Potent inhibition of anoxic depolarization by the sodium channel blocker dibucaine
Bibliographic record
Abstract
Recurring waves of peri-infarct depolarizations (PIDs) propagate across gray matter in the hours and days following stroke, expanding the primary site of injury. Ischemic depolarization (termed anoxic depolarization or AD in live brain slices) is PID-like but immediately arises in the more metabolically compromised ischemic core. This causes dramatic neuronal and astrocyte swelling and dendritic beading with spine loss within minutes, resulting in acute cell death. AD is evoked in rodent neocortical slices by suppressing the Na(+)/K(+)-ATPase pump with either oxygen/glucose deprivation (OGD) or exposure to ouabain. The process driving AD and PIDs remains poorly understood. Here we show that dibucaine is a potent drug inhibiting AD because of its high binding affinity to the Na(+) channel. Field recording reveals that, when superfused with ouabain (5 min), neocortical slices pretreated with 1 μM dibucaine for 45 min display either no AD or delayed AD onset compared with untreated controls. If ouabain exposure is extended to 10 min, 1 μM dibucaine is still able to delay AD onset by ∼ 60%. Likewise, it delays OGD-evoked AD onset by ∼ 54% but does not depress action potentials (APs) or evoked orthodromic field potentials. Increasing dibucaine to 10 μM inhibits AP firing, gradually putting the slice into a stasis that inhibits AD onset but also renders the slice functionally quiescent. Two-photon microscopy reveals that 10 μM dibucaine pretreatment prevents or helps reverse ouabain-induced structural neuronal damage. Although the therapeutic range of dibucaine is quite narrow, dibucaine-like drugs could prove therapeutically useful in inhibiting PIDs and their resultant neuronal damage.
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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".