Memantine agonist action at dopamine D2<sup>High</sup> receptors
Bibliographic record
Abstract
Memantine is reported to improve symptoms in moderate cases of Alzheimer's disease and Parkinson's disease, but is also known to trigger psychosis in some Parkinson patients. Because these clinical features suggested a possible dopamine component of memantine action, we measured the potency of memantine on the functional high-affinity state of dopamine D2 receptors, or D2(High). Using [(3)H]domperidone to label D2 receptors, the memantine dissociation constant at D2(High) was 917 +/- 23 nM for rat striatal D2 receptors and 137 +/- 19 nM for human cloned D2Long receptors. The memantine dissociation constant for striatal N-methyl-D-aspartate (NMDA) receptors labeled by [(3)H]MK 801 was 2200 +/- 400 nM. Memantine stimulated the incorporation of [(35)S]GTP-gamma-S into D2-expressing Chinese Hamster Ovary cells with a dissociation constant of 1200 +/- 400 nM. Memantine, between 200 and 2000 nM, directly acted on D2(High) to inhibit the release of prolactin from isolated anterior pituitary cells in culture. Because the memantine potencies at NMDA receptors and dopamine D2(High) receptors are of a similar order of magnitude, it is likely that the clinical features of memantine can be attributed to its action at both types of receptors.
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".