D-Serine: Potential Therapeutic Agent and/or Biomarker in Schizophrenia and Depression?
Bibliographic record
Abstract
D-Serine is a potent co-agonist at the NMDA glutamate receptor and has been the object of many preclinical studies to ascertain the nature of its metabolism, its regional and cellular distribution in the brain, its physiological functions and its possible clinical relevance. The enzymes involved in its formation and catabolism are serine racemase (SR) and D-amino acid oxidase (DAAO), respectively, and manipulations of the activity of those enzymes have been useful in developing animal models of schizophrenia and in providing clues to the development of potential new antipsychotic strategies. Clinical studies have been conducted in schizophrenia patients to evaluate body fluid levels of D-serine and/or to use D-serine alone or in combination with antipsychotics to determine its effectiveness as a therapeutic agent. D-serine has also been used in combination with DAAO inhibitors in preclinical investigations, and interesting results have been obtained. Genetic studies and postmortem brain studies have also been conducted on D-serine and the enzymes involved in its metabolism. It is also of considerable interest that in recent years clinical and preclinical investigations have suggested that D-serine may also have antidepressant properties. Clinical studies have also shown that D-serine may be a biomarker for antidepressant response to ketamine. Relevant to both schizophrenia and depression, preclinical and clinical studies with D-serine indicate that it may be effective in reducing cognitive dysfunction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".