Combinatorial Therapies After Spinal Cord Injury: How Can Biomaterials Help?
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
Traumatic spinal cord injury (SCI) results in an immediate loss of motor and sensory function below the injury site and is associated with a poor prognosis. The inhibitory environment that develops in response to the injury is mainly due to local expression of inhibitory factors, scarring and the formation of cystic cavitations, all of which limit the regenerative capacity of endogenous or transplanted cells. Strategies that demonstrate promising results induce a change in the microenvironment at- and around the lesion site to promote endogenous cell repair, including axonal regeneration or the integration of transplanted cells. To date, many of these strategies target only a single aspect of SCI; however, the multifaceted nature of SCI suggests that combinatorial strategies will likely be more effective. Biomaterials are a key component of combinatorial strategies, as they have the potential to deliver drugs locally over a prolonged period of time and aid in cell survival, integration and differentiation. Here we summarize the advantages and limitations of widely used strategies to promote recovery after injury and highlight recent research where biomaterials aided combinatorial strategies to overcome some of the barriers of spinal cord regeneration.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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