Design of Alginate/Gelatin Hydrogels for Biomedical Applications: Fine-Tuning Osteogenesis in Dental Pulp Stem Cells While Preserving Other Cell Behaviors
Bibliographic record
Abstract
Alginate/gelatin (Alg-Gel) hydrogels have been used experimentally, associated with mesenchymal stromal/stem cells (MSCs), to guide bone tissue formation. One of the main challenges for clinical application is optimizing Alg-Gel stiffness to guide osteogenesis. In this study, we investigated how Alg-Gel stiffness could modulate the dental pulp stem cell (DPSC) attachment, morphology, proliferation, and osteogenic differentiation, identifying the optimal conditions to uncouple osteogenesis from the other cell behaviors. An array of Alg-Gel hydrogels was prepared by casting different percentages of alginate and gelatin cross-linked with 2% CaCl2. We have selected two hydrogels: one with a stiffness of 11 ± 1 kPa, referred to as “low-stiffness hydrogel”, formed by 2% alginate and 8% gelatin, and the other with a stiffness of 55 ± 3 kPa, referred to as “high-stiffness hydrogel”, formed by 8% alginate and 12% gelatin. Hydrogel analyses showed that the average swelling rates were 20 ± 3% for the low-stiffness hydrogels and 35 ± 2% for the high-stiffness hydrogels. The degradation percentage was 47 ± 5% and 18 ± 2% for the low- and high-stiffness hydrogels, respectively. Both hydrogel types showed homogeneous surface shape and protein (Alg-Gel) interaction with CaCl2 as assessed by physicochemical characterization. Cell culture showed good adhesion of the DPSCs to the hydrogels and proliferation. Furthermore, better osteogenic activity, determined by ALP activity and ARS staining, was obtained with high-stiffness hydrogels (8% alginate and 12% gelatin). In summary, this study confirms the possibility of characterizing and optimizing the stiffness of Alg-Gel gel to guide osteogenesis in vitro without altering the other cellular properties of DPSCs.
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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) | 0.001 | 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".