In the recent survey, Japanese encephalitis (JE) is one of the most common mosquito-borne diseases, accounting for ∼30% of fatalities. The outbreaks of the JE virus (JEV) suggests that exhaustive study is essential for the prevention and management of the disease. The disease mainly spreads in humans and pigs by the vector: mosquito; as this is a major concern, this study had employed various bioinformatics tools to investigate the codon usage bias, evolutionary inference and selection pressure analysis of the Japanese encephalitis virus disease. The results indicated that the JE virus was biased and natural selection was the main factor shaping the codon usage that was determined and confirmed with the Nc, neutrality, PR2 plots and correlation analysis. The evolutionary analysis revealed that the virus had a substitution rate of 1.54 × 10^-4 substitution/site/year and the tMRCA was found to be in 1723. The transmission of the virus in the map found transmissions mostly from China and transmitted across Asia and Africa. The selection pressure analysis employed three methods which had 969th codon site as diversifying site and had many purifying sites that shows the virus had evolved rapidly. The inferences from this study would aid people to employ this methodology on various diseases and also perform insilico studies in the field of vaccinology and immunoinformatics.

Bacterial infections are one of the leading causes of morbidity, mortality, and healthcare complications in patients. Leptospirosis is found to be the most prevalent, re-emergent, and neglected tropical zoonotic disease worldwide. The adaptation to various environmental conditions has made Leptospira acquire a large genome (~4.6 Mb) and a complex outer membrane, making it unique among bacteria that mimic the symptoms of jaundice and hemorrhage. Sph2 is another important virulence factor that enhances hemolytic sphingomyelinase—capable of moving inside mitochondria—which increases the ROS level and decreases the mitochondrial membrane potential, thereby leading to cell apoptosis. In the present study, 25 suspected bovine serum samples were subjected to the Microscopic Agglutination Test (MAT) across the Mysuru region. Different samples, such as urine, serum, and aborted materials from the confirmed MAT-positive animals, were used for isolation and genomic detection by conventional PCR targeting virulence gene, Lipl32, using specific primers. Further, in vitro and in silico studies were performed on isolated cultures to assess the anti-leptospiral, anti-hemolytic, and sphingomyelinase enzyme inhibition using novel pseudopeptides. The microdilution technique (MDT) and dark field microscope (DFM) assays revealed that at a concentration of 62.5 μg/mL, the pseudopeptide inhibited 100% of the growth of Leptospira spp., suggesting its efficiency in the treatment of leptospirosis. The flow cytometry analyses show the potency of the pseudopeptide against sphingomyelinase enzymes using human umbilical vein endothelial cells (HUVECs). Thus, the present study demonstrated the efficacy of the pseudopeptide in the inhibition of the growth of Leptospira, and therefore, this can be used as an alternative drug for the treatment of leptospirosis.

Healthcare is a fundamental part of individual well-being, maintaining social order and developing economies. The delivery of organized preventive, diagnostic, curative and supportive services make up a healthcare system that includes primary, secondary and tertiary levels. Healthcare has developed from traditional, cultural-based forms of service delivery into an integrated, multi-disciplinary practice including medicine, nursing, behavioral science and public health. Contemporary healthcare systems are becoming patient-centered, relying on technology and delivering care through settings beyond hospitals (including community, telehealth and digital health platforms). New technologies including artificial intelligence, personalized or "precision" medicine and genomics will continue to transform the methods used for diagnosing, treating and accessing healthcare. However, despite significant advances, healthcare continues to be both an individual right and a collective obligation. Creating sustainable, fair and effective systems of healthcare delivery necessitates reconciling technological advancement with compassion; the need for efficient use of resources with the need for equitable access to those resources; and technological advancements with human dignity.

Artificial Intelligence (AI), robotics and automation are merging into an integrated platform to transform how healthcare delivers quality, efficiency, and patient centric care. From simple mechanical aids to sophisticated AI assisted surgery platforms, robotics have transformed in healthcare and from basic data entry, automation has also transformed from simple to complex intelligent process management. The core technologies that drive these transformations include, Computer Vision, Natural Language Processing, Reinforcement Learning, and Cloud Robotics via Internet of Things (IoT) driven robotics. Robots are now being used in a wide variety of applications in the healthcare industry, which include robotic-assisted surgery, hospital service robots, rehabilitation and assistive devices, and laboratory diagnostics and automation. As robotics continues to evolve, new emerging trends, which include Collaborative Robots (Cobots), Humanoid Assistants, Swarm Robotics, Generative AI, Digital Twin Technology will continue to redefine patient care, medical research and healthcare delivery. While there are many positive implications of utilizing AI and robotics in the healthcare industry, there are still several barriers or challenges to overcome prior to its full implementation in the healthcare delivery system, which include Cost, Accessibility, Data Security, Ethics and Workforce Displacement. In the future, AI driven robotics may play an important role in the advancement of personalized medicine, Smart Hospitals, and Home-Based Healthcare, creating a collaborative ecosystem between the human expertise and intelligent machines for safe and sustainable healthcare systems.

AI is changing how microbiologists do research and how drugs are developed with faster drug discovery, better vaccine designs and better ways to manage antibiotic resistance. AI also enables researchers to rapidly analyze massive amounts of data relating to genes and chemicals to help identify new compounds or molecules, to reuse existing drugs, and to predict the mechanisms that cause bacteria to develop resistance to antibiotics. In addition to these functions, AI helps researchers identify the best epitopes on an antigen for vaccine development as well as formulate vaccines that will elicit the strongest immune response. AI can also improve the efficiency of clinical trials by helping researchers find patients who fit into clinical studies, monitor the safety of those patients, and determine when to stop clinical studies early if there are concerns about patient safety. Further, AI can assist in identifying pathogens from genomic sequencing as well as annotating genes related to the function of those pathogens, which can aid in developing targeted therapies that take advantage of the unique characteristics of each individual's microbiome. Finally, AI-driven automation in the manufacturing process of pharmaceuticals has the ability to ensure quality control through real-time monitoring and predictive maintenance of equipment and processes to produce pharmaceutical products in a more timely manner. Overall, AI is changing the way researchers in microbiology and pharmacology conduct research and develop new pharmaceutical products. The use of AI is enabling researchers to move away from a traditional trial-and-error approach and toward a data-driven, predictive approach to conducting research. This transition toward using predictive models and analytical tools has resulted in reduced time-to-market and reduced costs associated with developing new drugs and vaccines. Furthermore, the use of AI has improved the safety and efficacy of clinical trials and research. Ultimately, the integration of AI into the scientific community is creating a revolution in personalized healthcare, providing researchers with innovative solutions to meet pressing global health challenges such as the emergence of new infectious diseases and multidrug-resistant pathogens.