New<i>in vitro</i>and<i>in vivo</i>models to evaluate antibiotic efficacy in<i>Staphylococcus aureus</i>prosthetic vascular graft infection
Bibliographic record
Abstract
OBJECTIVE: Prosthetic vascular graft infection (PVGI) is an emerging disease, mostly caused by staphylococci, with limited data regarding efficacy of current antistaphylococcal agents. We aimed to assess the efficacy of different antibiotic regimens. METHODS: Six different strains of MSSA and MRSA were used. We compared results of minimal biofilm inhibitory and eradicating concentrations (MBICs and MBECs) obtained with a Calgary Biofilm Pin Lid Device (CBPD) with those yielded by an original Dacron(®)-related minimal inhibitory and eradicating concentration measure model. We then used a murine model of Staphylococcus aureus vascular prosthetic material infection to evaluate efficacy of different antibiotic regimens: vancomycin and daptomycin combined or not with rifampicin for MRSA and the same groups with cloxacillin and cloxacillin combined with rifampicin for MSSA. RESULTS: We demonstrated that classical measures of MBICs and MBECs obtained with a CPBD could overestimate the decrease in antibiotic susceptibility in material-related infections and that the nature of the support used might influence the measure of biofilm susceptibility, since results yielded by our Dacron(®)-related minimal eradicating assay were lower than those found with a plastic device. In our in vivo model, we showed that daptomycin was significantly more bactericidal than comparators for some strains of MRSA or MSSA but not for all. For the majority of strains, it was as efficient as comparators. The addition of rifampicin to daptomycin did not enhance daptomycin efficacy. CONCLUSIONS: Despite the heterogeneity of results according to bacterial strains, these innovative models represent an option to better evaluate the in vitro efficacy of antibiotics on Dacron(®)-related biofilm S. aureus infections, and to screen different antibiotic regimens in a mouse model of PVGIs.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".