Protective effect of hydralazine on a cellular model of parkinson�s disease: a possible role of hypoxia-inducible factor (HIF)-1α
Bibliographic record
Abstract
Parkinson�s disease (PD) is a neurodegenerative disease accompanied by a low expression level of cerebral hypoxiainducible factor (HIF-1α). Hence, activating the hypoxia-signaling pathway may be a favorable therapeutic approach for curing PD. This study explored the efficacy of hydralazine, a well-known antihypertensive agent, for restoring the impaired HIF-1 signaling in PD, with the aid of 6-hydroxydopamine (6-OHDA)-exposed SH-SY5Y cells. The cytotoxicity of hydralazine and 6-OHDA on the SH-SY5Y cells were evaluated by MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and apoptosis detection assays. The activities of malondialdehyde, nitric oxide (NO), ferric reducing antioxidant power (FRAP), and superoxide dismutase (SOD) were also measured. Expression levels of HIF-1α and its downstream genes at the protein level were assessed by Western blotting. Hydralazine showed no toxic effects on SH-SY5Y cells, at the concentration of �50 α mol/L. Hydralazine decreased the levels of apoptosis, malondialdehyde, and NO, and increased the activities of FRAP and SOD in cells exposed to 6-OHDA. Furthermore, hydralazine up-regulated the protein expression levels of HIF-1α, vascular endothelial growth factor, tyrosine hydroxylase, and dopamine transporter in the cells also exposed to 6-OHDA, by comparison with the cells exposed to 6-OHDA alone. In summary, hydralazine priming could attenuate the deleterious effects of 6-OHDA on SH-SY5Y cells by increasing cellular antioxidant capacity, as well as the protein levels of HIF-1 α and its downstream target genes. © 2020, Canadian Science Publishing. All rights reserved.
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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".