Enhancing Osteogenic Potential in Bone Tissue Engineering: Optimizing Pore Size in Alginate–Gelatin Composite Hydrogels
Bibliographic record
Abstract
Bone tissue engineering relies on crucial scaffolds for tissue formation and stem cell differentiation. A composite scaffold of alginate‐gelatin effectively supports these processes. This study aims to design a porous alginate‐gelatin hydrogel and assess pore size effects on cell behavior, focusing on morphology, adhesion, and proliferation in distinct osteogenic environments. Hydrogels are prepared using various alginate‐gelatin concentrations: 4% alginate and 6% gelatin (4A6G) or 3% alginate and 5% gelatin (3A5G), cross‐linked with 2% CaCl2. Pore size optimization employs simple freezing and thawing cycles. Scanning electron microscopy reveals varying pore sizes: 340 µm ± 30 µm for 4A6G and 635 µm ± 25 µm for 3A5G. Stiffness measurements indicate significant differences: ≈26.3 kPa ± 0.6 KPa for 4A6G and 21.6 kPa ± 0.2 KPa for 3A5G. Cell interaction studies demonstrate higher adhesion and proliferation rates in larger‐pored hydrogels. Evaluation of bone tissue formation, including RT‐PCR, ALP activity, and ARS staining, reveal superior osteogenic potential in the 3A5G hydrogel compared to 4A6G. In conclusion, the 3A5G hydrogel (3% alginate and 5% gelatin) holds promise for bone tissue regeneration due to its biodegradability and favorable bone‐forming properties.
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".