This study aimed to investigate the individual characteristics of intolerance of uncertainty (IU) and its association with mental health symptoms among Chinese college students during COVID-19.

In total, 86,767 students completed the online survey in Guangdong province in June 2021. Data collected including socio-demographic and COVID-19-related information, IU, and mental health symptoms (depression, anxiety, insomnia, and suicidal ideation). Latent profile analysis was used to classify IU subgroups. Logistic regression was used to identify IU risk factors.

Four IU subgroups were identified, named low IU (n = 9,197, 10.6%), medium-low IU (n = 25,514, 29.4%), medium-high IU (n = 38,805, 44.7%), and high IU (n = 13,251, 15.3%). Scores of mental health symptoms varied from the degree of IU in the latent profiles. Mental health status was the worst in the high IU group. In addition, females, freshmen, and those perceiving more impacts from COVID-19 and spending longer time surfing COVID-19 information online were at risk of high IU.

Our findings showed that individuals differ in the total degree of intolerance of the uncertainties. Students with high IU were associated with worse mental health symptoms. Thus, taking actions to target individuals with high IU and developing their adaptive coping strategies are imperative during pandemics.

Late-onset depression (LOD) is featured by disrupted cognitive performance, which is refractory to conventional treatments and increases the risk of dementia. Aberrant functional connectivity among various brain regions has been reported in LOD, but their abnormal patterns of functional network connectivity remain unclear in LOD.

A total of 82 LOD and 101 healthy older adults (HOA) accepted functional magnetic resonance imaging scanning and a battery of neuropsychological tests. Static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) were analyzed using independent component analysis, with dFNC assessed via a sliding window approach. Both sFNC and dFNC contributions were classified using a support vector machine.

LOD exhibited decreased sFNC among the default mode network (DMN), salience network (SN), sensorimotor network (SMN), and language network (LAN), along with reduced dFNC of DMN-SN and SN-SMN. The sFNC of SMN-LAN and dFNC of DMN-SN contributed the most in differentiating LOD and HOA by support vector machine. Additionally, abnormal sFNC of DMN-SN and DMN-SMN both correlated with working memory, with DMN-SMN mediating the relationship between depression and working memory. The dFNC of SN-SMN was associated with depressive severity and multiple domains of cognition, and mediated the impact of depression on memory and semantic function.

This study displayed the abnormal connectivity among DMN, SN, and SMN that involved the relationship between depression and cognition in LOD, which might reveal mutual biomarkers between depression and cognitive decline in LOD.

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.

Psychomotor disturbance (PmD) is prevalent in major depressive disorder (MDD), with neural substrates implicated in disrupted motor circuits and the interaction to non-motor cortex. Our objective is to explore the functional connectivity pattern underlying PmD using functional magnetic resonance imaging (fMRI).

A total of 150 patients with MDD and 91 healthy controls (HCs) were included in this study. The patients were categorized into psychomotor (pMDD, n = 107) and non-psychomotor (npMDD, n = 43) groups based on the Hamilton Depression Rating Scale. Seed-based connectivity (SBC) analysis was conducted using predefined somatomotor and cerebellar network (SMN and CN) coordinates as seeds, to assess group differences and symptom correlations. Subsequently, we correlated the group-contrast SBC map with existing neurotransmitter maps to explore the neurochemical basis.

In pMDD patients compared to HC, we observed decreased connectivity, especially between the SMN and frontal cortex, within the bilateral SMN, and between the CN and right precentral cortex. Meanwhile, connectivity increased between the SMN and the middle cingulate cortex and between the CN and left precentral cortex in pMDD relative to npMDD and HC. Connectivity between the SMN and angular gyrus was positively correlated with the severity of PmD. Additionally, the aberrant SBC patterns in pMDD were linked to the distribution of dopamine D1 and D2 receptors.

This study provides insights into the aberrant connectivity within the motor circuits and its interactions with non-motor regions in PmD. It also suggests a potential role for dopaminergic dysregulation in the connectivity abnormalities associated with PmD.

Little is known regarding the shared genetic architecture underlying the phenotypic associations between depression and preterm birth (PTB). We aim to investigate the genetic overlap and causality of depression with PTB.

Leveraging summary statistics from the largest genome-wide association studies for broad depression (Ntotal = 807,533), major depression (Ntotal = 173,005), bipolar disorder (Ntotal = 414,466), and PTB (Ntotal = 226,330), we conducted a large-scale genome-wide cross-trait analysis to assess global and local genetic correlations, identify pleiotropic loci, and infer potential causal relationships

Positive genetic correlations were observed between PTB and broad depression (rg = 0.242), major depression (rg = 0.236), and bipolar disorder (rg = 0.133) using the linkage disequilibrium score regression, which were further verified by the genetic covariance analyzer. Local genetic correlation was identified at chromosome 11q22.3 (harbors NCAM1-TTC12-ANKK1-DRD2) for PTB with depression. Cross-trait meta-analysis identified two loci shared between PTB and broad depression, two loci shared with major depression, and five loci shared with bipolar disorder, among which three were novel (rs7813444, rs3132948 and rs9273363). Mendelian randomization demonstrated a significantly increased risk of PTB for genetic liability to broad depression (odds ratio [OR]=1.30; 95% confidence interval [CI]: 1.11-1.52) and major depression (OR=1.27; 95%CI: 1.08-1.49), and the estimates remained significant across the sensitivity analyses.

Our findings demonstrate an intrinsic link underlying depression and PTB and shed novel light on the biological mechanisms, highlighting an important role of early screening and effective intervention of depression in PTB prevention, and may provide novel treatment strategies for both diseases.

Remote injury assessment during natural disasters poses major challenges for healthcare providers due to the inaccessibility of disaster sites. This study aimed to explore the feasibility of using artificial intelligence (AI) techniques for rapid assessment of traumatic injuries based on gait analysis.

We conducted an AI-based investigation using a dataset of 4500 gait images across 3 species: humans, dogs, and rabbits. Each image was categorized as either normal or limping. A deep learning model, YOLOv5—a state-of-the-art object detection algorithm—was trained to identify and classify limping gait patterns from normal ones. Model performance was evaluated through repeated experiments and statistical validation.

The YOLOv5 model demonstrated high accuracy in distinguishing between normal and limp gaits across species. Quantitative performance metrics confirmed the model’s reliability, and qualitative case studies highlighted its potential application in remote, fast traumatic assessment scenarios.

The use of AI, particularly deep convolutional neural networks like YOLOv5, shows promise in enabling fast, remote traumatic injury assessment during disaster response. This approach could assist healthcare professionals in identifying injury risks when physical access to patients is restricted, thereby improving triage efficiency and early intervention.

Perimenopausal women often experience physiological and psychological decline due to the effects of oestrogen fluctuations and the decline of ovarian function, leading to significantly increased depression rates, decreases in the quality of life and mental health issues. Studies have shown that the gut microbiota exerts anti-perimenopausal depression (PMD) effects via the microbiota-gut-brain (MGB) axis, the mechanisms of which may be related to inflammation. In this review, we discuss the effects and mechanisms of gut microbiota in PMD and provide new insights for future PMD treatment.

This review elaborates on the role of MGB axis in PMD from different aspects of inflammation, including gut microbiota metabolites, inflammatory signaling pathways, and clinical applications.

Disorders of gut microbiota and decreased levels of gut microbiota metabolites (short-chain fatty acids, monoamine neurotransmitters) may cause PMD. The mechanism of intestinal microbiota-mediated inflammation may be related to TLR4/NF-κB pathway, NOD-like receptor protein 3 (NLRP3) inflammasome pathway and JAK-STAT pathway. At the same time, it was found that gut microbiota (probiotics, prebiotics, etc.) had good therapeutic potential in the treatment of PMD.

MGB axis mediated inflammation may play an important role in PMD. The application of gut microbiota in the treatment of PMD patients has profound clinical transformation value, but a lot of efforts are still needed.

Schizophrenia patients with auditory hallucinations have distinct morphological abnormalities, but whether this population have a progressive gray matter atrophy pattern and specific transmission chain of causal effects remains unclear. This study was designed to construct a causal structural covariance network in schizophrenia patients with persistent auditory hallucinations.

T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group) and 83 healthy controls (HC group). Stage-specific independent t tests of gray matter volume (GMV) comparisons between the two groups were used to depict the GMV atrophic pattern and locate the atrophic origin. In the pAH group, the causal structural covariance network (CaSCN) was constructed to map causal effects between the atrophic origin and other regions as the auditory hallucination severity increased.

With the ascending of hallucinatory severity, GMV reductions began from the thalamus, bilateral medial frontal gyri, left Rolandic operculum, and left calcarine, and expanded to other frontal and temporal regions, hippocampal complex, insula, anterior cingulate gyri, fusiform, and cerebellum. Using the peak region (thalamus) as the causal origin in the network, transitional nodes including the right opercular part of the inferior frontal gyrus, bilateral postcentral gyri, left thalamus, and right middle frontal gyrus received the casual information and projected to target nodes from the frontal, temporal, parietal, and occipital cortices, limbic system, and cerebellum.

Our study revealed causal effects from the thalamus and a specific transmission pattern of causal information within the network, indicating a thalamic–cortical–cerebellar circuitry dysfunction related to auditory hallucinations.