Biodegradable and Non-Biodegradable Biomaterials and Their Effect on Cell Differentiation
Bibliographic record
Abstract
Biomaterials for tissue scaffolds are key components in modern tissue engineering and regenerative medicine. Targeted reconstructive therapies require a proper choice of biomaterial and an adequate choice of cells to be seeded on it. The introduction of stem cells, and the transdifferentiation procedures, into regenerative medicine opened a new era and created new challenges for modern biomaterials. They must not only fulfill the mechanical functions of a scaffold for implanted cells and represent the expected mechanical strength of the artificial tissue, but furthermore, they should also assure their survival and, if possible, affect their desired way of differentiation. This paper aims to review how modern biomaterials, including synthetic (i.e., polylactic acid, polyurethane, polyvinyl alcohol, polyethylene terephthalate, ceramics) and natural (i.e., silk fibroin, decellularized scaffolds), both non-biodegradable and biodegradable, could influence (tissue) stem cells fate, regulate and direct their differentiation into desired target somatic cells.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".