首页|New Bacterial Infections and Mycoses Findings from IIT Reported (Machine Learning Enabled Multiplex Detection of Periodontal Pathogens By Surface-enhanced Raman Spectroscopy)
New Bacterial Infections and Mycoses Findings from IIT Reported (Machine Learning Enabled Multiplex Detection of Periodontal Pathogens By Surface-enhanced Raman Spectroscopy)
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NETL
NSTL
Elsevier
Current study results on Bacterial Infections and Mycoses have been published. According to news reporting from Chicago, Illinois, by NewsRx journalists, research stated, “Periodontitis is a chronic inflammation of the periodontium caused by a persistent bacterial infection, resulting in destruction of the supporting structures of teeth. Analysis of microbial composition in saliva can inform periodontal status.” Funders for this research include NIH National Institute of Dental & Craniofacial Research (NIDCR), National Institutes of Health (NIH) - USA. The news correspondents obtained a quote from the research from IIT, “Actinobacillus actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), and Streptococcus mutans (Sm) are among reported periodontal pathogens, and were used as model systems in this study. Our atomic force microscopic (AFM) study revealed that these pathogens are biological nanorods with dimensions of 0.6-1.1 mu m in length and 500-700 nm in width. Current bacterial detection methods often involve complex preparation steps and require labeled reporting motifs. Employing surface-enhanced Raman spectroscopy (SERS), we revealed cell-type specific Raman signatures of these pathogens for label-free detection. It overcame the complexity associated with spectral overlaps among different bacterial species, relying on high signal-tonoise ratio (SNR) spectra carefully collected from pure species samples. To enable simple, rapid, and multiplexed detection, we harnessed advanced machine learning techniques to establish predictive models based on a large set of raw spectra of each bacterial species and their mixtures. Using these models, given a raw spectrum collected from a bacterial suspension, simultaneous identification of all three species in the test sample was achieved at 95.6 % accuracy.”
ChicagoIllinoisUnited StatesNorth and Central AmericaBacterial Infections and MycosesCyborgsEmerging TechnologiesHealth and MedicineMachine LearningIIT
NETL
NSTL
Elsevier
