Bibliographic record
Abstract
Infections of orthopaedic implants, such as fracture fixation devices and total-joint prostheses, are devastating complications. Staphylococcus aureus (S. aureus) is a predominant pathogen causing orthopaedic-implant biofilm infections that can also internalise and persist in osteoblasts, thus resisting antibiotic therapy. Bacteriophages are a promising alternative treatment approach. However, data on the activity of bacteriophages against S. aureus, especially during intracellular growth, and against in vivo biofilm formation on metals are scarce. Therefore, the present study evaluated the in vitro efficacy of S. aureus bacteriophage 191219, alone as well as in combination with gentamicin and rifampicin, to eradicate S. aureus strains in their planktonic stage, during biofilm formation and after internalisation into osteoblasts. Further, the invertebrate model organism Galleria mellonella was used to assess the activity of the bacteriophage against S. aureus biofilm on metal implants with and without antibiotics. Results demonstrated the in vitro efficacy of bacteriophage 191219 against planktonic S. aureus. The phage was also effective against in vitro S. aureus biofilm formation in a dose-dependent manner and against S. aureus internalised in an osteoblastic cell line. Transmission electron microscopy (TEM) analysis showed bacteriophages on S. aureus inside the osteoblasts, with the destruction of the intracellular bacteria and formation of new bacteriophages. For the Galleria mellonella infection model, single administration of phage 191219 failed to show an improvement in survival rate but appeared to show a not statistically significant enhanced effect with gentamicin or rifampicin. In summary, bacteriophages could be a potential adjuvant treatment strategy for patients with implant-associated biofilm infections.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.074 | 0.059 |
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".