α7 nicotinic ACh receptor‐deficient mice exhibit sustained attention impairments that are reversed by β2 nicotinic ACh receptor activation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Disruptions of executive function, including attentional deficits, are a hallmark of a number of diseases. ACh in the prefrontal cortex regulates attentive behaviour; however, the role of α7 nicotinic ACh receptor (α7nAChR) in attention is contentious. EXPERIMENTAL APPROACH: In order to probe attention, we trained both wild-type and α7nAChR knockout mice on a touch screen-based five-choice serial reaction time task (5-CSRT). Following training procedures, we then tested sustained attention using a probe trial experiment. To further differentiate the role of specific nicotinic receptors in attention, we then tested the effects of both α7nAChR and β2nAChR agonists on the performance of both wild-type and knockout mice on the 5-CSRT task. KEY RESULTS: At low doses, α7nAChR agonists improved attentional performance of wild-type mice, while high doses had deleterious effects on attention. α7nAChR knockout mice displayed deficits in sustained attention that were not ameliorated by α7nAChR agonists. However, these deficits were completely reversed by the administration of a β2nAChR agonist. Furthermore, administration of a β2nAChR agonist in α7nAChR knockout mice elicited similar biochemical response in the prefrontal cortex as the administration of α7nAChR agonists in wild-type mice. CONCLUSIONS AND IMPLICATIONS: Our experiments reveal an intricate relationship between distinct nicotinic receptors to regulate attentional performance and provide the basis for targeting β2nAChRs pharmacologically to decrease attentional deficits due to a dysfunction in α7nAChRs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".