摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-药物和治疗的新研究-抗真菌药物是一篇报道的主题。根据NewsRx记者来自土耳其埃尔祖鲁姆的新闻报道，研究表明，“抗真菌药物很重要，因为它们在癌症治疗中的潜在应用，无论是单独应用还是传统治疗。阻止这些药物作用和限制其在癌症治疗中的使用的机制尚不完全清楚。”我们的新闻编辑从阿塔图尔克大学的研究中获得了一句话，“评估和鉴别Bcl-2家族的抗A成员对抗真菌药物诱导的细胞死亡的可能保护作用仍有科学上的不确定性，必须考虑。然而,细胞的复杂性给分析细胞的生化变化或分类带来了挑战。本研究首次提出了一种新的方法。采用表面增强拉曼光谱(SERS)方法,研究了bcl-2和Mcl-1蛋白对酮康唑(KET)和氟康唑(FLU)抗真菌DRU GS诱导的t细胞损伤的保护作用。该SERS平台建立了具有高信噪比的稳健拉曼光谱。通过先进的无监督和辅助机器学习方法对SERS光谱数据进行分析,使识别率达到100%(100%)在样品和生物分子鉴定中观察到的与脂质和蛋白质相关的各种SERS谱带表明，这些抗凋亡蛋白的表达减少了抗FU NGAL诱导的氧化生物分子损伤。此外，还进行了细胞活力测定、Annexin V-FITC/PI双染色以及总氧化剂和抗氧化状态分析来支持拉曼测量。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Drugs and Therapies - Antifungals is the subject of a report. According to news reporting originating from Erzurum, Turkey, by NewsRx correspondents, research stated, “Antifungal med ications are important due to their potential application in cancer treatment ei ther on their own or with traditional treatments. The mechanisms that prevent th e effects of these medications and restrict their usage in cancer treatment are not completely understood.” Our news editors obtained a quote from the research from Ataturk University, “Th e evaluation and discrimination of the possible protective effects of the anti-a poptotic members of the Bcl-2 family of proteins, critical regulators of mitocho ndrial apoptosis, against antifungal drug-induced cell death has still scientifi c uncertainties that must be considered. Novel, simple, and reliable strategies are highly demanded to identify the biochemical signature of this phenomenon. Ho wever, the complex nature of cells poses challenges for the analysis of cellular biochemical changes or classification. In this study, for the first time,we in vestigated the probable protective activities of Bcl-2 and Mcl-1 proteins agains t cell damage induced by ketoconazole (KET) and fluconazole (FLU) antifungal dru gs in a yeast model through surface-enhanced Raman spectroscopy (SERS) approach. The proposed SERS platform created robust Raman spectra with a high signal-to-n oise ratio. The analysis of SERS spectral data via advanced unsupervised and sup ervised machine learning methods enabled unquestionable differentiation (100 % ) in samples and biomolecular identification. Various SERS bands related to lipi ds and proteins observed in the analyses suggest that the expression of these an ti-apoptotic proteins reduces oxidative biomolecule damage induced by the antifu ngals. Also, cell viability assay, Annexin V-FITC/PI double staining, and total oxidant and antioxidant status analyses were performed to support Raman measurem ents.”
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