Rapid Induction of P/C-type Inactivation Is the Mechanism for Acid-induced K+ Current Inhibition
Bibliographic record
Abstract
Extracellular acidification is known to decrease the conductance of many voltage-gated potassium channels. In the present study, we investigated the mechanism of H(+)(o)-induced current inhibition by taking advantage of Na(+) permeation through inactivated channels. In hKv1.5, H(+)(o) inhibited open-state Na(+) current with a similar potency to K(+) current, but had little effect on the amplitude of inactivated-state Na(+) current. In support of inactivation as the mechanism for the current reduction, Na(+) current through noninactivating hKv1.5-R487V channels was not affected by [H(+)(o)]. At pH 6.4, channels were maximally inactivated as soon as sufficient time was given to allow activation, which suggested two possibilities for the mechanism of action of H(+)(o). These were that inactivation of channels in early closed states occurred while hyperpolarized during exposure to acid pH (closed-state inactivation) and/or inactivation from the open state was greatly accelerated at low pH. The absence of outward Na(+) currents but the maintained presence of slow Na(+) tail currents, combined with changes in the Na(+) tail current time course at pH 6.4, led us to favor the hypothesis that a reduction in the activation energy for the inactivation transition from the open state underlies the inhibition of hKv1.5 Na(+) current at low pH.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".