Capsular Biofilm Formation at the Interface of Textured Expanders and Human Acellular Dermal Matrix: A Comparative Scanning Electron Microscopy Study
Bibliographic record
Abstract
BACKGROUND: Despite benefits in reducing capsular contractures, textured implants have been associated with significant pitfalls, such a propensity for biofilm formation. Few studies have investigated whether the use of acellular dermal matrix on textured implants produces similar findings. This study aims to characterize biofilm formation at the capsular-acellular dermal matrix interface with scanning electron microscopy. METHODS: The authors performed a prospective observational pilot study in patients undergoing two-stage expander-to-permanent implant exchange. Patients were inflated with Biocell or Siltex expanders, and specimens from the capsular-pectoralis interface and capsular-acellular dermal matrix interface were obtained and examined under scanning electron microscopy for capsular ingrowth and biofilm formation using the Van Herdeen Biofilm Grading System and the Biofilm Thickness Grading Scale. RESULTS: Nine patients including 14 breasts (28 capsular samples in total) were examined. Thick biofilm formation was observed in all specimens from the capsular-acellular dermal matrix interface with Biocell and 25 percent of capsule-pectoralis interface, whereas no biofilm formation was found in Siltex implants. For Biocell implants, a significant difference in biofilm coverage between the upper and lower poles was observed using the Van Herdeen Biofilm Grading System (p = 0.0028) and the Biofilm Thickness Grading Scale (p = 0.0161). CONCLUSIONS: Biocell implants produce a significant rate of biofilm formation over acellular dermal matrix-covered capsules, which is not present in the muscular region or in Siltex implants. Further randomized controlled trials will further elucidate the clinical impact of using acellular dermal matrices with macrotextured implants. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".