Aim There is a lack of an overview of the factors associated with postacute COVID-19 musculoskeletal symptoms. The aims of this study were as follows: 1- to evaluate the most frequent admission symptoms and the frequency of musculoskeletal symptoms in postacute COVID-19 patients; and 2- to determine the related factors with the postacute COVID-19 musculoskeletal symptoms. Methods A total of 280 postacute COVID-19 patients (183 females, 97 males) were enrolled and divided into two groups: 1- patients whose musculoskeletal symptoms initiated with or were aggravated by COVID-19 (n = 240); and 2- patients whose musculoskeletal symptoms did not change with COVID-19 (n = 40). The variables were demographic and treatment data, symptoms on admission, postacute COVID-19 symptoms, laboratory results (complete blood count, erythrocyte sedimentation rate, C-reactive protein, ferritin and d-dimer), chest computed tomography findings and symptoms during acute COVID-19. Results Most of the patients have fatigue (71.8%), spine pain (70.7%) and myalgia (60.7%). The most common pain region was the back (30.4%). The frequency of dyspnoea was 30%, cough 18.5% and chest pain 10.7%. Having any chronic disease (P = .031), the duration of hospital stay (P = .016), frequency of back pain during acute COVID-19 (P = .018), tomography findings and d-dimer (P = .035) levels were significantly higher, and lymphocyte (P = .024) levels were significantly lower in the patients whose symptoms began with or were aggravated by COVID-19. Conclusion Back pain was the most frequent symptom on admission. The most common postacute COVID-19 musculoskeletal symptoms were fatigue, spine pain and myalgia. Lower lymphocyte and higher d-dimer levels, the presence of COVID-19 findings in tomography and back pain during acute COVID-19 infection, higher duration of hospital stay and having chronic diseases were related to post-COVID-19 musculoskeletal symptoms.

To investigate the role of oxidative stress on pseudoexfoliation formation and progression from pseudoexfoliation syndrome (XFS) to pseudoexfoliation glaucoma (XFG). This study investigated oxidative stress biomarkers in blood samples from 58 patients with XFG, 47 patients with XFS, and 134 healthy age- and sex-matched controls. The highest serum malondialdehyde (MDA) levels were measured in XFG patients (p<0.001), and MDA level was higher in XFS patients than controls (p<0.001). Superoxide dismutase (SOD) and catalase (CAT) enzyme activities were significantly lower in XFS and XFG patients than in the control group, whereas a significant increase was observed in glutathione (GSH) levels (p<0.001 for all). However, levels of these three biomarkers did not differ significantly between XFS and XFG patients (p=0.188, p=0.185, and p=0.733, respectively). Nitric oxide (NO) concentration was significantly lower in XFG patients compared to XFS patients and controls (p<0.001) but did not differ between XFS patients and controls (p=0.476). Elevated MDA levels suggest that lipid peroxidation is important in XFS and XFG development and progression from XFS to XFG. In addition, reduction in SOD and CAT enzyme activities is considered a deficiency in the enzymatic antioxidant protection system. Furthermore, GSH values may be evaluated as a compensatory response to oxidative stress in XFS and XFG. Alterations in NO indicate the role of a vascular regulatory factor in the progression from XFS to glaucoma.

Background: Psychoactive substances affect mainly central nervous system and brain function causing changes in behavior. Objectives: The purpose of this study was to determine the effects of different psychoactive substances on serum biochemical parameters. Patients and Methods: The study included 324 drug dependents, and 69 controls. The patient group was determined according to DSM-IV (The diagnostic and statistical manual of mental disorders, fourth edition) criteria. All patients and control subjects were tested for routine biochemical parameters and urine toxicology parameters for psychoactive substance use. Cases and controls with accompanying diseases like diabetes, cancer, metabolic disorders etc. are excluded from the study. Moreover, an association between urine toxicology results and changes in biochemical parameters was evaluated for statistical significance. Results: There was a statistically significant difference in the Gamma-Glutamyl Transferase (GGT), uric acid, creatinine, urea, albumin, Aspartate Aminotransferase (AST) medians between the dependent and control groups (P < 0.05). We found a statistically significant difference in sodium and albumin levels between the opium-dependent and control groups (P < 0.05). In the benzodiazepin dependent group, we found a significant difference in GGT, urea, glucose, sodium, T protein, and AST levels (P < 0.05). Moreover, a statistically significant difference was observed in triglyceride and GGT levels between the ethyl glucuronide and control groups (P < 0.05). Conclusions: In psychoactive substance dependents, serum routine biochemistry parameters can be used to predict the need for intensive monitoring and treatment programs. Keywords: Substance Addiction; Psychoactive Drugs; Biochemistry

We aimed to compare the performance of three different individual ROC methods (one from each of the broad categories of parametric, nonparametric and semiparametric analysis) for assessing continuous diagnostic tests: the binormal method as a parametric method, an empirical approach as a nonparametric method, and a semiparametric method using generalized linear models (GLM). We performed a simulation study with various sample sizes under normal, skewed, and monotone distributions. In the simulations, we used estimates of the ROC curve parameters 𝑎 and 𝑏, estimates of the area under the curve (AUC), the standard errors and root mean square errors (RMSEs) of these estimates, and the 95% AUC confidence intervals for comparison. The three methodologies were also applied to an acute coronary syndrome dataset in which serum myoglobin levels were used as a biomarker for detecting acute coronary syndrome. The simulation and application studies suggest that the semiparametric ROC analysis using GLM is a reliable method when the distributions of the diagnostic test results are skewed and that it provides a smooth ROC curve for obtaining a unique cutoff value. A sample size of 50 is sufficient for applying the semiparametric ROC method.