Serum levels of interleukin 6 in schizophrenic patients during treatment augmentation with sarcosine (results of the <scp>PULSAR</scp> study)
Bibliographic record
Abstract
OBJECTIVE: Augmentation of sarcosine, a natural inhibitor of the glycine transporter type I, normalizes glutamatergic neurotransmission, having beneficial impact on primary negative symptoms in schizophrenia and may also influence immune system and interleukin 6 (IL-6) levels. AIM: Finding a relationship between initial IL-6 serum concentrations or its changes and severity of symptoms as a result of sarcosine addition to stable antipsychotic treatment. METHOD: Fifity-eight individuals with schizophrenia with predominantly negative symptoms completed a 6-month randomized, double-blind placebo-controlled prospective study. Patients received 2 g of sarcosine (n = 29) or placebo (n = 30) daily per os. We measured IL-6 levels and severity of symptoms at the beginning, after 6 weeks and 6 months. As main clinical tools, we used Positive and Negative Syndrome Scale (PANSS) and Calgary depression scale for schizophrenia (CDSS). RESULTS: Augmentation with sarcosine had no effect on IL-6 serum levels in all time points. We noted significant improvements in negative symptoms, general psychopathology, and total PANSS score in the sarcosine group. We found correlation of initial serum IL-6 with severity of positive symptoms and negative association between IL-6 levels reduction and positive symptoms reduction. CONCLUSIONS: Sarcosine does not significantly affect IL-6 concentrations but IL-6 may be involved in mechanisms related to the presence of positive symptoms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".