Antidepressant effects of ketamine and the roles of AMPA glutamate receptors and other mechanisms beyond NMDA receptor antagonism
Bibliographic record
Abstract
The molecular mechanisms underlying major depressive disorder remain poorly understood, and current antidepressant treatments have many shortcomings. The recent discovery that a single intravenous infusion of ketamine at a subanesthetic dose had robust, rapid and sustained antidepressant effects in individuals with treatment-resistant depression inspired tremendous interest in investigating the molecular mechanisms mediating ketamine’s clinical efficacy as well as increased efforts to identify new targets for antidepressant action. We review the clinical utility of ketamine and recent insights into its mechanism of action as an antidepressant, including the roles of N-methyl-d-aspartate receptor inhibition, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor upregulation, activation of downstream synaptogenic signalling pathways and the production of an active ketamine metabolite, hydroxynorketamine. Emerging knowledge of the molecular mechanisms underlying both ketamine’s positive therapeutic and detrimental side effects will aid the development of a new generation of much-needed superior antidepressant agents.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".