2797. Antimicrobial Photodynamic Therapy (aPDT) Is Highly Effective Against Multidrug-Resistant HA- and CA- <i>S. aureus</i> Strains
Bibliographic record
Abstract
Abstract Background Methicillin-resistant Staphylococcus aureus (MRSA) is often associated with multidrug-resistant (MDR) infections found in healthcare settings, resulting in 48,000 deaths in the US and 1.27 million deaths worldwide each year. S. aureus strains are a common component of nasal microbiota and can be disseminated by both patients and healthcare workers, posing a substantial hospital-acquired infection risk. Antimicrobial photodynamic therapy (aPDT) combines the use of a photosensitizer (PS) with a specific wavelength of light to induce photochemical reactions lethal to a broad spectrum of microbes, without resistance induction. The objective of this study was to demonstrate the efficacy of aPDT against clinically-relevant multidrug-resistant S. aureus strains using a commercially-available photosensitizer formulation (SteriwaveTM, Ondine Biomedical Inc., Vancouver, BC). Resistance profile of MRSA strains Methods The MRSA strains used in this study included confirmed resistance to: cefoxitin, clindamycin, erythromycin, levofloxacin, mupirocin, oxacillin, penicillin, rifampicin, novobiocin, tetracyclines, trimethoprim/sulfamethoxazole, gentamicin, chloramphenicol, ciprofloxacin, moxifloxacin, linezolid, tedizolid quinupristin/dalfopristin and mupirocin. aPDT was carried out by exposing planktonic suspensions of each strain to photosensitizer formulations containing either 0.01% MB or SteriwaveTM (0.01% MB and 0.25% chlorhexidine gluconate in an aqueous excipient base). Illumination was performed at 670 nm, 150 mW/cm2 for 60 s (9 J/cm2). Samples were serially diluted and plated on TSA for CFU counting Results Relative to untreated controls, aPDT resulted in a minimum reduction of 3log10 (99.9%) against all MRSA strains tested, with the commercial SteriwaveTM formulation producing an average of 10X greater kills than aqueous methylene blue under the same illumination parameters. Conclusion aPDT is highly effective against a variety of clinically-relevant multidrug-resistant S. aureus strains in 60 s of treatment. The technique represents a promising alternative to antibiotics in healthcare systems where antimicrobial resistance strategies are important Disclosures Nicolas G. Loebel, PhD, ONDINE Biomedical Inc.: Board Member|ONDINE Biomedical Inc.: Employee|ONDINE Biomedical Inc.: Stocks/Bonds
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".