In this study, we aimed to develop a two-stage hierarchical diagnostic model based on machine learning with attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy in order to distinguish patients with bipolar disorder (BD) and major depressive disorder (MDD) from healthy controls, and to distinguish between the two psychiatric disorders. A total of 102 age- and sex-matched individuals (BD = 34, MDD = 34, Control = 34) were included in the study. In the first stage, psychiatric cases and healthy controls were classified with an accuracy rate of 87.2% using the Random Forest algorithm. The most decisive spectral wavenumbers in this distinction were 3006 cm[-1] and 1090 cm[-1]. In the second stage, the Radial Based Support Vector Machines (RBF-SVM) model was applied only on patient groups, and a Kappa value of 0.506 was obtained with a cross-validation accuracy of 75.1%. In this distinction, the 1148 cm[-1] and 1541 cm[-1] bands were determined as the strongest biomarkers. In regression analyses, a significant correlation was found between Beck Depression Inventory scores and FTIR spectra in the depression group, while no similar relationship was found with Young Mania Rating Scale scores in the bipolar group. The findings show that symptom severity in MDD creates state-dependent changes at the biochemical level, while the spectral profile provides trait-dependent information in BD. In conclusion, the integration of ATR-FTIR spectroscopy and machine learning stands out as an innovative and powerful analytical approach in the diagnosis of psychiatric disorders and the objective assessment of symptom severity.

Objective: Individuals with bipolar II disorder (BD-II) and depression face limited treatment options and are often excluded from psilocybin therapy trials due to concerns about precipitating mania or psychosis. The objective of this study was to evaluate the safety, tolerability, and preliminary efficacy of psilocybin therapy in individuals with BD-II and depression.

Motor symptoms have emerged as a promising target for understanding mechanisms, prognosis, and treatment in psychology. Although motor abnormalities are observed across mental health conditions, transdiagnostic research has been limited. It is not well understood whether motor symptoms are a broad endophenotypic marker or a predictor of specific symptoms. We leverage a hierarchical dimensional symptom structure to examine transdiagnostic relationships between psychological and self-reported motor symptoms in 3,460 participants in the Multi-Site Assessment of Psychosis-Risk study. Overall motor problems (β=0.07-0.25), dyskinesia (β=0.07-0.31), and coordination (β=0.05-0.23) were related positively at different strengths to all symptom domains. Amount of physical activity was positively related to mania and substance use (β=0.08-0.09) and negatively related to detachment, fear, and distress (β=0.05-0.27). These results suggest that motor domains may provide utility as both a general endophenotypic marker and more specific predictor of serious mental illness, with implications for research and clinical settings.

Sleep architecture alterations have been shown in major psychiatric disorders, but the contribution of demographic factors to between-study heterogeneity remains insufficiently characterized. This study reports a transdiagnostic systematic review and meta-regression of case-control polysomnographic studies comparing schizophrenia, bipolar disorder, and unipolar depression with healthy controls. The factors age and sex as possible moderators across harmonized case-control polysomnographic studies have been analyzed. Eligible studies reported diagnostic criteria, treatment status, age, and sex. Because the UD literature encompassed developmentally distinct populations, pediatric/adolescent and adult studies were analyzed separately. Standardized mean differences were synthesized using random-effects models with REML estimation. Univariable mixed-effects meta-regressions assessed study-level mean age and sex composition as moderators within each diagnostic group. Study-level mean age and sex composition significantly moderated delta sleep effect sizes in drug-naive schizophrenia, while study-level mean age significantly moderated REM time in drug-free schizophrenia. In bipolar disorder, study-level mean age moderated REM sleep time effect sizes during mania, whereas sex composition moderated delta sleep and REM density effect sizes. In UD, study-level mean age moderated total sleep time, and sex composition moderated wake after sleep onset only in pediatric/adolescent studies, whereas no significant moderation was observed in adult samples. No significant moderator effects were identified during bipolar depression. Overall, demographic composition explained only a modest proportion of between-study heterogeneity. Interpretation is limited by the ecological nature of study-level meta-regression and residual study heterogeneity.

Artificial induction of convulsions to treat mental illness has been explored for nearly a century, with earlier approaches that include insulin shock therapy, camphor-induced convulsive therapy, and metrazol-induced convulsive therapy. In 1938, Ugo Cerletti and Lucio Bini introduced electrically induced seizures as a more reliable and controllable therapeutic method, later incorporating the use of general anesthesia and muscle relaxants. Today, electroconvulsive therapy (ECT) involves the controlled electrical induction of a generalized cerebral seizure under general anesthesia to improve psychiatric symptoms. The primary indication for this intervention is severe or treatment-resistant major depressive disorder. However, ECT has also demonstrated efficacy in several other psychiatric and neuropsychiatric conditions, including bipolar disorder (particularly in cases featuring severe or treatment-resistant mania), catatonia, neuroleptic malignant syndrome, and schizophrenia spectrum disorders. Researchers and clinicians initially embraced ECT with considerable enthusiasm. However, stigma surrounding the treatment gradually emerged. Media, including films, television programs, and newspapers, often portrayed ECT negatively, while reports of inadequately administered treatments during the preanesthesia era further reinforced public fear and misunderstanding. These factors contributed to persistent misconceptions and apprehension about ECT that continue to influence public perception. Despite substantial advances in safety, technique, and patient outcomes, ECT remains underutilized in contemporary clinical practice because of ongoing stigma, limited public awareness, and restricted access to specialized ECT services.

The rapid integration of artificial intelligence (AI) into everyday life has created new intersections among digital technology, spirituality, mystical experience, and psychopathology. This scoping review aimed to map evidence on how AI mediates, reinforces, detects, predicts, or interprets religious and spiritual experiences at the boundary with psychosis and dissociation. PubMed, Web of Science, Embase, the Cochrane Library, and Scopus were searched through June 30, 2026, with supplementary reference-list screening and forward citation tracking. Study selection followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Nine studies from Türkiye, the USA, the UK, Poland, South Korea, and Israel were included. The evidence comprised clinical case reports, retrospective electronic health record analyses, qualitative studies, machine-learning investigations, and conceptual modeling. Conversational AI was reported as a catalyst, amplifier, co-author, or object of spiritually framed delusions, particularly in the presence of psychosis, mania, sleep disruption, substance exposure, or impaired reality testing. Conversely, natural language processing and machine-learning models showed preliminary potential for identifying documented psychotic episodes and predicting relapse. AI may therefore contribute to the reinforcement of psychopathological experiences while also offering preliminary support for psychiatric screening and risk prediction. Prospective, culturally diverse, and externally validated research is needed to clarify causal mechanisms and establish clinical, ethical, and technological safeguards.