Injectable Chitosan‐Based Scaffolds in Regenerative Medicine and their Clinical Translatability
Bibliographic record
Abstract
Injectable scaffolds (IS) are polymeric solutions that are injected in vivo and undergo gelation in response to physiological or non-physiological stimuli. Interest in using IS in regenerative medicine has been increasing this past decade. IS are administered in vivo using minimally invasive surgery, which reduces hospitalization time and risk of surgical wound infection. Here, chitosan is explored as an excellent candidate for developing IS. A literature search reveals that 27% of IS publications in the past decade investigated injectable chitosan scaffolds (ICS). This increasing interest in chitosan stems from its many desirable physicochemical properties. The first section of this Progress Report is a comprehensive study of all physical, chemical, and biological stimuli that have been explored to induce ICS gelation in vivo. Second, the use of ICS is investigated in four major regenerative medicine applications, namely bone, cartilage, cardiovascular, and neural regeneration. Finally, an overall critique of the ICS literature in light of clinical translatability is presented. Even though ICS have been widely explored in the literature, very few have progressed to clinical trials. The authors discuss the current barriers to moving ICS into the clinic and provide suggestions regarding what is needed to overcome those challenges.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".