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59. Characterization of the Differences in Cellular Response yo Silicone Particles in Smooth and Textured Breast Implant Capsules

2025· article· en· W4409786300 on OpenAlexaff
Sarah Petrecca, Hillary Nepon, Nikita Kalashnikov, Tassos Dionisopoulos, Peter Davison, Joshua Vorstenbosch

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2025
Typearticle
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsSiliconeImplantCharacterization (materials science)Materials scienceBreast implantBiomedical engineeringComposite materialNanotechnologyMedicineSurgery

Abstract

fetched live from OpenAlex

PURPOSE: Fibrous encapsulation of breast implants following placement is typically a benign process. However, deposition of silicone particles within the capsule induces chronic inflammation that leads to capsular contracture and is hypothesized to stimulate other breast implant capsule pathologies such as breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) and primary squamous cell carcinoma. Textured breast implants have a lower incidence of capsular contracture compared to smooth devices, but are linked to ALCL. While both smooth and textured breast implants can bleed small silicone particles into the capsule, textured implants may also shed larger surface silicone fragments into the capsule. This key difference could have important implications for breast implant capsule pathophysiology. To date, the localized cellular response surrounding silicone particles has not been investigated. We aim to determine the differences in silicone infiltration in smooth and textured breast implant capsules and characterize the specific cellular responses to silicone particles in the capsule. METHODS: Capsule tissue from macrotextured silicone (N=15) and smooth silicone (N=15) breast implants was collected from consenting patients undergoing revisions. Silicone infiltration and capsular thickness were assessed histologically. Capsular cell populations were measured by immunohistochemistry (macrophages, CD3+ total T-cells, CD4+ helper T-cells, fibroblasts, and myofibroblasts). RESULTS: Silicone infiltration was greater in textured implant capsules (radius=4.87 µm) than smooth implant capsules (radius= 4.28 µm) (p=0.47). Smooth implant capsules had an increased thickness (smooth implant capsule thickness=1058 µm, textured implant capsule thickness=848 µm) (p=0.27). In addition, smooth implant capsules displayed increased total macrophages (p=0.01), M2 macrophages (p=0.03), myofibroblasts (p=0.04) and fibrotic cells (p=0.08). Textured implant capsules had increased CD4+ T-cells (p=0.88). The presence of all immune cells was enriched in silicone-dense areas, irrespective of implant surface (p<0.001). CONCLUSION: Our results suggest that the immune response to silicone particles in capsular tissue varies according to both implant surface and amount of silicone infiltration. Smooth implant capsules exhibit a macrophage-dominant response with thicker capsules. Textured implant capsules display enhanced CD4+ T-cell infiltration, which may alter baseline capsular physiology and susceptibility to distinct capsular pathologies. The global increased immune response to silicone particles could underscore the link between the presence of silicone and capsular contracture. Expanding our knowledge of these differences could yield meaningful advancements in our understanding of the mechanisms underlying breast implant capsule-related pathology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.241
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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