Nanocurcumin as an Add-on to Antipsychotic Drugs for Treatment of Negative Symptoms in Patients With Chronic Schizophrenia
Bibliographic record
Abstract
PURPOSE/BACKGROUND: It is well documented that one of the pathophysiological mechanisms of negative symptoms in patients with schizophrenia is hypofunction of N-methyl-d-aspartate receptors. This double-blind, placebo-controlled clinical trial was designed to assess the efficacy and safety of nanocurcumin as an adjuvant agent on psychotic symptoms, especially negative symptoms, in patients with chronic schizophrenia. METHODS/PROCEDURES: Fifty-six inpatients with stable chronic schizophrenia and predominant negative symptoms were randomized in a 1:1 ratio to nanocurcumin soft gel capsule (160 mg/d) and control groups, along with their antipsychotic regimen for 16 weeks. The efficacy of treatment was assessed by Positive and Negative Syndrome Scale, Calgary Depression Scale for Schizophrenia, Clinical Global Impressions-Severity, and Clinical Global Impressions-Improvement scales. Extrapyramidal symptoms were evaluated by Simpson-Angus Scale and Barnes Akathisia Rating Scale. Patients were assessed at baseline and weeks 4, 8, 12, and 16 after the medication started. FINDINGS/RESULTS: No significant differences were observed in demographic or clinical variables between both groups at baseline. The nanocurcumin group showed significantly greater improvement on the negative subscale (P = 0.05), the general psychopathology subscale (P < 0.001), the positive subscale (P = 0.004), total Positive and Negative Syndrome Scale (P < 0.001), Clinical Global Impressions-Severity (P < 0.001), and Clinical Global Impressions-Improvement scores (P < 0.001) in comparison with the control group at the endpoint. Extrapyramidal symptom rating scales and Calgary Depression Scale for Schizophrenia and frequency of other adverse effects were comparable between 2 groups. IMPLICATIONS/CONCLUSIONS: The present study indicates nanocurcumin as a safe and potential adjunctive treatment strategy for treatment of primary negative symptoms of schizophrenia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".