Next-Generation strategies to combat antimicrobial resistance: Integrating genomics, CRISPR, and novel therapeutics for effective treatment

Engineering Science & Technology Journal, Journal Year: 2024, Volume and Issue: 5(7), P. 2284 - 2303

Published: July 24, 2024

Antimicrobial resistance (AMR) poses a significant threat to global public health, necessitating innovative strategies combat this escalating issue. This review outlines next-generation approaches integrating genomics, CRISPR technology, and novel therapeutics effectively address AMR. Genomic techniques enable comprehensive understanding of the genetic mechanisms underpinning resistance, facilitating development targeted interventions. By sequencing genomes resistant pathogens, researchers can identify genes, track their spread, predict emerging patterns. CRISPR-Cas systems offer revolutionary tool for combating AMR through precise genome editing. technology disrupt restore antibiotic sensitivity, develop bacteriophage therapies that selectively target bacteria. Moreover, CRISPR-based diagnostics rapid, accurate detection strains, enhancing infection control measures. The advent therapeutics, such as antimicrobial peptides, therapy, synthetic biology-derived compounds, provides alternative treatment options. These bypass traditional exhibit efficacy against multi-drug organisms. Additionally, artificial intelligence (AI) machine learning with genomics accelerate discovery new antibiotics trends, optimizing regimens. Implementing these requires robust collaboration, regulatory frameworks, investment in research development. combining CRISPR, we create multifaceted approach overcome AMR, ensuring effective treatments safeguarding health. integration represents paradigm shift strategy, offering hope future where infections be managed treated. Keywords: Integrating Genomics, Resistance, Therapeutic

Language: Английский

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SAS Meets Machine Learning: An Adaptive Framework for Healthcare Data Fusion

International Journal of Scientific Research in Computer Science Engineering and Information Technology, Journal Year: 2025, Volume and Issue: 11(1), P. 1456 - 1465

Published: Feb. 3, 2025

A Hybrid Approach to Healthcare Data Fusion using SAS and Machine Learning presents a novel framework for integrating traditional SAS-based data management capabilities with modern machine learning algorithms address the complex challenges of healthcare integration. This article introduces an adaptive architecture that leverages SAS's robust processing features alongside specialized models entity resolution, missing imputation, quality assessment. demonstrates significant improvements in completeness, accuracy, consistency compared methods alone, particularly when handling heterogeneous sources, including electronic health records, clinical trials, medical device outputs. Through comprehensive implemented at major hospital system, this showcases how hybrid methodology effectively resolves common integration such as semantic inconsistencies, temporal misalignment, variable while maintaining regulatory compliance. The proposed offers organizations scalable, maintainable solution combines reliability established procedures adaptability techniques, establishing new paradigm fusion.

Language: Английский

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Revolutionizing Patient Safety: The Economic and Clinical Impact of Artificial Intelligence in Hospitals

Hospitals, Journal Year: 2024, Volume and Issue: 1(2), P. 185 - 194

Published: Dec. 12, 2024

Artificial intelligence (AI) has emerged as a transformative force in enhancing patient safety within hospital settings. This perspective explores the various applications of AI improving outcomes, including early warning systems, predictive analytics, process automation, and personalized treatment. We also highlight economic benefits associated with implementation, such cost savings through reduced adverse events improved operational efficiency. Moreover, addresses how can enhance pharmacological treatments, optimize diagnostic testing, mitigate hospital-acquired infections. Despite promising advancements, challenges related to data quality, ethical concerns, clinical integration remain. Future research directions are proposed address these harness full potential healthcare.

Language: Английский

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