Blood biochemical parameters for assessment of COVID-19 in diabetic and non-diabetic subjects: a cross-sectional study.

This study aims to identify blood biomarkers for rapidly predicting progression and severity assessment of COVID-19 in type 2 diabetic (DM) and non-DM (NDM) patients. Among 211 hospitalized patients suspected of COVID-19, 98 were confirmed COVID-19 by rRT-PCR. The COVID-19 positive group contained 58 DM and 40 NDM patients with total death 9 of which 7 were males and 6 were DM, indicating males and DM individuals as more susceptible to COVID-19. Blood biomarkers notably serum ferritin, CRP, D-dimer, ALT, troponin I, and Hb1Ac were significantly (p < 0.05) higher in COVID-19 patients. Ferritin and HbA1c levels were significantly (p < 0.05) higher in DM than NDM COVID-19 patients. The present study suggests that ferritin and HbA1c levels for DM patients, and ferritin, D-dimer, ALT for NDM patients could be routinely used as biomarkers for progression and severity assessment of COVID-19. CRP and Troponin-I could be the predictor only for poor prognosis of COVID-19.

Antibacterial activity of graphene oxide nanosheet against multidrug resistant superbugs isolated from infected patients.

Graphene oxide (GO) is a derivative of graphene nanosheet which is the most promising material of the decade in biomedical research. In particular, it has been known as an antimicrobial nanomaterial with good biocompatibility. In this study, we have synthesized and characterize GO and checked its antimicrobial property against different Gram-negative and Gram-positive multidrug drug resistant (MDR) hospital superbugs grown in solid agar-based nutrient plates with and without human serum through the utilization of agar well diffusion method, live/dead fluorescent staining and genotoxicity analysis. No significant changes in antibacterial activity were found in these two different conditions. We also compare the bactericidal capability of GO with some commonly administered antibiotics and in all cases the degree of inhibition is found to be higher. The data presented here are novel and show that GO is an effective bactericidal agent against different superbugs and can be used as a future antibacterial agent.

Relationship among obesity, blood lipids and insulin resistance in Bangladeshi adults.

INTRODUCTION: Insulin resistance (IR) and abnormal lipid profiles are the risk factors for cardiovascular diseases in obesity. To clarify the relationship of the changes in insulin resistance, body weight and lipid profile, the present study was performed on Bangladeshi adults, total of 1500 individuals at the time of their general health examination in the hospital. METHODS: After exclusion of other endocrine diseases, the remaining 772 patients were classified as IR ≥ 2 and IR < 2 based on the homeostatic model assessment-estimated insulin resistance (HOMA-IR) index. The endocrine disease free subjects were further clustered based on age, gender and obesity. The anthropometric and biochemical profiles were statistically analyzed and correlated with IR ≥ 2 and IR<2 groups as well as other clusters of the subjects. Apart from some disparities, notable differences were observed in all anthropometric data. RESULTS: Total cholesterol (TC), triglyceride (TG), low density lipoprotein (LDL) and serum insulin levels were significantly higher in IR ≥ 2 group in comparison with IR<2 group. Obesity and dyslipidemia were associated as prevalent components of IR. Generalized linear model revealed that TC: LDL and TG: HDL had significant effect on IR. Age group II (41-60 years old) subjects had significantly higher lipid profile compared to age group I (20-40 years old) and age group III (61-80 years old). CONCLUSIONS: Results reported herein support the notion that lipoprotein ratios might be the reliable biomarkers to evaluate IR.

Computational Analysis of Damaging Single-Nucleotide Polymorphisms and Their Structural and Functional Impact on the Insulin Receptor.

Single-nucleotide polymorphisms (SNPs) associated with complex disorders can create, destroy, or modify protein coding sites. Single amino acid substitutions in the insulin receptor (INSR) are the most common forms of genetic variations that account for various diseases like Donohue syndrome or Leprechaunism, Rabson-Mendenhall syndrome, and type A insulin resistance. We analyzed the deleterious nonsynonymous SNPs (nsSNPs) in INSR gene based on different computational methods. Analysis of INSR was initiated with PROVEAN followed by PolyPhen and I-Mutant servers to investigate the effects of 57 nsSNPs retrieved from database of SNP (dbSNP). A total of 18 mutations that were found to exert damaging effects on the INSR protein structure and function were chosen for further analysis. Among these mutations, our computational analysis suggested that 13 nsSNPs decreased protein stability and might have resulted in loss of function. Therefore, the probability of their involvement in disease predisposition increases. In the lack of adequate prior reports on the possible deleterious effects of nsSNPs, we have systematically analyzed and characterized the functional variants in coding region that can alter the expression and function of INSR gene. In silico characterization of nsSNPs affecting INSR gene function can aid in better understanding of genetic differences in disease susceptibility.