Silk fibroin-derived polypeptides additives to promote hydroxyapatite nucleation in dense collagen hydrogels
Bibliographic record
Abstract
Silk fibroin-derived polypeptides (FDPs) are polypeptides resulting from the enzymatic separation of the hydrophobic crystalline (Cp) and hydrophilic electronegative amorphous (Cs) components of silk fibroin (SF). The role of these polypeptides in promoting the nucleation of hydroxyapatite (HA) has been previously investigated, yet is still not fully understood. Here we study the potential of HA mineralization via FDPs incorporated at 1:10, 1:2 and 1:1 in a plastically compressed (PC) and dense collagen (DC) scaffold. Scaffolds were immersed in simulated body fluid (SBF) at physiological conditions (pH = 7.4, 37°C) to promote biomineralization. The effect of Cs and Cp to promote HA nucleation was investigated at different time points, and compared to pure DC scaffolds. Characterization of Cs and Cp fragments using Liquid Chromatography-Mass Spectrometry (LCMS) showed little difference in the amino acid composition of the FDPs. Results obtained in vitro using Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR), Scanning Electron Microscopy (SEM) X-Ray Diffraction (XRD) and mass analysis showed little difference between scaffolds that incorporated Cs, Cp, and DC hydrogels. These results demonstrated that silk FDPs incorporation are not yet suitable to promote HA nucleation in vivo without further refining the collagen-FDP system.
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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".