Vitamin D contributes to mast cell stabilization
Bibliographic record
Abstract
BACKGROUND AND AIMS: Mast cells are the major effector cells in allergic disorders and many other informatory disorders. The mechanism of mast cell stabilization is not fully understood. Cumulative reports indicate that vitamin D (VitD) contributes to the homeostasis in the body. This study tests a hypothesis that VitD is required in the maintenance of the stability of mast cells. METHODS: The stability of mast cell lines, HMC1 cells, RBL-2H3 cells, p815 cells, and mouse bone marrow-derived mast cells (BMMC) was tested in the presence or absence of VitD3. RESULTS: Mast cells activated automatically in a VitD-deficient environment. Exposure to calcitriol in the culture increased the expression of VitD receptor (VDR) in mast cells. VDR formed complexes with Lyn in mast cells to inhibit the binding of Lyn to the β chain of FcεRI and MyD88, which decreased the phosphorylation of Syk, decreased the levels of MAPK and NF-κB. VDR bound to the promoter of TNF-α to decrease the acetylation of histone H3/H4, RNA polymerase II and OCT1 (a transcription factor of TNF-α) at the promoter locus and repressed the expression of TNF-α in mast cells. CONCLUSIONS: The data demonstrate that VitD is required to maintain the stability of mast cells. The deficiency of VitD results in mast cell activation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".