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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18 December 2025Rapid culture-free diagnosis of clinical pathogens via integrated microfluidic-Raman micro-spectroscopy
This study presents a rapid, culture-free diagnostic platform to support timely treatment of antimicrobial resistance. By combining microfluidic pathogen enrichment, Raman micro-spectroscopy, and deep-learning analysis, the system can identify infections directly from clinical samples and deliver results within 20 minutes, even at very low pathogen levels. Trained on a large database of bacterial and fungal […]
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18 December 2025Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance
Machine-learning models are increasingly used to predict antimicrobial resistance from bacterial genome data, but their performance is strongly undermined by hidden biases in how data are collected. Using more than 24,000 genomes from five different pathogens, this study shows that bacterial population structure and over-representation of human disease isolates can falsely link resistance to lineage […]
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18 December 2025Gene copy-number features generalize better than SNPs for antimicrobial resistance prediction in Staphylococcus aureus
Genome-based prediction of antimicrobial resistance in Staphylococcus aureus is substantially improved by using pan-genome gene copy-number features rather than traditional SNP-based models. Across 4,255 isolates and six antibiotics, machine-learning models trained on gene content achieved higher accuracy and far better generalization to previously unseen lineages, while SNP-based models performed well only when closely related strains […]
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