Purpose: Ovarian cancer (OC) is a leading cause of gynecologic cancer mortality, where accurate staging is vital for prognosis and treatment. This study aims to evaluate the prognostic potential of the Platelet-to-Lymphocyte Ratio (PLR), Prognostic Nutritional Index (PNI), and Hemoglobin, Albumin, Lymphocyte, Platelet (HALP) scores in OC staging. Methods: The study was conducted at the Sivas Cumhuriyet University Faculty of Medicine, Department of Obstetrics and Gynecology, between January 1, 2009, and December 31, 2021. A total of 96 OC patients were retrospectively analyzed. Preoperative PLR, PNI, and HALP scores were calculated from routine laboratory parameters. Associations between these scores and clinicopathological factors, including OC stage, were evaluated. The discriminatory power of each index was assessed through ROC curve and AUC (area under the curve) analysis. Results: The AUC for PLR was 0.689 (p < 0.05), indicating significant prognostic value, while the HALP score showed an AUC of 0.671 (p < 0.05). Both markers were significantly associated with OC stages, with higher PLR and lower HALP scores correlating with advanced stages. In contrast, PNI demonstrated a lower AUC of 0.398 (p > 0.05), indicating limited prognostic utility. Conclusion: PLR and HALP scores are useful prognostic markers for OC staging, offering cost-effective and accessible tools in clinical practice. PNI, however, was less predictive. These findings suggest that PLR and HALP could complement existing staging methods, aiding in clinical decision-making. Future large-scale studies are warranted to validate these results.

Amaç: Bu çalışmada amaç epilepsi tanısı almış ve antiepileptik ilaç kullanan gebelerde epilepsi hastalığının ve uygulanan antiepileptik tedavi protokolünün, anne, fetüs ve yenidoğan üzerine etkisinin değerlendirilmesidir. Gereç ve Yöntemler: Sivas Cumhuriyet Üniversitesi Tıp Fakültesi Kadın Hastalıkları ve Doğum Kliniği’nde 2015- 2020 tarihleri arasında gebelik takipleri yapılan ve epilepsi tanısı alan 55 gebe çalışmaya dahil edildi. Gebelerin yaşı, gebelik sayısı, doğum sayısı, epilepsi tanı yaşı, beden kitle indeksi (BKİ) , gebelikte sigara kullanımı, gebelik öncesi nöroloji kontrolüne gidip gitmedikleri, gebelik boyunca görülen nöbetlerin türü, hangi trimesterde nöbet geçirildiği ve sıklığı, gebelik sürecinde kullandıkları AEİ’lar ve tedavi protokolleri, gebelikte folik asit kullanımı değerlendirildi. Çalışmaya alınan gebelerde takipleri sırasında ortaya çıkan obstetrik komplikasyonlar ve doğum haftaları kaydedildi. Yenidoğan 1.-5. dk. Apgar skorları, fetal malformasyonlar ve fetal distres gibi erken neonatal sorunlar değerlendirildi. Bulgular: Yaşların ortanca değeri 26 (17-42) olarak bulunmuştur. Gebelik sayısının ortancası 2 (1-8)’dir. Doğum sayısının ortancası 1 (0-6)’dır. Çalışmaya alınan kadınların epilepsi tanı yaşlarının ortalaması ise 15 (3-29)’tir. Hastaların 34 (%61,9)’ü generalize, 2 (%3,6)’si parsiyel nöbet geçirmiştir. 19 (%34,5)’u ise gebelik sürecinde nöbet geçirmemiştir. Gebelik takibi boyunca komplikasyon gelişenlerin ortalaması % 43,6, gelişmeyenlerin ortalaması % 56,4 bulunmuştur. Hamilelikte trimesterlerde geçirilen nöbet sayısı ile 1. dakika Apgar skoru arasında istatistiksel farklılık vardır (p

Aim: In addition to affecting the individual sociologically and psychologically, heart disease also poses important problems in health systems. Evaluation of heart disease performances has gained great importance in terms of machine learning method. In the study, performances were compared with the machine learning method for risk methods that classify heart illness. Materials and Methods: The categorization process Throughout the research made use of the "Heart Disease Dataset," an open access dataset. F1-score, sensitivity, selectivity, accuracy, balanced accuracy, negative and positive predictive values were used to assess the performance of the categorisation model using the machine learning approach. Random forest method, one of the variable selection methods, was used. Results: According to the relational classification model's classification findings for heart disease, the accuracy, balanced accuracy, sensitivity, selectivity, positive predictive value, negative predictive value, and F1-score values were observed to be 0.997, 0.997, 0.995, 1, 1, and 0.995, respectively. Conclusion: The relational classification model proposed in the analysis obtained in the web-based open access dataset yielded distinctively successful results in classifying heart disease according to performance criteria.

Although there are studies on the effect of alexithymia on expressed emotion, emotion expression in people with alexithymia was not specifically examined. This study aimed to investigate the relationship between alexithymia and emotional expression styles and whether emotional intelligence and empathy mediate this relationship. A total of 254 teacher candidates were evaluated using the Toronto Alexithymia Scale, Empathy Quotient Scale, Trait Emotional Intelligence Questionnaire, and Emotional Expression Styles Inventory. We performed the analyses using structural equation models. Our results revealed that alexithymia indirectly affected the expression style of happiness and sadness emotions negatively and emotional intelligence and empathy played a mediator role in this effect of alexithymia. Also, an indirect positive relationship was identified between alexithymia and anger expression style, and it was found that this relationship was mediated by emotional intelligence. Accordingly, emotional expression styles were also related to other variables. The present study also determined that alexithymia level was significantly and negatively correlated with emotional intelligence and empathy levels. This is the first study to reveal that alexithymia is related to emotional expression styles and that emotional intelligence and empathy also have mediator roles in emotional expression styles. expression styles were also related to other variables.