Understanding COVID-19 outcome: Exploring the prognostic value of soluble biomarkers indicative of endothelial impairment.

Host proteins released by the activated endothelial cells during SARS-CoV-2 infection are implicated to be involved in coagulation and endothelial dysfunction. However, the underlying mechanism that governs the vascular dysfunction and disease severity in COVID-19 remains obscure. The study evaluated the serum levels of Bradykinin, Kallikrein, SERPIN A, and IL-18 in COVID-19 (N-42 with 20 moderate and 22 severe) patients compared to healthy controls (HC: N-10) using ELISA at the day of admission (DOA) and day 7 post-admission.

Machine learning-based protein signatures for differentiating hypertensive disorders of pregnancy.

Hypertensive disorders of pregnancy (HDP) result in major maternal and fetal complications. Our study aimed to find a panel of protein markers to identify HDP by applying machine-learning models. The study was conducted on a total of 133 samples, divided into four groups, healthy pregnancy (HP, n = 42), gestational hypertension (GH, n = 67), preeclampsia (PE, n = 9), and ante-partum eclampsia (APE, n = 15).

Urinary Metabolomics Identified Metabolic Perturbations Associated with Gutka, a Smokeless Form of Tobacco.

Gutka, a form of smokeless tobacco, is widely used in the Indian subcontinent and in other regions of South Asia. Smokeless tobacco exposure is most likely to increase the incidence of oral cancer in the Indian population, and metabolic changes are a hallmark of cancer. The development of biomarkers for early detection and better prevention measures for smokeless tobacco users at risk of oral cancer can be aided by studying urinary metabolomics and offering insight into altered metabolic profiles.

Integrated metabolomics and machine learning approach to predict hypertensive disorders of pregnancy.

Background: Hypertensive disorders of pregnancy account for 3% to 10% of maternal-fetal morbidity and mortality worldwide. This condition has been considered one of the leading causes of maternal deaths in developing countries, such as India.

Objective: This study aimed to discover hypertensive disorders of pregnancy-specific candidate urine metabolites as markers for hypertensive disorders of pregnancy by applying integrated metabolomics and machine learning approaches.