Secretin depolarizes nucleus tractus solitarius neurons through activation of a nonselective cationic conductance
Bibliographic record
Abstract
The recent suggestion that secretin may be useful in treating autism and schizophrenia has begun to focus attention on the mechanisms underlying this gut-brain peptide's actions in the central nervous system (CNS). In vitro autoradiographic localization of (125)I-secretin binding sites in rat brain shows the highest binding density in the nucleus tractus solitarius (NTS). Recent evidence suggests that intravenous infusion of secretin causes fos activation in NTS, a relay station playing important roles in the central regulation of autonomic functions. In this study, whole cell patch-clamp recordings were obtained from 127 NTS neurons in rat medullary slices. The mean resting membrane potential of these neurons was -54.7 +/- 0.3 mV, the mean input resistance was 3.7 +/- 0.2 GOmega, and the action potential amplitude of these neurons was always >70 mV. Current-clamp studies showed that bath application of secretin depolarized the majority (80.8%; 42/52) of NTS neurons tested, whereas the remaining cells were either unaffected (17.3%; 9/52) or hyperpolarized (1.9%; 1/52). These depolarizing effects were maintained in the presence of 5 microM TTX and found to be concentration dependent from 10(-12) to 10(-7) M. Using voltage-clamp techniques, we also identified modulatory actions of secretin on specific ion channels. Our results demonstrate that while secretin is without effect on net whole cell potassium currents, it activates a nonselective cationic conductance (NSCC). These results show that NTS neurons are activated by secretin as a consequence of activation of a NSCC and support the emerging view that secretin can act as a neuropeptide within the CNS.
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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".