Three-Dimensional Mineralization of Dense Nanofibrillar Collagen−Bioglass Hybrid Scaffolds
Bibliographic record
Abstract
Scaffolds for bone tissue engineering must meet a number of requirements such as biocompatibility, osteoconductivity, osteoinductivity, biodegradability, and appropriate biomechanical properties. A combination of type I collagen and 45S5 Bioglass may meet these requirements, however, little has been demonstrated on the effect of Bioglass on the potential of the collagen nanofibrillar three-dimensional mineralization and its influence on the structural and mechanical properties of the scaffolds. In this work, rapidly fabricated dense collagen-Bioglass hybrid scaffolds were assessed for their potential for immediate implantation. Hybrid scaffolds were conditioned, in vitro, in simulated body fluid (SBF) for up to 14 days and assessed in terms of changes in structural, chemical, and mechanical properties. MicroCT and SEM analyses showed a homogeneous distribution of Bioglass particles in the as-made hybrids. Mineralization was detected at day 1 in SBF, while ATR-FTIR microscopy and XRD revealed the presence of hydroxyl-carbonated apatite on the surface and within the two hybrid scaffolds at days 7 and 14. FTIR and SEM confirmed that the triple helical structure and typical banding pattern of fibrillar collagen was maintained as a function of time in SBF. Principal component analysis executed on ATR-FTIR microscopy revealed that the mineralization extent was a function of both Bioglass content and conditioning time in SBF. Tensile mechanical analysis showed an increase in the elastic modulus and a corresponding decrease in strain at ultimate tensile strength (UTS) as imparted by mineralization of scaffolds as a function of time in SBF and Bioglass content. Change in UTS was affected by Bioglass content. These results suggested the achievement of a hybrid matrix potentially suitable for bone tissue engineering.
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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".