دورية أكاديمية
أعرض في EDS

Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm.

التفاصيل البيبلوغرافية
العنوان: Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm.
المؤلفون: Barrera-Patiño CP; São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, Brazil., Soares JM; São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, Brazil., Branco KC; São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, Brazil., Inada NM; São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, Brazil., Bagnato VS; São Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense n° 400, Parque Arnold Schimidt, São Carlos 13566-590, SP, Brazil.; Biomedical Engineering, Texas A&M University, 400 Bizzell St, College Station, TX 77843, USA.
المصدر: Antibiotics (Basel, Switzerland) [Antibiotics (Basel)] 2023 Sep 30; Vol. 12 (10). Date of Electronic Publication: 2023 Sep 30.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101637404 Publication Model: Electronic Cited Medium: Print ISSN: 2079-6382 (Print) Linking ISSN: 20796382 NLM ISO Abbreviation: Antibiotics (Basel) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI AG, 2012-
مستخلص: FTIR (Fourier transform infrared spectroscopy) is one analytical technique of the absorption of infrared radiation. FTIR can also be used as a tool to characterize profiles of biomolecules in bacterial cells, which can be useful in differentiating different bacteria. Considering that different bacterial species have different molecular compositions, it will then result in unique FTIR spectra for each species and even bacterial strains. Having this important tool, here, we have developed a methodology aimed at refining the analysis and classification of the FTIR absorption spectra obtained from samples of Staphylococcus aureus , with the implementation of machine learning algorithms. In the first stage, the system conforming to four specified species groups, Control, Amoxicillin induced (AMO), Gentamicin induced (GEN), and Erythromycin induced (ERY), was analyzed. Then, in the second stage, five hidden samples were identified and correctly classified as with/without resistance to induced antibiotics. The total analyses were performed in three windows, Carbohydrates, Fatty Acids, and Proteins, of five hundred spectra. The protocol for acquiring the spectral data from the antibiotic-resistant bacteria via FTIR spectroscopy developed by Soares et al. was implemented here due to demonstrating high accuracy and sensitivity. The present study focuses on the prediction of antibiotic-induced samples through the implementation of the hierarchical cluster analysis (HCA), principal component analysis (PCA) algorithm, and calculation of confusion matrices (CMs) applied to the FTIR absorption spectra data. The data analysis process developed here has the main objective of obtaining knowledge about the intrinsic behavior of S. aureus samples within the analysis regions of the FTIR absorption spectra. The results yielded values with 0.7 to 1 accuracy and high values of sensitivity and specificity for the species identification in the CM calculations. Such results provide important information on antibiotic resistance in samples of S. aureus bacteria for potential application in the detection of antibiotic resistance in clinical use.
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معلومات مُعتمدة: GURI - M23303930 Governs University Research Initiative grant program; CAPES - finance code 001 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; CNPq-Grant No. 380930/2022-6 National Council for Scientific and Technological Development; FAPESP Grants No. CEPOF 2013/07276-1, INCT 2014/50857-8 São Paulo Research Foundation
فهرسة مساهمة: Keywords: FTIR spectroscopy; Staphylococcus aureus; amoxicillin induced; antibiotic-resistant bacteria; erythromycin induced; gentamicin induced; machine learning algorithms
تواريخ الأحداث: Date Created: 20231027 Latest Revision: 20231029
رمز التحديث: 20231029
مُعرف محوري في PubMed: PMC10604181
DOI: 10.3390/antibiotics12101502
PMID: 37887203
قاعدة البيانات: MEDLINE
الوصف
تدمد:2079-6382
DOI:10.3390/antibiotics12101502