Development of hydrogel‐based composite scaffolds containing eggshell particles for bone regeneration applications
Bibliographic record
Abstract
This study describes the development and characterization of novel composite scaffolds, made of an alginate-chitosan hydrogel matrix containing eggshell (ES) particles, for bone tissue engineering applications. Scaffolds with ES particles, either untreated or treated with phosphoric acid to create a nanotextured particle surface, were compared to scaffolds without particles. Results indicate that the nanotexturing process exposed occluded ES proteins orthologous to those in human bone extracellular matrix. Scaffolds with ES or nanotextured ES (NTES) particles had a higher porosity (81 ± 4% and 89 ± 5%, respectively) than scaffolds without particles (59 ± 5%) (p = .002 and p < .001, respectively). Scaffolds with NTES particles had a larger median pore size (113 μm [interquartile range [IQ]: 88-140 μm]) than scaffolds with ES particles (94 μm [IQ: 75-112 μm]) and scaffolds without particles (99 μm [IQ: 74-135 μm]) (p < .001 and p = .011, respectively). The compressive modulus of the scaffolds with ES or NTES particles remained low (3.69 ± 0.70 and 3.14 ± 0.62 kPa, respectively), but these scaffolds were more resistant to deformation following maximum compression than those without particles. Finally, scaffolds with ES or NTES particles allowed better retention of human mesenchymal stem cells during seeding (53 ± 12% and 57 ± 8%, respectively, vs. 17 ± 5% for scaffolds without particles; p < .001 in both cases), as well as higher cell viability up to 21 days of culture (67 ± 17% and 61 ± 11%, respectively, vs. 15 ± 7% for scaffolds without particles; p < .001 in both cases). In addition, alkaline phosphatase (ALP) activity increased up to 558 ± 164% on day 21 in the scaffolds with ES particles, and up to 567 ± 217% on day 14 in the scaffolds with NTES particles (p = .006 and p = .002, respectively, relative to day 0). Overall, this study shows that the physicochemical properties of the alginate-chitosan hydrogel scaffolds with ES or NTES particles are similar to those of cancellous bone. In addition, scaffolds with particles supported early osteogenic differentiation and therefore represent a promising new bone substitute, especially for non-load bearing applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".