Neutrophil-mediated biodegradation of medical implant materials
Bibliographic record
Abstract
During the acute inflammatory response to implanted medical devices, human neutrophils (PMN) release oxidative and hydrolytic activities which may ultimately contribute to the degradation of the biomaterial. In this study, the biological activities secreted by live PMNs which may contribute to biodegradation were investigated using a 14C label in the monomer unit of a poly(ester-urea-urethane) (PEUU) substrate. By using specific inhibitors, it was possible to propose a mechanism for PMN-mediated biodegradation. PMN, labeled with 3H-arachidonic acid, released significantly more 3H when adherent to PEUU than when adherent to tissue culture grade polystyrene (P<0.05). The phospholipase A2 (PLA2) inhibitors, aristolochic acid (ARIST) and quinacrine (QUIN), decreased the release of 3H and inhibited PEUU biodegradation (>50%, P<0.05). ARIST had no effect on cell viability, whereas QUIN significantly decreased it. The serine protease inhibitor, phenylmethylsulfonylfluoride inhibited biodegradation, but did not decrease cell survival. There is evidence to suggest that activation via the PLA2 pathway caused the release of hydrolytic activities which were able to elicit 14C release from PEUU. The role of oxidative compounds which were released via activation by phorbol myristate acetate (PMA), was not apparent, since PMA inhibited biodegradation and cell survival (>40%, P<0.05). This study has shown that it is possible to find out the differences in PMN activation through the PLA2 pathway when exposed to different material surfaces, making this a model system worthy of further investigation.
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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) | 0.001 | 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".