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

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.

Tumor radiosensitization by photobiomodulation.

PURPOSE: To investigate the safety of photobiomodulation therapy (PBM) in tumors and its potential as a radiosensitizer when combined with radiotherapy. METHODS: We have performed in vitro experiments in A431 cells to assess proliferation and cell cycle after PBM, as well as clonogenic assay and H2AX-gamma immunolabeling to quantify double strand breaks after the combination of PBM and radiation. In vivo experiments in xenografts included Kaplan-Meier survival analysis, optical coherence tomography (OCT) and histological analysis. RESULTS: PBM did not induce proliferation in vitro, but increased the G2/M fraction by 27% 24h after illumination, resulting in an enhancement of 30% in radiation effect in the clonogenic assay. The median survival of the PBM-RT group increased by 4 days and the hazard ratio was 0.417 (CI 95%: 0.173-1.006) when compared to radiation alone. OCT analysis over time demonstrated that PBM increases tumor necrosis due to radiation, and histological analysis showed that illumination increased cell differentiation and angiogenesis, which may play a role in the synergetic effect of PBM and radiation. CONCLUSION: PBM technique may be one of the most appropriate approaches for radiosensitizing tumors while protecting normal tissue because of its low cost and low training requirements for staff.