■由抗生素的过度使用和生物膜的发展引起的多药耐药细菌(MRB)的出现和迅速传播,对全球公共卫生构成了越来越大的威胁。纳米颗粒作为抗生素的替代品被证明具有通过新的抗微生物机制应对MRB感染的实质性能力。特别是,具有独特(生物)物理化学特性的碳点(CD)在通过破坏细菌壁来对抗MRB方面受到了相当大的关注,与DNA或酶结合,局部诱导高温,或形成活性氧。
■这里,在机器学习(ML)工具的帮助下,研究了各种CD的物理化学特征如何影响其抗菌能力。
■首先收集来自121个样品的CD的合成条件和固有特性,以形成原始数据集,以最小抑制浓度(MIC)为输出。四种分类算法(KNN,SVM,射频,和XGBoost)用输入数据进行训练和验证。发现集成学习方法在我们的数据上是最好的。此外,开发了ε-聚(L-赖氨酸)CD(PL-CD),以验证经过良好训练的ML模型在实验室中的实际应用能力,该模型具有两个管理预测的集成模型。
■因此,我们的结果表明,基于ML的高通量理论计算可用于预测和解码CD特性与抗菌效果之间的关系,加速高性能纳米粒子的开发和潜在的临床翻译。
UNASSIGNED: The emergence and rapid spread of multidrug-resistant bacteria (MRB) caused by the excessive use of antibiotics and the development of biofilms have been a growing threat to global public health. Nanoparticles as substitutes for antibiotics were proven to possess substantial abilities for tackling MRB infections via new antimicrobial mechanisms. Particularly, carbon dots (CDs) with unique (bio)physicochemical characteristics have been receiving considerable attention in combating MRB by damaging the bacterial wall, binding to DNA or enzymes, inducing hyperthermia locally, or forming reactive oxygen species.
UNASSIGNED: Herein, how the physicochemical features of various CDs affect their antimicrobial capacity is investigated with the assistance of machine learning (ML) tools.
UNASSIGNED: The synthetic conditions and intrinsic properties of CDs from 121 samples are initially gathered to form the raw dataset, with Minimum inhibitory concentration (MIC) being the output. Four classification algorithms (KNN, SVM, RF, and XGBoost) are trained and validated with the input data. It is found that the ensemble learning methods turn out to be the best on our data. Also, ε-poly(L-lysine) CDs (PL-CDs) were developed to validate the practical application ability of the well-trained ML models in a laboratory with two ensemble models managing the prediction.
UNASSIGNED: Thus, our results demonstrate that ML-based high-throughput theoretical calculation could be used to predict and decode the relationship between CD properties and the anti-bacterial effect, accelerating the development of high-performance nanoparticles and potential clinical translation.