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.

This study investigated the intricate interplay between Crimean‐Congo hemorrhagic fever virus infection and alterations in amino acid metabolism. The primary aim is to elucidate the impact of Crimean‐Congo hemorrhagic fever (CCHF) on specific amino acid concentrations and identify potential metabolic markers associated with viral infection. One hundred ninety individuals participated in this study, comprising 115 CCHF patients, 30 CCHF negative patients, and 45 healthy controls. Liquid chromatography‐tandem mass spectrometry techniques were employed to quantify amino acid concentrations. The amino acid metabolic profiles in CCHF patients exhibit substantial distinctions from those in the control group. Patients highlight distinct metabolic reprogramming, notably characterized by arginine, histidine, taurine, glutamic acid, and glutamine metabolism shifts. These changes have been associated with the underlying molecular mechanisms of the disease. Exploring novel therapeutic and diagnostic strategies addressing specific amino acids may offer potential means to mitigate the severity of the disease.

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.

Background Obesity and overweight are significant public health problems due to higher risk for coronary artery disease (CAD). It is very important to determine new predictive markers to identify the CAD risk in obese and overweight. To aim this, we analyzed HDL-C subgroups (HDL2-C and HDL3-C) and their paraoxonase-1 (PON-1) activity in obese, overweight and normal weight subjects. Method 71 obese, 40 overweight and 30 healthy subjects as a control group were enrolled the study. Serum lipids levels were determined with enzymatic colorimetric method. Further, PON-1 activities and HDL-C levels were determined by spectrophotometric methods. Non-HDL3-C concentrations were calculated with the subtraction of HDL3-C from total HDL-C. Results The mean serum levels of total HDL-C, HDL3-C, Non-HDL3-C and ApoA1 were higher in control group than obese and overweight groups. There were a statistically significant difference between obese and control group in terms of Lp(a), hsCRP and HOMA index. Higher total PON-1, non-HDL3 PON-1 and HDL3 PON-1 activities were found in the control group compared to obese and overweight groups. Total HDL was weakly negative correlated with the HOMA index, BMI and waist circumference. There was a weak negative correlation between non-HDL3-C and waist circumference. Conclusion Altered HDL-subgroups pattern and decreased PON-1 activities may cause increased risk for CVD in obese and overweight individuals. Therefore determination of HDL subgroups and their PON-1 activity may improve risk prediction compared with measuring total HDL-C levels and its PON-1 activity alone. Body weight and insulin resistance appear to have a role in the decreased HDL-C levels and PON-1activity in obese. Further studies should be conducted to shed more light on impacts of these markers in CVD.