Inflammasome components Asc and caspase-1 mediate biomaterial-induced inflammation and foreign body response
Bibliographic record
Abstract
Implantation of biomaterials and devices into soft tissues leads to the development of the foreign body response (FBR), which can interfere with implant function and eventually lead to failure. The FBR consists of overlapping acute and persistent inflammatory phases coupled with collagenous encapsulation and currently there are no therapeutic options. Initiation of the FBR involves macrophage activation, proceeding to giant cell formation, fibroblast activation, and collagen matrix deposition. Despite the recognition of this sequence of events, the molecular pathways required for the FBR have not been elucidated. We have identified that the acute inflammatory response to biomaterials requires nucleotide-binding domain and leucine-rich repeat-containing 3 (Nlrp3), apoptosis-associated speck-like protein containing CARD (Asc), and caspase-1, as well as plasma membrane cholesterol, and Syk signaling. Full development of the FBR is dependent on Asc and caspase-1, but not Nlrp3. The common antiinflammatory drug aspirin can reduce inflammasome activation and significantly reduce the FBR. Taken together, these findings expand the role of the inflammasome from one of sensing damage associated molecular patterns (DAMPs) to sensing all particulate matter irrespective of size. In addition, implication of the inflammasome in biomaterial recognition identifies key pathways, which can be targeted to limit the FBR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".