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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31 March 2025New antibiotic that kills drug-resistant bacteria discovered in technician’s garden
Researchers have discovered a new antibiotic molecule that targets a wide range of disease-causing bacteria, including those resistant to commercial drugs, and is not toxic to human cells. The molecule was found in soil samples from a garden technician’s garden, demonstrating the potential of antibiotics. The molecule targets the ribosome, the protein-making factory of bacteria, […]
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31 March 2025Topological data analysis captures Horizontal Gene Transfer in Antimicrobial Resistance gene families among clinically relevant bacteria
Antibiotic resistance, causing 10 million deaths annually by 2050, is driven by horizontal gene transfer in hospitals. The Critical Assessment of Massive Data Analysis 2023 challenge analyzed resistance markers from 146 Johns Hopkins bacterial isolates using persistent homology, a topological data analysis method. The study found that on average, two 1-holes form for every group […]
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31 March 2025Integrating socioeconomic deprivation indices and electronic health record data to predict antimicrobial resistance
Machine learning models have been developed to predict the presence of Antimicrobial Resistance (AMR) organisms in blood cultures at the first patient encounter. The models use three supervised classifiers: penalized logistic regression, random forest, and XGBoost, and classify five AMR organisms: ESBL, CRE, AmpC, MRSA, and VRE. The combination of ADI and SVI increases predictive […]
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