The biological response of poly(<scp>L</scp>‐lactide) films modified by different biomolecules: Role of the coating strategy
Bibliographic record
Abstract
The interactions between the surface of synthetic scaffolds and cells play an important role in tissue engineering applications. To improve these interactions, two strategies are generally followed: surface coating with large proteins and surface grafting with small peptides. The proteins and peptides more often used and derived from the extracellular matrix, are fibronectin, laminin, and their active peptides, RGD and SIKVAV, respectively. The aim of this work was to compare the effects of coating and grafting of poly(L-lactide) (PLLA) films on MRC5 fibroblast cells. Grafting reactions were verified by X-ray photoelectron spectroscopy. Cell adhesion and proliferation on coated and grafted PLLA surfaces were measured by cell counting. Vinculin localization and distribution were performed on cell cultured on PLLA samples using a fluorescence microscopy technique. Finally, western blot was performed to compare signals of cell adhesion proteins, such as vinculin, Rac1, and RhoA, as well as cell proliferation, such as PCNA. These tests showed similar results for fibronectin and laminin coated PLLA, while RGD grafting is more effective compared with SIKVAV grafting. Considering the overall view of these results, although coating and grafting can both be regarded as effective methods for surface modification to enhance cell adhesion and proliferation on a biomaterial, RGD grafted PLLA show better cell adhesion and proliferation than coated PLLA, while SIKVAV grafted PLLA show similar adhesion but worse proliferation. These data verified different biological effects depending on the surface modification method used.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".