Time Evolution of Bacterial Resistance Observed with Principal Component Analysis
<b>Background/Objectives</b>: In recent work, we have demonstrated that principal component analysis (PCA) and Fourier Transformation Infrared (FTIR) spectra are powerful tools for analyzing the changes in microorganisms at the biomolecular level to detect changes in bacteria with resist...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Antibiotics |
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| Online Access: | https://www.mdpi.com/2079-6382/14/7/729 |
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| author | Claudia P. Barrera Patiño Mitchell Bonner Andrew Ramos Borsatto Jennifer M. Soares Kate C. Blanco Vanderlei S. Bagnato |
| author_facet | Claudia P. Barrera Patiño Mitchell Bonner Andrew Ramos Borsatto Jennifer M. Soares Kate C. Blanco Vanderlei S. Bagnato |
| author_sort | Claudia P. Barrera Patiño |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: In recent work, we have demonstrated that principal component analysis (PCA) and Fourier Transformation Infrared (FTIR) spectra are powerful tools for analyzing the changes in microorganisms at the biomolecular level to detect changes in bacteria with resistance to antibiotics. Here biochemical structural changes in <i>Staphylococcus aureus</i> were analyzed over exposure time with the goal of identifying trends inside the samples that have been exposed to antibiotics for increasing amounts of time and developed resistance. <b>Methods</b>: All studied data was obtained from FTIR spectra of samples with induced antibiotic resistance to either Azithromycin, Oxacillin, or Trimethoprim/Sulfamethoxazole following the evolution of this development over four increasing antibiotic exposure periods. <b>Results</b>: The processing and data analysis with machine learning algorithms performed on this FTIR spectral database allowed for the identification of patterns across minimum inhibitory concentration (MIC) values associated with different exposure times and both clusters from hierarchical classification and PCA. <b>Conclusions</b>: The results enable the observation of resistance development pathways for the sake of knowing the present stage of resistance of a bacterial sample. This is carried out via machine learning methods for the purpose of faster and more effective infection treatment in healthcare settings. |
| format | Article |
| id | doaj-art-c48bf75282ea41758f2f631f58a00a0e |
| institution | DOAJ |
| issn | 2079-6382 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibiotics |
| spelling | doaj-art-c48bf75282ea41758f2f631f58a00a0e2025-08-20T02:45:48ZengMDPI AGAntibiotics2079-63822025-07-0114772910.3390/antibiotics14070729Time Evolution of Bacterial Resistance Observed with Principal Component AnalysisClaudia P. Barrera Patiño0Mitchell Bonner1Andrew Ramos Borsatto2Jennifer M. Soares3Kate C. Blanco4Vanderlei S. Bagnato5Sao Carlos Institute of Physics (IFSC), University of Sao Paulo (USP), Sao Carlos 13566-590, SP, BrazilBiomedical Engineering, Texas A&M University, 400 Bizzell St, College Station, TX 77843, USABiomedical Engineering, Texas A&M University, 400 Bizzell St, College Station, TX 77843, USASao Carlos Institute of Physics (IFSC), University of Sao Paulo (USP), Sao Carlos 13566-590, SP, BrazilSao Carlos Institute of Physics (IFSC), University of Sao Paulo (USP), Sao Carlos 13566-590, SP, BrazilSao Carlos Institute of Physics (IFSC), University of Sao Paulo (USP), Sao Carlos 13566-590, SP, Brazil<b>Background/Objectives</b>: In recent work, we have demonstrated that principal component analysis (PCA) and Fourier Transformation Infrared (FTIR) spectra are powerful tools for analyzing the changes in microorganisms at the biomolecular level to detect changes in bacteria with resistance to antibiotics. Here biochemical structural changes in <i>Staphylococcus aureus</i> were analyzed over exposure time with the goal of identifying trends inside the samples that have been exposed to antibiotics for increasing amounts of time and developed resistance. <b>Methods</b>: All studied data was obtained from FTIR spectra of samples with induced antibiotic resistance to either Azithromycin, Oxacillin, or Trimethoprim/Sulfamethoxazole following the evolution of this development over four increasing antibiotic exposure periods. <b>Results</b>: The processing and data analysis with machine learning algorithms performed on this FTIR spectral database allowed for the identification of patterns across minimum inhibitory concentration (MIC) values associated with different exposure times and both clusters from hierarchical classification and PCA. <b>Conclusions</b>: The results enable the observation of resistance development pathways for the sake of knowing the present stage of resistance of a bacterial sample. This is carried out via machine learning methods for the purpose of faster and more effective infection treatment in healthcare settings.https://www.mdpi.com/2079-6382/14/7/729antibiotic-resistant bacteria<i>Staphylococcus aureus</i>Fourier Transformation Infraredminimum inhibitory concentrationmachine learning algorithms |
| spellingShingle | Claudia P. Barrera Patiño Mitchell Bonner Andrew Ramos Borsatto Jennifer M. Soares Kate C. Blanco Vanderlei S. Bagnato Time Evolution of Bacterial Resistance Observed with Principal Component Analysis Antibiotics antibiotic-resistant bacteria <i>Staphylococcus aureus</i> Fourier Transformation Infrared minimum inhibitory concentration machine learning algorithms |
| title | Time Evolution of Bacterial Resistance Observed with Principal Component Analysis |
| title_full | Time Evolution of Bacterial Resistance Observed with Principal Component Analysis |
| title_fullStr | Time Evolution of Bacterial Resistance Observed with Principal Component Analysis |
| title_full_unstemmed | Time Evolution of Bacterial Resistance Observed with Principal Component Analysis |
| title_short | Time Evolution of Bacterial Resistance Observed with Principal Component Analysis |
| title_sort | time evolution of bacterial resistance observed with principal component analysis |
| topic | antibiotic-resistant bacteria <i>Staphylococcus aureus</i> Fourier Transformation Infrared minimum inhibitory concentration machine learning algorithms |
| url | https://www.mdpi.com/2079-6382/14/7/729 |
| work_keys_str_mv | AT claudiapbarrerapatino timeevolutionofbacterialresistanceobservedwithprincipalcomponentanalysis AT mitchellbonner timeevolutionofbacterialresistanceobservedwithprincipalcomponentanalysis AT andrewramosborsatto timeevolutionofbacterialresistanceobservedwithprincipalcomponentanalysis AT jennifermsoares timeevolutionofbacterialresistanceobservedwithprincipalcomponentanalysis AT katecblanco timeevolutionofbacterialresistanceobservedwithprincipalcomponentanalysis AT vanderleisbagnato timeevolutionofbacterialresistanceobservedwithprincipalcomponentanalysis |