Recent Advances in Natural Gum-Based Biomaterials for Tissue Engineering and Regenerative Medicine: A Review
Bibliographic record
Abstract
The engineering of tissues under a three-dimensional (3D) microenvironment is a great challenge and needs a suitable supporting biomaterial-based scaffold that may facilitate cell attachment, spreading, proliferation, migration, and differentiation for proper tissue regeneration or organ reconstruction. Polysaccharides as natural polymers promise great potential in the preparation of a three-dimensional artificial extracellular matrix (ECM) (i.e., hydrogel) via various processing methods and conditions. Natural polymers, especially gums, based upon hydrogel systems, provide similarities largely with the native ECM and excellent biological response. Here, we review the origin and physico-chemical characteristics of potentially used natural gums. In addition, various forms of scaffolds (e.g., nanofibrous, 3D printed-constructs) based on gums and their efficacy in 3D cell culture and various tissue regenerations such as bone, osteoarthritis and cartilage, skin/wound, retinal, neural, and other tissues are discussed. Finally, the advantages and limitations of natural gums are precisely described for future perspectives in tissue engineering and regenerative medicine in the concluding remarks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 0.002 |
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".