首页|University of Waterloo Reports Findings in Bladder Cancer (Multispectral 3D DNA Machine Combined with Multimodal Machine Learning for Noninvasive Precise Diagno sis of Bladder Cancer)
University of Waterloo Reports Findings in Bladder Cancer (Multispectral 3D DNA Machine Combined with Multimodal Machine Learning for Noninvasive Precise Diagno sis of Bladder Cancer)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Bladder Can cer is the subject of a report. According to news reporting originating in Water loo, Canada, by NewsRx journalists, research stated, "Extracellular vesicle (EV) molecular phenotyping offers enormous opportunities for cancer diagnostics. How ever, the majority of the associated studies adopted biomarker-based unimodal an alysis to achieve cancer diagnosis, which has high false positives and low preci sion." The news reporters obtained a quote from the research from the University of Wat erloo, "Herein, we report a multimodal platform for the high-precision diagnosis of bladder cancer (BCa) through a multispectral 3D DNA machine in combination w ith a multimodal machine learning (ML) algorithm. The DNA machine was constructe d using magnetic microparticles (MNPs) functionalized with aptamers that specifi cally identify the target of interest, i.e., five protein markers on bladder-can cer-derived urinary EVs (uEVs). The aptamers were hybridized with DNA-stabilized silver nanoclusters (DNA/AgNCs) and a G-quadruplex/hemin complex to form a sens ing module. Such a DNA machine ensured multispectral detection of protein marker s by fluorescence (FL), inductively coupled plasma mass spectrometry (ICPMS), a nd UV-vis absorption (Abs). The obtained data sets then underwent uni- or multim odal ML for BCa diagnosis to compare the analytical performance. In this study, urine samples were obtained from our prospective cohort ( = 45). Our analytical results showed that the 3D DNA machine provided a detection limit of 9.2 x 10 pa rticles mL with a linear range of 4 x 10 to 5 x 10 particles mL for uEVs. Moreov er, the multimodal data fusion model exhibited an accuracy of 95.0% , a precision of 93.1%, and a recall rate of 93.2% on average, while those of the three types of unimodal models were no more than 91 %."
WaterlooCanadaNorth and Central Amer icaBiomarkersBladder CancerCancerCyborgsDiagnostics and ScreeningEme rging TechnologiesHealth and MedicineMachine LearningOncology
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