Use of tendon to produce decellularized sheets of mineralized collagen fibrils for bone tissue repair and regeneration
Bibliographic record
Abstract
With demand for alternatives to autograft and allograft materials continuing to rise, development of new scaffolds for bone tissue repair and regeneration remains of significant interest. Engineered collagen-calcium phosphate (CaP) constructs can offer desirable attributes, including absence of foreign body response and possession of inherent osteogenic potential. Despite their promise, current collagen-CaP constructs are limited to nonload-bearing applications. In this article, we describe a process for creating decellularized sheets of highly aligned, natively cross-linked, and mineralized collagen fibrils, which may be useful for developing multilaminate collagen-CaP constructs with improved mechanical properties. Decellularized bovine tendons were cryosectioned to produce thin sheets of aligned collagen fibrils. Mineralization of the sheets was then performed using an alternate soaking method incorporating a polymer-induced liquid precursor (PILP) process to promote intrafibrillar mineralization, along with incorporation of physiologically relevant amounts of citrate, Mg, and carbonate. Characteristics of the produced scaffolds were assessed using energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Scaffolds were also compared with both native bovine cortical bone and pure hydroxyapatite using X-ray powder diffraction (XRD), and Fourier transform infrared spectroscopy attenuated total reflection (FTIR-ATR). Structural and chemical analyses show that the scaffold preparation process that we described is successful in creating mineralized collagen sheets, possessing a mineral phase similar to that found in bone as well as a close association between collagen fibrils and mineral plates.
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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".