Functionalized collagen-based biomaterials via self-assembly: implications for gastrointestinal health
Bibliographic record
Abstract
Abstract Collagen, one of the most abundant proteins in human physiology, maintains the morphology and structure of skin and tissues, serving as an important raw material for the repair of damaged tissues. Collagen's widespread application in biomedicine stems from its myriad beneficial properties, including its diverse sourcing, exceptional biocompatibility, sustainability, low immunogenicity, porous nature, and biodegradability. In addition, collagen can self-assemble with other molecules through multiple interactions to form a variety of structures, thereby enhancing its biological functions. In recent years, gastrointestinal diseases have attracted much attention due to their high prevalence and complexity. In this context, collagen-based biomaterials, such as collagen scaffolds and hydrogels, have demonstrated an important role in the treatment of gastrointestinal diseases. This review aims to summarize the research progress of collagen-based biomaterials for the treatment of gastrointestinal diseases in recent years, with a focus on their self-assembly properties and application advantages. Our goal is to explore innovative methods for producing collagen-based biomaterials, aiming to broaden their potential applications and enhance precise therapeutic effects to expand their clinical applications. Graphical Abstract
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) | 0.001 | 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".