Chemical hypoxia in human pluripotent NT2 stem cell-derived neurons: Effect of hydroxamic acid and benzamide-based epigenetic drugs
Bibliographic record
Abstract
Hypoxia-induced oxidative stress contributes to neuronal damage leading to many neurodegenerative disorders. Hypoxia promotes many downstream effectors such as hypoxia-inducible factor-1α (HIF-1α) in order to restore respiratory homeostasis due to low oxygen availability and increased ROS. Use of histone deacetylase (HDAC) inhibitors may modulate hypoxia-induced neuronal cell damage. In this study, we used two chemically diverse HDAC inhibitors to investigate their effect on hypoxia-exposed neuronal cells. Human pluripotent NT-2 stem cell-derived neuronal differentiated cells were exposed to CoCl2 pre-treatment for 6h to induce hypoxia, prior to supplementation of HDAC inhibitor (SAHA or MGCD0103). Treatment with HDAC inhibitor improved cell viability in hypoxia-induced neuronal cells. The increased HIF1α expression in hypoxia-induced neuronal cells was blunted by these HDAC inhibitors with a concomitant decrease in ROS production. CoCl2 treatment caused an increase in IL-1β, which was significantly inhibited by these HDAC inhibitors. Furthermore, apoptosis induced in these CoCl2 treated neuronal cells was mitigated by SAHA as well MGCD0103 suggesting that these HDAC inhibitors are capable of reducing cellular toxicity, inflammation and apoptosis, and thus, could be beneficial as therapeutic molecules for many neuropathological conditions.
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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 | 0.000 |
| 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".