👤 Burçin Çolak

Studies have reported that the prevalence of aggression is higher in individuals with schizophrenia compared to the general population. Various factors, including genetic variations, contribute to the emergence of aggression in patients with schizophrenia. Among these, the monoamine oxidase A (MAOA) and brain-derived neurotrophic factor (BDNF) genes are considered key genetic factors potentially influencing aggressive behavior in schizophrenia. This study investigated the association of BDNF rs6265 and MAOA rs1465108 polymorphisms with aggression in schizophrenia. A total of 150 patients diagnosed with schizophrenia were included in the study. The MAOA rs1465108 and BDNF rs6265 polymorphisms were analyzed using the Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) method. Aggression was evaluated using the Buss-Perry Aggression Questionnaire. Suicide risk, childhood trauma, and impulsivity which were related to aggression were evaluated using the Suicide Probability Scale, the Childhood Trauma Questionnaire, and the Barratt Impulsiveness Scale, respectively. Negative and positive symptoms of schizophrenia were assessed using the Scale for the Assessment of Negative Symptoms (SANS) and the Scale for the Assessment of Positive Symptoms (SAPS), respectively. No direct genotype associations were observed between aggression and the BDNF rs6265 and MAOA rs1465108 polymorphisms. However, impulsivity, SAPS, and SANS scores were significantly associated with aggression. These findings highlight that aggression in schizophrenia is primarily shaped by environmental and clinical factors rather than by BDNF or MAOA variants. Show less

The aim of this study was to both classify data of familial adenomatous polyposis patients with and without duode- nal cancer and to identify important genes that may be related to duodenal cancer by XGboost model. The current study was performed using expression profile data from a series of duodenal samples from familial adenomatous polyposis patients to explore variations in the familial adenomatous polyposis duodenal adenoma-carcinoma sequence. The expression profiles obtained from cancerous, adenomatous, and normal tissues of 12 familial adenomatous polyposis patients with duodenal cancer and the tissues of 12 familial adenomatous polyposis patients without duodenal cancer were compared. The ElasticNet approach was utilized for the feature selection. Using 5-fold cross-validation, one of the machine learning approaches, XGboost, was utilized to classify duodenal cancer. Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score performance metrics were assessed for model performance. According to the variable importance obtained from the modeling, ADH1C, DEFA5, CPS1, SPP1, DMBT1, VCAN-AS1, APOB genes (cancer vs. adenoma); LOC399753, APOA4, MIR548X, and ADH1C genes (adenoma vs. adenoma); SNORD123, CEACAM6, SNORD78, ANXA10, SPINK1, and CPS1 (normal vs. adenoma) genes can be used as predictive biomarkers. The proposed model used in this study shows that the aforementioned genes can forecast the risk of duodenal cancer in patients with familial adenomatous polyposis. More comprehensive analyses should be performed in the future to assess the reliability of the genes determined. Show less