Do Bacteria and Biofilm Play a Role in Double-Capsule Formation around Macrotextured Implants?
Bibliographic record
Abstract
BACKGROUND: The double capsule is a complication mostly described in aggressive macrotextured implants. Mechanical shear stress applied onto an immature periprosthetic capsule has been linked to their formation. The authors aim to demonstrate the role of bacterial phenotype and biofilm in the development of the double capsule. METHODS: Seven double capsules formed at the interface of macrotextured breast expander implants were studied using scanning electron microscopy. Two samples for each surface of the inner capsule layer (the prosthesis interface and the intercapsular space) were analyzed for bacteria cell size, bacterial density, and biofilm deposition. RESULTS: Although all routine bacterial cultures were negative, the prosthesis interface had both higher bacteria load and biofilm deposition compared with the intercapsular space (Mann-Whitney U test, p = 0.004 and p = 0.008, respectively). Moreover, bacteria cell sizes were significantly smaller at the prosthesis interface in six of seven samples. Comparison of bacteria density and biofilm dispersion showed an increase of biofilm extracellular matrix deposition over 2000 cells/mm (linear regression, p = 0.0025). These results indicate a common trend among bacteria species. CONCLUSIONS: Bacterial expression between the different surfaces of the double capsule displays significant differences; bacteria at the prosthesis interface are mostly in a biofilm state, whereas they demonstrate a planktonic phenotype at the intercapsular space. When a sufficient amount of bacteria are present at a specific location, quorum sensing may trigger a biofilm phenotypic switch in planktonic bacteria cells. Biofilm formation may alter capsule formation through immune response, thereby weakening capsule strength and facilitating extracellular matrix delamination and double-capsule formation. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, V.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".