Modulation of Dopaminergic and Glutamatergic Brain Function: PET Studies on Parkinsonian Rats
Bibliographic record
Abstract
UNLABELLED: Degeneration of dopaminergic neurons of the substantia nigra pars compacta is a cardinal feature of Parkinson's disease (PD). Although uncertain, the pathology has been suggested to derive from a malfunction of the complex interaction between dopaminergic and metabotropic glutamate receptors (mGluRs). To further address this issue, we investigated the imaging profile and expression of dopamine D(2) receptors and mGluRs in a classic parkinsonian rodent model induced by the toxin 6-hydroxydopamine. METHODS: Adult male Sprague-Dawley rats (250-300 g) received a stereotaxic injection of 8 mug/2 muL of 6-hydroxydopamine (n = 6) or saline solution (n = 4) in the right medial forebrain bundle. Small-animal PET was performed on all rats 4 wk after the surgical procedure to assess dopamine transporter (DAT) status using (11)C-2beta-carbomethoxy-3beta-(4-fluorophenyl)-tropane (CFT), as well as dopamine D(2) receptor and mGluR(5) modulation using (11)C-raclopride and 2-(11)C-methyl-6-(2-phenylethynyl)-pyridine ((11)C-MPEP), respectively. Behavioral studies were also conducted 6 wk after lesioning by d-amphetamine challenge. Immunohistochemistry and Western blotting were carried out at 8 wk after lesioning to confirm dopamine fiber, neuronal loss, and level of striatal mGluR(5) expression. RESULTS: PET images showed decreased (11)C-CFT binding on the lesioned side, including the structures of the striatum, hippocampus, and cortex, compared with the contralateral intact side. Interestingly, dopamine D(2) receptors and mGluR(5) upregulation were observed in the right striatum, hippocampus, and cortex, using (11)C-raclopride and (11)C-MPEP, respectively. A negative correlation was also found between the percentage change in mGluR(5) expression and DAT function. Finally, tyrosine hydroxylase immunoreactivity confirmed both dopamine fiber loss (t test, P < 0.01) and neuronal loss (t test, P < 0.01) on the lesioned side. These changes were accompanied by a strongly enhanced mGluR(5) expression in the right striatum of the lesioned side analyzed by Western plot. CONCLUSION: These findings support the existence of compensatory mechanisms in nigrostriatal dopamine degeneration and provide new insights that help further dissect some of the pathways underlying neurodegeneration. In addition, these results reconfirm that PET is a valuable tool for multilevel receptor studies, significantly contributing to the understanding of pathogenic mechanisms and ultimately opening new avenues in the study of neuroprotective approaches toward PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".