Uric acid (UA) has been shown to have neuroprotective or neurotoxic properties, in relation to specific tissues and diseases that have been studied. Previous studies provided contradictory results on the role of UA in schizophrenia as a neurodegenerative disorder. The aim of this brief report was an additional analysis of UA sera levels in different phases of schizophrenia. Here, 86 patients with first-episode psychosis (FEP) vs. 45 patients with schizophrenia in relapse (SC in relapse) vs. 35 healthy control subjects (HC) were studied before and 1 month after antipsychotic therapy. Further, we aimed to explore the possible correlation of UA with scores presenting clinical features and with serum concentrations of the proinflammatory cytokines interleukin (IL)-6 and IL-17. When comparing the data between all three groups, we did not find significant differences in UA levels, either before or after the applied therapy. Also, comparing sera concentrations of UA in every single group, the analysis did not reveal statistically significant differences between FEP patients, but statistically, a significant difference was found in SC in relapse before and after treatment (334.71 ± 116.84 vs. 289.37 ± 109.15 μmol/L, p = 0.05). Uric acid serum levels correlated with negative sub-score (p = 0.001, r = 0.306), general sub-score (p = 0.015, r = 0.236), and total PANSS score (p = 0.009, r = 0.3) after 1 month of therapy. We have established a statistically significant positive correlation between serum concentrations of UA and IL-6 in exacerbation (p = 0.01, r = 0.220) and with IL-17 after treatment and in the stabilization of psychosis (p = 0.01, r = 0.34), suggesting potential cascades in different phases of schizophrenia that potentiate inflammation.

In modern clinical practice and research on behavioral changes in patients with oncological problems, there are several one-sided approaches to these problems. Strategies for early detection of behavioral changes are considered, but they must take into account the specifics of the localization and phase in the course and treatment of somatic oncological disease. Behavioral changes, in particular, may correlate with systemic proinflammatory changes. In the up-to-date literature, there are a lot of useful pointers on the relationship between carcinoma and inflammation and between depression and inflammation. This review is intended to provide an overview of these similar underlying inflammatory disturbances in both oncological disease and depression. The specificities of acute and chronic inflammation are considered as a basis for causal current and future therapies. Modern therapeutic oncology protocols may also cause transient behavioral changes, so assessment of the quality, quantity, and duration of behavioral symptoms is necessary to prescribe adequate therapy. Conversely, antidepressant properties could be used to ameliorate inflammation. We will attempt to provide some impetus and present some unconventional potential treatment targets related to inflammation. It is certain that only an integrative oncology approach is justifiable in modern patient treatment.

Neuroimmune, clinical and treatment challenges in multiple sclerosis-related psychoses

Background: Early prediction of COVID-19 patients’ mortality risk may be beneficial in adequate triage and risk assessment. Therefore, we aimed to single out the independent morality predictors of hospitalized COVID-19 patients among parameters available on hospital admission. Methods: An observational, retrospective–prospective cohort study was conducted on 703 consecutive COVID-19 patients hospitalized in the University Clinical Center Kragujevac between September and December 2021. Patients were followed during the hospitalization, and in-hospital mortality was observed as a primary end-point. Within 24 h of admission, patients were sampled for blood gas and laboratory analysis, including complete blood cell count, inflammation biomarkers and other biochemistry, coagulation parameters, and cardiac biomarkers. Socio-demographic and medical history data were obtained using patients’ medical records. Results: The overall prevalence of mortality was 28.4% (n = 199). After performing multiple regression analysis on 20 parameters, according to the initial univariate analysis, only four independent variables gave statistically significant contributions to the model: SaO2 < 88.5 % (aOR 3.075), IL-6 > 74.6 pg/mL (aOR 2.389), LDH > 804.5 U/L (aOR 2.069) and age > 69.5 years (aOR 1.786). The C-index of the predicted probability calculated using this multivariate logistic model was 0.740 (p < 0.001). Conclusions: Parameters available on hospital admission can be beneficial in predicting COVID-19 mortality.