Karina Nowakowska

Schizophrenia spectrum disorders impact about 1% of people globally, with 70% experiencing auditory hallucinations, whereas 30% of them have persistent symptoms despite effective pharmacological treatment. The main objective of this study is to generate preliminary data on the potential efficacy of VR-based Avatar therapy in treating residual auditory hallucinations. By exploring its potential efficacy across different countries, this study aims to provide initial insights into strategies for integrating VR-based Avatar therapy into clinical practice while also conducting an exploratory trial-based assessment of its cost-effectiveness compared to the waitlist (treatment-as-usual). This is a multicentre, non-randomised, waitlist-controlled, single-group, pre-post design study, intended to inform future randomised controlled trials by providing implementation-related insights. The study involves 90 participants. After enrolment, all patients will first be placed on a three-month waiting list, followed by a subsequent three-month period during which they will participate in a total of seven individual VR-based Avatar therapy sessions. Patients’ conditions will be assessed at three measurement points (week 0, 12, and 24). The primary outcome of the study is the change in auditory verbal hallucinations, including their frequency, intensity, content, and the patient’s coping strategies. Secondary outcomes focus on evaluating changes in comorbid affective symptoms, as well as in functionality. Although Avatar therapy shows promise, few studies focus on its VR-assisted form, often with small sample sizes and limited cultural contexts. More research with larger, diverse populations is needed to confirm its effectiveness. Additionally, assessing the cost-effectiveness of VR-based Avatar therapy is crucial for its integration into clinical practice. The study was registered at ClinicalTrials.gov under the identifier NCT06505564 at 2024.07.19.

Delirium is a neuropsychiatric syndrome that is pathophysiologically related to both mental (dementia, depression) and physical illness. Its occurrence results in a poor prognosis. This study investigates whether specific miRNAs (miR-9-3p, miR-34c-5p, miR-96-5p, miR-183-5p, and miR-374-3p) related to brain function are associated with an increased risk of postoperative delirium. A total of 224 adult individuals scheduled for elective cardiac surgery were eligible to participate in the study. Delirium diagnosis was established with the use of the Confusion Assessment Method. Following miRNA expression profiling, cDNA synthesis was conducted on the samples obtained one day before and the day after surgery, from 60 delirium patients and 60 randomly selected non-delirium individuals. Univariate comparisons revealed that preoperative miR-96-5p (p = 0.05) and miR-183-5p (p = 0.001), along with postoperative miR-34c-5p (p = 0.009), miR-96-5p (p = 0.07), and miR-183-5p (p = 0.05), were associated with the risk of post-surgery delirium. However, after conducting multivariate logistic regression analysis, only miR-183-5p was found to be independently associated with the risk of delirium development. Other predictors of delirium included an ongoing episode of depression, peripheral vascular disease, female gender, active smoking, and increased postoperative pCO2 concentration. The current study revealed that preoperatively decreased expression of miR-183-5p predicts delirium development after cardiac surgery.

Background: Delirium is a neuropsychiatric syndrome that is pathophysiologically related to both mental (dementia, depression) and physical illness. Its occurrence results in a poor prognosis. Specific miRNAs play a role in regulating central nervous system development, neuroinflammation, and neurodegeneration. This study investigates whether specific miRNAs (miR-9-3p, miR-34c-5p, miR-96-5p, miR-183-5p, and miR-374-3p) related to brain function are associated with an increased risk of postoperative delirium. Methods: A total of 224 adult individuals scheduled for elective cardiac surgery were eligible to participate in the study. Prior to surgery, each patient underwent psychiatric evaluation to identify major depressive disorder based on the DSM-5 criteria. Delirium diagnosis was established with the use of the Confusion Assessment Method. Following miRNA expression profiling, cDNA synthesis was conducted on 60 delirium patients and 60 randomly selected non-delirium individuals. MiRNA dqPCR analysis was performed on the serum samples obtained one day before and the day after surgery. Results: Of the 177 patients finally included, 34% (61 cases) experienced delirium. Univariate comparisons revealed that preoperative miR-96-5p (p=0.05) and miR-183-5p (p=0.001), along with postoperative miR-34c-5p (p=0.009), miR-96-5p (p=0.07), and miR-183-5p (p=0.05), were associated with the risk of post-surgery delirium. However, after conducting multivariate logistic regression analysis, only miR-183-5p was found to be independently associated with the risk of delirium development. Other predictors of delirium included an ongoing episode of depression, peripheral vascular disease, female gender, active smoking, and increased postoperative pCO2 concentration. Conclusion: The current study revealed that preoperatively decreased expression of miR-183-5p predicts delirium development after cardiac surgery.

Several studies have demonstrated a critical association between cardiovascular disease (CVD) and mental health, revealing that approximately one-third of individuals with CVD also experience depression. This comorbidity significantly increases the risk of cardiac complications and mortality, a risk that persists regardless of traditional factors. Addressing this issue, our study pioneers a straightforward, explainable, and data-driven pipeline for predicting depression in CVD patients. Methods: Our study was conducted at a cardiac surgical intensive care unit. A total of 224 participants who were scheduled for elective coronary artery bypass graft surgery (CABG) were enrolled in the study. Prior to surgery, each patient underwent psychiatric evaluation to identify major depressive disorder (MDD) based on the DSM-5 criteria. An advanced data curation workflow was applied to eliminate outliers and inconsistencies and improve data quality. An explainable AI-empowered pipeline was developed, where sophisticated machine learning techniques, including the AdaBoost, random forest, and XGBoost algorithms, were trained and tested on the curated data based on a stratified cross-validation approach. Results: Our findings identified a significant correlation between the biomarker “sRAGE” and depression (r = 0.32, p = 0.038). Among the applied models, the random forest classifier demonstrated superior accuracy in predicting depression, with notable scores in accuracy (0.62), sensitivity (0.71), specificity (0.53), and area under the curve (0.67). Conclusions: This study provides compelling evidence that depression in CVD patients, particularly those with elevated “sRAGE” levels, can be predicted with a 62% accuracy rate. Our AI-driven approach offers a promising way for early identification and intervention, potentially revolutionizing care strategies in this vulnerable population.