首页|Tongji University Reports Findings in Sepsis (Machine learning reveals ferroptos is features and a novel ferroptosis classifier in patients with sepsis)

Tongji University Reports Findings in Sepsis (Machine learning reveals ferroptos is features and a novel ferroptosis classifier in patients with sepsis)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Blood Diseases and Con ditions - Sepsis is the subject of a report. According to news originating from Shanghai, People's Republic of China, by NewsRx correspondents, research stated, "Sepsis is an organ malfunction disease that may become fatal and is commonly a ccompanied by severe complications such as multiorgan dysfunction. Patients who are already hospitalized have a high risk of death due to sepsis." Our news journalists obtained a quote from the research from Tongji University, "Even though early diagnosis is very important, the technology and clinical appr oaches that are now available are inadequate. Hence, there is an immediate neces sity to investigate biological markers that are sensitive, specific, and reliabl e for the prompt detection of sepsis to reduce mortality and improve patient pro gnosis. Mounting research data indicate that ferroptosis contributes to the occu rrence, development, and prevention of sepsis. However, the specific regulatory mechanism of ferroptosis remains to be elucidated. This research evaluated the e xpression profiles of ferroptosis-related genes (FRGs) and the diagnostic signif icance of the ferroptosis-related classifiers in sepsis. We collected three peri pheral blood data sets from septic patients, integrated the clinical examination data and mRNA expression profile of these patients, and identified 13 FRGs in s epsis through a co-expression network and differential analysis. Then, an optima l classifier tool for sepsis was constructed by integrating a variety of machine learning algorithms. Two key genes, ATG16L1 and SRC, were shown to be shared be tween the algorithms, and thus were identified as the FRG signature of classifie r. The tool exhibited satisfactory diagnostic efficiency in the training data se t (AUC = 0.711) and two external verification data sets (AUC = 0.961; AUC = 0.91 3). In the rat cecal ligation puncture sepsis model, in vivo experiments verifie d the involvement of ATG16L1 and SRC in the early sepsis process."

ShanghaiPeople's Republic of ChinaAs iaBlood Diseases and ConditionsBloodstream InfectionCyborgsEmerging Tech nologiesHealth and MedicineMachine LearningRisk and PreventionSepsisSe pticemia

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(MAY.30)