In vitro characterization of novel nanostructured collagen-hydroxyapatite composite scaffolds doped with magnesium with improved biodegradation rate for hard tissue regeneration
Bibliographic record
Abstract
New materials are required for bone healing in regenerative medicine able to temporarily substitute damaged bone and to be subsequently resorbed and replaced by endogenous tissues. Taking inspiration from basic composition of the mammalian bones, composed of collagen, apatite and a number of substitution ions, among them magnesium (Mg2+), in this work, novel composite scaffolds composed of collagen(10%)-hydroxyapatite (HAp)(90%) and collagen(10%)-HAp(80%)-Mg(10%) were developed. The lyophilization was used for composites preparation. An insight into the nanostructural nature of the developed scaffolds was performed by Scanning Electron Microscopy coupled with Energy Dispersive X-Ray and Transmission Electron Microscopy coupled with Energy Dispersive X-Ray. The HAp nanocrystallite clusters and Mg nanoparticles were homogeneously distributed within the scaffolds and adherent to the collagen fibrils. The samples were tested for degradation in Simulated Body Fluid (SBF) solution by soaking for up to 28 days. The release of Mg from collagen(10%)-HAp(80%)-Mg(10%) composite during the period of up to 21 days was attested, this composite being characterized by a decreased degradation rate with respect to the composite without Mg. The developed composite materials are promising for applications as bone substitute materials favouring bone healing and regeneration.
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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".