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RNA viruses have high rate of replication and mutation that help them adapt and change according to their environmental conditions. Many viral mutants are the cause of various severe and lethal diseases. Vaccines, on the other hand have the capacity to protect us from infectious diseases by eliciting antibody or cell-mediated immune responses that are pathogen-specific. While there are a few reviews pertaining to the use of artificial intelligence (AI) for SARS-COV-2 vaccine development, none focus on peptide vaccination for RNA viruses and the important role played by AI in it. Peptide vaccine which is slowly coming to be recognized as a safe and effective vaccination strategy has the capacity to overcome the mutant escape problem which is also being currently faced by SARS-COV-2 vaccines in circulation.Here we review the present scenario of peptide vaccines which are developed using mathematical and computational statistics methods to prevent the spread of disease caused by RNA viruses. We also focus on the importance and current stage of AI and mathematical evolutionary modeling using machine learning tools in the establishment of these new peptide vaccines for the control of viral disease.

Staphylococcus aureus is a major gram positive bacterial pathogen that causes a wide spectrum of clinical infections, ranging from localized soft-tissue infections to life-threatening bacteremia and endocarditis. S. aureus can infect tissues when the skin or mucosal barriers have been breached. This can lead to many different types of infections, including boils, carbuncles (a collection of boils) and abscesses. Deeply penetrating S. aureus infections can be severe. The incidence of methicillin resistant S. aureus (MRSA) in India ranges from 30-70%. The present study investigates the detection of S. aureus from pus swabs of hospitalized patients (Capital Hospital, Bhubaneswar) having skin infections and abscesses and its' susceptibility pattern to different antibiotics. Out of 230 samples collected 204 (88.9%) were culture positive for different bacterial pathogens from which S. aureus was 54 (23%). The incidence rate of S. aureus among male and female group studied was 56.3% and 43.7%, respectively. The isolated S. aureus was found to be resistant to most of the antibiotics such as azithromycin, doxycycline, ciprofloxacin, tetracycline, gentamycin, ofloxacin, chloramphenicol, ampicillin and oxacillin. Among the various antibiotics, the isolated S. aureus strains revealed resistant to methicillin (MRSA) and vancomycin (VRSA) were 90.7% and 14.8, respectively. The MRSA strains were confirmed genotypically by amplification of methicillin resistant (mec A) gene. S. aureus identification and its antibiogram profile are highly essential for implementation of treatment and control of the infection in Odisha.

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