Dense fibrillar collagen-based hydrogels as functional osteoid-mimicking scaffolds
Bibliographic record
Abstract
There is an increasing need to generate novel materials for the treatment and augmentation of bone defects, affecting millions of people worldwide. Fibrillar type I collagen is the most abundant tissue matrix protein in bone, providing its key native scaffolding material. However, while in vitro reconstituted collagen hydrogels of physically entangled, nano-fibred meshes, have long served as three-dimensional cultures, their highly-hydrated nature impacts their physiological relevance. In an effort to create biomimetic collagen gels, approaches have been undertaken to generate osteoid-like environments with increased collagen concentrations, controlled fibrillar orientation, defined micro-architectures, and tailored mechanical properties. This review describes the state-of-the-art on collagen densification techniques, exploring their advantages, limitations and future perspectives for applications as bone grafts. Ultimately, by successfully mimicking the organic milieu of bone through acellular or cell-mediated mineralisation of the designed osteoid-like structure, functional collagen scaffolds with potential applications in bone tissue engineering can be realised.Abbreviations: 3D: three-dimensional; BG: bioactive glass; CFD: collagen fibrillar density; CHA: carbonated-hydroxyapatite; Col1: Type I collagen; ECM: extracellular matrix; GAE: gel aspiration-ejection; HHC: highly hydrated collagen; MSC: mesenchymal stem cell; NCPs: non-collagenous proteins; PC: plastic compression; PILP: polymer-induced liquid precursor; SBF: simulated body fluid
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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".