Cognitive deficits and immune system dysregulation are core features of psychotic disorders. Among inflammatory markers, interleukin-6 (IL-6) and tumour necrosis factor-alpha (TNF-α) have been linked to both psychosis pathophysiology and related cognitive impairments.

We investigated associations among IL-6, TNF-α, and neurocognitive performance in 107 participants: individuals at clinical high risk for psychosis (CHR-P, n = 35), first-episode psychosis (FEP, n = 39), and healthy controls (HC, n = 33). Assessments included memory, processing speed, executive function, and social cognition. Cytokines were measured from fasting serum samples. Analyses included ANOVA, correlations, and multivariate regressions controlling for age, sex, IQ, group, and symptom severity.

TNF-α levels were significantly elevated in FEP compared to CHR-P (p = 0.0251); IL-6 differences were non-significant. FEP showed poorer performance in multiple cognitive domains, especially social cognition. CHR-P individuals exhibited intermediate profiles between FEP and HC in cognition. In adjusted regression models, IL-6 was significantly associated with undermentalization on the MASC task (β = 0.28, p = 0.0337) and showed a trend-level association with slower processing speed (β = 0.98, p = 0.075). TNF-α levels predicted poorer facial emotion recognition (β = −1.37, p = 0.0022). IQ and group were significant covariates in most models.

Our findings suggest that peripheral inflammation, particularly IL-6 and TNF-α, may selectively impact social cognitive functioning in early psychosis. Though modest, these associations highlight potential inflammatory contributions to functional impairment and support further investigation of immunological targets in early intervention.

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), but initial outcomes can be modest.

To compare SSRI dose optimisation with four alternative second-line strategies in MDD patients unresponsive to an SSRI.

Of 257 participants, 51 were randomised to SSRI dose optimisation (SSRI-Opt), 46 to lithium augmentation (SSRI+Li), 48 to nortriptyline combination (SSRI+NTP), 55 to switch to venlafaxine (VEN) and 57 to problem-solving therapy (SSRI+PST). Primary outcomes were week-6 response/remission rates, assessed by blinded evaluators using the 17-item Hamilton Depression Rating Scale (HDRS-17). Changes in HDRS-17 scores, global improvement and safety outcomes were also explored. EudraCT No. 2007-002130-11.

Alternative second-line strategies led to higher response (28.2% v. 14.3%, odds ratio = 2.36 [95% CI 1.0–5.6], p = 0.05) and remission (16.9% v. 12.2%, odds ratio = 1.46, [95% CI 0.57–3.71], p = 0.27) rates, with greater HDRS-17 score reductions (−2.6 [95% CI −4.9 to −0.4], p = 0.021]) than SSRI-Opt. Significant/marginally significant effects were only observed in both response rates and HDRS-17 decreases for VEN (odds ratio = 2.53 [95% CI 0.94–6.80], p = 0.067; HDRS-17 difference: −2.7 [95% CI −5.5 to 0.0], p = 0.054) and for SSRI+PST (odds ratio = 2.46 [95% CI 0.92 to 6.62], p = 0.074; HDRS-17 difference: −3.1 [95% CI −5.8 to −0.3], p = 0.032). The SSRI+PST group reported the fewest adverse effects, while SSRI+NTP experienced the most (28.1% v. 75%; p < 0.01), largely mild.

Patients with MDD and insufficient response to SSRIs would benefit from any other second-line strategy aside from dose optimisation. With limited statistical power, switching to venlafaxine and adding psychotherapy yielded the most consistent results in the DEPRE'5 study.

Moral distress affects a significant proportion of clinicians who have received requests and participated in euthanasia or physician-assisted suicide (E/PAS) globally. It has been reported that personal and professional support needs are often unaddressed, with only a minority of those reporting adverse impacts seeking support.

This study aimed to review studies from 2017 to 2023 for the perceived risks, harms, and benefits to doctors of administering E/PAS and the ethical implications for the profession of medicine resulting from this practice.

The search explored original research papers published in peer-reviewed English language literature between June 2017 and December 2023 to extend prior reviews. This included both studies reporting quantitative and qualitative data, with a specific focus on the impact on, or response from, physicians to their participation in E/PAS. The quantitative review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The qualitative review used the Critical Appraisal Skills Programme to assess whether studies were valid, reliable, and trustworthy.

Thirty studies (quantitative n = 5, qualitative n = 22, mixed methods n = 3) were identified and fulfilled acceptable research assessment criteria. The following 5 themes arose from the synthesis of qualitative studies: (1) experience of the request prior to administration; (2) the doctor’s role and agency in the death of a patient; (3) moral distress post-administration; (4) workload and burnout; and (5) professional guidance and support. Both quantitative and qualitative studies showed a significant proportion of clinicians (45.8–80%) have been adversely affected by their involvement in E/PAS, with only a minority of those reporting adverse impacts seeking support.

Participation in E/PAS can reward some and cause moral distress in others. For many clinicians, this can include significant adverse personal and professional consequences, thereby impacting the medical profession as a whole.

The duration of undiagnosed or untreated bipolar disorder (DUBD) has become a focus of research interest. However, its relationship with clinical characteristics and outcomes remains poorly understood.

The objective of this systematic review and meta-analysis was to examine DUBD and explore its relationships with clinical characteristics and outcomes in bipolar disorder.

We conducted a systematic search of the literature to identify studies reporting on DUBD and its relationships with clinical characteristics and outcomes including frequency of relapse into mood episodes, severity and persistence of mood symptoms, functional and cognitive measures, suicidality, hospital admission rate, and comorbidities such as substance use disorders.

Thirty articles met inclusion criteria for the systematic review, and 23 studies were included in the three different sets of meta-analyses. The pooled mean DUBD across all studies was 9.10 years. Early onset, depression as the polarity of the first mood episode, lifetime suicide attempts, comorbid anxiety and alcohol use disorders, and family history of bipolar disorder were associated with significantly longer DUBD, whereas diagnosis of bipolar I disorder and lifetime psychotic symptoms were associated with shorter DUBD. Studies that investigated outcomes subsequent to the diagnosis of bipolar disorder yielded conflicting results.

DUBD may be associated with certain adverse outcomes. This association indicates the importance of adopting a more comprehensive approach to assessing mood disorders, with an emphasis on prioritising early screening for bipolar disorder. The significant heterogeneity among included studies suggests a need for improved methodological rigour in future research.

Studies conducted during the COVID-19 pandemic found high occurrence of suicidal thoughts and behaviours (STBs) among healthcare workers (HCWs). The current study aimed to (1) develop a machine learning-based prediction model for future STBs using data from a large prospective cohort of Spanish HCWs and (2) identify the most important variables in terms of contribution to the model’s predictive accuracy.

This is a prospective, multicentre cohort study of Spanish HCWs active during the COVID-19 pandemic. A total of 8,996 HCWs participated in the web-based baseline survey (May–July 2020) and 4,809 in the 4-month follow-up survey. A total of 219 predictor variables were derived from the baseline survey. The outcome variable was any STB at the 4-month follow-up. Variable selection was done using an L1 regularized linear Support Vector Classifier (SVC). A random forest model with 5-fold cross-validation was developed, in which the Synthetic Minority Oversampling Technique (SMOTE) and undersampling of the majority class balancing techniques were tested. The model was evaluated by the area under the Receiver Operating Characteristic (AUROC) curve and the area under the precision–recall curve. Shapley’s additive explanatory values (SHAP values) were used to evaluate the overall contribution of each variable to the prediction of future STBs. Results were obtained separately by gender.

The prevalence of STBs in HCWs at the 4-month follow-up was 7.9% (women = 7.8%, men = 8.2%). Thirty-four variables were selected by the L1 regularized linear SVC. The best results were obtained without data balancing techniques: AUROC = 0.87 (0.86 for women and 0.87 for men) and area under the precision–recall curve = 0.50 (0.55 for women and 0.45 for men). Based on SHAP values, the most important baseline predictors for any STB at the 4-month follow-up were the presence of passive suicidal ideation, the number of days in the past 30 days with passive or active suicidal ideation, the number of days in the past 30 days with binge eating episodes, the number of panic attacks (women only) and the frequency of intrusive thoughts (men only).

Machine learning-based prediction models for STBs in HCWs during the COVID-19 pandemic trained on web-based survey data present high discrimination and classification capacity. Future clinical implementations of this model could enable the early detection of HCWs at the highest risk for developing adverse mental health outcomes.

NCT04556565

Antidepressants are essential in managing depression, including treatment-resistant cases. Public perceptions of these medications, shaped by social media platforms like X (formerly Twitter), can influence treatment adherence and outcomes. This study explores public attitudes toward antidepressants through sentiment and topic modeling analysis of tweets in English and Spanish from 2007 to 2022.

Tweets mentioning antidepressants approved for depression were collected. The analysis focused on selective serotonin reuptake inhibitors (SSRIs) and glutamatergic drugs. Sentiment analysis and topic modeling were conducted to identify trends, concerns, and emotions in discussions across both languages.

A total of 1,448,674 tweets were analyzed (1,013,128 in English and 435,546 in Spanish). SSRIs were the most mentioned antidepressants (27.9% in English, 58.91% in Spanish). Pricing and availability were key concerns in English tweets, while Spanish tweets highlighted availability, efficacy, and sexual side effects. Glutamatergic drugs, especially esketamine, gained attention (15.61% in English, 25.23% in Spanish), evoking emotions such as fear, sadness, and anger. Temporal analysis showed significant increases in discussions, with peaks in 2012 and 2021 for SSRIs in Spanish, and exponential growth from 2018 to 2021 for glutamatergic drugs. Emotional tones varied across languages, reflecting cultural differences.

Social media platforms like X provide valuable insights into public perceptions of antidepressants, highlighting cultural variations in attitudes. Understanding these perceptions can help clinicians address concerns and misconceptions, fostering informed treatment decisions. The limitations of social media data call for careful interpretation, emphasizing the need for continued research to improve pharmacovigilance and public health strategies.