首页|University Gadjah Mada Reports Findings in Sepsis (A machine learning-based elec tronic nose for detecting neonatal sepsis: Analysis of volatile organic compound biomarkers in fecal samples)
University Gadjah Mada Reports Findings in Sepsis (A machine learning-based elec tronic nose for detecting neonatal sepsis: Analysis of volatile organic compound biomarkers in fecal samples)
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New research on Blood Diseases and Con ditions-Sepsis is the subject of a report. According to news originating from Yogyakarta, Indonesia, by NewsRx correspondents, research stated, "Neonatal seps is is a global health threat, contributing to high morbidity and mortality rates among newborns. Recognizing the profound impact of neonatal sepsis on long-term health outcomes emphasizes the critical need for timely detection to mitigate i ts consequences and ensure optimal health for the affected newborns." Our news journalists obtained a quote from the research from University Gadjah M ada, "Currently, various diagnostic approaches have been implemented, but they a re limited by their invasiveness, high costs, centralized testing, frequent dela ys or inaccuracies in results, and the need for sophisticated laboratory equipme nt. We introduced a novel, non-invasive, cost-efficient, and easy-to-use technol ogy that can provide rapid results at a point-of-care. The technology utilized a lab-built metal oxide semiconductor-based electronic nose (cNose) combined with volatile organic compound (VOC) biomarkers identified through gas chromatograph y-mass spectrometry (GC-MS) analysis. The system was evaluated using fecal profi ling tests involving a total of 32 samples, including 17 positive and 15 negativ e sepsis, confirmed by blood culture. To assess the performance in discriminatin g patients from healthy controls, four machine learning algorithms were implemen ted. Based on the cross-validation results, the MLPNN model provided the best re sults in distinguishing between neonates with positive and negative sepsis, achi eving high-performance results of 90.63% accuracy, 88.24% sensitivity, and 93.33% specificity at a 95% confide nce interval. Specific VOCs associated with neonatal sepsis, such as alcohols, a cids, and esters, were successfully identified through GC-MS analysis, further v alidating the diagnostic capability of the cNose device."
YogyakartaIndonesiaAsiaBiomarkersBlood Diseases and ConditionsBloodstream InfectionCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineMachine LearningOrganic ChemicalsSepsisSepticemiaTechnologyVolatile Organic Compounds
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