MétaCan
Menu
Back to cohort
Record W1994295602 · doi:10.1002/jbm.a.30181

<i>In vitro</i> interaction between silicone gel and human monocyte‐macrophages

2004· article· en· W1994295602 on OpenAlexaff
F. Tavazzani, Shaomo Xing, Jennifer E Waddell, Diane E. Smith, E. Boynton

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2004
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsSiliconeMacrophagePhagocytosisForeign-body giant cellMaterials scienceInflammationGiant cellMonocyteIn vitroFibrosisSecretionImmune systemCell biologyPathologyImmunologyBiologyMedicineComposite materialBiochemistry

Abstract

fetched live from OpenAlex

Controversy remains regarding the ability of silicone materials to induce a specific immune reaction versus a nonspecific inflammatory response. Histopathological analysis of the tissue around failed breast implants reveals chronic inflammation with silicone gel droplets either surrounded by giant cells or engulfed by macrophages, areas of fibrosis, and necrosis. Macrophages are the key cells engulfing or forming foreign body giant cells. To address the mechanisms of silicone-induced inflammation a model of human monocyte-derived macrophages (MDMs) was developed. After sonication of silicone gel, the silicone droplets were embedded in Type I collagen and used to coat glass coverslips; human MDMs were subsequently seeded on the coverslips and maintained in culture for up to 7 days. The advantage of the model was that human macrophages could be studied histologically, and cytochemically as they interacted with well-characterized silicone materials. Initial analysis of the human macrophages shows phagocytosis of the silicone gel within hours of exposure to the material. Analysis for pro-inflammatory cytokines reveals significant transient secretion of IL-1 (p < 0.01) over controls by human macrophages upon exposure to silicone gel at 24 h.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.071
GPT teacher head0.416
Teacher spread0.345 · 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 designBench or experimental
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

Citations23
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Biomedical Materials Research Part ASame topicTissue Engineering and Regenerative MedicineFrench-language works237,207