首页|Peter MacCallum Cancer Centre Reports Findings in Artificial Intelligence [Rapid screening of bacteriostatic and bactericidal antimicrobial agents against Escherichia coli by combining machine learning (artificial intelligence) and UV-VIS ...]
Peter MacCallum Cancer Centre Reports Findings in Artificial Intelligence [Rapid screening of bacteriostatic and bactericidal antimicrobial agents against Escherichia coli by combining machine learning (artificial intelligence) and UV-VIS ...]
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NETL
NSTL
Royal Soc Chemistry
New research on Artificial Intelligence is the subject of a report. According to news originating from Melbourne, Australia, by NewsRx correspondents, research stated, “Antibiotics are compounds that have a particular mode of action upon the microorganism they are targeting. However, discovering and developing new antibiotics is a challenging and timely process.” Our news journalists obtained a quote from the research from Peter MacCallum Cancer Centre, “Antibiotic development process can take up to 10-15 years and over $1billion to develop a single new therapeutic product. Rapid screening tools to understand the mode of action of the new antimicrobial agent are considered one of the main bottle necks in the antimicrobial agent development process. Classical approaches require multifarious microbiological methods and they do not capture important biochemical and organism therapeutic-interaction mechanisms. This work aims to provide a rapid antibiotic-antimicrobial biochemical diagnostic tool to reduce the timeframes of therapeutic development, while also generating new biochemical insight into an antimicrobial-therapeutic screening assay in a complex matrix. The work evaluates the effect of antimicrobial action through ‘traditional’ microbiological analysis techniques with a high-throughput rapid analysis method using UV-VIS spectroscopy and chemometrics. Bacteriostatic activity from tetracycline and bactericidal activity from amoxicillin were evaluated on a system using nonresistant O157:H7 by confocal laser scanning microscopy (CLSM), scanning electron microscopy (SEM), and UV-VIS spectroscopy (high-throughput analysis). The data were analysed using principal component analysis (PCA) and support vector machine (SVM) classification.”
MelbourneAustraliaAustralia and New ZealandAntimicrobialsArtificial IntelligenceBiochemicalsBiochemistryChemicalsCyborgsDrugs and TherapiesEmerging TechnologiesHealth and MedicineMachine Learning
NETL
NSTL
Royal Soc Chemistry
