Barrera Patiño Claudia Patricia, Soares Jennifer Machado, Blanco Kate Cristina, Bagnato Vanderlei Salvador
São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense No. 400, Parque Arnold Schimidt, São Carlos CEP 13566-590, SP, Brazil.
Biomedical Engineering, Texas A&M University, 400 Bizzell St., College Station, TX 77843, USA.
Antibiotics (Basel). 2024 Aug 30;13(9):821. doi: 10.3390/antibiotics13090821.
Recent studies introduced the importance of using machine learning algorithms in research focused on the identification of antibiotic resistance. In this study, we highlight the importance of building solid machine learning foundations to differentiate antimicrobial resistance among microorganisms. Using advanced machine learning algorithms, we established a methodology capable of analyzing the FTIR structural profile of the samples of and (Gram-positive), as well as and (Gram-negative), demonstrating cross-sectional applicability in this focus on different microorganisms. The analysis focuses on specific biomolecules-Carbohydrates, Fatty Acids, and Proteins-in FTIR spectra, providing a multidimensional database that transcends microbial variability. The results highlight the ability of the method to consistently identify resistance patterns, regardless of the Gram classification of the bacteria and the species involved, reinforcing the premise that the structural characteristics identified are universal among the microorganisms tested. By validating this approach in four distinct species, our study proves the versatility and precision of the methodology used, in addition to bringing support to the development of an innovative protocol for the rapid and safe identification of antimicrobial resistance. This advance is crucial for optimizing treatment strategies and avoiding the spread of resistance. This emphasizes the relevance of specialized machine learning bases in effectively differentiating between resistance profiles in Gram-negative and Gram-positive bacteria to be implemented in the identification of antibiotic resistance. The obtained result has a high potential to be applied to clinical procedures.
最近的研究介绍了在专注于鉴定抗生素耐药性的研究中使用机器学习算法的重要性。在本研究中，我们强调建立坚实的机器学习基础以区分微生物之间的抗微生物耐药性的重要性。使用先进的机器学习算法，我们建立了一种能够分析金黄色葡萄球菌和枯草芽孢杆菌（革兰氏阳性）以及大肠杆菌和铜绿假单胞菌（革兰氏阴性）样本的傅里叶变换红外光谱（FTIR）结构特征的方法，证明了在针对不同微生物的这一研究重点中的横断面适用性。该分析聚焦于FTIR光谱中的特定生物分子——碳水化合物、脂肪酸和蛋白质，提供了一个超越微生物变异性的多维数据库。结果突出了该方法能够始终如一地识别耐药模式的能力，无论所涉及细菌的革兰氏分类和物种如何，强化了所识别的结构特征在所测试微生物中具有普遍性的前提。通过在四个不同物种中验证这种方法，我们的研究证明了所用方法的通用性和精确性，此外还为开发一种用于快速、安全鉴定抗微生物耐药性的创新方案提供了支持。这一进展对于优化治疗策略和避免耐药性传播至关重要。这强调了专门的机器学习基础在有效区分革兰氏阴性菌和革兰氏阳性菌的耐药谱以用于抗生素耐药性鉴定方面的相关性。所获得的结果具有很高的应用于临床程序的潜力。
