Cognitive and behavioral symptoms of major depressive disorder (MDD) are linked to aberrant changes in the controllability of brain networks. However, previous studies examined network controllability using white matter tractography, neglecting the contributions of gray matter. We aimed to examine differences in the controllability of morphometric networks between patients with MDD and demographic-matched healthy controls and identify the associated neurobiological signatures.

Based on the structural and diffusion MRI data from two independent cohorts, we calculated the controllability of morphometric similarity networks for each participant. A generalized additive model was used to investigate the case–control differences in regional controllability and their cognitive and behavioral associations. We investigated the associations between imaging-derived controllability and neurotransmitters, brain metabolism, and gene transcription profiles using multivariate linear regression and partial least squares regression analyses.

In both cohorts, depression-related abnormalities of morphometric network controllability were primarily located in the prefrontal, cingulate, and visual cortices, contributing to memory, sensation, and perception processes. These abnormalities in network controllability were spatially aligned with the distributions of serotonergic transmission pathways as well as with altered oxygen and glucose metabolism. In addition, these abnormalities spatially overlapped with differentially expressed genes enriched in annotations related to protein catabolism and mitochondria in neuronal cells and were disproportionately located on chromosome 22.

Collectively, neuroimaging evidence revealed aberrant morphometric network controllability underlying MDD-related cognitive and behavioral deficits, and the associated genetic and molecular signatures may help identify the neurobiological mechanisms underlying MDD and provide feasible therapeutic targets.

This study aimed to determine the optimal Biological Effective Dose (BED)-based compensation strategy for treatment interruptions in left-sided breast cancer radiotherapy, with a focus on evaluating cardiac substructures to address a previously unmet clinical need.

Twenty patients with left-sided breast cancer who had received radiotherapy were retrospectively enrolled.

Simulations assumed treatment interruptions (number of interruption days) occurred after the first week, ranging from 1 to 10 days. Three BED-based compensation strategies were evaluated: (A) maintaining total fractions and days while delivering twice-daily treatments; (B) maintaining total days while increasing the dose per fraction; and (C) keeping the dose per fraction constant while extending the overall treatment course. Original uninterrupted plans served as the baseline. BEDs for the planning target volume (PTV), simultaneous integrated boost (SIB), cardiac substructures and other organs at risk (OARs) were calculated. Physical and BED differences among the schemes were systematically compared.

Compared to the original scheme, physical doses to PTV and SIB were lower in Scheme B but higher in Scheme C. As interruptions increased from 1 to 10 days, PTV and SIB doses in Scheme B decreased to minimum values of 42.71 Gy and 50.58 Gy, respectively, while Scheme C resulted in maximum values of 58.60 Gy and 67.15 Gy. Analysis of BED changes (ΔBED) in OARs revealed that the left anterior descending artery (LAD) was the most affected cardiac substructure, with ΔBED values of 0.41, –1.20 and 0.60 for Schemes A, B and C, respectively, at 10 interruption days. Among other OARs, the left lung showed the highest ΔBED changes (0.39, –0.30 and 0.32, respectively). Most OAR comparisons reached statistical significance (ANOVA, p < 0.05).

Compensation strategies for radiotherapy interruptions significantly influence the BED of OARs, particularly in the LAD and left lung. Scheme B most effectively reduced the BED of OARs but requires replanning. Schemes A and C offer clinical convenience at the cost of a higher BED of OARs. The choice of compensation strategy should be individualised based on clinical priorities and patient-specific anatomy.

The treatment response for the negative symptoms of schizophrenia is not ideal, and the efficacy of antidepressant treatment remains a matter of considerable controversy. This systematic review and meta-analysis aimed to assess the efficacy of adjunctive antidepressant treatment for negative symptoms of schizophrenia under strict inclusion criteria.

A systematic literature search (PubMed/Web of Science) was conducted to identify randomized, double-blind, effect-focused trials comparing adjuvant antidepressants with placebo for the treatment of negative symptoms of schizophrenia from database establishment to April 16, 2025. Negative symptoms were examined as the primary outcome. Data were extracted from published research reports, and the overall effect size was calculated using standardized mean differences (SMD).

A total of 15 articles, involving 655 patients, were included in this review. Mirtazapine (N = 2, n = 48, SMD −1.73, CI −2.60, −0.87) and duloxetine (N = 1, n = 64, SMD −1.19, CI −2.17, −0.21) showed significantly better efficacy for negative symptoms compared to placebo. In direct comparisons between antidepressants, mirtazapine showed significant differences compared to reboxetine, escitalopram, and bupropion, but there were no significant differences between other antidepressants or between antidepressants and placebo. No publication bias for the prevalence of this condition was observed.

These findings suggest that adjunctive use of mirtazapine and duloxetine can effectively improve the negative symptoms of schizophrenia in patients who are stably receiving antipsychotic treatment. Therefore, incorporating antidepressants into future treatment plans for negative symptoms of schizophrenia is a promising strategy that warrants further exploration.

Major depressive disorder (MDD) patients exhibit a mood-congruent emotional processing bias within the amygdala toward negative facial stimuli at both unconscious and conscious levels. Therefore, our study aimed to investigate the temporal and spatial dynamics of amygdala along with its interactions with the whole brain during implicit and explicit conditions in MDD.

Thirty MDD patients and 26 healthy controls (HCs) underwent magnetoencephalography (MEG) recordings and performed implicit and explicit emotional face recognition tasks with happy, sad, and neutral facial expressions. Using the amygdala as a seed region, time frequency representations (TFR) and functional connectivity (FC) were calculated. Pearson correlation analyses measured the relationship between TFR and FC values with clinical symptoms.

During implicit processing, MDD patients exhibited left amygdala activation in the gamma power (60–70 Hz) before 250 ms in response to sad facial stimuli compared to HCs. In the implicit mode, there were increased FC between the right amygdala and several brain regions in the occipitoparietal lobes, as well as higher FC between the left amygdala and putamen in MDD patients. Additionally, the right amygdala was positively correlated with the severity of depression and anxiety during implicit processing.

MDD patients had lateralized amygdala activation in response to sad facial expressions during unconscious emotional recognition of facial stimuli. Our study provided valuable insights into the spatiotemporal dynamics of facial emotional recognition associated with depressive and anxiety-related cognitive bias during implicit and explicit processing.

The COVID-19 pandemic exacerbated psychological distress, but limited information is available on the shifts in mental health symptoms and their associated factors across different stages. This study was conducted to more reliably estimate shifts in mental health impacts and to identify factors associated with symptoms at different pandemic stages.

We performed a national repeated cross-sectional study at stable (2021), recurrence (2022), and end-of-emergency (2023) stages based on representative general national population with extensive geographic coverage. Anxiety, depression, post-traumatic stress disorder (PTSD) and insomnia symptoms were evaluated by GAD-7, PHQ-9, IES-R and ISI scales, respectively, and their associated factors were identified via multivariable linear regression.

Generally, 42,000 individuals were recruited, and 36,218, 36,097 and 36,306 eligible participants were included at each stage. The prevalence of anxiety, depression and insomnia symptoms increased from 13.7–16.4% at stable to 17.3–22.2% at recurrence and decreased to 14.5–18.6% at end of emergency, while PTSD symptom continuously increased from 5.1% to 7.6% and 9.2%, respectively (all significant, P < 0.001). Common factors associated with mental health symptoms across all stages included centralized quarantine, frontline work and residence in initially widely infected areas. Centralized quarantine was linked to anxiety, depression, PTSD and insomnia during the stable, recurrence and end-of-emergency stages. Frontline workers exhibited higher risks of anxiety, depression and insomnia throughout these stages. Individuals in initially widely infected areas were more likely to experience depression and PTSD, particularly during the stable and recurrence stages. Stage-specific risk factors were also identified. Lack of outdoor activity was associated with anxiety, depression and insomnia during the stable and recurrence stages. Residents in high-risk areas during the recurrence stage correlated with increased anxiety and insomnia. Suspected infection was tied to anxiety and insomnia in the recurrence and end-of-emergency stages, while the death of family or friends was linked to PTSD during recurrence and to depression, PTSD and insomnia at the end-of-emergency stage.

Mental health symptoms increased when pandemic recurred, and could remain after end-of-emergency, requiring prolonged interventions. Several key factors associated with mental symptoms and their variations were identified at different pandemic stages, suggesting different at-risk populations.

Schizophrenia progresses through high-risk, first-episode, and chronic stages, each associated with altered spontaneous brain activity. Resting state functional MRI studies highlight these changes, but inconsistencies persist, and the genetic basis remains unclear.

A neuroimaging meta-analysis was conducted to assess spontaneous brain activity alterations in each schizophrenia stage. The largest available genome-wide association study (GWAS) summary statistics for schizophrenia (N = 53,386 cases, 77,258 controls) were used, followed by Hi-C-coupled multimarker analysis of genomic annotation (H-MAGMA) to identify schizophrenia-associated genes. Transcriptome-neuroimaging association and gene prioritization analyses were performed to identify genes consistently linked to brain activity alterations. Biological relevance was explored by functional enrichment.

Fifty-two studies met the inclusion criteria, covering the high-risk (Nhigh-risk = 409, Ncontrol = 475), first-episode (Ncase = 1842, Ncontrol = 1735), and chronic (Ncase = 1242, Ncontrol = 1300) stages. High-risk stage showed reduced brain activity in the right median cingulate and paracingulate gyri. First-episode stage revealed increased activity in the right putamen and decreased activity in the left gyrus rectus and right postcentral gyrus. Chronic stage showed heightened activity in the right inferior frontal gyrus and reduced activity in the superior occipital gyrus and right postcentral gyrus. Across all stages, 199 genes were consistently linked to brain activity changes, involved in biological processes such as nervous system development, synaptic transmission, and synaptic plasticity.

Brain activity alterations across schizophrenia stages and genes consistently associated with these changes highlight their potential as universal biomarkers and therapeutic targets for schizophrenia.

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.