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Since February-2020, the world has been battling a tragic public-health crisis with the emergence and spread of 2019-nCoV. Due to the lack of information about the pathogenesis-specific treatment of Covid-19, early diagnosis and timely treatment are important. However, there is still a lack of information about routine-blood-parameteres (RBP) findings and effects in the disease process. Although the literature includes various interventions, existing studies need to be generalized and their reliability improved. In this study, the efficacy of routine blood values used in the diagnosis and prognosis of Covid-19 and independent biomarkers obtained from them were evaluated retrospectively in a large patient group. Low lymphocyte (LYM) and white-blood-cell (WBC), high CRP and Ferritin were effective in the diagnosis of the disease. The d-CWL= and d-CFL= biomarkers derived from them were the most important risk factors in diagnosing the disease and were more successful than direct RBP values. High d-CWL and d-CFL values ​​largely confirmed the Covid-19 diagnosis. The most effective RBP in the prognosis of the disease was CRP. (d-CIT) = CRP*INR*Troponin; (d-CT) = CRP*Troponin; (d-PPT) = PT*Troponin*Procalcitonin biomarkers were found to be more successful than direct RBP values and biomarkers used in previous studies in the prognosis of the disease. Finally, an open-access data source consisting of RBC was created for studies to be carried out in the fight against COVİD-19. In this study, biomarkers derived from RBP were found to be more successful in both diagnosis and prognosis of Covid-19 than previously used direct RBP and biomarkers.

It remains important to investigate the changing and impact of routine blood values (RBVs) in order to predict mortality and follow an appropriate treatment in COVID-19 patients. In the study, the importance of RBVs in the mortality of patients with COVID-19 was investigated. The changes in the biochemical, hematological, and immunological parameters of patients who recovered (n = 4364) and died (n = 233) from COVID-19 over time and their relationship with the mortality of the disease were evaluated retrospectively. Odds ratios of the parameters affecting one-month mortality were calculated by running multiple-logistic-regression analysis. The cut off values and diagnostic efficiencies of the parameters that posed a risk for mortality were obtained via receiver operating curve analysis. It was determined that the C-reactive protein (CRP), D-dimer, procalcitonin, erythrocyte-sedimentation-rate (ESR), troponin values were at abnormal levels until death occurred in the patients who died. In addition, the procalcitonin levels were consistently high in patients who died. The patients who died generally had a sustained increase in their leukocyte and neutrophil levels and biochemical variables, and an ongoing decrease in lymphopenia and eosinopenia levels. Although significant changes were observed in liver function tests, cardiac troponin, hemogram values, kidney function tests and parameters related to inflammation in deceased patients, high ESR, international-normalized-ratio (INR), prothrombin-time (PT), CRP, D-dimer, ferritin and red-cell-distribution width (RDW) values, respectively, were the most effective predictive mortality risk biomarkers of COVID-19. In addition, neutrophilia, leukocytosis, thrombocytopenia, erythrocytopenia were other risk predictors of mortality. Indicators was found in this study can be successfully used to predict mortality from COVID-19.

Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.

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Glutathione S-transferases are multifunctional enzymes for the cellular defense against xenobiotics and provide protection for organism. In this study, the inhibition effects of some antibiotics were investigated against GST obtained from albino-rats kidney, liver, and heart tissues. Ninety-six albino-rats were randomly divided into 16 groups (n:6). The first four groups were control groups that were administrated blank enjection and decapitated at 1-7 h. The other groups were administrated the antibiotics. In all tissues, GST activity was increased in antibiotics groups at 1st and 3rd hours compared to control groups, while it began to fall at 5th and 7th hours (p < .05). In kidney tissues, it was lower than the same control group the cefuroxime and cefoperazone groups at 7th hours (p < .05). In addition, almost all antibiotic groups of kidney tissues had higher GST activity at all hours than those of control groups, but it was higher only at 5th hours in heart tissues (p < .05).

ObjectiveIn stored red blood cells (RBCs), which are used in diseases (e.g., acute blood loss and leukaemia), storage lesions arise by oxidative stress and other factors over time. This study investigated the protective effects of resveratrol and serotonin on stored RBCs.MethodsBlood from each donor (n = 10) was placed in different bags containing 70 mL of citrate phosphate dextrose (total volume: 500 mL) and divided into three groups (n = 30): control, 60 µg/mL resveratrol, and 60 µg/mL serotonin. Malondialdehyde (MDA) and glutathione (GSH) levels, activity of glutathione peroxidase (GSH-Px), catalase, and carbonic anhydrase (CA), and susceptibility to oxidation in RBCs, and pH in whole blood were measured at baseline and on days 7, 14, 21, and 28.ResultsMDA levels and susceptibility to oxidation were increased in all three groups time-dependently, but this increase was greater in the serotonin group than in the other groups. Activity of GSH-Px, CAT, and CA, as well as GSH levels, were decreased in the control and serotonin groups time-dependently, but were significantly preserved in the resveratrol group. The pH was decreased in all groups time-dependently.ConclusionOur study shows that resveratrol attenuates susceptibility to oxidation of RBCs and protects their antioxidant capacity, and partially preserves CA activity time-dependently.