Coronavirus disease 2019 (COVID‐19) is an infectious respiratory disease caused by a new strain of the coronavirus. There is limited data on the pathogenesis and the cellular responses of COVID‐19. In this study, we aimed to determine the variation of metabolites between healthy control and COVID‐19 via the untargeted metabolomics method. Serum samples were obtained from 44 COVID‐19 patients and 41 healthy controls. Untargeted metabolomics analyses were performed by the LC/Q‐TOF/MS (liquid chromatography quadrupole time‐of‐flight mass spectrometry) method. Data acquisition, classification, and identification were achieved by the METLIN database and XCMS. Significant differences were determined between patients and healthy controls in terms of purine, glutamine, leukotriene D4 (LTD4), and glutathione metabolisms. Downregulations were determined in R‐S lactoglutathione and glutamine. Upregulations were detected in hypoxanthine, inosine, and LTD4. Identified metabolites indicate roles for purine, glutamine, LTD4, and glutathione metabolisms in the pathogenesis of the COVID‐19. The use of selective leukotriene D4 receptor antagonists, targeting purinergic signaling as a therapeutic approach and glutamine supplementation may decrease the severity and mortality of COVID‐19.

Background: Low-density lipoprotein cholesterol (LDL-C) is an important biomarker for determining cardiovascular risk and regulating lipid lowering therapy. Therefore, the accurate estimation of LDL-C concentration is essential in cardiovascular disease diagnosis and prognosis. Sampson recently proposed a new formula for the estimation of LDL-C. However, little is known regarding the validation of this formula. Objectives: This study aimed to validate this new formula with other well-known formulas in Turkish population, composed of adults. Methods: A total of 88,943 participants above 18 years old at Sivas Cumhuriyet University Hospital (Sivas, Turkey) were included to this study. LDL-C was directly measured by homogeneous assays, i.e., Roche, Beckman and Siemens and estimated by Friedewald’s, Martin-Hopkins’, extended Martin-Hopkins’ and Sampson’s formulas. The concordances between the estimations obtained by the formulas and the direct measurements were evaluated both in general and separately for the LDL-C, TG and non-HDL-C sublevels. Linear regression analysis was applied and residual error plots were generated between each estimation and direct measurement method. Coefficient of determination (R²) and mean absolute deviations were also calculated. Results: The results showed that the extended Martin-Hopkins approach provided the most concordant results with the direct assays for LDL-C estimation. The results also showed that the highest concordances were obtained between the direct assays with the extended Martin-Hopkins formula calculated with the median statistics obtained from our own population. On the other hand, it was observed that the results of the methods may differ in different assays. The extended Martin-Hopkins approach, calculated from the median statistics of our population, gave the most concordant results in patients with “low LDL-C level (LDL-C levels < 70 mg/dL) or hypertriglyceridemia (TG levels ≥ 400 mg/dL)”. Conclusions: Although the results of the formulas in different assays may vary, the extended Martin-Hopkins approach was the best one with the highest overall concordances. The validity of the Martin Hopkins’ and Sampson’s formulas has to be further investigated in different populations.

Objectives: It is vital to determine the intensive care unit (ICU) requirement at an early stage to reduce the mortality rate in COVID-19 patients. The aim of the study was to find reliable predictive markers to determine the ICU requirement. Methods: We retrospectively reviewed the clinical and laboratory records of 151 COVID-19 patients. The predictive abilities of biochemical parameters and computed tomography (CT) score were evaluated to determine of ICU requirement. Results: The area under curve (AUC) values for procalcitonin, D-Dimer, C reactive protein (CRP), glucose, lactate dehydrogenase (LDH) and CT score were found higher than those for other parameters in the prediction of ICU requirement. The negative predictive values of these markers were higher than their positive predictive values. CT score was found to be highly correlated with fibrinogen and CRP. The glucose levels [odd ratio (OR): 95% CI; 1.07, p-value: 0.014] and CT score [OR: 95% CI; 1.05, p-value: 0.022] were associated with ICU requirement in COVID-19 patients. Conclusions: CT score, procalcitonin, D-Dimer, CRP, glucose, and LDH are potential predictors to rule out ICU requirement on hospital admission. Fibrinogen and CRP can be used to follow up and predict lung damages in patients with COVID-19.

Can platelet-rich plasma be used safely in intra-abdominal operations?