Neuropeptide FF and neuropeptide VF inhibit GABAergic neurotransmission in parvocellular neurons of the rat hypothalamic paraventricular nucleus
Bibliographic record
Abstract
Neuropeptide FF (NPFF) and neuropeptide VF (NPVF) are octapeptides belonging to the RFamide family of peptides that have been implicated in a wide variety of physiological functions in the brain, including central autonomic and neuroendocrine regulation. The effects of these peptides are mediated via NPFF1 and NPFF2 receptors that are abundantly expressed in the rat brain, including the hypothalamic paraventricular nucleus (PVN), an autonomic nucleus critical for the secretion of neurohormones and the regulation of sympathetic outflow. In this study, we examined, using whole cell patch-clamp recordings in the brain slice, the effects of NPFF and NPVF on inhibitory GABAergic synaptic input to parvocellular PVN neurons. Under voltage-clamp conditions, NPFF and NPVF reversibly and in a concentration-dependent manner reduced the evoked bicuculline-sensitive inhibitory postsynaptic currents (IPSCs) in parvocellular PVN neurons by 25 and 31%, respectively. RF9, a potent and selective NPFF receptor antagonist, blocked NPFF-induced reduction of IPSCs. Recordings of miniature IPSCs in these neurons following NPFF and NPVF applications showed a reduction in frequency but not amplitude, indicating a presynaptic locus of action for these peptides. Under current-clamp conditions, NPVF and NPFF caused depolarization (6-9 mV) of neurons that persisted in the presence of TTX but was abolished in the presence of bicuculline. Collectively, these data provide evidence for a disinhibitory role of NPFF and NPVF in the hypothalamic PVN via an attenuation of GABAergic inhibitory input to parvocellular neurons of this nucleus and explain the central autonomic effects of NPFF.
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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".