Delayed Administration of a Bio-Engineered Zinc-Finger VEGF-A Gene Therapy Is Neuroprotective and Attenuates Allodynia Following Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Following spinal cord injury (SCI) there are drastic changes that occur in the spinal microvasculature, including ischemia, hemorrhage, endothelial cell death and blood-spinal cord barrier disruption. Vascular endothelial growth factor-A (VEGF-A) is a pleiotropic factor recognized for its pro-angiogenic properties; however, VEGF has recently been shown to provide neuroprotection. We hypothesized that delivery of AdV-ZFP-VEGF--an adenovirally delivered bio-engineered zinc-finger transcription factor that promotes endogenous VEGF-A expression--would result in angiogenesis, neuroprotection and functional recovery following SCI. This novel VEGF gene therapy induces the endogenous production of multiple VEGF-A isoforms; a critical factor for proper vascular development and repair. Briefly, female Wistar rats--under cyclosporin immunosuppression--received a 35 g clip-compression injury and were administered AdV-ZFP-VEGF or AdV-eGFP at 24 hours post-SCI. qRT-PCR and Western Blot analysis of VEGF-A mRNA and protein, showed significant increases in VEGF-A expression in AdV-ZFP-VEGF treated animals (p<0.001 and p<0.05, respectively). Analysis of NF200, TUNEL, and RECA-1 indicated that AdV-ZFP-VEGF increased axonal preservation (p<0.05), reduced cell death (p<0.01), and increased blood vessels (p<0.01), respectively. Moreover, AdV-ZFP-VEGF resulted in a 10% increase in blood vessel proliferation (p<0.001). Catwalk™ analysis showed AdV-ZFP-VEGF treatment dramatically improves hindlimb weight support (p<0.05) and increases hindlimb swing speed (p<0.02) when compared to control animals. Finally, AdV-ZFP-VEGF administration provided a significant reduction in allodynia (p<0.01). Overall, the results of this study indicate that AdV-ZFP-VEGF administration can be delivered in a clinically relevant time-window following SCI (24 hours) and provide significant molecular and functional benefits.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".