Injectable Chitosan‐Based Scaffolds in Regenerative Medicine and their Clinical Translatability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it