The histone deacetylase inhibitor suberoylanilide hydroxamic acid attenuates human astrocyte neurotoxicity induced by interferon-γ
Bibliographic record
Abstract
BACKGROUNDS: Increasing evidence shows that the histone deacetylase inhibitor suberoylanilide hydroxamic acid (SAHA) possesses potent anti-inflammatory and immunomodulatory properties. It is tempting to evaluate the potential of SAHA as a therapeutic agent in various neuroinflammatory and neurodegenerative disorders. METHODS: We examined the effects of SAHA on interferon (IFN)-γ-induced neurotoxicity of human astrocytes and on IFN-γ-induced phosphorylation of signal transducer and activator of transcription (STAT) 3 in human astrocytes. We also studied the effects of SAHA on the astrocytic production of two representative IFN-γ-inducible inflammatory molecules, namely IFN-γ-inducible T cell α chemoattractant (I-TAC) and intercellular adhesion molecule-1 (ICAM-1). RESULTS: SAHA significantly attenuated the toxicity of astrocytes activated by IFN-γ towards SH-SY5Y human neuronal cells. In the IFN-γ-activated astrocytes, SAHA reduced the STAT3 phosphorylation. SAHA also inhibited the IFN-γ-induced astrocytic production of I-TAC, but not ICAM-1. These results indicate that SAHA suppresses IFN-γ-induced neurotoxicity of human astrocytes through inhibition of the STAT3 signaling pathway. CONCLUSION: Due to its anti-neurotoxic and anti-inflammatory properties, SAHA appears to have the therapeutic or preventive potential for a wide range of neuroinflammatory disorders associated with activated astrocytes.
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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".