Vol 7, No 2 (2026)

The total digital transformation has confronted psychiatry with a unique challenge: the human-made information environment has simultaneously become a source of new psychopathology and a tool for overcoming it. In this article, the authors propose examining the dialectic of two fields: cyber psychiatry (which studies the pathogenic influence of the digital world) and digital psychiatry (which develops assistive technologies). Particular attention is paid to the key risk of the current stage — reification, i.e., the substitution of live clinical thinking with the illusory objectivity of algorithms.

The discussion addresses why the absence of operational management and clear rules of responsibility when using artificial intelligence in psychiatry creates a “hole” in the professional future. The authors substantiate the thesis that the only reasonable way forward is a hybrid model of care, in which digital tools serve as an extension of the physician's capabilities, not a replacement, and the “human in the loop” possesses real, rather than declarative, authority.

BACKGROUND: There is ongoing debate about the appropriate measurement level for symptom severity scores derived from clinical rating scales. The use of statistically inappropriate analytical methods for such data may distort results and lead to misinterpretations.

AIM: To study the applicability of linear, beta, beta-binomial, and ordinal regression models for assessing changes in schizophrenia symptoms over time using the PANSS.

METHODS: The study cohort comprised patients diagnosed with schizophrenia and schizoaffective disorder. Symptom severity was quantified using the Positive and Negative Syndrome Scale (PANSS). Observation period, sex, brexpiprazole prescription upon admission, brexpiprazole monotherapy, and dropout from the study prior to completion of follow-up were used as covariates. Bayesian mixed-effects regression models were fitted to the obtained dataset: Model 1 (normal distribution), Model 2 (ordered beta distribution), Model 3 (beta-binomial distribution), and Model 4 (ordinal regression). The applicability of the models was assessed using the fit index γ, the proportion of predicted values that corresponded with any possible schizophrenia symptom score on the PANSS. A model was considered consistent at a γ=1. Additionally, the 95% Highest Density Interval (HDI) was calculated for γ.

RESULTS: The study enrolled 24 patients with schizophrenia (75% of whom were men) aged 20 to 45 years. The fit index γ was 0.00 (95% HDI 0.00–0.00) for Model 1; 0.17 (95% HDI 0.14–0.21) for Model 2; 1.00 (95% HDI 1.00–1.00) for Model 3; and 1.00 (95% HDI 1.00–1.00) for Model 4. Model 1 allows results that fall outside the range of the PANSS. Models 1 and 2 can produce fractional values.

CONCLUSION: Statistical models designed to analyze continuous variables (linear and beta regression) are inapplicable for ordinal variables and, in particular, changes in schizophrenia symptoms over time on the PANSS. Beta-binomial and ordinal regression models are recommended for rating scores.

BACKGROUND: Children with autism spectrum disorder (ASD) frequently exhibit restrictive eating behaviors that may meet the criteria for avoidant/restrictive food intake disorder (ARFID). This disorder is characterized not only by behavioral disturbances but also by somatic impairments and functional complications. Timely identification of ARFID allows for therapeutic and nutritional interventions aimed at restoring fully balanced nutrition in order to achieve the highest possible level of development and health for every child with ASD. Despite high prevalence of avoidant/restrictive eating behaviors in children, the associated adverse health outcomes, and the frequency of clinical encounters, ARFID is often underdiagnosed and inadequately addressed within both psychiatric and pediatric care settings, highlighting the need for a multidisciplinary diagnostic approach.

AIM: To develop and implement a multidisciplinary approach to the diagnosis of ARFID in children with ASD.

METHODS: A multistage mixed-methods design was applied to address the aim of the study. In the first stage, diagnostic criteria for ARFID were clustered. In the second stage, a conventional algorithm for examining patients with ASD to diagnose ARFID was developed. This was followed by a cross-sectional study, during which a comprehensive examination of patients with ASD was performed and approaches to the diagnosis of ARFID in children with autism were developed.

RESULTS: A behavioral pattern of selective/restrictive food intake was identified in 71 (88.8%) children with autism out of 80. Somatic disorders (deviation of body weight from the norm, carbohydrate and/or lipid metabolism disorders, vitamin deficiencies, anemia) were observed in 66 (82.5%) children, and functional impairment — in 11 (19.7%). This defines an additional criterion necessary for diagnosing ARFID as a comorbid disorder and for implementing therapeutic interventions aimed at treating the identified pathology and restoring a balanced diet.

CONCLUSION: This study proposes the identification of three diagnostic clusters of ARFID: behavioral, somatic, and functional impairment. These findings highlight the importance of a multidisciplinary approach and the need to assess somatic consequences when diagnosing ARFID and developing individualized treatment strategies.

BACKGROUND: Alcohol consumption is one of the most significant global public health challenges. The pathophysiology of alcohol use disorder remains only partially elucidated, and direct biomarkers for this disorder have yet to be identified. Some studies have investigated the profiles of small extracellular vesicles (sEVs) in different bodily fluids. However, the findings across these studies are heterogeneous, and the methodological quality of these studies has not been systematically assessed.

AIM: To summarize the results of studies examining the sEVs' profiles in alcohol use disorders.

METHODS: The review included original research articles that explored the sEVs' profiles in alcohol use disorders. The MEDLINE (for studies published up to November 2025) and Scopus (for studies published up to January 10, 2026) were searched. The analysis of the included studies was conducted using a descriptive evidence synthesis approach based on the categories of lipid profile, protein profile, and signaling molecules. The methodological quality of the studies was assessed using the Newcastle–Ottawa Scale (NOS) adapted for extracellular vesicle research.

RESULTS: Ten studies were included. All studies employed a cross-sectional design, were conducted in small cohorts, and included patients with acute and chronic alcohol intoxication, as well as those with alcohol use disorder. Significant differences in the sEVs' profiles were identified in individuals with alcohol use disorders compared to healthy controls. These differences encompassed modifications in lipid and protein profiles, decreased expression of hsa-miR-144-5p, hsa-miR-182-5p, hsa-miR-142-5p, hsa-miR-7-5p, hsa-miR-15b-5p, and hsa-circ-0004771, an increased expression of miR-30a-5p and miR-194-5p, as well as alterations in inflammatory response parameters. The majority of these differences were related to the regulation of inflammation.

CONCLUSION: In alcohol use disorders, sEVs differ in lipid and protein profiles as well as in microRNA expression. New studies are needed to investigate the impact of sEVs on the regulation of systemic inflammation in alcohol use disorders using a prospective design with repeated measurements of specific sEV characteristics.