Nucleation of biomimetic hydroxyapatite nanoparticles on the surface of human type I collagen using a hybrid all-atom and coarse-grained model
Bibliographic record
Abstract
Inorganic mineral/collagen composite materials are one of the most attractive implant materials for bone repair engineering. Mineralized collagen composites have a similar hierarchical structure and biological activity to natural bone; however, the mechanism of the mineralization process is complex, and the properties of mineralized materials are difficult to control during the preparation process. Currently, this is a significant challenge in coarse-grained organic-inorganic systems. Thus, a coarse-grained/all-atom multiscale model was employed to investigate the biomineralization process. Based on the free energy of the all-atom ion association, we obtained the coupling parameters of the multiscale model, which were similar to those of the all-atom model. In this multiscale simulation model, coarse-grained models were used for type I collagen protein and water molecules and all-atom models for phosphate and calcium ions. The coarse-grained/all-atom multiscale model of mineralized collagen identified the same nucleation site and calcium phosphate aggregation process as the all-atom model. Additionally, the calcium phosphate clusters still retained site-selectivity around the coarse-grained collagen surface during the nucleation process. At the same time, the clusters tended to have a certain crystal structure morphology during the long-time simulation. This new strategy will help accelerate biomaterial design and optimization.
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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 | 0.001 |
| 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 | 0.001 | 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".