摘要
一位新闻记者-机器人和机器学习的工作人员新闻编辑每日新闻-血液疾病和疾病的新研究-脓毒症是一篇报道的主题。根据NewsRx记者从中国贵阳发回的新闻报道，研究人员称：“本研究旨在探讨脓毒症和急性肾损伤(AKI)的潜在机制，包括脓毒症相关的AKI(SA-AKI)，这是危重脓毒症患者常见的并发症。GWAS数据分析AKI与脓毒症之间的基因关联。”本文作者引用了贵州大学的一篇文章，系统地应用了三种不同的机器学习算法(LASO、SVM-RFE、RF)来严格地识别和验证SA-AKI的特征基因，通过ROC曲线和生存分析来评估它们的诊断和预后价值，并对这些基因的功能和免疫学方面、潜在的药物靶点和作用机制进行了研究。建立脓毒症小鼠模型，检测这些特征基因的可靠性。LDSC证实AKI与脓毒症之间存在正相关，但没有发现明显的共享L OCI。双向MR分析显示AKI与脓毒症的风险相互增加。随后鉴定出311个脓毒症和AKI共同的关键基因，其中42个与脓毒症预后显著相关。通过LASO、SVM-RFE和RF算法选择的模型显示出对脓毒症、AKI和SA-AKI的良好预测性能。这些模型在训练和测试数据集中显示出接近完美的AUC，在脓毒症小鼠模型中显示出完美的AUC。本研究确定了62种治疗脓毒症和AKI的潜在药物，并构建了一个ceRNA网络，所鉴定的SIG天然基因具有潜在的临床应用价值，包括脓毒症和AKI的预后评估和靶向治疗策略。
Abstract
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 reporting origina ting from Guiyang, People's Republic of China, by NewsRx correspondents, researc h stated, "This study aimed to explore the underlying mechanisms of sepsis and a cute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent c omplication in critically ill sepsis patients. GWAS data was analyzed for geneti c association between AKI and sepsis." Our news editors obtained a quote from the research from Guizhou University, "Th en, we systematically applied three distinct machine learning algorithms (LASSO, SVM-RFE, RF) to rigorously identify and validate signature genes of SA-AKI, ass essing their diagnostic and prognostic value through ROC curves and survival ana lysis. The study also examined the functional and immunological aspects of these genes, potential drug targets, and ceRNA networks. A mouse model of sepsis was created to test the reliability of these signature genes. LDSC confirmed a posit ive genetic correlation between AKI and sepsis, although no significant shared l oci were found. Bidirectional MR analysis indicated mutual increased risks of AK I and sepsis. Then, 311 key genes common to sepsis and AKI were identified, with 42 significantly linked to sepsis prognosis. Six genes, selected through LASSO, SVM-RFE, and RF algorithms, showed excellent predictive performance for sepsis, AKI, and SA-AKI. The models demonstrated near-perfect AUCs in both training and testing datasets, and a perfect AUC in a sepsis mouse model. Significant differ ences in immune cells, immune-related pathways, HLA, and checkpoint genes were f ound between high- and low-risk groups. The study identified 62 potential drug t reatments for sepsis and AKI and constructed a ceRNA network. The identified sig nature genes hold potential clinical applications, including prognostic evaluati on and targeted therapeutic strategies for sepsis and AKI."
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