Efficacy of low-fat milk and yogurt fortified with vitamin D3 on systemic inflammation in adults with abdominal obesity
Bibliographic record
Abstract
Abstract Background The prevalence of vitamin D deficiency is increasing globally and is associated with an increased risk of metabolic syndrome, autoimmune disease, and cardiovascular disease. Vit D deficiency is also associated with increased systemic inflammation. The current study aimed to determine the efficacy of low-fat milk and yogurt fortified with 1500 IU nano-encapsulated vitamin D, on systemic inflammation in abdominal obese participants. Method This multi-center study was conducted using a 2.5-month parallel total-blind randomized clinical trial design. Two hundred and eighty nine subjects were allocated to four groups: low-fat milk fortified by 1500 IU nano-encapsulated vitamin D3 (200 mL/day). Simple milk (200 mL/day), low-fat yogurt fortified by 1500 IU nano-encapsulated vitamin D3 (150 g/day), and simple yogurt (150 g/day). Results The results showed that serum levels of neutrophils, lymphocytes, platelets and red blood cell distribution width (RDW) were significantly lower before and after the intervention in fortified dairy groups. The results showed that serum levels of neutrophils, lymphocytes, platelets, and RDW before and after intervention in the fortified dairy groups were significantly lower (p < 0.05). The values of = neutrophil to lymphocyte ratio (NLR), platelets to lymphocyte ratio, and RDW to platelets ratio (RPR) reduced significantly in the fortification group (p < 0.05). Conclusion Fortification with nano-encapsulated vitamin D3 of dairy products may decrease inflammation in individuals with abdominal obesity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".