Optical coherence tomography-based detection of orthopaedic implant biofilms formed by Methicillin-resistant S.aureus (MRSA)
Bibliographic record
Abstract
Orthopaedic implant-associated infections cause serious complications primarily attributed to bacterial biofilm formation and often characterized by increased antibiotic resistance and diminished treatment response. There is currently a lack of imaging modalities that can directly visualize biofilms to determine the location and extent of contamination. Optical coherence tomography (OCT) is a portable, non-invasive, high-resolution imaging modality with the potential to fulfill this unmet need. In this study, we aim to evaluate the efficacy of OCT in detecting biofilms formed by life- and limb-threatening bacteria on orthopaedic implants. Bioluminescent strain SAP231 of methicillin-resistant <i>S. aureus</i> (MRSA) was used to grow biofilms on the surfaces of titanium and stainless-steel orthopaedic hardware situated inside custom-designed macrofluidic devices, allowing continuous nutrient broth supply and waste removal. Three-dimensional OCT images of each piece of hardware were obtained every 24 hours with subsequent bioluminescence imaging using the PerkinElmer IVIS Spectrum. OCT texture analysis based on multi-parametric fitting approach was developed and validated against IVIS quantification for accurate identification of live MRSA signatures. The monitoring of biofilm formation and measurement of film thicknesses starting at 12 micrometers and reaching 180 micrometers in 72 hours on metal hardware is demonstrated. This proof-of-concept study highlights the ability of OCT to detect and quantify the formation of MRSA bacterial biofilms in a high fidelity orthopaedic implant biofilm model in vitro, opening avenues for translation of this technique to preclinical models of contaminated orthopaedic trauma surgery 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 | 0.002 |
| 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".