Clinical and metabolic response to vitamin D plus probiotic in schizophrenia patients
Bibliographic record
Abstract
BACKGROUND: This study determined the effects of a novel combination of vitamin D and probiotic on metabolic and clinical symptoms in chronic schizophrenia. METHODS: CFU/day probiotic (n = 30) or placebo (n = 30) for 12 weeks. RESULTS: Vitamin D and probiotic co-supplementation was associated with a significant improvement in the general (- 3.1 ± 4.7 vs. + 0.3 ± 3.9, P = 0.004) and total PANSS scores (- 7.4 ± 8.7 vs. -1.9 ± 7.5, P = 0.01). Vitamin D and probiotic co-supplementation also significantly increased total antioxidant capacity (+ 51.1 ± 129.7 vs. -20.7 ± 53.3 mmol/L, P = 0.007), and significantly decreased malondialdehyde (- 0.3 ± 0.9 vs. + 0.2 ± 0.4 μmol/L, P = 0.01) and high sensitivity C-reactive protein levels (- 2.3 ± 3.0 vs. -0.3 ± 0.8 mg/L, P = 0.001) compared with the placebo. Moreover, taking vitamin D plus probiotic significantly reduced fasting plasma glucose (- 7.0 ± 9.9 vs. -0.2 ± 9.9 mg/dL, P = 0.01), insulin concentrations (- 2.7 ± 2.3 vs. + 0.4 ± 2.0 μIU/mL, P < 0.001), homeostasis model of assessment-estimated insulin resistance (- 0.8 ± 0.7 vs. + 0.1 ± 0.7, P < 0.001), triglycerides (- 7.8 ± 25.2 vs. + 10.1 ± 30.8 mg/dL, P = 0.01) and total cholesterol levels (- 4.9 ± 15.0 vs. + 5.9 ± 19.5 mg/dL, P = 0.04) and total-/HDL-cholesterol ratio (- 0.1 ± 0.6 vs. + 0.3 ± 0.8, P = 0.04). CONCLUSION: Probiotic and vitamin D for 12 weeks to chronic schizophrenia had beneficial effects on the general and total PANSS score, and metabolic profiles. TRIAL REGISTRATION: This study was retrospectively registered in the Iranian website ( www.irct.ir ) for clinical trials registration ( http://www.irct.ir : IRCT2017072333551N2). 07-31-2017 2.
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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 | 0.000 |
| 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".