Novel genipin‐collagen immobilization of polylactic acid (PLA) fibers for use as tissue engineering scaffolds
Bibliographic record
Abstract
The material surface plays an important role in the case of biomaterials used as tissue engineering scaffolds. On exposure to a biological environment, extra cellular matrix (ECM) proteins are adsorbed non-specifically onto the surface and cells interact indirectly with the surface through the adsorbed proteins. Most synthetic polymeric biomaterials lack the desirable surface properties for cells as well as have poor cellular adhesion due to their hydrophobic nature. The main objective of this study was to harness surface functionalization technologies to fabricate scaffolds that would be biocompatible and support the adhesion and proliferation of fibroblast cells. The collagen was immobilized on the surface of functionalized PLA via a novel natural cross-linking molecule genipin which resulted in improved cell proliferation of human dermal fibroblasts as compared to the PLA surface coated with collagen without genipin. It is believed that genipin helps reduce steric problems between the functional groups and large protein molecules, and enables immobilized peptide to move more freely in a biological environment.
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) | 0.001 | 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) | 0.000 | 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".