Prenatal vitamin D<sub>3</sub>supplementation suppresses LL-37 peptide expression in<i>ex vivo</i>activated neonatal macrophages but not their killing capacity
Bibliographic record
Abstract
Vitamin D has regulatory effects on innate immunity. In the present study, we aimed to assess the effect of prenatal vitamin D₃ (vitD₃) supplementation on neonatal innate immunity in a randomised, placebo-controlled trial by evaluating cathelicidin (LL-37) expression and the killing capacity of macrophages. Healthy pregnant women (n 129) attending a clinic in Dhaka were randomised to receive either a weekly oral dose of 0·875 mg vitD₃ or placebo starting from 26 weeks of gestation up to delivery. Serum, plasma and monocyte-derived macrophages (MDM) were obtained from the cord blood. 25-Hydroxyvitamin D (25(OH)D) concentration was measured in serum. MDM were stimulated with or without Toll-like-receptor 4 ligand (TLR4L). Innate immune function was assessed by measuring LL-37 peptide levels in the culture supernatant of MDM by ELISA, LL-37 transcript levels by quantitative PCR, and ex vivo bactericidal capacity of MDM. VitD₃ supplementation did not increase LL-37 peptide levels in plasma or in the extracellular fluid of macrophages with or without TLR4L induction. However, stimulated intracellular LL-37 expression (ratio of stimulated:unstimulated MDM) was significantly reduced in the vitamin D group v. placebo (P=0·02). Multivariate-adjusted analyses showed that intracellular LL-37 peptide concentration from stimulated MDM was inversely associated with 25(OH)D concentration in serum (P=0·03). TLR4L stimulation increased the bactericidal capacity of MDM compared with the unstimulated ones (P=0·01); however, there was no difference in killing capacity between the two groups. A weekly dose of 0·875 mg vitD₃ to healthy pregnant women suppressed the intracellular LL-37 peptide stores of activated macrophages, but did not significantly affect the ex vivo bactericidal capacity of cord blood MDM.
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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 | 0.001 |
| 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) | 0.001 | 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".