A Sequential Intrastriatal Dopaminergic Graft Strategy in the Rodent Model for Parkinson's Disease: Implications for Graft Survival and Targeting
Bibliographic record
Abstract
Optimal placement of intrastriatal dopaminergic grafts is likely crucial to optimize clinical recovery in Parkinson's disease (PD). The target sites of dopaminergic grafts vary among clinical trials and may partially explain the variable results in clinical efficacy reported thus far. In this study we hypothesized that a subsequent dopaminergic graft may promote functional recovery following a suboptimal initial graft. To test this hypothesis, rats with unilateral 6-hydroxydopamine lesions of the right nigrostriatal pathway were randomly divided into three groups. The first group received 900,000 fetal nigral cells in the medial striatum only (n = 6). The second group received 900,000 cells in both the medial and lateral striatum simultaneously (1.8 million total; n = 8). The final group received a second graft of 900,000 cells in the lateral striatum 6 weeks following initial transplantation of a medial graft (n = 6). Amphetamine-induced circling behavior was significantly reduced in both simultaneous and sequential graft groups at 9 and 12 weeks following transplantation of the initial graft. However, no recovery was noted in the single medial graft group at those time points. Furthermore, increased survival of dopaminergic cells was observed in the lateral graft of sequentially grafted animals compared with the medial graft. We conclude that a well-positioned subsequent graft can restore function in animals with a suboptimal initial graft and that the initial graft may improve survival of the second graft. These results are further discussed in relation to their important clinical implication for 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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".