Therapeutic Approaches Targeting Vascular Repair After Experimental Spinal Cord Injury: A Systematic Review of the Literature
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) disrupts the spinal cord vasculature resulting in ischemia, amplification of the secondary injury cascade and exacerbation of neural tissue loss. Restoring functional integrity of the microvasculature to prevent neural loss and to promote neural repair is an important challenge and opportunity in SCI research. Herein, we summarize the course of vascular injury and repair following SCI and give a comprehensive overview of current experimental therapeutic approaches targeting spinal cord microvasculature to diminish ischemia and thereby facilitate neural repair and regeneration. A systematic review of the published literature on therapeutic approaches to promote vascular repair after experimental SCI was performed using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standards. The MEDLINE databases PubMed, Embase, and OVID MEDLINE were searched using the keywords "spinal cord injury," "angiogenesis," "angiogenesis inducing agents," "tissue engineering," and "rodent subjects." A total of 111 studies were identified through the search. Five main therapeutic approaches to diminish hypoxia-ischemia and promote vascular repair were identified as (1) the application of angiogenic factors, (2) genetic engineering, (3) physical stimulation, (4) cell transplantation, and (5) biomaterials carrying various factor delivery. There are different therapeutic approaches with the potential to diminish hypoxia-ischemia and promote vascular repair after experimental SCI. Of note, combinatorial approaches using implanted biomaterials and angiogenic factor delivery appear promising for clinical translation.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".