MMP-9 Serum Levels in Schizophrenic Patients during Treatment Augmentation with Sarcosine (Results of the PULSAR Study)
Bibliographic record
Abstract
AIM: Find changes in matrix metallopeptidase-9 (MMP-9) levels during augmentation of antipsychotic treatment with sarcosine and a relationship between schizophrenia symptoms severity and initial level of MMP-9. METHOD: Fifty-eight patients with diagnosis of schizophrenia with predominant negative symptoms participated in a six-month prospective RCT (randomized controlled trial). The patients received two grams of sarcosine (n = 28) or placebo (n = 30) daily. At the beginning, after six weeks and after six months MMP-9 levels were measured. Severity of symptomatology was assessed with the Positive and Negative Syndrome Scale (PANSS) and Calgary Depression Scale for Schizophrenia (CDSS). RESULTS: MMP-9 serum levels were stable after six weeks and six months in both groups. We noted improvement in negative symptoms, general psychopathology and total PANSS score in sarcosine group compared to placebo; however, there was no correlations between serum MMP-9 concentrations and PANSS scores in all assessments. Initial serum MMP-9 concentrations cannot be used as an improvement predictor acquired during sarcosine augmentation. CONCLUSIONS: Our results indicate that either MMP-9 is not involved in the N-methyl-d-aspartate (NMDA)-dependent mechanism of sarcosine action in terms of clinical parameters or sarcosine induced changes in peripheral MMP-9 concentrations cannot be detected in blood assessments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".