Prokineticin 2 depolarizes paraventricular nucleus magnocellular and parvocellular neurons
Bibliographic record
Abstract
Blind whole-cell patch-clamp techniques were used to examine the effects of prokineticin 2 (PK2) on the excitability of magnocellular (MNC), parvocellular preautonomic (PA), and parvocellular neuroendocrine (NE) neurons within the hypothalamic paraventricular nucleus (PVN) of the rat. The majority of MNC neurons (76%) depolarized in response to 10 nm PK2, effects that were eliminated in the presence of tetrodotoxin (TTX). PK2 also caused an increase in excitatory postsynaptic potential (EPSP) frequency, a finding that was confirmed by voltage clamp recordings demonstrating increases in excitatory postsynaptic current (EPSC) frequency. The depolarizing effects of PK2 on MNC neurons were also abolished by kynurenic acid (KA), supporting the conclusion that the effects of PK2 are mediated by the activation of glutamate interneurons within the hypothalamic slice. PA (68%) and NE (67%) parvocellular neurons also depolarized in response to 10 nm PK2. However, in contrast to MNC neurons, these effects were maintained in TTX, indicating that PK2 directly affects PA and NE neurons. PK2-induced depolarizations observed in PA and NE neurons were found to be concentration-related and receptor mediated, as experiments performed in the presence of A1MPK1 (a PK2 receptor antagonist) abolished the effects of PK2 on these subpopulations of neurons. The depolarizing effects of PK2 on PA and NE neurons were also shown to be abolished by PD 98059 (a mitogen activated protein kinase (MAPK) inhibitor) suggesting that PK2 depolarizes PVN parvocellular neurons through a MAPK signalling mechanism. In combination, these studies have identified separate cellular mechanisms through which PK2 influences the excitability of different subpopulations of PVN neurons.
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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 | 0.001 |
| 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".