FTIR-Derived Feature Insights for Predicting Time-Dependent Antibiotic Resistance Progression
These results indicate that antibiotic resistance should be viewed as a temporally adaptive trajectory rather than a static state. FTIR-based biochemical profiling, when integrated with ML, enables projection of phenotypic transitions and supports real-time therapeutic decision-making. This strategy represents a shift toward adaptive antimicrobial management, with the potential to personalize interventions based on dynamic resistance monitoring through spectral biomarkers.
Authors
- Kate Cristina Blanco (ORCID: https://orcid.org/0000-0003-0361-9725)
- Vanderlei Salvador Bagnato (ORCID: https://orcid.org/0000-0003-4833-239X)
- Jennifer M. Soares (ORCID: https://orcid.org/0000-0002-2978-7076)
- Mitchell Bonner
- Andrew Ramos Borsatto
Institutions
- Institute of Physics (PL)
- Institute of Mathematics and Computer Science (MD)
- Texas A&M University (US)
Publication Details
- Journal
- Antibiotics
- Published
- 2025-08-15
- DOI
- https://doi.org/10.3390/antibiotics14080831
- Primary Topic
- Spectroscopy Techniques in Biomedical and Chemical Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Cancer Prevention and Research Institute of Texas
- Fundação de Amparo à Pesquisa do Estado de São Paulo