The neurokinin‐3 (NK<sub>3</sub>) and the neurokinin‐1 (NK<sub>1</sub>) receptors are differentially targeted to mesocortical and mesolimbic projection neurons and to neuronal nuclei in the rat ventral tegmental area
Bibliographic record
Abstract
Tonic activation of neurokinin-3 (NK(3)) receptors in dopamine neurons of the ventral tegmental area (VTA) has been implicated in the pathophysiology of schizophrenia. This psychiatric disorder is associated with a dysfunctional activity in VTA projection neurons that can affect cognitive function at the level of the medial prefrontal cortex (mPFC) as well as motor and motivational states controlled in part by mesolimbic output to the nucleus accumbens (Acb). To determine the relevant sites for NK(3) receptor activation within this neuronal network, we used confocal and electron microscopy to examine NK(3) receptors (Cy5; immunogold) and retrograde labeling of fluorogold (FG, FITC; immunoperoxidase) in the VTA of rats receiving either Acb or mPFC injections of FG. Comparison was made with neurokinin-1 (NK(1)) receptors, which are also present, but less abundant then NK(3) receptors, in dopaminergic and GABAergic VTA neurons. There were no observable differences between NK(3) and NK(1) receptors in their primary locations in the cytoplasm and on the plasma membrane of VTA somata and dendrites with or without FG. Dendrites labeled with FG retrogradely transported from mPFC, however, contained more NK(3) or less NK(1) immunogold particles (plasmalemmal + cytoplasmic) then those retrogradely labeled following FG injection in the Acb. Moreover, only the NK(3) receptors were detected in neuronal nuclei in the VTA and in the nuclei of human HEK-293T NK(3)-transfected cells. The enrichment of NK(3) receptors in mesocortical projection neurons and nuclear distribution of these receptors may provide insight for understanding the selective antipsychotic effectiveness of NK(3) antagonists.
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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 | 0.001 |
| 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".