This study investigates the role of inflammatory markers in perception disorders associated with major psychiatric pathology, focusing on schizophrenia and acute psychotic disorder. Guided by the vulnerability–stress–inflammation model, this research explores how genetic predispositions, maternal immune activation, and chronic low-grade neuroinflammation contribute to disease onset and progression. A sample of 135 patients (69 with schizophrenia and 66 with acute psychotic disorder) admitted to a psychiatric hospital between October 2024 and February 2025 was analyzed. Demographic and clinical data, along with biological markers—such as white blood cells, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune–inflammation index (SII), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR)—were assessed. Results indicated elevated median values for SII, CRP, and MLR, with statistically significant differences compared to normal reference ranges, suggesting persistent systemic inflammation in psychotic disorders. While acute psychotic disorders showed wider value ranges, schizophrenia patients demonstrated higher median levels, consistent with chronic inflammation. No significant differences were observed between the two groups after Bonferroni correction, though CRP values suggested a trend toward greater inflammation in schizophrenia. These findings reinforce the inflammatory hypothesis of psychosis and highlight the potential of biomarkers to refine diagnosis, guide treatment strategies, and support future research into immunomodulatory therapies.

Severe depressive disorders with psychotic features in adolescents present diagnostic and therapeutic challenges, often exceeding the limitations of existing guidelines. A 19-year-old patient with four-year history of treatment-resistant severe depression and psychotic features, addressing a broad spectrum of symptoms, including persistent negative symptoms, cognitive impairments, and motor disturbances such as bradykinesia and hypokinesia, was comprehensively assessed. The therapeutic trajectory included multiple pharmacological regimens, neuroimaging evaluations, and standardized scales such as the Positive and Negative Syndrome Scale (PANSS) to monitor clinical outcomes. Initial treatments yielded limited improvements. However, the final regimen resulted in significant symptomatic relief, reflected by marked reductions in PANSS scores (from 105 to 65 over six months). Neuroimaging revealed structural anomalies, including a medial temporal dysplastic lesion and pituitary microadenoma, which contributed to the psychopathology. Symptoms such as anhedonia, avolition, alogia, bradykinesia, episodic derealization, and cognitive impairments showed substantial improvement following tailored interventions. This case underscores the importance of personalized treatment strategies that integrate advanced neuroimaging and innovative pharmacological approaches to address atypical presentations of treatment-resistant psychiatric disorders effectively. Quantifiable improvements in PANSS scores highlight the efficacy of such interventions in managing complex adolescent psychopathologies.

Objective: We examined the psychological mechanisms underlying cyberchondria by testing whether rumination mediates the association between intolerance of uncertainty and cyberchondria and whether this indirect effect is moderated by prior medical experiences and perceived access to healthcare. Methods and Measures: A cross-sectional design was employed with a non-clinical sample of 96 Romanian adults. Participants completed validated self-report measures of intolerance of uncertainty (IUS-12), rumination (Ruminative Responses Scale), and cyberchondria (Cyberchondria Severity Scale). Additional items assessed medical history and perceived access to healthcare. Moderated mediation analyses with bootstrapped confidence intervals were conducted, controlling for relevant sociodemographic variables. Results: Higher intolerance of uncertainty was associated with higher cyberchondria both directly and indirectly through rumination, which accounted for more than half of the total effect. The rumination–cyberchondria association, and the indirect effect of IU, were significantly stronger among individuals who had experienced a recent acute medical episode, whereas chronic illness did not significantly moderate this pathway. Cyberchondria levels were lowest among participants reporting very good access to healthcare. Conclusions: Cyberchondria appears to arise from the interaction of intolerance of uncertainty, ruminative thinking, and contextual health experiences. Targeting rumination and uncertainty tolerance may be particularly important following acute medical events.

This proof-of-concept corpus study examined the technical feasibility of generating and clinically appraising synthetic psychiatric dialogues about hallucinations. A corpus of 200 dialogues was generated with GPT-4o-mini using synthetic personas, structured prompt attributes, and knowledge graph representations. According to the primary clinician’s annotations, hallucinations were rated as present in $n=94$ dialogues, absent in $n=91$, and No/Unclear in $n=15$. Among the hallucination-positive dialogues, auditory phenomena were recorded in $n=57$, tactile phenomena in $n=16$, olfactory phenomena in $n=14$, and visual phenomena in $n=13$; modalities were not mutually exclusive. The corpus had two clinician annotation layers. The primary psychiatrist provided the annotations used in the descriptive corpus analysis, while a second psychiatrist independently rated the same 200 dialogues in predefined domains for the interrater agreement analyses. Four-category realism showed 75.5% exact agreement (Cohen’s $\kappa =0.549$), and three-category hallucination presence showed 75.0% exact agreement ($\kappa =0.552$). Exploratory within-corpus comparisons examined associations of the primary hallucination-presence rating with realism and exact symptom labels. The resulting corpus is presented as a structured resource for method development and future educational or computational evaluation, not as a validated clinical or decision-support instrument.

Introduction: Depression, a pervasive mood disorder, significantly impairs one’s quality of life. Early Maladaptive Schemas (EMS), ingrained thought patterns stemming from early life experiences, play a pivotal role in shaping adult beliefs and behaviors. This study delves into the relevance of specific EMS domains—Emotional Inhibition (EI), Negativity/Pessimism (NP), and Social Isolation/Alienation (SI)—in influencing the severity of depression among medical students and diagnosed patients.Objectives: Our primary goal was to assess the correlation between specific EMS domains and depression severity in medical students and clinically diagnosed patients. We aimed to elucidate whether these schemas could serve as indicators for potential depressive tendencies or if they had a stronger association in those already diagnosed with depression.Methods: We conducted a prospective cross-sectional analysis involving 73 medical students and 61 diagnosed depression patients (aged 18-32). Four key variables—Depression, EI, NP, and SI—were measured using the Beck Depression Inventory-2 and The Young Schema Questionnaire-Short-form-3 in the Romanian context. Statistical analyses, including correlation coefficients and t-tests, were employed to explore the relationships between EMS domains and depression severity.Results: In the non-clinical sample, we identified moderate, statistically significant correlations between depression and EI (r=0.63), NP (r=0.71), and SI (r=0.59). Conversely, the clinical sample exhibited slightly weaker, yet significant correlations (EI-r=0.42, NP-r=0.39, SI-r=0.29). Notably, significant differences emerged between the groups in all measured variables. These findings imply that while a positive correlation between EMS variables and depression exists in both samples, the association weakens in diagnosed patients, indicating that these schemas may be less predictive in this population.Conclusions: Our study underscores the importance of understanding EMS domains in assessing depression severity. While specific schemas—EI, NP, and SI—correlate with depression in both medical students and diagnosed patients, this link is notably weaker in the latter group. Elevated EMS variables suggest a potential for future subclinical depression in medical students, but they might not strongly predict depression in those already diagnosed. These nuanced insights have implications for preventive interventions and therapeutic approaches tailored to individuals at different stages of depression, thereby enhancing targeted mental health care strategies.Disclosure of Interest: None Declared