Relationships Between Calcium and pH in the Regulation of the Slow Afterhyperpolarization in Cultured Rat Hippocampal Neurons
Bibliographic record
Abstract
The Ca(2+)-dependent slow afterhyperpolarization (AHP) is an important determinant of neuronal excitability. Although it is established that modest changes in extracellular pH (pH(o)) modulate the slow AHP, the relative contributions of changes in the priming Ca(2+) signal and intracellular pH (pH(i)) to this effect remain poorly defined. To gain a better understanding of the modulation of the slow AHP by changes in pH(o), we performed simultaneous recordings of intracellular free calcium concentration ([Ca(2+)](i)), pH(i), and the slow AHP in cultured rat hippocampal neurons coloaded with the Ca(2+)- and pH-sensitive fluorophores fura-2 and SNARF-5F, respectively, and whole cell patch-clamped using the perforated patch technique. Decreasing pH(o) from 7.2 to 6.5 lowered pH(i), reduced the magnitude of depolarization-evoked [Ca(2+)](i) transients, and inhibited the subsequent slow AHP; opposite effects were observed when pH(o) was increased from 7.2 to 7.5. Although decreases and increases in pH(i) (at a constant pH(o)) reduced and augmented, respectively, the slow AHP in the absence of marked changes in preceding [Ca(2+)](i) transients, the inhibition of the slow AHP by decreases in pH(o) was correlated with low pH(o)-dependent reductions in [Ca(2+)](i) transients rather than the decreases in pH(i) that accompanied the decreases in pH(o). In contrast, high pH(o)-induced increases in the slow AHP were correlated with the accompanying increases in pH(i) rather than high pH(o)-dependent increases in [Ca(2+)](i) transients. The results indicate that changes in pH(o) modulate the slow AHP in a manner that depends on the direction of the pH(o) change and substantiate a role for changes in pH(i) in modulating the slow AHP during changes in pH(o).
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".