Bacterial collagen-templated synthesis and assembly of inorganic particles
Bibliographic record
Abstract
Abstract Collagen has been used as a common template for mineralization and assembly of inorganic particles, because of the special arrangement of its fibrils and the presence of charged residues. Streptococcal bacterial collagen, which is inherently secreted on the surface of Streptococcus pyogenes, has been progressively used as an alternative for type I animal collagen. Bacterial collagen is rich in charged amino acids, which can act as a substrate for the nucleation and growth of inorganic particles. Here, we show that bacterial collagen can be used to nucleate three different inorganic materials: hydroxyapatite crystals, silver nanoparticles, and silica nanoparticles. Collagen/mineral composites show an even distribution of inorganic particles along the collagen fibers, and the particles have a more homogenous size compared with minerals that are formed in the absence of the collagen scaffold. Furthermore, the gelation of silica occurring during mineralization represents a means to produce processable self-standing collagen composites, which is challenging to achieve with bacterial collagen alone. Overall, we highlight the advantage of simply combining bacterial collagen with minerals to expand their applications in the fields of biomaterials and tissue engineering, especially for bone regenerative scaffolds.
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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 | 0.001 |
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".