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 hypoxia-inducible 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 μmol/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-1α and its downstream target genes.
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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 | 0.000 | 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 | 0.000 |
| 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".