Changes in macrophage function and morphology due to biomedical polyurethane surfaces undergoing biodegradation
Bibliographic record
Abstract
Monocytes are recruited to the material surface of an implanted biomedical device recognizing it as a foreign body. Differentiation into macrophages subsequently occurs followed by fusion to form foreign body giant cells (FBGCs). Consequently, implants can become degraded, cause chronic inflammation or become isolated by fibrous encapsulation. In this study, a relationship between material surface chemistry and the FBGC response was demonstrated by seeding mature monocyte-derived macrophages (MDMs) on polycarbonate-based polyurethanes that differed in their chemical structures (synthesized with poly(1,6-hexyl 1,2-ethyl carbonate) diol, and either (14)C-hexane diisocyanate and butanediol (BD) (referred to as HDI) or 4,4'-methylene bisphenyl diisocyanate and (14)C-BD (referred to as MDI)) and material degradation assessed. At 48 h of cell-material interaction, the FBGC attached to HDI were more multinucleated (73%) compared to MDI or the polystyrene (PS) control (21 and 36%, respectively). There was a fivefold increase in the synthesis and secretion of a protein with an approximate molecular weight of 48 kDa and a pI of 6.1 (determined by two-dimensional gel electrophoresis) only from cells seeded on HDI. Immunoprecipitation confirmed that MSE and CE were synthesized and secreted de novo. Immunoblotting also showed an increase in secreted monocyte-specific esterase (MSE) and cholesterol esterase (CE) from cells seeded on HDI relative to PS and MDI. Significantly more radiolabel ((14)C) release and esterase activity were elicited by MDMs on HDI than MDI (P < 0.05). The material that was more degradable (HDI), elicited greater protein synthesis and esterase secretion as well as more multinucleated MDMs than MDI, suggesting that the material surface chemistry modulates the function of MDM at the site of an inflammatory response to an implanted device.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".