Mass Spectrometric and Artificial Intelligence-Based Identification of the Secretome of Plasmodium falciparum Merozoites to Provide Novel Candidates for Vaccine Development Pipeline.

Purpose: Merozoites are the only extracellular form of blood stage parasites, making it a worthwhile target. Multiple invasins that are stored in the merozoite apical organelles, are secreted just prior to invasion, and mediates its interaction with RBC. A comprehensive identification of all these secreted invasins is lacking and this study addresses that gap.

AI-based pipeline for early screening of lung cancer: integrating radiology, clinical, and genomics data.

Background: The prognosis of lung carcinoma has changed since the discovery of molecular targets and their specific drugs. Somatic Epidermal Growth Factor Receptor () mutations have been reported in lung carcinoma, and these mutant proteins act as substrates for targeted therapies. However, in a resource-constrained country like India, panel-based next-generation sequencing cannot be made available to the population at large.

Structural similarities between SAM and ATP recognition motifs and detection of ATP binding in a SAM binding DNA methyltransferase.

S-adenosylmethionine (SAM) is a ubiquitous co-factor that serves as a donor for methylation reactions and additionally serves as a donor of other functional groups such as amino and ribosyl moieties in a variety of other biochemical reactions. Such versatility in function is enabled by the ability of SAM to be recognized by a wide variety of protein molecules that vary in their sequences and structural folds. To understand what gives rise to specific SAM binding in diverse proteins, we set out to study if there are any structural patterns at their binding sites.

Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development.

Vaccine development is a complex and long process. It involves several steps, including computational studies, experimental analyses, animal model system studies, and clinical trials. This process can be accelerated by using in silico antigen screening to identify potential vaccine candidates.

Effect of micro-osteoperforations on the rate of orthodontic tooth movement and expression of biomarkers: a randomized controlled clinical trial.

Introduction: Micro-osteoperforation is a minimally invasive technique that has been used to accelerate orthodontic tooth movement and reduce treatment duration. However, literature presents conflicting reports about this technique.

Objective: To evaluate the effectiveness of micro-osteoperforations on the rate of canine retraction and expression of biomarkers in gingival crevicular fluid (GCF).

Vaxi-DL: A web-based deep learning server to identify potential vaccine candidates.

The development of a new vaccine is a challenging exercise involving several steps including computational studies, experimental work, and animal studies followed by clinical studies. To accelerate the process, in silico screening is frequently used for antigen identification. Here, we present Vaxi-DL, web-based deep learning (DL) software that evaluates the potential of protein sequences to serve as vaccine target antigens.

Identification of vaccine targets in pathogens and design of a vaccine using computational approaches.

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate.

Prevalence and determinants of the dangerous selfie among medical and nursing students: a cross-sectional study from eastern India.

Background: Globally, there has been an exponential rise in smartphone use and selfie taking among youth. To make selfies exciting, dangerous selfies are often taken that may lead to catastrophic consequences, including death. This study aims to estimate the prevalence of dangerous selfies and to determine the factors associated with dangerous selfies among medical and nursing students in India.