Peptide-Based Functional Biomaterials for Soft-Tissue Repair
Bibliographic record
Abstract
Synthetically derived peptide-based biomaterials are in many instances capable of mimicking the structure and function of their full-length endogenous counterparts. Combine this with the fact that short mimetic peptides are easier to produce when compared to full length proteins, show enhanced processability and ease of modification, and have the ability to be prepared under well-defined and controlled conditions; it becomes obvious why there has been a recent push to develop regenerative biomaterials from these molecules. There is increasing evidence that the incorporation of peptides within regenerative scaffolds can result in the generation of structural recognition motifs that can enhance cell attachment or induce cell signaling pathways, improving cell infiltration or promote a variety of other modulatory biochemical responses. By highlighting the current approaches in the design and application of short mimetic peptides, we hope to demonstrate their potential in soft-tissue healing while at the same time drawing attention to the advances made to date and the problems which need to be overcome to advance these materials to the clinic for applications in heart, skin, and cornea repair.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".