首页|Ataturk University Reports Findings in Antifungals (Machine learning-assisted SE RS approach enables the biochemical discrimination in Bcl-2 and Mcl-1 expressing yeast cells treated with ketoconazole and fluconazole antifungals)
Ataturk University Reports Findings in Antifungals (Machine learning-assisted SE RS approach enables the biochemical discrimination in Bcl-2 and Mcl-1 expressing yeast cells treated with ketoconazole and fluconazole antifungals)
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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.”
ErzurumTurkeyEurasiaAntifungalsA ntiinfectivesAzole AntifungalsBiochemicalsBiochemistryChemicalsCyborgsDermatological AgentsDrugs and TherapiesEmerging TechnologiesFluconazole TherapyHealth and MedicineKetoconazole TherapyMachine LearningPharmaceu ticalsTopical Antifungals
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