Optical coherence tomography for label-free detection and characterization of methicillin-resistant S. aureus biofilms
Bibliographic record
Abstract
SignificanceOrthopedic implant-associated infections cause serious complications primarily attributed to bacterial biofilm formation and are often characterized by increased antibiotic resistance and diminished treatment response. Yet, no methods currently exist to identify biofilms intraoperatively—surgeons rely solely on their eyes and hands and cannot detect or differentiate infected tissue to determine the location and extent of contamination.AimAs the first step in addressing this unmet clinical need, here, we develop an optical coherence tomography (OCT)-based imaging method capable of detection in situ and quantification of one of the most dangerous orthopedic biofilms formed by methicillin-resistant Staphylococcus aureus (MRSA).ApproachGrowing biofilms on orthopedic hardware, we identify MRSA distinct optical signature through histogram-based multi-parametric texture analysis of OCT images and support the findings with bioluminescence imaging and scanning electron microscopy. Under identical experimental conditions, we identify an optical signature of Escherichia coli (E. coli) biofilms and use it to distinguish and quantify both species within MRSA–E. coli biofilms.ResultsThe developed OCT-based methodology was successfully tested for (1) MRSA colonies delineation, (2) detection of metal hardware (an important feature for clinical translation where the metal surface of most orthopedic hardware is not flat), (3) automated quantification of biofilm thickness and roughness, and (4) identification of pores and, therefore, ability to evaluate the role of porosity—one of the critical biological metrics in relation to biofilm maturity and response to treatment. For the first time, we demonstrated complex pore structures of thick (>100 microns) MRSA biofilms in situ with an unprecedented level of detail.ConclusionsThe proposed rapid noninvasive detection/quantification of MRSA biofilms on metal surfaces and delineation of their complex network of pores opens new venues for label-free MRSA detection in preclinical models of trauma surgery, expansion to other bacterial strains, and further clinical translation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".