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-干细胞研究的新研究-间充质干细胞是一篇报道的主题。根据NewsRx编辑对波兰Warszawa的新闻报道，研究表明：“全面了解细胞分化的分子机制需要一个整体的观点。我们将无标记FTIR和拉曼高光谱成像与数据挖掘相结合，以检测分子细胞组成，从而实现对细胞分化的无创性监测和生物化学异质性的识别。”我们的新闻记者从军事医学研究所的研究中获得了一句话：“小鼠脂肪来源的间充质干细胞(AD-MSCs)进行脂肪生成，随后进行拉曼和红外成像、油红和免疫荧光。数据分析工作流程(IRRSmetrics4stem)被设计为识别脂肪生成的光谱预测因子，并测试机器学习方法(ML)(层次聚类、PCA、PCA)。为了控制Ad-MSCs的不同分化程度，IRRSmetrics4stem为深入了解成脂细胞的化学作用提供了新的思路。本文利用单细胞追踪技术，建立了Ad-MSCs分化过程中脂质、蛋白质和DNA变化的IRS指标，所选ML方法的检测效率超过90%，证明了IRS指标的高敏感性。重要的是，IRS指标明确地识别了Ad-
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Stem Cell Research-M esenchymal Stem Cells is the subject of a report. According to news reporting ou t of Warszawa, Poland, by NewsRx editors, research stated, "The full understandi ng of molecular mechanisms of cell differentiation requires a holistic view. Her e we combine label-free FTIR and Raman hyperspectral imaging with data mining to detect the molecular cell composition enabling noninvasive monitoring of cell d ifferentiation and identifying biochemical heterogeneity." Our news journalists obtained a quote from the research from the Military Instit ute of Medicine, "Mouse adipose-derived mesenchymal stem cells (AD-MSCs) undergo ing adipogenesis were followed by Raman and FT-IR imaging, Oil Red, and immunofl uorescence. A workflow of the data analysis (IRRSmetrics4stem) was designed to i dentify spectral predictors of adipogenesis and test machine-learning (ML) metho ds (hierarchical clustering, PCA, PLSR) for the control of the AD-MSCs different iation degree. IRRSmetrics4stem provided insights into the chemism of adipogenes is. With single-cell tracking, we established IRRS metrics for lipids, proteins, and DNA variations during AD-MSCs differentiation. The over 90% p redictive efficiency of the selected ML methods proved the high sensitivity of t he IRRS metrics. Importantly, the IRRS metrics unequivocally recognize a switch from proliferation to differentiation."
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