Arginine-glycine-glutamine and serine-isoleucine-lysine-valine-alanine-valine modified poly(<scp>l</scp>-lactide) films: Bioactive molecules used for surface grafting to guide cellular contractile phenotype
Bibliographic record
Abstract
Tissue engineering is defined as "an interdisciplinary field that applies the principles of engineering and life sciences toward the development of biological substitutes that restore, maintain, or improve tissue function." The biological substitutes can be developed with the help of natural or synthetic materials. Polymeric materials are primarily used, because of the high variability in mechanical, physical, and chemical properties. Biodegradable polymers are object of the majority of studies, because of the ability to be degraded by the host organism, avoiding late stent thrombosis unlike permanent grafts. Poly-l-lactide acid (PLLA) is one of the most used polymers in research. In order to improve the material's bioactivity, in this work, PLLA surface was modified by grafted arginine-glycine-glutamine (RGD), a fibronectin derived adhesion motif, and serine-isoleucine-lysine-valine-alanine-valine (SIKVAV), a laminin derived motif, and rat cardiac (H9C2) and mouse (C2C12) myoblasts proliferation and differentiation on modified PLLA were evaluated. In order to verify the surface modification, x-ray photoelectron spectroscopy analysis was performed. After seeding, cells' viability was confirmed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay while proliferating cell nuclear antigen expression was used to investigate cell proliferation. Myf5, Myogenin and Myosin heavy chain were used to analyze cell differentiation. Moreover, RGD peptide slightly inhibited rat myoblast (H9C2) proliferation, whereas less strong effect was observed on C2C12. However, both cell lines showed to enhance the contractile phenotype in the presence of SIKVAV peptides. These results suggest that bioactive molecules grafting could be useful on polymeric scaffolds for guiding cell phenotype expression, and, to ultimately maintain adequate biological characteristics suitable for the tissue functional regeneration.
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) | 0.000 | 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".