Gelatin‐polysaccharide composite scaffolds for 3D cell culture and tissue engineering: Towards natural therapeutics
Bibliographic record
Abstract
Gelatin is a promising material as scaffold with therapeutic and regenerative characteristics due to its chemical similarities to the extracellular matrix (ECM) in the native tissues, biocompatibility, biodegradability, low antigenicity, cost-effectiveness, abundance, and accessible functional groups that allow facile chemical modifications with other biomaterials or biomolecules. Despite the advantages of gelatin, poor mechanical properties, sensitivity to enzymatic degradation, high viscosity, and reduced solubility in concentrated aqueous media have limited its applications and encouraged the development of gelatin-based composite hydrogels. The drawbacks of gelatin may be surmounted by synergistically combining it with a wide range of polysaccharides. The addition of polysaccharides to gelatin is advantageous in mimicking the ECM, which largely contains proteoglycans or glycoproteins. Moreover, gelatin-polysaccharide biomaterials benefit from mechanical resilience, high stability, low thermal expansion, improved hydrophilicity, biocompatibility, antimicrobial and anti-inflammatory properties, and wound healing potential. Here, we discuss how combining gelatin and polysaccharides provides a promising approach for developing superior therapeutic biomaterials. We review gelatin-polysaccharides scaffolds and their applications in cell culture and tissue engineering, providing an outlook for the future of this family of biomaterials as advanced natural therapeutics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".