Sex-dependent differences in stress-induced depression in Wistar rats are accompanied predominantly by changes in phosphatidylcholines and sphingomyelins.

With a high annual and lifetime prevalence, depression is becoming the leading contributor to the global disease burden. During the COVID-19 crisis, the depression and mood disorders accelerated significantly. Despite the growing evidence, the precise underlying mechanisms of depression disorders (DD) remain unknown. When studying DD in humans, there are many uncontrollable factors such as medication history, age of the patient or living conditions. In this regard, animal models provide an essential step for examining neural circuitry or molecular and cellular pathways in a controlled environment. As far as we know, women have a consistently higher prevalence of DD than men. Thus, the aim of our study was to evaluate sex-related changes in blood metabolites in a model of stress-induced depression in Wistar rats. Pregnant females were stressed using restriction of mobility in the final week of the pregnancy three times a day for 45 minutes each, three following days. After the birth, the progeny aged 60 days was stressed repeatedly. The perturbation in overall energy metabolism as well as in lipid metabolism was found. While in males, phosphatidylcholines (the most phosphatidylcholine with acyl-alkyl residue sum C40:4 - PC ae C40:4), sphingomyelins, and acylcarnitines were changed, in females, lipid metabolism perturbation was seen with the most critical alteration in hydroxysphingomyelin with acyl residue sum C16:1 (SM OH C16:1). Our results confirm that the animal model may be used further in the research of depression. Our results may provide an essential insight into the sex-dependent pathogenesis of depression and contribute to the search for effective treatment and prevention of depression with respect to sex.

Fuzzy Decision Tree Based Method in Decision-Making of COVID-19 Patients' Treatment

A new method in decision-making of timing of tracheostomy in COVID-19 patients is developed and discussed in this paper. Tracheostomy is performed in critically ill coronavirus disease (COVID-19) patients. The timing of tracheostomy is important for anticipated prolonged ventilatory wean when levels of respiratory support were favorable. The analysis of this timing has been implemented based on classification method. One of principal conditions for the developed classifiers in decision-making of timing of tracheostomy in COVID-19 patients was a good interpretation of result. Therefore, the proposed classifiers have been developed as decision tree based because these classifiers have very good interpretability of result. The possible uncertainty of initial data has been considered by the application of fuzzy classifiers. Two fuzzy classifiers as Fuzzy Decision Tree (FDT) and Fuzzy Random Forest (FRF) have been developed for the decision-making in tracheostomy timing. The evaluation of proposed classifiers and their comparison with other show the efficiency of the proposed classifiers. FDT has best characteristics in comparison with other classifiers.