Inorganic/Organic Biocomposite Cryogels for Regeneration of Bony Tissues
Bibliographic record
Abstract
The present work focuses on the physical, mechanical and in vitro properties of porous inorganic/organic biocomposite scaffolds of polyvinyl alcohol-tetraethylorthosilicate-alginate-calcium oxide (PTAC). These scaffolds are prepared by means of cryogelation technology and are intended for bone tissue engineering applications. The biocomposite cryogels have much more favorable physical and biological properties compared to the previous work of our group on the same composition in the form of pellets and foams. The optimized and heat-treated PTAC biocomposite cryogels show homogenous porosity and good mechanical properties and also exhibit the formation of a hydroxyapatite-like layer on their surface on coming in contact with simulated body fluid (SBF). Furthermore, the biocomposite cryogels showed good biocompatibility with L929 fibroblasts. Also, the influence of pre-soaking in SBF to that of non-soaked scaffolds was compared in terms of proliferation of MG-63 osteoblast-like osteosarcoma cells on these scaffolds and it was found that the pre-soaking caused a decrease in cell proliferation. Finally, the response of human osteoblasts on these scaffolds was analyzed by MTT assay, scanning electron microscopy, energy dispersive X-ray spectroscopy and micro X-ray computing tomography. The cells revealed good biocompatibility with the biocomposite cryogels and were mostly present as cell sheets on the surface with thick bundles of collagenous extracellular matrix during initial period of incubation. During later phases, the formation of calcium phosphate-like mineral deposits was observed on the surface of the cryogels suggesting a high potential of the biocomposite cryogels towards bone regeneration. Therefore, the PTAC biocomposite cryogels, due to their favorable properties and high biocompatibility with human osteoblasts can be suggested as potential scaffolds for bone tissue engineering applications.
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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".