Dopamine‐induced toxicity is synergistically potentiated by simultaneous HSP‐90 and Akt inhibition in oligodendrocyte progenitors
Bibliographic record
Abstract
Oligodendrocyte progenitors are highly susceptible to various insults. Their limited antioxidant defenses and high levels of apoptotic factors, such as Bax and pro-caspase-3 contribute to their sensitivity. We previously showed that dopamine (DA) is toxic to oligodendrocyte progenitors by inducing superoxide generation, lowering glutathione levels and promoting apoptosis through caspase-3 activation. In contrast, factors that contribute to cell survival and defense against dopamine (DA) toxicity are less studied. Here, we explored the role of two molecules which play important roles in cell survival, namely the heat shock protein 90 (HSP-90) and the protein kinase Akt, using the selective inhibitors, 17-AAG and Akt inhibitor III, respectively. The HSP-90 inhibitor caused a decrease in P-Akt level, induced caspase-3 activation, increased nuclear condensation and caused a loss in cell viability. Furthermore, 17-AAG potentiated DA-induced apoptosis by enhancing caspase-3 activation. In addition, the Akt inhibitor alone exacerbated DA toxicity and in combination with 17-AAG caused synergistic potentiation of DA toxicity by enhancing caspase-3 activation. Together, these results indicate that HSP-90 is essential for oligodendrocyte progenitor survival. Both HSP-90 and Akt play important roles in concert in the defense against DA-induced apoptosis.
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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".