Natural Antimicrobial Peptide Regulation by Cytokines and Vitamin D in human vaginal epithelial (VK2/E6E7) cells
Bibliographic record
Abstract
Introduction: Natural antimicrobial peptides (NAPs) are an important part of the innate immune defences against bacterial infection and production of NPAs can be stimu- lated by 1,25-dihydroxyvitamin D3 (VitD3). The role of NAPs in infection-associated preterm birth is still unclear. Previously, we identified two cytokines (GM-CSF and MCP1) that were elevated in cervico-vaginal fluid of women at high risk of spontaneous preterm birth (STPB). This study assessed the effect of GM-CSF, MCP1 and IL-1b and VitD3 and calcipotriol on NAP expression in human vaginal epithelial cells. Methods: VK2/E6E7 cells (~80% confluence, growth factor deprived) were treated with IL-1b, MCP-1, GM-CSF (1 or 10ng/ml for 6 h, n = 6), Vit D or calcipotriol (10 )9 –10 )6 M for 6 h, n = 3). RNA was extracted and NAP (SKALP, SLPI, hBD2) mRNA expression determined by qPCR. Data were analysed using ANOVA with Bonferroni’s multiple comparison test, or linear regression. Results: SKALP and hBD2 were significantly increased by IL-1b (p < 0.05 and p < 0.01 respectively). GM-CSF inhib- ited SKALP mRNA expression (p < 0.05). Both VitD3 and calcipotriol increased SKALP, hBD2 and SLPI mRNA expression in a concentration dependent manner (p < 0.01). MCP1 had no effect on any of the NAPs assessed. Conclusion: GM-CSF suppression of SKALP provides a novel mechanism may explain, in part, impaired immune defences against ascending infection in women at risk of preterm labour. The evidence that VitD3 increases NAP expression in VK2/E6E7 indicates that vitamin D supple- mentation could be considered as a potential therapy to sti- mulate immune defences in women at risk of spontaneous preterm birth.
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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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".