Differentiating methicillin resistant and susceptible Staphylococcus aureus from ocular infections using photoacoustic labeling
Bibliographic record
Abstract
Introduction Antibiotic resistance in bacterial species constitutes a growing problem in the clinical management of infections. Not only does it limit therapeutic options, but application of ineffective antibiotics allows resistant species to progress prior to prescribing more effective treatment to patients. Methicillin resistance in Staphylococcus aureus is a major problem in clinical infections as it is the most common hospital acquired infection. Methods We developed a photoacoustic flow cytometer using engineered bacteriophage as probes for rapid determination of methicillin resistance in Staphylococcus aureus with thirteen clinical samples obtained from keratitis patients. This method irradiates cells under flow with 532 nm laser light and selectively generates acoustic waves in labeled bacterial cells, thus enabling detection and enumeration of them. Staphylococcus aureus isolates were classified from culture isolation as either methicillin resistant or susceptible using cefoxitin disk diffusion testing. The photoacoustic method enumerates bacterial cells before and after treatment with antibiotics. Decreasing counts of bacteria after treatment indicate susceptible strains. We quantified the bacterial cells in the treated and untreated samples. Results Using k-means clustering on the data, we achieved 100% concordance with the classification of Staphylococcus aureus resistance using culture. Discussion Photoacoustics can be used to differentiate methicillin resistant and susceptible strains of bacteria from ocular infections. This method may be generalized to other bacterial species using appropriate bacteriophages and testing for resistance using other antibiotics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".