Exposure to Nicotine Produces an Increase in Dopamine D2<sup>High</sup>Receptors: A Possible Mechanism for Dopamine Hypersensitivity
Bibliographic record
Abstract
Dopamine D2 receptors exist in both low- and high-affinity states (D2High), the latter being the functionally relevant state. Cocaine self-administration produces an increase in D2High, a phenomenon that could explain why cocaine administration results in hypersensitivity to dopamine, even though drug addicts were found to have a decreased number of striatal dopamine D2 receptors. As nicotine acts through the same mesocortical dopaminergic signaling pathways as other stimulant drugs, which are known to increase the levels of D2High, we hypothesized that nicotine exposure could produce an increase in D2High levels. We determined D2High levels in rats after nicotine administration (1.5 mg/kg/day; 14 days), in rats voluntarily self-administering nicotine using an intravenous self-administration (IVSA) protocol (mean dose 0.5 mg/kg/day; 14 days), as well as after a prolonged withdrawal. An increase in the levels of D2High was found in rats who had nicotine administered at a uniform dose, as well as in rats who self-administered nicotine via IVSA, but these changes appear to normalize over time, as indicated by lower D2High levels in rats after a prolonged withdrawal period. We suggest that nicotine-induced elevation in D2High levels could be participating in hypersensitivity to dopamine following nicotine exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".