Rescue of lesioned adult rat spinal motoneurons by adenoviral gene transfer of glial cell line-derived neurotrophic factor
Bibliographic record
Abstract
Glial cell line-derived neurotrophic factor (GDNF) has been shown to protect cranial and spinal motoneurons, that suggests potential uses of GDNF in the treatment of spinal cord injury and motor neuron diseases. We examined neuroprotective effect of human GDNF encoded by an adenovirus vector (AxCAhGDNF) on the death of lesioned adult rat spinal motoneurons. The seventh cervical segment (C7) ventral and dorsal roots and dorsal root ganglia of adult Fisher 344 rats were avulsed, and AxCAhGDNF, AxCALacZ (adenovirus encoding beta-galactosidase gene) or PBS was inoculated in C7 vertebral foramen. One week after the avulsion and treatment with AxCALacZ, the animals showed expression of beta-galactosidase activity in lesioned spinal motoneurons. Animals avulsed and treated with AxCAhGDNF showed intense immunolabeling for GDNF in lesioned spinal motoneurons and expression of virus-induced human GDNF mRNA transcripts in the lesioned spinal cord tissue. Nissl-stained cell counts revealed that the treatment with AxCAhGDNF significantly prevented the loss of lesioned ventral horn motoneurons 2 to 8 weeks after avulsion, as compared to AxCALacZ or PBS treatment. Furthermore, the AxCAhGDNF treatment ameliorated choline acetyltransferase immunoreactivity in the lesioned motoneurons after avulsion. These results indicate that the adenovirus-mediated gene transfer of GDNF may prevent the degeneration of motoneurons in adult humans with spinal cord injury and motor neuron diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".