There has been debate about the frequency and severity of antidepressant withdrawal effects.

We set out to appraise and reanalyze an influential systematic review by Henssler and colleagues that concluded that withdrawal effects are not particularly common and rarely severe. We repeated the meta-analysis, including only studies where data were derived from systematic measures of withdrawal symptoms.

Most data in the Henssler review are derived from pharmaceutical industry-sponsored efficacy studies in which withdrawal was a minor consideration. Shortcomings of the review include the use of spontaneously reported adverse events to estimate withdrawal symptoms, potential misclassification of withdrawal symptoms as relapse, inclusion of data from retrospective case-note studies, short duration of prior antidepressant use, short observation periods, the overlooking of differences between placebo and drug withdrawal effects, and the use of questionable proxies for severe withdrawal. There were also discrepancies and uncertainties in some figures used. In our reanalysis, we included only the five studies that used a systematic and relevant method to assess the incidence of any withdrawal symptom. Prior treatment was short-term (12 weeks or less) in all but one of these. The pooled percentage was 55% (95% confidence interval, CI, 31% to 81%; N = 601) without subtracting nocebo effects, with high heterogeneity.

Henssler's review is based on unreliable data and does not provide an adequate basis for the evaluation of antidepressant withdrawal effects. Further good-quality research on antidepressant withdrawal is required.

Pediatric nurses operate in high-stakes environments characterized by emotional, cognitive, and physical demands. Excessive workload can undermine nurses' ability to perform effectively, particularly when compounded by low self-efficacy. Core competencies such as clinical judgment, evidence-based practice, and communication are essential to pediatric nursing performance and are susceptible to the influence of psychological and environmental stressors.

This study aimed to examine the mediating role of self-efficacy in the relationship between workload and core competencies among pediatric nurses in Egyptian governmental hospitals.

A descriptive cross-sectional study was conducted from November 2024 to February 2025 with 198 pediatric nurses recruited from PICUs, NICUs, and pediatric wards using convenience sampling. Data were collected via self-administered questionnaires including the NASA Task Load Index (workload), General Self-Efficacy Scale, and the Core Competence Scale for Paediatric Specialist Nurses. Structural Equation Modeling (SEM) was used to assess direct and indirect effects.

Nurses reported high workload (M = 63.79, SD = 10.21), low self-efficacy (M = 18.90, SD = 4.12), and low core competencies (M = 76.74, SD = 11.56). SEM results showed that workload negatively predicted self-efficacy (β = -0.285, p < 0.001), and self-efficacy positively predicted core competencies (β = 2.186, p < 0.001). Self-efficacy mediated the relationship between workload and core competencies, with a significant indirect effect (β = -0.624, p < 0.001).

Self-efficacy acts as a psychological buffer against the negative effects of workload on professional performance. Enhancing nurses' self-efficacy through targeted interventions may mitigate workload-related declines in core competencies and improve pediatric nursing care.

Not applicable.

Positive symptoms are a prominent feature of schizophrenia. Despite antipsychotic treatment, ~30% of patients develop refractory positive symptoms (RPSs). Current research fails to elucidate the potential neurophysiological mechanisms underlying RPSs, thereby hindering the development of additional treatments. This study, which included 37 patients with RPSs and 40 with non-refractory positive symptoms (NRPSs), aimed to explore their underlying neural mechanisms. Outcome measures were relative power spectrum density and interregional synchronization across frequency bands and theta-gamma phase-amplitude coupling (θ-γ PAC). The single-frequency analysis indicated that RPSs exhibited elevated theta power and reduced lateralization in the left temporal lobe and temporo-parietal junction, along with enhanced functional connectivity in the left frontocentral region. The cross-frequency analysis revealed that RPSs exhibited slightly higher θ-γ coupling at the left temporo-parietal junction compared to NRPSs. Correlation analysis revealed significant associations among theta power, the lateralization index, functional connectivity, and the severity of positive symptoms. The aberrant activation of the theta rhythm in the left temporo-parietal region may lead to increased functional asymmetry in the brain, impeding interregional and inter-frequency information transmission and thus significantly impairing the normal processing of auditory information. These findings offer potential insights into the neurophysiological basis of positive symptoms in schizophrenia and may inform future clinical interventions.

In recent years, several large clinical trials have demonstrated the survival benefits of immunotherapies, mainly immune checkpoint inhibitors (ICIs), in patients with gastric cancer (GC). However, not every GC patient responds equally to immunotherapy. Compared with patients with intestinal GC (IGC), patients with diffuse GC (DGC) are less likely to obtain a survival benefit from the currently approved ICIs. This histological determinant of immunotherapy efficacy in GC has attracted less attention, exposing some patients with DGC to unnecessary risks. Limited data suggest that the cold tumor immune microenvironment, which is shaped by histological and molecular characteristics, challenges the success of immunotherapy in patients with GC. Here, we review the possible mechanisms of resistance and propose new avenues to overcome resistance to immunotherapy in DGC.

Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer's disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. We utilised Gradient Boosting Machines (GBMs), biological pathway-informed Neural Networks (NNs), and Model-based Multifactor Dimensionality Reduction (MB-MDR) models. ML approaches successfully captured all genome-wide significant genetic variants identified in the training set and 22% of associations from larger meta-analyses. They highlight 6 novel loci which replicate in an external dataset, including variants which map to ARHGAP25, LY6H, COG7, SOD1 and ZNF597. They further identify novel association in AP4E1, refining the genetic landscape of the known SPPL2A locus. Our results demonstrate that machine learning methods can achieve predictive performance comparable to classical approaches in genetic epidemiology and have the potential to uncover novel loci that remain undetected by traditional GWAS. These insights provide a complementary avenue for advancing the understanding of AD genetics.

Impaired cognition in liver recipients has been studied in the immediate posttransplant period but is poorly understood in the long term, despite its importance to quality of life. In a single-center cohort of liver recipients transplanted in 2010-2022 and >1 year after transplant, we assessed cognitive performance using a telephone-based battery. We compared depression, anxiety, and self-reported function by cognitive performance using descriptive statistics. Among 120 participants (median age 65, median 7.3 y after transplant), 25% had below-expectation cognition, 53% at-expectation cognition, and 22% above-expectation. Baseline characteristics were similar between groups. Below-expectation performance was most commonly observed in verbal learning (28%) and verbal memory (22%). Overall, 46% had symptoms of depression (38%) and/or anxiety (28%); anxiety was less common among those with above-expectation cognition (0%) versus below-expectation (34%) or at-expectation cognition (38%, p=0.01). The impaired global daily function was reported by 36% of recipients but was not associated with objective cognitive performance. Below-expectation cognition was prevalent among 25% of liver recipients at least 1 year after transplant and was associated with a higher likelihood of reporting psychiatric distress. These findings underscore the need for longitudinal assessment of cognitive and mental health outcomes among recipients of liver transplants.

Although foundation models have advanced many medical imaging fields, their absence in neuroimage analysis limits progress in neuroscience and clinical practice. Brain functional connectivity (FC) analysis is central to understanding brain function and widely used in neuroscience. We propose a foundation model tailored for brain functional connectivity networks (FCN). Our graph transformer model integrates node and edge embeddings to extract FCN features and adapts flexibly to classification, regression, and clustering via task-specific adapters. We validate the model on fMRI data from 10,718 subjects across multiple tasks: gender classification, mental disorder classification (distinguishing schizophrenia or autism from healthy population), brain age prediction, and depressive and anxiety disorder biotyping. Compared to 14 competing methods, our model consistently outperforms them. Moreover, it facilitates biomarker discovery by identifying task-specific FC patterns. In summary, we present a novel, versatile foundation model for FCN that advances neuroimaging research through scalable and interpretable analysis.

ObjectiveTo describe demographic and practice characteristics of psychiatrists in British Columbia (BC), Manitoba (MB) and Ontario (ON) and explore how practice characteristics change by psychiatrist sex/gender and years since medical school graduation.MethodWe conducted a repeated cross-sectional study of all practising psychiatrists who had patient interactions and submitted billings from the fiscal years (FY) 2012/2013 to 2021/2022 using linked administrative data in BC, MB and ON. Psychiatrist demographic variables included age, sex/gender, years since medical school graduation and their practice location. Psychiatrist practice characteristics included visit and patient volume, service settings and patient diagnoses. We used measures of central tendency to describe demographic and practice characteristics and quantify change over time using percentage change.ResultsThe number of psychiatrists increased from 2012/2013 to 2021/2022 (percentage change, BC: 15.4%, MB: 20.0%, ON: 11.8%) and kept up with population increases, shown by stable per-capita supply of psychiatrists. The median age of psychiatrists in all provinces decreased over the study period. The percentage of female psychiatrists in practice increased in all provinces, but more in BC and ON than in MB. On average, psychiatrists are seeing a greater number of patients per year in 2021/2022 than in 2012/2013 (percentage change, BC: 14.6%, MB: 6.5%, ON: 11.1%), and more than half of patients are seen, on average, for one or two visits in all three provinces. More patients receive substance use and psychosis diagnoses over the 10-year study.ConclusionsDuring the past decade, psychiatric practice characteristics show modest changes despite changing psychiatrist demographics and subtle shift towards more consultative practices. While provinces demonstrated similar trends, differences underscore the importance of conducting pan-Canadian analyses to highlight particularities in workforce patterns.