Background: Characterized by demyelination, inflammation and axonal damage, multiple sclerosis (MS) is one of the most common disorders of central nervous system led by the immune system. There is an urgent and obvious need for biomarkers for the diagnosis and follow-up of MS. Objective: To investigate serum levels of sestrin2 (SESN2), a protein that responds to acute stress, in MS patients. Methods: A total of 85 participants, 40 patients diagnosed previously with relapsing-remitting MS and 45 healthy controls, were included. Serum SESN2 parameters were investigated in blood samples drawn from each participant in the patient and control groups. Results: SESN2 levels were significantly lower in MS patients than in controls (z: -3.06; p=0.002). In the ROC analysis of SESN2, the predictive level for MS was 2.36 ng/mL [sensitivity, 72.50%; specificity, 55.56%; p=0.002; area under the curve (AUC)=0.693]. For the cut-off value in both groups, SESN2 was an independent predictor for MS [Exp (B)=3.977, 95% confidence interval (95%CI) 1.507-10.494 and p=0.013]. Conclusions: The decreased expression of SESN2 may play a role in MS pathogenesis, and SESN2 could be used as a biomarker for MS and as immunotherapeutic agent to treat MS.

Objective: One of the serious mental disorders in which people interpret reality in an abnormal situation is schizophrenia. A combination of extremely disordered thoughts, delusions, and hallucinations occurs due to schizophrenia, and the person's daily functions are seriously impaired due to this disease. For general cognitive activity analysis, electroencephalography signals are widely used as a low-resolution diagnostic tool. This study aimed to diagnose schizophrenia using the transfer learning method by including the EEGs of 73 patients diagnosed with schizophrenia, and 67 patients from the healthy group. Material and Method: In the first step of the study, digital electroencephalography signal data was converted into spectrograms to make them usable. In the classification phase, ResNet18, ResNet50 and EfficientNet models, which are FastAI, and Convolutional Neural Network (CNN) based deep learning models, were used. Results: Despite the complexity of electroencephalography data, CNN-based models in the study were successful in capturing different aspects of neurophysiological activity. The best performance was obtained from the ResNet-50 model with an accuracy rate of 97%. Afterwards, the classification process was finalized with 95% ResNet-18, and 83% EfficientNet models, respectively. Conclusion: It is thought that the classification performance of the result obtained in the application is promising, and may be a guide for future studies.

Neocuproine is a free radical scavenger. Neuroblastoma cancer is seen mostly in children. It accounts for approximately 15% of all pediatric cancer deaths. Treatments against SH-SY5Y may be ineffective due to the development of drug resistance, resulting in poor survival in high-risk patients. The goal of this study was to find out how the anti-cancer concentration of Neocuproine affected the levels of oxidants, antioxidants, and cytokines in the SH-SY5Y cell line. Five different concentrations of Neocuproine (12.5, 25, 50, 100, and 200 µM) were applied to the cell line for 24 h. MTT cytotoxicity tests, total oxidant status and total antioxidant capacity tests, and anti-inflammatory cytokine tests were used to look at the uses of Neocuproine. In our results, 100 µM Neocuproine application reduced cancer cell viability up to 69.76%. In addition, it was determined that 100 µM Neocuproine reduced oxidant activity, increased antioxidant capacity, and regulated anti-inflammatory cytokine levels in neuroblastoma cells, consistent with the cytotoxicity test. As a result, it was concluded that Neocuproine has an anti-cancer effect on the SH-SY5Y cell line.

Although the underlying cause of ischemic stroke is vascular occlusion, the post-stroke pathophysiology remains unclear. Therefore, numerous biomarkers have been investigated for both diagnostic and prognostic purposes. In our study, the diagnostic value of the parameters NLR, PLR, RDW-CV, RDW-SD, HIF-1, and DEC-1 was comprehensively evaluated. NLR, Blood samples from patients with ischemic stroke and healthy controls were analyzed for NLR, PLR, RDW-CV, RDW-SD, HIF-1, and DEC-1 levels. Demographic and clinical characteristics of both patient and control groups were recorded. The relationship between HIF-DEC measurements and laboratory parameters was subsequently assessed. For statistical analysis, the Mann-Whitney U test was used for comparisons between groups, and the Chi-Square test was applied for categorical variables. RDW-SD values were found to be higher in patients diagnosed with cerebrovascular accident (CVA) while DEC levels were lower in the stroke group. Although no significant correlation was observed between HIF levels and other parameters in either the CVA or control groups, there was a weak inverse correlation found between DEC and NLR (r:-0.258), and between DEC and RDW-CV (r:-0.268), and a weak linear correlation between DEC and RDW (r:0.319) in the whole group. No significant correlations were found between DEC and other measurements within either the control or CVA groups individually. Our study may indicate that pathways increasing the levels of DEC-1, which acts on the PI3K/Akt pathway, could lead the development of new therapeutic strategies for ischemic stroke.