Maternal vitamin D deficiency impairs Treg and Breg responses in offspring mice and deteriorates allergic airway inflammation
Bibliographic record
Abstract
BACKGROUND: Vitamin D (VitD) can regulate immune responses and maternal VitD-deficiency can affect immune responses in the offspring. This study aimed at investigating the effects of maternal VitD-deficiency during pregnancy on Treg and Breg responses in offspring mice with house dust mite (HDM)-induced allergic airway inflammation. METHODS: Female BALB/c mice were randomized and fed with normal chow or VitD-deficient diet until their offspring weaned. The offspring mice were fed with normal chow and injected with vehicle or HDM to induce allergic airway inflammation. The levels of serum 25(OH)D, cytokines and infiltrate numbers as well as percentages of Tregs and Bregs in the bronchoalveolar lavage fluid (BALF) were analyzed. The relative levels of VitD receptor (VDR), VitD-binding protein (VDBP), Cytochromes P450 (CYP) 27b1, and CYP24A1 mRNA transcripts in the lungs of different groups of mice were measured. RESULTS: Maternal VitD-deficiency significantly reduced serum 25(OH)D levels in offspring mice. VitD-deficiency significantly increased the relative levels of VDR, VDBP and CYP27B1 mRNA transcripts, but decreased CYP24A1 expression in the lungs of mice. In comparison with the control mice, significantly elevated levels of pro-inflammatory cytokines, increased numbers of lymphocytes and eosinophils, but decreased levels of anti-inflammatory cytokines were detected in the BALF of VitD-deficient mice. VitD-deficiency significantly increased the frequency of Th1, Th2, Th9, Th17 cells, but decreased regulatory T (Tregs) and B cells (Bregs) in the BALF of mice with allergic airway inflammation. CONCLUSION: Maternal VitD-deficiency lowed serum 25(OH)D levels and enhanced HDM-induced allergic airway inflammation in the offspring by impairing Breg and Treg responses.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".