Understanding the most relevant hematological/biochemical characteristics, pre-existing health conditions and complications in survivors and non-survivor will aid in predicting COVID-19 patient mortality, as well as intensive care unit (ICU) referral and death. A literature review was conducted for COVID-19 mortality in PubMed, Scopus, and various preprint servers (bioRxiv, medRxiv and SSRN), with 97 observational studies and preprints, consisting of survivor and non-survivor sub-populations. This meta/network analysis comprised 19,014 COVID-19 patients, consisting of 14,359 survivors and 4655 non-survivors. Meta and network analyses were performed using META-MAR V2.7.0 and PAST software. The study revealed that non-survivors of COVID-19 had elevated levels of gamma-glutamyl transferase and creatinine, as well as a higher number of neutrophils. Non-survivors had fewer lymphocytes and platelets, as well as lower hemoglobin and albumin concentrations. Age, hypertension, and cerebrovascular disease were shown to be the most influential risk factors among non-survivors. The most common complication among non-survivors was heart failure, followed by septic shock and respiratory failure. Platelet counts, creatinine, aspartate aminotransferase, albumin, and blood urea nitrogen levels were all linked to ICU admission. Hemoglobin levels preferred non-ICU patients. Lower levels of hemoglobin, lymphocytes, and albumin were associated with increased mortality in ICU patients. This meta-analysis showed that inexpensive and fast biochemical and hematological tests, as well as pre-existing conditions and complications, can be used to estimate the risk of mortality in COVID-19 patients.

Type 2 diabetes (T2D) known as a complex metabolic disorder may cause health problems and changes in blood biochemical markers. A growing number of studies have looked into several biomarkers and their connections with T2D risk. However, few have explored the interconnection of these biomarkers, as well as the prospective alterations in the diabetes biomarker correlation network. We conducted a secondary analysis in order to introduce a multi-level approach to establish a relationship between diabetes, pre-diabetes, blood biochemical markers, age, and body mass index (BMI). The dataset was obtained from the Mendeley Data (available at https://data.mendeley.com/datasets/wj9rwkp9c2/1. In this study, three groups were established: non-diabetic (n = 103), pre-diabetic (n = 53), and diabetic (n = 844). According to the Heatmap analysis, non-diabetic and pre-diabetic individuals had the lowest BMI, age, and HbA1c. Diabetes and pre-diabetes were correlated with BMI (r = 0.58 and − 0.27, respectively), age (r = 0.47 and − 0.28, respectively), and HbA1c (r = 0.55 and − 0.21, respectively) using Pearson analysis. Using multivariate analysis, we found that diabetes, BMI, age, HbA1c, cholesterol, triglyceride, LDL, VLDL, and HDL were all associated. Network analysis revealed a connection between BMI and diabetes at the highest cut-off point. Moreover, receiver operating characteristic (ROC) analysis validated the network findings, revealing that BMI (area under the ROC curve, AUC = 0.95), HbA1c (AUC = 0.94), and age (AUC = 0.84) were the best predictors of diabetes. In conclusion, our multi-step study revealed that identifying significant T2D predictors, such as BMI and HbA1c, required a series of mathematical analyses.

Toll-like receptor 2 (TLR2) is a major membrane-bound receptor with ligand and species specificity that activates the host immune response. Heterodimerization of TLR2 with TLR1 (TLR2/1) or TLR6 (TLR2/6), triggered by ligand binding, is essential to initiating the signaling pathway. Bovine TLR2 (bTLR2) heterodimerization has not been defined yet compared with human and mouse TLR2s (hTLR2 and mTLR2). The aim of the present study was to model bovine TLRs (TLRs 1, 2 and 6) and create the heterodimeric forms of the bovine TLR2 using molecular dynamics (MD) simulations. We compared the intermolecular interactions in bTLR2/1-PAM3 and bTLR2/6-PAM2 with the hTLR2 and mTLR2 complexes through docking simulations and subsequent MD analyses. The present computational findings showed that bTLR2 dimerization could have a biological function and activate the immune response, similar to hTLR2 and mTLR2. Agonists and antagonists that are designed for hTLR2 and mTLR2 can target bTLR2. However, the experimental approaches to comparing the functional immune response of TLR2 across species were missing in the present study. This computational study provides a structural analysis of the bTLR2 interaction with bTLR1 and bTLR6 in the presence of an agonist/antagonist and reveals the three-dimensional structure of bTLR2 dimerization. The present findings could guide future experimental studies targeting bTLR2 with different ligands and lipopeptides.

The effect of soybean meal (SBM) replacement with fermented SBM (FSBM) on ruminal fermentation and bacterial abundance in Holstein calves was investigated in this study. Thirty nine calves were randomized to: (1) control: 27% SBM + 0% FSBM (FSBM0, n = 13); (2) 18% SBM + 9% FSBM (FSBM9, n = 13); and (3) 13.5% SBM + 13.5% FSBM (FSBM13, n = 13). SBM contained a greater amount of large peptides containing 3 to 10 amino acids (AAs), while FSBM had a greater amount of ammonia nitrogen (NH₃–N), free AAs, and small peptides containing 2 to 3 AAs. The calves fed FSBM13 had the lowest acetic acid, NH₃–N, and the ratio of acetate to propionate, with the greatest concentration of caproic acid, valeric acid and isovaleric acid in ruminal fluid. Compared to those fed FSBM9 or FSBM13, the calves fed FSBM0 had the greatest proportion of Butyrivibrio fibrisolvens and Ruminococcus albus in rumen fluid. However, the ruminal abundance of Prevotella ruminicola in calves fed FSBM13 was greater than in calves fed FSBM0. Network analysis showed that the abundance of the Ruminococcus albus was associated with large peptides, and butyric acid was correlated with small peptide. Taken together, our findings suggest that FSBM may have the potential to boost calf performance by changing the fermentation products and the relative abundance of some members of the bacterial community in the rumen.

High-protein diets often lead to an increase in urea concentration in follicular fluid of dairy cows, which may reduce oocyte competence. In the present study, maturation media were supplemented with urea (0, 20, 40 mg/dl), and amino acids (AAs) turnover was evaluated in the 24-h spent media of specimens (cell types), bovine cumulus-oocyte complexes (COCs), cumulus cells (CCs), or denuded oocytes (DOs). The main effects of urea and cell type, and their interaction were significant on the individual turnover (expect threonine, glycine, and tyrosine) and total turnover, depletion, and appearance of AAs. The results showed a high level of urea and DOs increased the depletion of all AAs and that of essential and non-AAs, respectively. Sensitivity analysis revealed the highest sensitivity of isoleucine, lysine, and tryptophan to urea, especially in DOs. Principal component analysis (PCA) evaluated the strong correlations between the turnover of: (1) glutamine, aspartic acid or glycine, and developmental competence and fertilization of COCs; (2) serine, isoleucine, valine or glutamic acid, and cleavage rate of DOs; and (3) serine, glutamine, aspartic acid or alanine, and CCs viability. In conclusion, urea significantly changed the turnover of AAs by COCs, CCs and DOs, and reduced the subsequent developmental competence of bovine oocytes.