Vaccine development using artificial intelligence and machine learning: A review.

The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immunogenicity, and designing delivery systems that ensure optimal efficacy. Artificial intelligence (AI) can facilitate this process by using machine learning methods to analyze large and diverse datasets, suggest novel vaccine candidates, and refine their design and predict their performance.

Profiling of Antimicrobial Resistance Genes and Integron from Isolates Using Whole Genome Sequencing.

This study is designed to investigate for the antibiotic resistance genes (ARGs) and integrons from healthy as well as diarrhoeic/diseased animals/birds' faecal samples. A total of eight samples were selected for the study; from each animal, two samples were taken, one from healthy animals/birds and one from diarrhoeic/diseased animals/birds. Antibiotic sensitivity testing (AST) and whole genome sequencing (WGS) was performed for selected isolates.