Erythropoietin as a Modulator of Pathology in a Toxicant Mouse Model of Parkinson’s Disease
Bibliographic record
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder and has no known disease-modifying treatments.Due to the complexity of the disease pathology, effective treatments for PD will likely involve a combination of treatment factors.The current experiments sought to profile the pro-survival effects of the trophic cytokine, erythropoietin (EPO), in a 6-hydroxydopamine (6-OHDA) mouse model of PD.Methods: To this end, male C57Bl/6 mice were used in a series of four experiments investigating the potential antiapoptotic, anti-inflammatory and antioxidant effects of the trophic cytokine.EPO's ability to protect dopaminergic terminals in the striatum, cell bodies in the substantia nigra (SNc) and modulate 6-OHDA-induced motor deficits was characterized at different doses of 6-OHDA and EPO.Results: Our results did indeed demonstrate EPO's ability to exert pro-survival effects that were brain region-specific.While intra-nigral EPO was ineffective, intra-striatal EPO preserved striatal terminals and nigral soma in two different 6-OHDA lesion models.EPO further attenuated apomorphine-induced rotations at two doses of 6-OHDA.EPO demonstrated antiapoptotic signalling through phosphorylation of Akt and the Bcl-2 associated proteins and antiinflammatory activity through modulation of microglial morphology.Finally, EPO demonstrated antioxidant activity through elevated levels of striatal glutathione peroxidase, in addition to retrograde signalling that resulted in elevated levels of glutathione peroxidase in the SNc in response to EPO treatment.Conclusions: In short, EPO appears to modify antioxidant and antiapoptotic factors and act in a brain-region specific manner to mitigate neuronal loss.Taken together, the results of the current set of experiments indicate EPO's potential for use as an adjuvant therapy in the treatment of 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".