Incorporation of the Amniotic Membrane as an Immunomodulatory Design Element in Collagen Scaffolds for Tendon Repair
Bibliographic record
Abstract
Tendon injuries often require surgical intervention and even then result in poor outcomes due to scar formation and repeated failure. Biomaterial implants offer the potential to address multiple underlying concerns preventing improved tendon repair. Here, we describe modifications to the composition of an anisotropic collagen–glycosaminoglycan (CG) scaffold biomaterial, incorporating amniotic membrane (AM)-derived matrix to alter the inflammatory response and establish conditions for improved regenerative repair. We explored two methods of AM matrix incorporation to address multiple concerns associated with tendon repair. Amniotic membrane-derived matrix was incorporated directly into the scaffold microstructure during fabrication to form a C/AM composite. Alternatively, decellularized amniotic matrix was wrapped around the traditional collagen–chondroitin sulfate (C/CS) scaffold to form a core–shell composite (C/CS plus AM wrap) in a manner similar to current collagen membrane wraps used in rotator cuff and Achilles tendon surgeries to improve the mechanical strength of the repair. Human mesenchymal stem cells (MSCs) cultured within these materials were evaluated for metabolic health and immunomodulatory gene expression in response to inflammatory media challenge of interleukin 1 β and tumor necrosis factor α. The scaffolds were able to maintain MSC metabolic activity in all media conditions over the course of a 7 day culture. Expression of genes encoding for pro-inflammatory cytokines were down-regulated in AM containing scaffolds, suggesting the potential to employ AM-modified CG scaffolds for tendon-repair applications.
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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) | 0.000 | 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".