The Combined Use of Gentamicin and Silver Nitrate in Bone Cement for a Synergistic and Extended Antibiotic Action against Gram-Positive and Gram-Negative Bacteria
Bibliographic record
Abstract
Using bone cement as a carrier, gentamicin was for years the default drug to locally treat orthopedic infections but has lost favor due to increasing bacterial resistance to this drug. The objective of this study was to investigate the effect of combining gentamicin with silver nitrate in bone cement against S. aureus and P. aeruginosa. Antibacterial effects (CFU counts) of gentamicin and silver were initially studied followed by studies using subtherapeutic concentrations of each in combination. The release rates from cement were measured over 10 days and day 7 release samples were saved and analyzed for antibiotic activity. A strong synergistic effect of combining silver with gentamicin was found using both dissolved drugs and using day 7 bone cement release media for both Gram-positive and Gram-negative bacteria. The cement studies were extended to vancomycin and tobramycin, which are also used in bone cement, and similar synergistic effects were found for day 7 release media with P. aeruginosa but not S. aureus. These studies conclude that the combined use of low loadings of gentamicin and silver nitrate in bone cement may offer an economical and much improved synergistic method of providing anti-infective orthopedic treatments in the clinic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".