The cellular and molecular properties of capsule surrounding silicone implants in humans vary uniquely according to the tissue type adjacent to the implant
Bibliographic record
Abstract
The foreign body reaction (FBR) to biomaterials results in fibrous encapsulation. Excessive capsule fibrosis (capsular contracture) is a major challenge to the long-term stability of implants. Clinical data suggests that the tissue type in contact with silicone breast implants alters susceptibility to developing capsular contracture; however, the tissue-specific inflammatory and fibrotic characteristics of capsule have not been well characterized at the cellular and molecular level. In this study, 60 breast implant capsule samples are collected from patients and stratified by the adjacent tissue type including subcutaneous tissue, glandular breast tissue, or muscle tissue. Capsule thickness, collagen organization, immune and fibrotic cellular populations, and expression of inflammatory and fibrotic markers is quantified with histological staining, immunohistochemistry, and real-time PCR. The findings suggest there are significant differences in M1-like macrophages, CD4+ T cells, CD26+ fibroblasts, and expression of IL-1β, IL-6, TGF-β, and collagen type 1 depending on the tissue type abutting the implant. Subglandular breast implant capsule displays a significant increase in inflammatory and fibrotic markers. These findings suggest that the tissue microenvironment contributes uniquely to the FBR. This data could provide new avenues for research and clinical applications to improve the site-specific biocompatibility and longevity of implantable devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".