versus HC).

The stacking model effectively classified SCZ and MDD from HC, contributing to understanding transdiagnostic mechanisms of anhedonia.

Existing evidence on the association between combined lifestyle and depressive symptoms is limited to the general population and is lacking in individuals with subthreshold depression, a high-risk group for depressive disorders. Furthermore, it remains unclear whether an overall healthy lifestyle can mitigate the association between childhood trauma (CT) and depressive symptoms, even in the general population. We aimed to explore the associations of combined lifestyle, and its interaction with CT, with depressive symptoms and their subtypes (i.e. cognitive-affective and somatic symptoms) among adults with subthreshold depression.

This dynamic cohort was initiated in Shenzhen, China in 2019, including adults aged 18–65 years with the Patient Health Questionnaire-9 (PHQ-9) score of ≥ 5 but not diagnosed with depressive disorders at baseline. CT (present or absent) was assessed with the Childhood Trauma Questionnaire-Short Form. Combined lifestyle, including no current drinking, no current smoking, regular physical exercise, optimal sleep duration and no obesity, was categorized into 0–2, 3 and 4–5 healthy lifestyles. Depressive symptoms were assessed using the PHQ-9 during follow-up. This cohort was followed every 6 months, and as of March 2023, had been followed for 3.5 years.

This study included 2298 participants (mean [SD] age, 40.3 [11.1] years; 37.7% male). After fully adjusting for confounders, compared with 0–2 healthy lifestyles, 3 (β coefficient, −0.619 [95% CI, −0.943, −0.294]) and 4–5 (β coefficient, −0.986 [95% CI, −1.302, −0.671]) healthy lifestyles were associated with milder depressive symptoms during follow-up. There exists a significant synergistic interaction between a healthy lifestyle and the absence of CT. The CT-stratified analysis showed that compared with 0–2 healthy lifestyles, 3 healthy lifestyles were associated with milder depressive symptoms in participants with CT, but not in those without CT, and 4–5 healthy lifestyles were associated with milder depressive symptoms in both participants with and without CT, with a stronger association in those with CT. The lifestyle-stratified analysis showed that CT was associated with more severe depressive symptoms in participants with 0–2 healthy lifestyles, but not in those with 3 or 4–5 healthy lifestyles. Cognitive-affective and somatic symptoms showed similar results.

In this 3.5-year longitudinal study of adults with subthreshold depression, an overall healthy lifestyle was associated with subsequent milder depressive symptoms and their subtypes, with a stronger association in adults with CT than those without CT. Moreover, an overall healthy lifestyle mitigated the association of CT with depressive symptoms and their subtypes.