Adrenomedullin influences magnocellular and parvocellular neurons of paraventricular nucleus via separate mechanisms
Bibliographic record
Abstract
We previously reported that adrenomedullin (AM) decreases blood pressure following microinjection into the paraventricular nucleus of the hypothalamus (PVN) of the rat. With the use of whole cell recordings in rat hypothalamic slice preparations, we characterized the effects of AM on electrophysiologically identified PVN neurons and described the membrane events underlying such actions. AM hyperpolarized magnocellular (type I) neurons in a dose-dependent manner, a response associated with an increase in the frequency and amplitude of inhibitory postsynaptic potentials. Blockade of action potentials with tetrodotoxin (TTX) abolished AM effects on membrane potential and synaptic activity in magnocellular neurons, suggesting direct actions on inhibitory interneurons. Furthermore, blockade of inhibitory synaptic transmission with the GABA(A) receptor antagonist bicuculline methiodide also abolished AM effects on membrane potential in magnocellular neurons. In contrast, parvocellular (type II) neurons depolarized following AM receptor activation. AM effects on parvocellular neurons were dose dependent and were maintained in the presence of TTX, indicating direct effects on this population of neurons. Voltage-clamp recordings from parvocellular neurons showed AM enhances a nonselective cationic conductance, suggesting a potential mechanism through which AM influences membrane potential. These observations show clear population-specific actions of AM on separate identified groups of PVN neurons. Such effects on magnocellular neurons likely contribute to the hypotensive actions of this peptide in PVN. Although the effects on parvocellular neurons may also contribute to such cardiovascular effects of AM, it is more likely that actions on this population of PVN neurons underlie the previously demonstrated activational effects of AM on the hypothalamic-pituitary-adrenal axis.
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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".