Differential distribution of SK channel subtypes in the brain of the weakly electric fish <i>Apteronotus leptorhynchus</i>
Bibliographic record
Abstract
Calcium signals in vertebrate neurons can induce hyperpolarizing membrane responses through the activation of Ca(2+)-activated potassium channels. Of these, small conductance (SK) channels regulate neuronal responses through the generation of the medium after-hyperpolarization (mAHP). We have previously shown that an SK channel (AptSK2) contributes to signal processing in the electrosensory system of Apteronotus leptorhynchus. It was shown that for pyramidal neurons in the electrosensory lateral line lobe (ELL), AptSK2 expression selectively decreases responses to low-frequency signals. The localization of all the SK subunits throughout the brain of Apteronotus then became of substantial interest. We have now cloned two additional SK channel subunits from Apteronotus and determined the expression patterns of all three AptSK subunits throughout the brain. In situ hybridization experiments have revealed that, as in mammalian systems, the AptSK1 and 2 channels showed a partially overlapping expression pattern, whereas the AptSK3 channel was expressed in different brain areas. The AptSK1 and 2 channels were the primary subunits found in the major electrosensory processing areas. Immunohistochemistry further revealed distinct compartmentalization of the AptSK1 and 2 channels in the ELL. AptSK1 was localized to the apical dendrites of pyramidal neurons, whereas AptSK2 channels are primarily somatic. The distinct expression patterns of all three AptSK channels may reflect subtype-specific contributions to neuronal function, and the high homology between subtypes from a number of species suggests that the functional roles for each channel subtype are conserved from early vertebrate evolution.
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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".