Background: The high and increasing prevalence of internet use in the general population and the significant burden of depression and schizophrenia urge us to investigate the patterns of internet use among patients with these illnesses. The aim of this study is to assess internet use and mental health-related internet use among patients suffering from schizophrenia and depression. Methods: A total of 104 patients with psychosis and 105 patients with depression were surveyed to assess their internet use and mental health-related internet use. Results: The majority of participants were internet users (87.6%), with 66.7% of internet users with psychosis and 71.4% of internet users with depression using it as a source of information on mental health. Participants with psychosis significantly more attributed the internet and mental health internet forums as helpful in coping with their mental illness and were more interested in the utilization of online mental health services than participants with depression. Conclusions: General internet use in patients with schizophrenia and depression corresponds with the internet use of the general population; however, they use it more often as a source of health information than the general population. Mental health service providers should offer more online interventions and treatment programs to patients with psychosis and depression, as our study suggests there is an unmet need for online mental health services for such patients.

Poor insight and high level of self-stigma are often present among patients with schizophrenia and are related to poorer treatment adherence, poorer social function and rehabilitation, aggressive behavior, higher level of depression,...

Obstructive sleep apnea (OSA) is a common sleep disorder leading to increased risk of developing cardiovascular diseases (CVDs) by supporting a low-grade chronic inflammation as one of the pathological mechanisms. The continuous positive airway pressure (CPAP) device is used as an effective treatment for moderate and severe OSA. Neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), white blood cell–mean platelet volume ratio (WMR), C-reactive protein–albumin ratio (CAR) and fibrinogen–albumin ratio (FAR) are new potential inflammatory biomarkers that are widely available and were shown to be possibly favorable screening or follow-up tools for moderate- or severe-grade OSA, as well as indirect indicators for cardiovascular risk. Our study evaluated the impact of CPAP therapy in patients with severe OSA and acceptable therapy adherence on NLR, PLR, WMR, FAR and CAR. Of 57 patients who were initially enrolled and had no exclusion criteria, 37 had a satisfactory CPAP adherence (usage of ≥4 h per night) after a minimum of 6 months of therapy. There was a statistically significant difference in NLR (2.122 ± 0.745 before therapy vs. 1.888 ± 0.735 after therapy) and FAR (86.445 ± 18.763 before therapy vs. 77.321 ± 19.133 after therapy) suggesting a positive effect of the CPAP therapy on chronic inflammatory states, thereby possibly reducing cardiovascular risk.

Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) has gained attention as a potential objective tool for diagnosing depression. This study aimed to evaluate the effectiveness of EEG in identifying MDD by analyzing 140 EEG recordings from patients diagnosed with depression and healthy volunteers. Using various machine learning (ML) classification models, we achieved up to 80% accuracy in distinguishing individuals with MDD from healthy controls. Despite its promise, this approach has limitations. The variability in the clinical and biological presentations of depression, as well as patient-specific confounding factors, must be carefully considered when integrating ML technologies into clinical practice. Nevertheless, our findings suggest that an EEG-based ML model holds potential as a diagnostic aid for MDD, paving the way for further refinement and clinical application.

Obstructive sleep apnea (OSA) is a significant risk factor for cardiovascular disease (CVD) with increasing prevalence. An important mechanism of CVD development is a dysregulation of the autonomic nervous system (ANS). This prospective and controlled cohort study aimed to investigate ANS function in OSA including the response to long-term continuous positive airway pressure (CPAP) therapy by analyzing 24 h Holter electrocardiogram and 24 h Holter ambulatory blood pressure recording parameters. The study enrolled 57 patients who were newly diagnosed with severe OSA. After 6 months of CPAP therapy, 37 patients had a good therapy adherence (usage of CPAP device >4 h per night), and their data were analyzed. The difference in nocturnal diastolic blood pressure values before and after CPAP therapy reached statistical significance (76 (68–84) vs. 74 (63–80) mmHg, p = 0.0439). Lower nocturnal values after CPAP therapy of SDNN (101.5 vs. 95 ms, p = 0.0492) and RMSSD (29.5 vs. 26 ms, p = 0.0193) were found. An increase in diurnal spectral power (1742 vs. 2112 ms², p = 0.0282) and a decrease in nocturnal spectral power (3256 vs. 2124 ms², p = 0.0097), nocturnal VLF band (2493 vs. 1485.4 ms², p = 0.0176), nocturnal LF band (638.7 vs. 473 ms², p = 0.0097), and nocturnal HF band (234.9 vs. 135.7 ms², p = 0.0319) was found. The results showed an imbalance of the ANS with a sympathetic predominance, especially during the night hours and in those with arterial hypertension. The impact of CPAP therapy on the improvement in ANS parameters was more pronounced at night, in men, and those with arterial hypertension.

Background: Suicide is a major public health challenge requiring early detection of suicidal ideation (SI). Traditional direct questioning methods suffer from stigma and disclosure bias, failing to identify many at-risk individuals. While machine learning (ML) models show promise, most lack external validation. Indirect screening, using psychosocial data rather than direct SI questions, offers a scalable alternative. This study aimed to externally validate an indirect, ML-based SI screening tool. We tested if a model trained on a Slovenian general population sample retained predictive accuracy when applied to an independent Croatian sample during a period of societal stress (pandemic and earthquakes), assessing performance across age and gender subgroups. Methods: A logistic regression model was trained on a Slovenian sample ( N = 2,989) and validated on a Croatian sample ( N = 2,364). The model used only indirect predictors, including sociodemographics, life satisfaction, behavioral changes, and Brief COPE subscales. The target outcome was the presence of SI (SIDAS score > 0). Performance was measured by the area under the receiver operating characteristic curve (AUROC). Results: The model demonstrated strong external validity on the entire Croatian sample, achieving an AUROC of 0.80. Performance remained robust across subgroups: males (AUROC = 0.83), females (AUROC = 0.79), younger adults (AUROC = 0.77), and older adults (AUROC = 0.81). Self-blame, behavioral disengagement, and relationship dissatisfaction were key predictors. Conclusions: An indirect, ML-based screening tool can reliably identify SI risk in the general population. The model demonstrated strong cross-national transferability and resilience during a societal crisis, proving it is a feasible and valid strategy for population-level prevention.