No abstract

Latent Onset Autoimmune Diabetes in Adults (LADA) is an autoimmune disorder between T1DM and T2DM and is often misdiagnosed as T2DM due to its late onset. The disease is characterized by β-cell failure and slow progression to insulin dependence. Early diagnosis is significant in limiting disease progression. C-peptide levels and autoantibodies against β-cells are the most critical diagnostic biomarkers in LADA. The review aims to understand the biomarkers used to diagnose LADA, and the treatment approaches followed. We have summarized LADA's pathophysiology and the autoantibodies involved in the condition, diagnostic approaches, and challenges. There are clear shortcomings concerning the feasibility of autoantibody testing. Finally, we explored the treatment strategies involved in the management of LADA. In Conclusion, the usual management includes treatment with metformin and the addition of low doses of insulin. The scope of newer oral hypoglycaemic agents such as GLP-1RA and DPP-4 inhibitors are brought into use. Since the disease is not entirely understood in the research level and clinical practice, we hope to encourage further research in this field to find the prevalence. Large randomized controlled trials are required to compare the efficacy of different treatment options available.

Background: Artificial intelligence postulates that computers will eventually supervise performing tasks through various pattern recognition with less or without human interventions and assistance. It appears to mimic human cognitive functions. Resembling the human brain, it receives various forms of raw data that are stored, aligned, surveyed, interpreted, analyzed, and converted to single processed data, making it easy to conclude and understand. Recently, in the digital world, machine learning, deep learning, neural network and AI applications are expanding widely, where humans have expertise. Methodology: A detailed literature survey was performed through an online database, such as ScienceDirect, Google Scholar, Scopus, Cochrane, and PubMed. The search keywords were Machine Learning OR Deep Learning OR Neural Networks OR Applications OR Pharmaceutical Innovations OR Technology OR Artificial Intelligence AND [Pharmaceutical Sectors OR Clinical Pharmacology OR Healthcare OR Medical OR Pharmacovigilance OR Clinical Trials OR Regulatory OR Challenges. The literature search was limited to studies published in English. Results: It was found that there is an immense growth of artificial intelligence in the sector of the pharmaceutical industry applied in drug discovery and drug development, clinical trials, and the pharmacovigilance sector. It has several clinical applications of AI as a tool in health care and biomedical research besides clinical practice. It also shows several challenges faced and methods to overcome them. Conclusion: AI has great potential and future as a valuable tool in the healthcare and pharmaceutical industry by applying a scientific approach and averting real-life challenges.

Background: Acute encephalitis syndrome (AES) is a major public health concern in India and the Japanese Encephalitis (JE) Virus is the most common cause of viral encephalitis in Asia affecting children under the age of 15 years. In India, despite the introduction of the JE vaccine (SA-14-14-2) in the immunization programme, JE continues to account for 15–20% of AES cases to date. The present study evaluates the immunogenicity of live attenuated SA-14-14-2 JE vaccine in terms of persistence of the humoral response after two doses. Methods: A cross-sectional study was conducted among 266 children belonging to one of the JE endemic regions of Uttar Pradesh, India. Blood samples were taken from children (2–10 years) and grouped according to the duration (in years) after two doses of the vaccine (five groups with a class interval of two years). Informed written consent was obtained from the parents/guardians. All the samples collected were tested for the presence of anti-JEV-specific IgG antibodies by enzyme-linked immunosorbent assay (ELISA) and further confirmed by micro neutralization test (MNT) and immunofluorescence assays. Results: Of the 266 samples tested by ELISA for anti-JEV-specific IgG antibodies, 260 (97.74%) were negative and six (2.26%) were equivocal. The geometric mean immune status ratio across the five groups, 0–2 years (n = 59), 2–4 years (n = 73), 4–6 years (n = 65), 6–8 years (n = 48) and 8–10 years (n = 21) post two doses of SA-14-14-2 JE vaccine was 1.143, 1.059, 1.138, 1.075 and 1.130 respectively and the geometric mean titre (GMT) obtained from MNT across the five groups were 10.77, 8.400, 8.453, 9.517 and 9.674 respectively. Conclusion: The study showed a decreasing trend of anti-JEV specific IgG antibody titers across the five groups based on the duration following two doses of SA-14-14-2 vaccine. The results emphasize the significance of booster doses of vaccine for children living in endemic areas.