The Chicken Egg: An Advanced Material for Tissue Engineering
Bibliographic record
Abstract
The chicken egg, an excellent natural source of proteins, has been an overlooked native biomaterial with remarkable physicochemical, structural, and biological properties. Recently, with significant advances in biomedical engineering, particularly in the development of 3D in vitro platforms, chicken egg materials have increasingly been investigated as biomaterials due to their distinct advantages such as their low cost, availability, easy handling, gelling ability, bioactivity, and provision of a developmentally stimulating environment for cells. In addition, the chicken egg and its by-products can improve tissue engraftment and stimulate angiogenesis, making it particularly attractive for wound healing and tissue engineering applications. Evidence suggests that the egg white (EW), egg yolk (EY), and eggshell membrane (ESM) are great biomaterial candidates for tissue engineering, as their protein composition resembles mammalian extracellular matrix proteins, ideal for cellular attachment, cellular differentiation, proliferation, and survivability. Moreover, eggshell (ES) is considered an excellent calcium resource for generating hydroxyapatite (HA), making it a promising biomaterial for bone regeneration. This review will provide researchers with a concise yet comprehensive understanding of the chicken egg structure, composition, and associated bioactive molecules in each component and introduce up-to-date tissue engineering applications of chicken eggs as biomaterials.
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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 | 0.001 | 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 | 0.001 |
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".