Differential effects of calcineurin inhibition and protein kinase A activation on nucleus accumbens amphetamine-produced conditioned place preference in rats.
Bibliographic record
Abstract
The nucleus accumbens (NAc) plays a critical role in amphetamine-produced conditioned place preference (CPP). In previous studies inhibition or activation of cyclic adenosine monophosphate-dependent protein kinase (PKA) blocked NAc amphetamine-produced CPP. PKA activation unrelated to ongoing DA transmission may disrupt reward-related learning. Calcineurin (CN) down-regulates downstream PKA targets. Unlike PKA activation, CN inhibition may preserve and enhance reward-related learning. The PKA signalling cascade is negatively regulated by calcineurin (CN). We tested the hypothesis that post-training CN inhibition in NAc will enhance NAc amphetamine-produced CPP and that PKA activation will block CPP. Eight but not four or two 30-min conditioning sessions were sufficient to establish significant CPP. Immediate post-training, NAc injection of the calcineurin inhibitor FK506 (5.0 but not 1.0 µg in 0.5 µL per side) led to a significant amphetamine CPP in rats receiving four but not two training sessions; the 5.0-µg dose had no effect on rats trained with eight sessions. Injections of the PKA activator Sp-cAMPS (2.5 or 10.0 µg in 0.5 µL per side) failed to affect CPP following two or four training sessions and blocked CPP produced by a standard 8-day conditioning schedule. Results suggest that CN acts as a negative regulator in the establishment of NAc amphetamine-produced CPP, a form of reward-related learning.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".