Curcumin as Add-On to Antipsychotic Treatment in Patients With Chronic Schizophrenia: A Randomized, Double-Blind, Placebo-Controlled Study
Bibliographic record
Abstract
BACKGROUND: Introduction of old and new generations of antipsychotics leads to significant improvements in the positive symptoms of schizophrenia. However, negative symptoms remain refractory to conventional trials of antipsychotic therapy. Recently, there were several open clinical human trials with curcumin. Curcumin is a natural polyphenol, which has a variety of pharmacological activities, including antioxidative and neuroprotective effects. The studies showed that curcumin improved the negative symptoms of schizophrenia. The purpose of our study was to examine the efficacy of curcumin as an add-on agent to regular antipsychotic medications in patients with chronic schizophrenia. METHODS: Thirty-eight patients with chronic schizophrenia were enrolled in a 24-week, double-blind, randomized, placebo-controlled study. The subjects were treated with either 3000 mg/d curcumin or placebo combined with antipsychotics from January 2015 to February 2017. The outcome measures were the Positive and Negative Symptoms Scale (PANSS) and the Calgary Depression Scale for Schizophrenia. RESULTS: Analysis of variance showed significant positive changes in both groups from baseline to the end of the study in all scales of measurement. There was a significant response to curcumin within 6 months in total PANSS (P = 0.02) and in the negative symptoms subscale (P = 0.04). There were no differences in the positive and general PANSS subscales, and the Calgary Depression Scale for Schizophrenia scores between the treatment and placebo groups. No patient complained of any adverse effect. CONCLUSIONS: The promising results of curcumin as an add-on to antipsychotics in the treatment of negative symptoms may open a new and safe therapeutic option for the management of schizophrenia. However, these results should be replicated in further studies.ClinicalTrials.gov Identifier: NCT02298985.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".