Polycarbonate-urethane hard segment type influences esterase substrate specificity for human-macrophage-mediated biodegradation
Bibliographic record
Abstract
Previous studies have shown that esterase activity can degrade a variety of polyurethanes (PUs), including polycarbonate-based PUs (PCNUs). When cultured on PCNUs, differing in their chemistries, monocyte-derived macrophages (MDM) synthesized and secreted different amounts of both cholesterol esterase (CE) and monocyte-specific esterase (MSE). MDM were seeded on PCNUs synthesized with hexane diisocyanate (HDI) or 4,4'-methylene-bis-phenyl diisocyanate (MDI), PCN and [14C]butanediol (BD) in the ratio 3:2:1 (referred to as HDI321 or MDI321). The effect of phenylmethylsulfonyl fluoride (PMSF, a serine esterase and proteinase inhibitor), sodium fluoride (NaF, a MSE inhibitor) and sodium taurocholate (NaT, a CE stimulator) was assessed on degradation (measured by radiolabel release (RR)) and esterase activity in MDM lysate. The results were compared to the effect that these reagents had on commercially available CE and carboxyl esterase (CXE), which has a specificity similar to MSE. NaF inhibited CXE- and MDM-mediated RR to the same extent as for both PCNUs. However, the MDM-mediated RR from MDI321 was 1.8-times higher than HDI321 in the presence of NaT (P = 0.005). This study suggests that the difference in diisocyanate chemistry may dictate the relative contribution of each esterase to a specific material's degradation. This may be related to both the substrate specificity of each esterase, as well as by the relative amount of each esterase that the specific biomaterial substrates induce the cells to synthesize and secrete.
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".