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Background: Many noninvasive methods, including amino transaminase (AST)/amino transaminase (ALT) ratio (AAR), AST-to-platelet ratio index (APRI), Bonacini cirrhosis discriminant score (CDS), fibrosis-4 (FIB4) index, and age-platelet index (API), have been described to determine the stage of hepatic fibrosis. However, these methods are developed for patients with chronic hepatitis C (CHC) and produce conflicting results in the prediction of liver fibrosis in patients with chronic hepatitis B (CHB). Objectives: The aim of this study was to evaluate the relationship between 7 noninvasive models, including AAR, APRI, CDS, API, FIB-4, neutrophil-to-lymphocyte ratio (NLR), and red cell distribution width (RDW)-to-platelet ratio (RPR) in patients with CHB. Methods: The study population included all patients with CHB, undergoing liver biopsy to determine HBsAg and HBV DNA positivity in more than 6 months. Results: A total of 2520 treatment-naive CHB patients from 40 different centers were included in the study. In total, 62.6% of the patients were male, and the mean age

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Introduction: This study was conducted to determine the relationship between sleep status and sociodemographic features, body mass index (BMI), blood pressure (BP), additional diseases, duration of diabetes mellitus diagnosis, received therapies, and laboratory values in patients with diabetes mellitus. Methods:The study was carried out with 200 adult diabetic patients applying to the Internal Medicine Department of Tepecik Training and Research Hospital with normal cognitive function and without any known psychiatric disorder. The research was conducted between July 10 and August 31, 2019. Sociodemographic data of patients were recorded, and the Pittsburgh Sleep Quality Index (PSQI) was applied. Those with a total score of 5 or above were considered poor, while those less than 5 were deemed good. Results: Of the 200 patients, 64% (n = 128) were female, and 36% (n = 72) were male. The mean total PDQI score was 10.44. Sleep quality was good in 7.5%, while poor in 92.5%. In both genders, the mean total score was greater than five. However, there was a statistically significant difference according to gender. Namely, 3.9% of women had good sleep quality, which was 13.9% among men (p=0.010). Higher educational status was related to higher sleep quality (p=0.007). Sleep quality was further impaired in patients with additional hypertension, hyperlipidemia, or coronary artery disease (p=0.010). The sleep quality of those living with their spouses was lower than that of other participants (p=0.033). There was no statistically significant difference between mean PUKI total scores and diabetes type, BMI, BP, lipid values, fasting & postprandial blood glucose, creatinine, or ALT values (p>0.05). Conclusion:Sleep quality is poor in diabetics. Clinicians should handle patients' sleep status during diabetes mellitus follow-up. Interventions to increase sleep quality in diabetics will no doubt improve the quality of life.