首页|Beijing National Laboratory for Molecular Sciences (BNLMS) Reports Findings in L ung Cancer (Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strateg y Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectromet ry ...)
Beijing National Laboratory for Molecular Sciences (BNLMS) Reports Findings in L ung Cancer (Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strateg y Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectromet ry ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology-Lung Cancer is the subject of a report. According to news reporting out of Beijing, People' s Republic of China, by NewsRx editors, research stated, "Lung cancer ranks amon g the cancers with the highest global incidence rates and mortality. Swift and e xtensive screening is crucial for the early-stage diagnosis of lung cancer." Our news journalists obtained a quote from the research from Beijing National La boratory for Molecular Sciences (BNLMS), "Laser desorption/ionization mass spect rometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample proce ssing, rapid speed, and high-throughput performance. As n-type semiconductors, t itanate-based perovskite materials can generate charge carriers under ultraviole t light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO (STO/Rh)-assisted LDI-MS combined with mac hine learning to establish a method for urine-based lung cancer screening. We di rectly analyzed urine metabolites from lung cancer patients (LCs), pneumonia pat ients (PNs), and healthy controls (HCs) without employing any pretreatment. Thro ugh the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 fo r LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related p athways through metabolic enrichment analysis." According to the news editors, the research concluded: "These results suggest th e potential of this method for rapidly distinguishing LCs in clinical applicatio ns and promoting precision medicine." This research has been peer-reviewed.
BeijingPeople's Republic of ChinaAsi aCancerCyborgsEmerging TechnologiesHealth and MedicineLung CancerLun g Diseases and ConditionsLung NeoplasmsMachine LearningOncology
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