摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一篇报道的主题。根据来自中国辽宁的消息，NewsRx的记者报道，研究表明：“脓毒症，导致严重的器官和组织损伤甚至死亡，尚未完全阐明。因此，了解脓毒症相关免疫反应的关键机制将导致更多潜在的治疗策略。”我们的新闻记者引用了北方战区司令部总医院的一项研究:“研究了GSE167363数据集中4例脓毒症患者和2例健康对照的单细胞RNA数据。假颞叶细胞电泳分析脓毒症时中性粒细胞簇。采用hdWGCNA方法,分析了脓毒症时中性粒细胞簇。”探讨了嗜中性粒细胞的关键基因模块,采用多种机器学习方法筛选和验证了嗜中性粒细胞hub基因,并利用SCENIC技术研究了调控hub基因的转录因子。定量逆转录-聚合酶链反应检测两种脓毒症小鼠外周血中性粒细胞hu B基因的mRNA表达，发现两种新的中性粒细胞亚型在脓毒症时显著增加，这两种亚型在中性粒细胞不同识别的后期富集，hdWGCNA分析揭示了3种不同的模块(绿松石、棕褐色和黑色)。8种机器学习方法显示8个hub基因(ALPL、ACTB、CD177、GAPDH、SLC25A37、S100A8、S100A9和STXBP2)具有较高的准确性和鲁棒性,scenesic分析显示APLP、CD177、GAPDH、S100A9和STXBP2与多种转录因子密切相关,最后发现ALPL、CD177、S100A9、S100A8、S100A8、s100a在CLP和lps诱导的脓毒症小鼠外周血中性粒细胞中,STXBP2表达明显上调,发现了脓毒症小鼠中性粒细胞的新聚集。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news originating from Liaoning, Peopl e's Republic of China, by NewsRx correspondents, research stated, "Sepsis, causi ng serious organ and tissue damage and even death, has not been fully elucidated . Therefore, understanding the key mechanisms underlying sepsis-associated immun e responses would lead to more potential therapeutic strategies." Our news journalists obtained a quote from the research from the General Hospita l of Northern Theater Command, "Single-cell RNA data of 4 sepsis patients and 2 healthy controls in the GSE167363 data set were studied. The pseudotemporal traj ectory analyzed neutrophil clusters under sepsis. Using the hdWGCNA method, key gene modules of neutrophils were explored. Multiple machine learning methods wer e used to screen and validate hub genes for neutrophils. SCENIC was then used to explore transcription factors regulating hub genes. Finally, quantitative rever se transcription-polymerase chain reaction was to validate mRNA expression of hu b genes in peripheral blood neutrophils of two mice sepsis models. We discovered two novel neutrophil subtypes with a significant increase under sepsis. These t wo neutrophil subtypes were enriched in the late state during neutrophils differ entiation. The hdWGCNA analysis of neutrophils unveiled that 3 distinct modules (Turquoise, brown, and blue modules) were closely correlated with two neutrophil subtypes. 8 machine learning methods revealed 8 hub genes with high accuracy an d robustness (ALPL, ACTB, CD177, GAPDH, SLC25A37, S100A8, S100A9, and STXBP2). T he SCENIC analysis revealed that APLP, CD177, GAPDH, S100A9, and STXBP2 were sig nificant associated with various transcriptional factors. Finally, ALPL, CD177, S100A8, S100A9, and STXBP2 significantly up regulated in peripheral blood neutro phils of CLP and LPS-induced sepsis mice models. Our research discovered new clu sters of neutrophils in sepsis."
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