Bone Environment Influences Irreversible Adhesion of a Methicillin-Susceptible Staphylococcus aureus Strain
Bibliographic record
Abstract
Prosthesis and joint infections are an important threat in public health, especially due to the development of bacterial biofilms and their high resistance to antimicrobials. Biofilm-associated infections increase mortality and morbidity rates as well as hospitalization costs. Prevention is the best strategy for this serious issue, so there is an urgent need to understand the signals that could induce irreversible bacterial adhesion on a prosthesis. In this context, we investigated the bone environment and its consequence on a methicillin-susceptible Staphylococcus aureus strain adhesion. Using static and dynamic biofilm models, we tested various bone environment factors and showed that the presence of Mg2+, lack of oxygen and starvation each increased bacterial adhesion. It was observed that human osteoblast-like cell culture supernatants increased bacterial adhesion capacity by 2-fold (p=0.015) compared to the medium control. Moreover, the stimulation of osteoblast-like cells with TNF-α to mimic inflammatory conditions increased bacterial adhesion by almost 5-fold (p=0.003). Interestingly, the effect of osteoblast-like cell supernatants on bacterial adhesion could be counteracted by the activity of synthetic antibiofilm peptides. In conclusion, the bone environment and products of osteoblast-like cells directly influenced S. aureus biofilm formation, which demonstrates an interkingdom communication between host bone cells and bacteria.
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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) | 0.002 | 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".