Erythropoietin and GDNF enhance ventral mesencephalic fiber outgrowth and capillary proliferation following neural transplantation in a rodent model of Parkinson's disease
Bibliographic record
Abstract
Low dopaminergic cell survival and suboptimal fiber reinnervation are likely major contributing factors for the limited benefits of neural transplantation in Parkinson's disease (PD) patients. Glial cell lined-derived neurotrophic factor (GDNF) has been shown to enhance dopaminergic cell survival and fiber outgrowth of the graft site as well as promote behavioral recovery in rodent models of PD, while erythropoietin (EPO) can produce dopaminergic neuroprotective effects against 6-hydroxydopamine (6-OHDA) exposure on cultured neurons and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-treated mice. The aim of this study was to determine if fetal ventral mesencephalic (FVM) tissue exposed to hibernation media containing a combination of GDNF and EPO could enhance dopaminergic graft survival, striatal reinnervation and functional recovery in a 6-OHDA rodent model of PD. FVM tissue was dissected from 14-day-old rat fetuses and placed for 6 days in hibernation media alone, and in hibernation media that received either a daily administration of GDNF, EPO or a combination of GDNF and EPO. Following hibernation, FVM cells were transplanted as a single cell suspension into the striatum of unilateral 6-OHDA-lesioned rats. Rotational behavioral assessment revealed animals that received FVM tissue exposed to GDNF, EPO or the combination of both drugs had accelerated functional recovery. Immunohistochemical and stereological assessment revealed a significant increase in graft fiber density and angiogenesis into the graft when compared with control. These findings suggest that the hibernation of FVM tissue in a combination of GDNF and EPO can enhance graft efficacy and may have important implications for tissue preparation protocols for clinical neural transplantation in PD.
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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) | 0.000 | 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".