Objective: The objective of the study is to find out the resistance pattern of pathogenic organisms isolated from intra-abdominal infection (IAI). Methods: A total of 500 samples were collected from suspected IAIs of patients reporting to the hospital and cultured. Identification of the isolates was done using standard identification protocol. Antimicrobial susceptibility was performed by Kirby-Bauer disc diffusion method and interpretation was done using Central Laboratory Standard Institute guidelines. Results: Out of 500 samples, 170 were culture positive and 330 showed no growth. Gram-negative organisms (n=127) outnumbered the Gram-positive organisms (n=23). Among the Gram-negative organisms, Escherichia coli (n=67) was the most commonly isolated bacilli followed by Klebsiella sp. (n=32), Pseudomonas sp. (n=25), Acinetobacter baumannii (n=18), and Klebsiella oxytoca (n=05). Among Gram-positive organisms Staphylococcus aureus (n=17) and Enterococcus spp (n=06) isolates of were grown in culture. Among Gram-negative bacilli, Imipenem followed by Gentamicin was the most effective drug but in Acinetobacter spp. The second most effective drug was Tigecycline. Among Gram-positive isolates, Linezolid was the most effective drug. Conclusion: Prompt starting of empirical antimicrobials based on the local susceptibility pattern, followed by modification of treatment in accordance with the antimicrobial susceptibility report can significantly reduce the morbidity and the mortality associated with IAIs.

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This study focuses on the future of AI in the energy sector, examining how AI can be used to improve efficiency and sustainability in the sector. The study aims to provide a realistic baseline of AI technology that can be used to compare efforts, ambitions, new applications, and challenges around the world. We covered three main topics: (i) how AI is being used in solar and hydrogen power generation; (ii) how AI is being used in supply and demand management control; and (iii) the latest advances in AI technology. In this research we explored how AI techniques outperform traditional models in controllability, energy efficiency optimization, cyber-attack prevention, IoT, big data handling, smart grid, robotics, predictive maintenance control, and computational efficiency. Our study found that AI is becoming an important tool for a new and data-intensive energy industry, which is providing a key magic tool to increase operational performance and efficiency in an increasingly cut-throat environment. KEYWORDS: Artificial Intelligence; Renewable Energy; Energy Demand; Decision Making; Big Data; Energy Digitization

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