A Dopamine D4 Receptor Antagonist Attenuates Ischemia-Induced Neuronal Cell Damage via Upregulation of Neuronal Apoptosis Inhibitory Protein
Bibliographic record
Abstract
Neuronal apoptosis inhibitory protein (NAIP/BIRC1), the inhibitor of apoptosis protein (IAP) family member, suppresses neuronal cell death induced by a variety of insults, including cell death from ischemia and stroke. The goal of the present study was to develop an efficient method for identification of compounds with the ability to upregulate endogenous NAIP and to determine the effects on these compounds on the cellular response to ischemia. A novel NAIP-enzyme-linked immunosorbent assay (ELISA)-based in vitro drug-screening system is established. Use of this system identified an antagonist of dopamine D4 receptor, termed L-745,870, with a potent NAIP upregulatory effect. L-745,870-mediated NAIP upregulation in neuronal and nonneuronal cultured cells resulted in decreased vulnerability to oxidative stress-induced apoptosis. Reducing NAIP expression via RNA interference techniques resulted in prevention of L-745,870-mediated protection from oxidative stress. Further, systemic administration of L-745,870 attenuated ischemia-induced damage of the hippocampal CA1 neurons and upregulated NAIP expression in the rescued hippocampal CA1 neurons in a gerbil model. These data suggest that the NAIP upregulating compound, L-745,870, has therapeutic potential in acute ischemic disorders and that our NAIP-ELISA-based drug screening may facilitate the discovery of novel neuroprotective compounds.
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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".