The interaction between hydrolytic and oxidative pathways in macrophage‐mediated polyurethane degradation
Bibliographic record
Abstract
Although relatively resistant to oxidation, polycarbonate-based polyurethanes (PCNUs) are degraded by monocyte-derived macrophages (MDM) by a co-mediated mechanism involving both hydrolytic and oxidative pathways. Since a previous study showed that PCNU pretreatment with H(2)O(2) modulated degradation by esterases, human MDM were used to further elucidate this dual pathway mechanism of degradation for (14)C-radiolabeled PCNUs (synthesized with 1,6-hexane diisocyanate:polycarbonatediol: butanediol with different stoichiometry (HDI431 and HDI321) or another diisocyanate 4,4'-methylene bisphenyl diisocyanate (MDI321)). Scanning electron microscopy of PCNU slips pretreated with 20% H(2)O(2) showed that HDI431 had visible holes with more radiolabel release than from the other PCNUs. When MDM were seeded on H(2)O(2)-treated PCNUs, degradation of HDI321 and MDI321, but not HDI431 was decreased. Esterase activity was inhibited in MDM on all surfaces except MDI321, whereas inhibition of acid phosphatase occurred on all surfaces. The material surface itself, induced H(2)O(2) release from live MDM, with more H(2)O(2) elicited by phorbol myristate acetate treated MDM when cultured on HDI431 but not the other materials. H(2)O(2) pretreatment affected cell function by chemically altering the material surface and MDM-mediated degradation, known to be dependent on surface chemistry. The findings highlight that both oxidative and hydrolytic mechanisms need to be understood in order to tailor material chemistry to produce desired cell responses for in vivo applications.
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 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".