Ep 35: Christer Malmberg & rapid diagnostics. MALDI-TOF machine learning. Global burden of AMR.
Happy 2022 to you all! We are glad start a new year with you with this episode. This time, we bring you an interview with our first UAC PhD graduate, Christer Malmberg, who defended his industry-PhD thesis last year, where he developed a new rapid method of testing antibiotic susceptibility. We talk about his experience throughout his studies, what this new method brings to the table, and what he wishes for the future. In the news section, we continue talking about diagnostics, looking into a recent study that explores machine learning and the readily available MALDI-TOF system to provide early information that can help guide treatment recommendations for infections. We also talk about the new seminal paper published last month in The Lancet, presenting the most up-to-date and comprehensive data on the global burden of AMR. We hope you enjoy!
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Relevant links:
- Check how the Gradientech method works here.
- This Week in Microbiology (TWiM) podcast episode mentioned.
Further Reading:
- Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning. First article covered in the news section. Read-only version here.
- AI spots antibiotic resistance 24 hours faster than old methods. Futurity popular of the first article covered in the news.
- Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Second article covered in the news section.
- Antimicrobial resistance now a leading cause of death worldwide, study finds. Coverage in the The Guardian of the second article. Good to share with friends and family.
- Why do antibiotics exist? mBio paper mentioned as part of the TWiM episode.
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