Background: Fecal microbiota transplantation (FMT) from humans with specific medical conditions to animal models can demonstrate causality by inducing or exacerbating pathophenotypes, linking the gut microbiota to health outcomes. Methods: We conducted a scoping review searching MEDLINE, EMBASE, Scopus, and Web of Science through July 2024 to identify human noninfectious diseases studied using FMT in animal models, investigate FMT methodologies, and assess the feasibility of systematic reviews on the role of the microbiota in specific diseases. Results: From 605 reports of 489 studies, we found that inflammatory bowel diseases, irritable bowel syndrome, obesity, colorectal cancer, and depression were the most commonly studied, with cancer research focusing on immunotherapy non-responsiveness. In a random sample of studies, gastrointestinal outcomes were most frequently reported, with remarkably high rates (> 80%) of successful induction of disease-specific alterations for intestinal barrier function, gastrointestinal inflammation, circulating immune parameters, and fecal metabolites. Most studies used C57BL/6 mice and oral gavage administration, with recipients being either germ-free or antibiotic-pretreated. We created tables linking conditions with publications to facilitate future systematic reviews. Conclusions: Although human-to-animal FMT studies cover diverse conditions, methodological heterogeneity and inconsistent reporting hinder comparability. Standardized protocols and guidelines are needed. For several conditions, sufficient literature exists to assess the role of the gut microbiota in human health through systematic reviews.

Objectives: Mood disorders are trending to be among the leading causes of years lived with disability. Despite multiple treatment options, around 30% patients with major depressive disorder (MDD) develop treatment resistant depression (TRD) and fail to respond to current pharmacological therapies. This study aimed to explore the potential benefits of nutritional treatment strategies, along with their molecular mechanisms of action, focusing especially on low-carbohydrate diet (LCHD), ketogenic diet (KD) and other strategies based on carbohydrates intake reduction.Methods: A comprehensive literature review was conducted to determine the impact of LCHD on alleviating depressive symptoms in patients with MDD, along with an explanation of its mode of action.Results: The study revealed significant impact of nutritional interventions based on restriction in carbohydrate intake such as LCHD, KD or sugar-sweetened beverages (SSB) exclusion on anxiety or depression symptoms reduction, mood improvement and lower risk of cognitive impairment or depression. The efficacy of these approaches is further substantiated by their underlying molecular mechanisms, mainly brain-derived neurotrophic factor (BDNF) which is a potential key target of sugar restriction diets in terms of neuroplasticity.Discussion: Healthcare professionals may consider implementing LCHD strategies for MDD and TRD patients to modify the disease process, maintain euthymia, and prevent depressive episode relapses. Ranging from the exclusion of SSB to the adherence to rigorous LCHD regimens, these nutritional approaches are safe, straightforward to implement, and may confer benefits for well-being and relapse prevention in this specific patient population.

Background: Ketamine is currently gaining attention as a rapid-acting antidepressant for treatment-resistant depression (TRD). However, many patients fail to respond, and limited data exist on predictors of non-response. This study aims to characterize the sociodemographic and clinical features associated with non-response to ketamine among TRD patients. Methods: This is a post-hoc analysis of a naturalistic observational study, which enrolled 40 inpatients with treatment-resistant major depressive disorder and analyzed sociodemographic and clinical features in responders and non-responders stratified per Montgomery-Åsberg Depression Rating Scale (MADRS) during short-term ketamine administration (intravenous dosage: 0,5 mg/kg and orally: 2.0 or 2.5 mg/kg) that comprise over 4 weeks. Results: In this study, 30 patients (75%) were classified as non-responders. No significant differences were detected among sociodemographic and clinical features beyond the history of substance use disorder (SUD) – only 53.3% of non-responders reported prior SUD (vs. 100%; p = 0.0075) and a lower number of psychiatric comorbidities (p = 0.0381). Conclusion: This study highlights key characteristics of TRD non-responders to ketamine, including lower rates of SUD and fewer psychiatric comorbidities. These findings suggest that a higher burden of traditional TRD risk factors may not limit ketamine efficacy and could even enhance response compared to “pure” major depressive disorder. Identifying potential non-responders early can optimize treatment decisions, reduce ineffective exposure, and guide future research on improving TRD management.

Objectives: Ketamine is a prototypical rapid‐acting antidepressant for treatment‐resistant bipolar depression (TRBD), yet many patients do not achieve a meaningful response. This study explored features of ketamine nonresponse in TRBD. Methods: In a post hoc analysis of a naturalistic study, 35 TRBD patients received a four‐week ketamine regimen (intravenous 0.5 mg/kg or oral 2.0/2.5 mg/kg). Response was measured using the Montgomery‐Åsberg Depression Rating Scale, and baseline sociodemographic and clinical features were compared between responders and nonresponders. Results: Fourteen patients (40%) were nonresponders. They had a higher median number of psychiatric comorbidities (2 vs. 1; p = 0.0366), were more likely to have any psychiatric comorbidity (78.6% vs. 33.3%; p = 0.0153), and had greater prior benzodiazepine use (64.3% vs. 23.8%; p = 0.0332). No significant links emerged between individual comorbidities or baseline suicidality and response. Conclusion: Ketamine demonstrates a favorable safety and tolerability profile for short time use in TRBD regardless of isolated baseline characteristics, although a more severe comorbidity burden and benzodiazepine use appear to be associated with nonresponse. Trial Registration: NCT04226963 and NCT05565352

Treatment-resistant depression (TRD) remains a significant public-health challenge, with many patients failing to respond to conventional therapies. Ketamine has emerged as a rapid-acting intervention, but quantitative outcomes alone do not capture patients’ lived experiences, which shape engagement, acceptability, and adherence. We conducted a narrative review of qualitative and mixed-methods studies to enable conceptual integration and thematic synthesis of patients’ experiences with ketamine treatment for depression, guided by established narrative review methodology and the SANRA framework. A targeted search of MEDLINE and Scopus (November 2025) identified studies reporting adult patients’ perspectives on therapeutic ketamine or esketamine use, with qualitative data synthesized iteratively in keeping with narrative review principles. Across the literature, patients’ perspectives coalesce around key thematic domains, including motivations and expectations for treatment, the phenomenology of the treatment experience, post-treatment trajectories, side effects and reasons for discontinuation, relational and environmental factors, and information and education needs. By focusing on these thematic groups, the review highlights the experiential dimensions that influence the perceived value and acceptability of ketamine, underscoring the need for patient-centered service design. Integrating these insights can guide the development of ketamine programs that are both evidence-based and aligned with patients’ priorities and perspectives.

Background: Suicide remains a leading cause of death among youth, yet effective tools to predict suicide attempts (SA) in individuals under 18 are scarce. This study aims to develop machine learning (ML) models to predict SA in paediatric populations using Google Trends data. Methods: Relative Search Volumes (RSVs) from Google Trends were analysed for terms linked to suicide risk factors. Pearson Correlation Coefficients (PCC) identified terms strongly associated with SA rates. Based on these, several ML models were developed and evaluated, including Random Forest Regression, Support Vector Regression (SVR), XGBoost, and Linear Regression. Model performance was assessed using metrics such as PCC, mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results: Terms related to suicide prevention and symptoms, including psychiatrist and anxiety disorder, showed the strongest correlations with SA rates (PCC ≥ 0.90). Random Forest Regression emerged as the top-performing ML model (PCC = 0.953, MAPE = 20.12%, RMSE = 17.21), highlighting burnout, anxiety disorder, antidepressants, and psychiatrist as key predictors of SA. Other models’ scores were XGBoost (PCC = 0.446, MAPE = 22.57%, RMSE = 18.03), SVR (PCC = 0.833, MAPE = 42.23%, RMSE = 47.32) and Linear Regression (PCC = 0.947, MAPE = 23.64%, RMSE = 17.66). Conclusions: Google Trends–based ML models suggest potential utility for short-term prediction of youth SA. These preliminary findings support the utility of search data in identifying real-time suicide risk in paediatric populations.