Differential Expression of Functionally Identified and Immunohistochemically Identified NK<sub>1</sub>Receptors on Sympathetic Neurons
Bibliographic record
Abstract
We have used multiple-labeling immunohistochemistry, intracellular dye-filling, and intracellular microelectrode recordings to characterize the distribution of tachykinin receptors and substance P boutons on subpopulations of neurons within the guinea pig celiac ganglion. Superfusion of substance P (SP, 1 microM for 1 min) depolarized 42% of tonic neurons and inhibited afterhyperpolarizations in 66% of long afterhyperpolarizing (LAH) neurons without significant desensitization. Twenty-one percent of tonic neurons and 24% of LAH neurons responded to the NK(3) agonist senktide but did not respond to SP, indicating SP did not activate NK(3) receptors at this concentration. All effects of SP were abolished by the selective NK(1) receptor antagonist, SR140333, but not by the selective NK(3) receptor antagonist, SR142801, suggesting that exogenous SP activated a receptor with NK(1) pharmacology. No dye-filled LAH neuron and only 50% of tonic neurons responding to SP expressed NK(1) receptor immunoreactivity (NK(1)-IR). All neurons responding to SP had SP immunoreactive fibers within one cell diameter, indicating good spatial matching between SP release sites and target neurons. These results indicate that SP may act via a receptor with NK(1)-like pharmacology that has a C terminus not recognized by antibodies to the intracellular domain of the conventional NK(1) receptor. Inward currents evoked by SP acting on this NK(1)-like receptor or senktide acting through NK(3) receptors had identical current-voltage relationships. In LAH neurons, both agonists suppressed I(sAHP) without reducing I(AHP). Responses evoked by SP and senktide were resistant to PKC inhibitors, suggesting that the transduction mechanisms for the NK(1)-like receptor and the NK(3) receptor may be similar.
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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".