Slowly Inactivating Potassium Conductance (ID): A Potential Target for Stroke Therapy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Excessive accumulation of extracellular glutamate results in the death of most, but not all, neurons in the central nervous system. Understanding the unique properties of cells that can withstand this excitotoxic challenge may identify specific targets for novel stroke therapies. METHODS: A combination of in vivo methods for analysis of excitotoxic cell death after activation of N-methyl-D-aspartate (NMDA) receptors and in vitro patch-clamp analysis of specific conductances in hypothalamic slices and dissociated cells has been used to assess the roles of specific potassium conductances in delayed cell death after NMDA receptor activation. RESULTS: We report that a specific D-type potassium conductance (I(D)), necessary for the rapid repolarization of the membrane after a strong depolarization, serves such a protective purpose in magnocellular neurons of the paraventricular nucleus. Manipulations that inhibit this current (4-aminopyridine or angiotensin II) increase neuronal excitability and augment cell death after NMDA receptor activation. In addition, this protection is not observed in magnocellular neurons of spontaneously hypertensive rats, and intriguingly it can be reestablished by blocking angiotensin II receptors in these animals. CONCLUSIONS: These observations provide a persuasive experimental explanation for the unexpected finding that therapeutic treatments for hypertension that block central as well as peripheral angiotensin type 1 receptors reduce the severity and occurrence of stroke.
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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) | 0.002 | 0.001 |
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".