Chronic Nicotine Selectively Enhances α4β2* Nicotinic Acetylcholine Receptors in the Nigrostriatal Dopamine Pathway
Bibliographic record
Abstract
These electrophysiological experiments, in slices and intact animals, study the effects of in vivo chronic exposure to nicotine on functional alpha4beta2* nAChRs in the nigrostriatal dopaminergic (DA) pathway. Recordings were made in wild-type and alpha4 nicotinic acetylcholine receptor (nAChR) subunit knock-out mice. Chronic nicotine enhanced methyllycaconitine citrate hydrate-resistant, dihydro-beta-erythroidine hydrobromide-sensitive nicotinic currents elicited by 3-1000 mum ACh in GABAergic neurons of the substantia nigra pars reticulata (SNr), but not in DA neurons of the substantia nigra pars compacta (SNc). This enhancement leads to higher firing rates of SNr GABAergic neurons and consequently to increased GABAergic inhibition of the SNc DA neurons. In the dorsal striatum, functional alpha4* nAChRs were not found on the neuronal somata; however, nicotine acts via alpha4beta2* nAChRs in the DA terminals to modulate glutamate release onto the medium spiny neurons. Chronic nicotine also increased the number and/or function of these alpha4beta2* nAChRs. These data suggest that in nigrostriatal DA pathway, chronic nicotine enhancement of alpha4beta2* nAChRs displays selectivity in cell type and in nAChR subtype as well as in cellular compartment. These selective events augment inhibition of SNc DA neurons by SNr GABAergic neurons and also temper the release of glutamate in the dorsal striatum. The effects may reduce the risk of excitotoxicity in SNc DA neurons and may also counteract the increased effectiveness of corticostriatal glutamatergic inputs during degeneration of the DA system. These processes may contribute to the inverse correlation between tobacco use and Parkinson's disease.
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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".