Microstructural and tensile properties of elastin‐based polypeptides crosslinked with Genipin and pyrroloquinoline quinone
Bibliographic record
Abstract
Abstract Elastin is an elastomeric, self‐assembling extracellular matrix protein with potential for use in biomaterials applications. Here, we compare the microstructural and tensile properties of the elastin‐based recombinant polypeptide (EP) EP20‐244 crosslinked with either genipin (GP) or pyrroloquinoline quinone (PQQ). Recombinant EP‐based sheets were produced via coacervation and subsequent crosslinking. The micron‐scale topography of the GP‐crosslinked sheets examined with atomic force microscopy revealed the presence of extensive mottling compared with that of the PQQ‐crosslinked sheets, which were comparatively smoother. Confocal microscopy showed that the subsurface porosity in the GP‐crosslinked sheets was much more open. GP‐crosslinked EP‐based sheets exhibited significantly greater tensile strength (P ≤ 0.05). Mechanistically, GP appears to yield a higher crosslink density than PQQ, likely due to its capacity to form short‐range and long‐range crosslinks. In conclusion, GP is able to strongly modulate the microstructural and mechanical properties of elastin‐based polypeptide biomaterials forming membranes with mechanical properties similar to native insoluble elastin. © 2006 Wiley Periodicals, Inc. Biopolymers 85: 199–206, 2007. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com
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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".