Intra-Accumbens Injection of a Dopamine Aptamer Abates MK-801-Induced Cognitive Dysfunction in a Model of Schizophrenia
Bibliographic record
Abstract
Systemic administration of the noncompetitive NMDA-receptor antagonist, MK-801, has been proposed to model cognitive deficits similar to those seen in patients with schizophrenia. The present work investigated the ability of a dopamine-binding DNA aptamer to regulate these MK-801-induced cognitive deficits when injected into the nucleus accumbens. Rats were trained to bar press for chocolate pellet rewards then randomly assigned to receive an intra-accumbens injection of a DNA aptamer (200 nM; n = 7), tris buffer (n = 6) or a randomized DNA oligonucleotide (n = 7). Animals were then treated systemically with MK-801 (0.1 mg/kg) and tested for their ability to extinguish their bar pressing response. Two control groups were also included that did not receive MK-801. Data revealed that injection of Tris buffer or the random oligonucleotide sequence into the nucleus accumbens prior to treatment with MK-801 did not reduce the MK-801-induced extinction deficit. Animals continued to press at a high rate over the entire course of the extinction session. Injection of the dopamine aptamer reversed this MK-801-induced elevation in lever pressing to levels as seen in rats not treated with MK-801. Tests for activity showed that the aptamer did not impair locomotor activity. Results demonstrate the in vivo utility of DNA aptamers as tools to investigate neurobiological processes in preclinical animal models of mental health 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".