Identification of High Death Risk Coronavirus Disease-19 Patients using Blood Tests.

Background: The coronavirus disease (COVID-19) pandemic has made a great impact on health-care services. The prognosis of the severity of the disease help reduces mortality by prioritizing the allocation of hospital resources. Early mortality prediction of this disease through paramount biomarkers is the main aim of this study. Materials and Methods: In this retrospective study, a total of 205 confirmed COVID-19 patients hospitalized from June 2020 to March 2021 were included. Demographic data, important blood biomarkers levels, and patient outcomes were investigated using the machine learning and statistical tools. Results: Random forests, as the best model of mortality prediction, (Matthews correlation coefficient = 0.514), were employed to find the most relevant dataset feature associated with mortality. Aspartate aminotransferase (AST) and blood urea nitrogen (BUN) were identified as important death-related features. The decision tree method was identified the cutoff value of BUN >47 mg/dL and AST >44 U/L as decision boundaries of mortality (sensitivity = 0.4). Data mining results were compared with those obtained through the statistical tests. Statistical analyses were also determined these two factors as the most significant ones with P values of 4.4 × 10-7 and 1.6 × 10-6, respectively. The demographic trait of age and some hematological (thrombocytopenia, increased white blood cell count, neutrophils [%], RDW-CV and RDW-SD), and blood serum changes (increased creatinine, potassium, and alanine aminotransferase) were also specified as mortality-related features (P < 0.05). Conclusions: These results could be useful to physicians for the timely detection of COVID-19 patients with a higher risk of mortality and better management of hospital resources.

In silico identification and characterization of antineoplastic asparaginase enzyme from endophytic bacteria.

It is generally accepted that L-asparagine is an important amino acid required for the fast growth of cells. Cancerous cells receive this amino acid from extracellular sources. The depletion of L-asparagine from its surrounding environments by asparaginase enzyme can be used as a therapeutic strategy in cancer patients. This therapeutic enzyme is produced commercially mainly from bacteria such as Escherichia coli and Erwinia chrysanthemi. The side effects of such drugs have persuaded scientists to find new enzyme sources. In this study, in silico approach was applied to investigate L-asparaginase producing endophytic bacteria that produce more compatible enzymes within the body. Protein-protein basic local alignment search tool with E. coli and E. chrysanthemi asparaginase enzyme sequences against 262 endophytic bacteria were performed. The results with identity more than 35%, coverage more than 80%, and E-value less than 10-4 were selected. Then, some of bioinformatics tools were used to characterize them. A total of nine sequences consisting of seven known and two hypothetical proteins were identified in six bacterial species. The results showed that some of the asparaginase enzymes produced by endophytic bacteria possess more suitable immunological indices compared with asparaginase enzymes of E. coli and E. chrysanthemi. Herbaspirillum rubrisubalbicans was predicted to produce a nonallergen and nonantigen asparaginase enzyme. The number of antigenic determinants was predicted to be lower in asparaginase enzymes produced by Bacillus amyloliquefaciens, H. rubrisubalbicans, and H. seropedicae. Moreover, the number of high-scored B-cell epitopes was lower in enzyme sequences related to the mentioned bacteria and Paenibacillus polymyxa. The number of discontinuous epitopes and the number of T-cell epitopes were lower in B. amyloliquefaciens produced enzymes. Therefore, the therapeutic use of these enzymes is possible.

In Silico Identification of Probable Drug and Vaccine Candidates Against Antibiotic-Resistant Acinetobacter baumannii.

Acinetobacter baumannii is known as a Gram-negative bacterium that has become one of the most important health problems due to antibiotic resistance. Today, numerous efforts are being made to find new antibiotics against this nosocomial pathogen. As an alternative solution, finding bacterial target(s), necessary for survival and spread of most resistant strains, can be a benefit exploited in drug and vaccine design. In this study, a list of extensive drug-resistant and carbapenem-resistant (multidrug resistant) A. bumannii strains with complete sequencing of genome were prepared and common hypothetical proteins (HPs) composed of more than 200 amino acids were selected. Then, a number of bioinformatics tools were combined for functional assignments of HPs using their sequence. Overall, among 18 in silico investigated proteins, the results showed that 7 proteins implicated in transcriptional regulation, pilus assembly, protein catabolism, fatty acid biosynthesis, adhesion, urea catalysis, and hydrolysis of phosphate monoesters have theoretical potential of involvement in successful survival and pathogenesis of A. baumannii. In addition, immunological analyses with prediction softwares indicated 4 HPs to be probable vaccine candidates. The outcome of this work will be helpful to find novel vaccine design candidates and therapeutic targets for A. baumannii through experimental investigations.