The stability and self-assembly of tri-calcium silicate and hydroxyapatite scaffolds in bone tissue engineering applications
Bibliographic record
Abstract
The fabrication of scaffolds for bone tissue engineering (BTE) applications often involves the utilization of two distinct categories of biomaterials, namely calcium phosphates and calcium silicates. The selection of these materials is based on their biocompatibility, bioactivity, and mechanical characteristics that closely resemble those of natural bone. The present research examined the utilization of hydroxyapatite (HAP) and tri-calcium silicate (TCS), which are among the most commonly utilized materials in calcium phosphates and calcium silicates, in the context of bone scaffolding applications. A molecular dynamics simulation was conducted to investigate the impact of different concentrations of ceramic nanoparticles, when combined with sodium alginate (SA) hydrogel, on the fabrication of bone scaffolds.The stability and self-assembly were assessed through several parameters, such as the solvent-accessible surface area (SASA), radius of gyration (Rg), radial distribution function (g(r)), root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), hydrogen bonding, van der Waals, electrostatic, and total energies. The findings indicate that the addition of 10 wt% HAP and TCS to the SA hydrogel matrix results in a more compact, stable, and potentially less hydrated structure. Accordingly, the experimental validation of these simulation approved our in silico findings. Experimental rheology and mechanical properties evaluation validate our simulation results, indicating a superior characteristic of TCS10 and HAP10 inks and 3D-printed scaffolds among other composition ratios. This could potentially benefit the in vitro and in vivo performance of the scaffold and its interaction with cells. The aforementioned traits are considered fundamental for the successful execution of the scaffold in the field of BTE. The findings indicate that TCS samples exhibit superior properties when compared to HAP samples, specifically in terms of composition with SA hydrogel.
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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".