Mechanically Stable and Tunable Photoactivated Peptide‐Based Hydrogels for Soft Tissue Adhesion
Bibliographic record
Abstract
ABSTRACT Compared to the use of natural‐based products, peptide‐based materials can be produced synthetically to reduce cost, batch variability, and avoid pathogen transmission while providing flexible platforms with suitable tissue and cell compatibilities for biomedical uses. In this study, we present a rationally designed, collagen‐like peptide (CLP) hydrogel platform utilizing supramolecular self‐assembly and light‐triggered thiol‐ene crosslinking to form mechanically stable and tunable materials for use as soft tissue adhesives. By screening and characterizing a library of synthetic peptides, ideal candidates for hydrogel formation are identified. Upon adjusting the peptide concentration or structural properties such as junction functionality and choice of reactive group, the mechanical properties of these peptide hydrogels can be optimized to generate robust biomaterials capable of closing wounds with strength comparable to commercial tissue adhesives such as LiquiBand. These peptide materials are also cytocompatible and biodegradable, indicating their potential as adhesives for soft tissue 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 | 0.000 | 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".