Smart Innovation
Antimicrobial resistance (AMR)
AMR develops when bacteria, fungi or viruses are exposed to antibiotics, antifungals or antivirals. As a result, the antimicrobials become ineffective and infections may persist. In addition, medical interventions including surgery, chemotherapy and stem cell therapy may become impossible.
AMR is considered the biggest global threat of Health and Food Safety.
AMR Insights
For Researchers and Entrepreneurs who wish to investigate, develop and commercialize novel vaccines, diagnostics and antimicrobials to prevent Antimicrobial resistance, AMR Insights offers selected, global information and data, specific education and extensive networking and partnering opportunities.
AMR Insights is for:
- Researchers at Universities and University Medical Centers
- Researchers at Research Institutes
- R&D professionals in Pharma, Biopharma and Diagnostics companies
- Entrepreneurs in start-up’s and spin off companies
- Innovators, Venture Capitalists.
Latest Topics
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20 April 2026The application of artificial intelligence in surveillance and control for antimicrobial resistance in hospital-acquired infections
This narrative review highlights how artificial intelligence (AI) can significantly enhance surveillance and control of antimicrobial resistance (AMR) in hospital-acquired infections (HAIs), addressing the limitations of traditional, often fragmented and reactive systems. AI applications—including machine learning models using microbiology data, electronic health records, and hospital workflows—can predict the emergence and spread of resistant pathogens, enable […]
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20 April 2026In silico and in vitro assessment of antimicrobial activity of Arctium lappa L. leaf and flower essential oil against WHO priority pathogens
A study on essential oil from Arctium lappa (burdock) highlights its potential as a novel, multitarget antimicrobial agent against resistant pathogens. Chemical analysis identified key components—1,3-cyclooctadiene, caryophyllene oxide, and aromadendrene—while in vitro testing showed strong activity against MRSA, Pseudomonas aeruginosa, and Candida auris, with effectiveness comparable to standard drugs. Computational analyses further demonstrated that these […]
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17 April 2026Machine learning-based prediction of multi-level antimicrobial resistance in Klebsiella pneumoniae using whole-genome sequencing data
This study presents an advanced machine learning approach (MLWA) that uses whole-genome sequencing (WGS) data from over 5,200 bacterial strains to accurately predict antimicrobial resistance across multiple levels, including resistant/susceptible, intermediate categories, and resistance intensity. The models demonstrated high performance (AUC > 0.9, ~96% agreement) across 11 antibiotics and remained robust across different regions, strain […]
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