Seroprevalence of SARS-CoV-2 IgG in blood donors in a teaching institute from Western part of Maharashtra.

Introduction: COVID-19 is a disease caused by the severe acute respiratory syndrome coronavirus 2 that has appeared as a global pandemic in recent times. Currently, the transmission rate has slowed down significantly, but the definite pathological reason behind this is still unknown. Therefore, the prevalence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody must be studied to establish the relation between the rate of transmission and antibody presence. Materials and Methods: A clinical assessment was performed to evaluate the seroprevalence of SARS-CoV-2 Immunoglobulin G (IgG) antibodies among 299 healthy volunteers in the period of February to May 2021. Serum samples were analyzed using chemiluminescent microparticle immunoassay (CMIA) technology to detect the presence of IgG antibodies. Result: It was observed that 21% of the participants were seropositive, and 78% of the population was seronegative across the different genders. This confirmed that the generation of antibodies is independent of gender. Simultaneously, a t-test was performed that further suggested no statistical correlation between gender and seroprevalence. Moreover, a comprehensive analysis was performed to establish the relation between age and blood group with the seroprevalence. However, there was no statistical relationship found among these parameters. Conclusion: This study assisted in examining the underlying causes of high or low seroprevalence among healthy volunteers.

Assessment of risk factors associated with drug-resistant tuberculosis in pulmonary tuberculosis patients.

INTRODUCTION: Tuberculosis remains a global health problem worldwide and the risk progression of Tuberculosis to Drug Resistant Tuberculosis is influenced by various factors. These include immunocompromised status, past history of tuberculosis, life style and nutritional level. Hence, identifying the population at risk of multidrug-resistant tuberculosis is essential and may help in developing appropriate case-finding strategies. Therefore, the present study was designed to study the contributing risk-factors associated with Drug resistant Tuberculosis. MATERIALS AND METHODS: In this prospective observational study, we assessed 189 Pulmonary tuberculosis diagnosed patients during the period of 2 years at government recognized tertiary care centers. Data was collected from all these patients checked to investigate risk factors associated with Drug resistant tuberculosis development by multivariant analysis. RESULTS: Of the 189 participants, 36 were diagnosed with drug resistant tuberculosis and 153 with drug sensitive tuberculosis. Factors associated with drug resistant tuberculosis include low-weight (OR 8.50; p = 0.0008430991), low-BMI (p = 0.0000527166), lower economic status (OR-2.1351; p = 0.048608696) and tobacco (OR-4.5192; p = 0.0023003189) were found clinically and statistically significant in development of drug resistant tuberculosis. Binary logistic regression was performed to ascertain the effects of various statistically significant factors. Drug resistant tuberculosis patients were 7.77 times more likely to be tobacco users than drug sensitive tuberculosis. CONCLUSIONS: Our study suggests that, there is a compelling and urgent need for increasing public awareness, initiating better nutrition and food programs, regular screening, and better management & control of MDR-TB.

Assessing organophosphate insecticide retention in muscle tissues of juvenile common carp fish under acute toxicity tests.

Organophosphate insecticide spray poses potential threat of contamination of environmental components their accumulation in aquatic organisms. Although various physiological deficits associated with their exposure in fishes are documented, yet their retention in their edible muscle tissues has been poorly studied. In this context, the study was undertaken to ascertain the bioaccumulation of two organophosphate insecticide compounds (dimethoate and chlorpyrifos) in the muscles of juvenile Cyprinus carpio. The study could provide insight into the risks to human health associated with consuming contaminated fish flesh. The fishes exposed to various concentrations of dimethoate and chlorpyrifos in-vivo for 96 to ascertain the uptake and retention of these insecticides in the muscle. Results indicated that fish muscles accumulated the residues at all the concentrations with the recovery of 2.99% (0.032 ppm) of dimethoate exposed to LC50 concentrations. In contrast, the chlorpyrifos residues were found Below the Detection Level (BDL) in the fishes exposed to LC50 concentrations. The percentage bioaccumulation of dimethoate in fish muscle was 88.10%, and that of chlorpyrifos was BDL. The bio-concentration factor was dose-dependent and increased with increasing doses of both insecticides. The study invites attention to human health risk assessment in the regions where contaminated fish are consumed without scientific supervision.

Development of a machine learning model to predict risk of development of COVID-19-associated mucormycosis.

Aim: The study aimed to identify quantitative parameters that increase the risk of rhino-orbito-cerebral mucormycosis, and subsequently developed a machine learning model that can anticipate susceptibility to developing this condition. Methods: Clinicopathological data from 124 patients were used to quantify their association with COVID-19-associated mucormycosis (CAM) and subsequently develop a machine learning model to predict its likelihood. Results: Diabetes mellitus, noninvasive ventilation and hypertension were found to have statistically significant associations with radiologically confirmed CAM cases. Conclusion: Machine learning models can be used to accurately predict the likelihood of development of CAM, and this methodology can be used in creating prediction algorithms of a wide variety of infections and complications.

Fungal infections caused by the Mucorales order of fungi usually target patients with a weakened immune system. They are usually also associated with abnormal blood sugar states, such as in diabetic patients. Recent work during the COVID-19 outbreak suggested that excessive steroid use and diabetes may be behind the rise in fungal infections caused by Mucorales, known as mucormycosis, in India, but little work has been done to see whether we can predict the risk of mucormycosis. This study found that these fungal infections need not necessarily be caused by Mucorales' species, but by a wide variety of fungi that target patients with weak immune systems. Secondly, we found that diabetes, breathing-assisting devices and high blood pressure states had associations with COVID-19-associated fungal infections. Finally, we were able to develop a machine learning model that showed high accuracy when predicting the risk of development of these fungal infections.