Local circuitry regulates the excitability of rat neurohypophysial neurones
Bibliographic record
Abstract
The importance of angiotensin II (AII) and glutamate has long since been recognized in neuroendocrine regulation. However, the mechanisms by which AII and glutamate modulate the excitability of the paraventricular nucleus (PVN) have largely remained a mystery until recently. It is now apparent that AII and glutamate are potent stimulators of both magnocellular and parvocellular neurones in the rat PVN. While glutamate, the predominant excitatory neurotransmitter in the CNS, ubiquitously excites PVN neurones, AII appears to mediate excitability of the PVN by both direct and indirect mechanisms. Interestingly, both of these neurotransmitters, upon exciting the PVN, activate an inhibitory feedback system, which is capable of diminishing the initial stimulus. Physiologically, this moderates the output signals from the PVN, and probably also regulates neuropeptide release from the neurohypophysis. The importance of this negative-feedback loop is evident in the pathophysiological implications of a disruption in the system. Evidence suggests that a breakdown in this system may be responsible in part for the onset and maintenance of both congestive heart failure and hypertension. Future studies will continue to characterize both the actions of glutamate and AII in the PVN, and to further elucidate the mechanisms which control the excitability of the PVN.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".