335. Using high temperatures to eradicate prosthetic joint associated biofilms on metal implants using alternating magnetic field: Efficacy and safety implications
Bibliographic record
Abstract
Abstract Background Prosthetic joint infection (PJI) is a significant complication of modern arthroplasty. Revision surgery is frequently required due to the formation pf biofilm. The presence of biofilm makes non surgical treatment difficult in part because traditional antibiotics are unable to penetrate this structure. We have developed a noninvasive way to eradicate biofilm off the outer surface of metal implant utilizing alternating magnetic fields (AMF). AMF creates focused surface heating on metal lic implants and can be delivered in a fashion spares significant heating of surrounding tissue. The study was to determine efficacy and safety of AMF when combined with traditional antibiotics in animal models of implant infection. Methods Pseudomonas aeruginosa (PA) and staphylococcus aureus (SA) were grown individually on stainless steel ball that were implanted into the thigh muscle of the mice. Mice placed in a custom built solenoid coil for AMF treatments. AMD exposures generating peak temperature of 80 or 65 C on the implant were delivered once a day. Treatment groups included AMF alone, antibiotic alone, and combination therapy. Antibiotics tested included ciprofloxacin, ceftraixone and rifampin. Residual biofilm was measured by CFU counts. Histopathology was analyzed to determine area of damage in response to AMF treatment. Results Combination of a single AMF pulse with antibiotics lead to a greater biofilm reduction than either treatment alone. PA with AMD (80 C peak) and ciprofloxacin resulted in >2 log reduction of biofilm (p< 0.0001) compared to minimal reduction (AMF or ciprofloxacin alone) at Day 4. Similar treatment outcome was seen with SA and ceftraixone with combination treatment resulting on multi log reduction. Combined treatment effects were seen at lower temperatures (65 C). Histopathologic analysis demonstrates a small area of tissue damage at the end of treatment (Day 4). In mice that were survived fro additional 28-days after the final treatment, the tissue showed no signs of damage. Conclusion AMF combined with antibiotics leads to enhanced reduction of biofilm on metallic implants in vivo. This non invasive approach to eradicating biofilm could serve as a new paradigm in treating these challenging infections. Disclosures All Authors: No reported disclosures
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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) | 0.001 | 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 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".