Machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys - Singh, Prateek; Ujjainiya, Rajat; Prakash, Satyartha; Naushin, Salwa; Sardana, Viren; Bhatheja, Nitin; Singh, Ajay Pratap; Barman, Joydeb; Kumar, Kartik; Gayali, Saurabh; Khan, Raju; Rawat, Birendra Singh; et al. (Computers in Biology and Medicine, 2022)
Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.
Keywords: BBV152; Covaxin; COVID-19; Ensemble methods; Infection; machine learning; SARS-CoV-2

Discovery of free glycated amines and glycated urea in diabetic plasma: potential implications in diabetes - Shaikh, Rashdajabeen Q.; Das, Sancharini; Chaurasiya, Arvindkumar; Ashtamy, Murali G.; Sheikh, Amreen B.; Fernandes, Moneesha; Tiwari, Shalbha; Unnikrishnan, Ambika G.; Kulkarni, Mahesh J. (ACS Omega, 2024)
The role of protein glycation in the pathogenesis of diabetes has been well established. Akin to proteins, free amino acids and other small-molecule amines are also susceptible to glycation in hyperglycemic conditions and may have a role in the pathogenesis of the disease. However, information about glycation of free amino acids and other small-molecule amines is relatively obscure. In the quest to discover small-molecule glycated amines in the plasma, we have synthesized glycated amino acids, glycated creatine, and glycated urea, and by using a high-resolution accurate mass spectrometer, a mass spectral library was developed comprising the precursor and predominant fragment masses of glycated amines. Using this information, we report the discovery of the glycation of free lysine, arginine, and leucine/isoleucine from the plasma of diabetic patients. This has great physiological significance as glycation of these amino acids may create their deficiency and affect vital physiological processes such as protein synthesis, cell signaling, and insulin secretion. Also, these glycated amino acids could serve as potential markers of diabetes and its complications. While other amines, such as creatinine and urea, accumulate in the plasma and act as biomarkers of diabetic nephropathy. For the first time, we report the detection of glycated urea in diabetic plasma, which is confirmed by matching the precursor and fragment masses with the in vitro synthesized glycated urea by using C-12(6) and C-13(6)-glucose. Further, we quantified glycated urea detected in two forms, monoglycated urea (MGU) and diglycated urea (DGU), by a targeted mass spectrometric approach in the plasma of healthy, diabetic, and diabetic nephropathy subjects. Both MGU and DGU showed a positive correlation with clinical parameters, such as blood glucose and HbA1c. Given that urea gets converted to glycated urea in hyperglycemic conditions, it is crucial to quantify MGU and DGU along with the urea for the diagnosis of diabetic nephropathy and study their physiological role in diabetes.