Multivariate indicators of disease severity in COVID-19

Calprotectin, a Promising Serological Biomarker for the Early Diagnosis of Superinfections with Multidrug-Resistant Bacteria in Patients with COVID-19

Bacterial and fungal superinfections are common in COVID-19, and early diagnosis can enable timely intervention. Serum calprotectin levels increase with bacterial, fungal, and viral infections. This study evaluated serum calprotectin as a diagnostic and prognostic tool for microbial superinfections in COVID-19. Serum samples from adult patients with moderate and severe COVID-19 were collected during hospitalization from 2020 to 2024. Calprotectin levels were measured using an enzyme-linked immunosorbent assay in 63 patients with moderate COVID-19, 60 patients with severe COVID-19, and 34 healthy individuals. Calprotectin serum levels were elevated in patients with moderate COVID-19 compared with controls, and these levels were further increased in the severe cases. Patients with severe COVID-19 and vancomycin-resistant enterococci (VRE) bacteremia had elevated calprotectin levels, but their C-reactive protein and procalcitonin levels were not increased. Fungal superinfections and herpes simplex virus reactivation did not change the calprotectin levels. A calprotectin concentration of 31.29 µg/mL can be used to diagnose VRE bloodstream infection with 60% sensitivity and 96% specificity. These data suggest that serum calprotectin may be a promising biomarker for the early detection of VRE bloodstream infections in patients with COVID-19.

Influence of simultaneous comorbidities on COVID-associated acute respiratory distress syndrome mortality in people with diabetes

Objectives

This study analyzed the influence of 23 comorbidities on COVID-associated acute distress respiratory syndrome (CARDS) mortality in people with a history of diabetes mellitus.

Methods

An observational, analytical, cross sectional study was utilized to investigate data from 6723 health services in Brazil, comprising 5433 people with diabetes. Adjusted logistic regression models for demographic factors such as age, sex, and race were used to analyze the association between CARDS mortality and comorbidities.

Results

Persons with two (p < 0.001), three (p < 0.001), four (p < 0.001), and five (p < 0.001) simultaneous comorbidities had a higher chance of dying. We identified that diabetes patients who had concomitant metabolic diseases (p = 0.019), neurological disorders (p < 0.001), or were smokers (p < 0.001) had a higher predicted mortality risk based on CADRS.

Conclusion

The number of comorbidities plays a determining role in CARDS mortality in people with diabetes, especially those who suffer from smoking and neurological diseases simultaneously.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Interplay of Demographic Influences, Clinical Manifestations, and Longitudinal Profile of Laboratory Parameters in the Progression of SARS-CoV-2 Infection: Insights from the Saudi Population

Understanding the factors driving SARS-CoV-2 infection progression and severity is complex due to the dynamic nature of human physiology. Therefore, we aimed to explore the severity risk indicators of SARS-CoV-2 through demographic data, clinical manifestations, and the profile of laboratory parameters. The study included 175 patients either hospitalized at King Abdulaziz Medical City-Riyadh or placed in quarantine at designated hotels in Riyadh, Saudi Arabia, from June 2020 to April 2021. Hospitalized patients were followed up through the first week of admission. Demographic data, clinical presentations, and laboratory results were retrieved from electronic patient records. Our results revealed that older age (OR: 1.1, CI: [1.1-1.12]; p < 0.0001), male gender (OR: 2.26, CI: [1.0-5.1]; p = 0.047), and blood urea nitrogen level (OR: 2.56, CI: [1.07-6.12]; p = 0.034) were potential predictors of severity level. In conclusion, the study showed that apart from laboratory parameters, age and gender could potentially predict the severity of SARS-CoV-2 infection in the early stages. To our knowledge, this study is the first in Saudi Arabia to explore the longitudinal profile of laboratory parameters among risk factors, shedding light on SARS-CoV-2 infection progression parameters.

Cholesterol and COVID-19-therapeutic opportunities at the host/virus interface during cell entry

The rapid development of vaccines to combat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections has been critical to reduce the severity of COVID-19. However, the continuous emergence of new SARS-CoV-2 subtypes highlights the need to develop additional approaches that oppose viral infections. Targeting host factors that support virus entry, replication, and propagation provide opportunities to lower SARS-CoV-2 infection rates and improve COVID-19 outcome. This includes cellular cholesterol, which is critical for viral spike proteins to capture the host machinery for SARS-CoV-2 cell entry. Once endocytosed, exit of SARS-CoV-2 from the late endosomal/lysosomal compartment occurs in a cholesterol-sensitive manner. In addition, effective release of new viral particles also requires cholesterol. Hence, cholesterol-lowering statins, proprotein convertase subtilisin/kexin type 9 antibodies, and ezetimibe have revealed potential to protect against COVID-19. In addition, pharmacological inhibition of cholesterol exiting late endosomes/lysosomes identified drug candidates, including antifungals, to block SARS-CoV-2 infection. This review describes the multiple roles of cholesterol at the cell surface and endolysosomes for SARS-CoV-2 entry and the potential of drugs targeting cholesterol homeostasis to reduce SARS-CoV-2 infectivity and COVID-19 disease severity.