Translating Curry Extract to Novel Therapeutic Approach in Schizophrenia: the Emerging Role of Epigenetics Signaling
Bibliographic record
Abstract
Despite pharmacotherapeutic advances in schizophrenia, persistent negative symptoms and cognitive impairment often persist in patients diagnosed as schizophnrenia. Growing evidence suggesting that epigenetic dysregulation may play a pivotal role in schizophrenia. No study has been conducted to examine whether targeting epigenetics with HDAC (histone deacetylase) translates to efficacious treatment in schizophrenia. Curcumin (Diferuloylmethane), extracted from Curcuma Longata, exhibits HDAC inhibition and hence may be beneficial in the treatment of schizophrenia. We chose standardized Curcumin C-3 complex combined with Bioperine™: SupercuminTM to examine the efficacy of Curcumin C-3 complex in improving core symptoms and cognitive deficits as assessed with Vital Sign-CNS neurocognitive tests in patients diagnosed as schizophrenia. We recruited community dwelling patients diagnosed as schizophrenia with persistent negative symptoms (Scale for Assessment of Negative Symptoms: SANS score> 30) to participate in the open-label parallel-group randomized study. We randomized 17 subjects into Group 1 (1 g daily) and Group 2 (4 g daily) respectively for 16 weeks. The subjects were maintained on current antipsychotic therapy throughout the study. We found that Supercurcumin™ 1 g (Group 1) and 4 g (Group 2) groups significantly improved the total, and general psychopathology sub-scales PANSS-(Positive and Negative Symptoms scale). Within group analysis favored statistically significant (p < 0.05) mean changes in total PANSS score, PANSS-negative and PANSS-general psychopathology compared with baseline. Both groups improved in cognition. SupercurcuminTM was well tolerated. We conclude curcumin in targeting HDAC opens new therapeutics frontier in schizophrenia.
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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 | 0.000 |
| 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) | 0.002 | 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".