Healthcare Transformation: Artificial Intelligence Is the Dire Imperative of the Day.

At present, healthcare systems around the world are confronted with unprecedented challenges caused by aging demographics, increasing chronic diseases, and resource challenges. In this scenario, artificial intelligence (AI) emerges as a disruptive technology that can provide solutions to these complicated problems. This review article outlines the vital role played by AI in altering the health landscape. The constant demand for effective and accessible healthcare demands the use of new solutions. AI can be described as an important imperative, enabling advancements in many areas of the delivery of healthcare. This review article explores the possibilities of use of AI to aid in the field of healthcare assistants, diagnosing, disease prediction, and personalized treatment and the discovery of drugs, telemedicine and remote monitoring of patients, robotic-assisted procedures imaging for pathology and radiology analysis, and the analysis of genomic data. By analyzing the existing research and cases, we explain how AI-driven technology can optimize processes in healthcare, improve diagnosis accuracy, improve the quality of treatment, and simplify administrative tasks. By highlighting the most successful AI applications and laying out possible future developments, the review article will provide insight for healthcare professionals, policymakers, researchers, and other stakeholders in harnessing the power of AI to transform healthcare delivery and enhance the quality of care for patients.

JN.1 variant in enduring COVID-19 pandemic: is it a variety of interest (VoI) or variety of concern (VoC)?

The emergence of the SARS-CoV-2 Omicron variant, classified as a Variant of Concern (VoC) in November 2021, marked a significant shift in the COVID-19 landscape. This study investigates the subsequent development of a novel Omicron sublineage, JN.1, which displays distinctive mutations in the spike protein. The study delves into the phylogenetic differences between these variants and their potential implications. A comprehensive analysis of the genomic profiles and mutation patterns of JN.1 and BA.2.86 was conducted, utilizing SARS-CoV-2 database. The study explores the unique mutations, such as S:L455S in JN.1, associated with increased transmissibility and immune escape. Furthermore, a comparison with prevalent strains like XBB.1.5 and HV.1 highlights the substantial genetic divergence of JN.1. JN.1, first detected in August 2023, exhibits a notable spike protein mutation profile, including the reappearance of earlier variants' mutations (E484K and P681R). The variant's increased transmissibility and immune evasion potential are attributed to specific spike protein mutations like R21T, S50L, V127F, R158G, and others. The study also explores the distribution and prevalence of JN.1 globally, with a focus on the rising cases in India. JN.1 poses a unique challenge as one of the most immune-evading variants, with potential implications for COVID-19 transmission. The study emphasizes the importance of monitoring and understanding emerging variants, especially those with distinct spike protein mutations. The observed cases in India highlight the need for vigilance and prompt public health responses. As JN.1 continues to evolve, ongoing surveillance, vaccination strategies, and adherence to preventive measures are crucial to mitigating its potential impact on global public health.

Empirical evidence for economic viability of direct seeded rice in peninsular India: An action-based research.

Purpose: This study identified critical constraints in technology adoption for Direct Seeded Rice (DSR) compared with puddled transplanted rice (PTR) practices. We present the impact of DSR technology adoption on paddy yield, income generation, and cost incurred on various farm operations. Furthermore, the study investigates whether a dry DSR practice provides more economic and production benefits than a wet DSR. Methodology: We used a multi-stage sampling (from state to district-to-village-to-farmers) and conducted a face-to-face questionnaire survey to collect primary farm-level data. We collected 669 farm and household-level data and analyzed the impact of DSR and dry DSR adoption over PTR and wet DSR, respectively. Initially, the study employed probit regression analysis to identify the DSR adoption determinants. Subsequently, using the Propensity Score Matching approach, the study measures the impact of DSR adoption over PTR in terms of yield, income, and cost management. Finally, using the PSM approach, the study estimated the impact of dry DSR adoption over wet DSR. Findings: Probit estimates suggest that variables like education, membership in farmers' organizations, farm experience, institutional credit, crop insurance, off-farm income, and smartphone and television ownership positively regulate DSR adoption. The impact assessment analysis reveals that the adoption of DSR over PTR results in marginal yield improvement. However, the cost of irrigation, land preparation, and fertilization is significantly lower in DSR, resulting in an additional income of ₹5192/acre for DSR adopters. Moreover, a comparative analysis between dry DSR and wet DSR indicates that farmers can achieve ₹2467/acre by adopting dry DSR. Practical implications: Our research findings designate the necessity for implementing policies and strategies to promote the adoption of DSR among non-adopters. Besides economic benefits, adopting the DSR method can yield environmental benefits, improve soil health, mitigate soil erosion, and decrease water use.