Vitamin D3 encapsulated in polymeric nanoparticles to dampen the pro-inflammatory immune response
Bibliographic record
Abstract
1α25-dihydroxyvitamin D3, the active metabolite of vitamin D3 (VD3), is a modulator of inflammation well-known for its ability to promote anti-inflammatory and tolerogenic immune responses. It is therefore an attractive agent for the attenuation of inflammatory responses and the development of tolerogenic immunity in autoimmune diseases. To overcome VD3 toxicity and enhance its in vivo performance, nanoparticles (NPs) have emerged as a promising delivery platform. Therefore, in this study, we have developed VD3-loaded polymeric nanoparticles (VD3-NPs) as a therapeutical strategy for the treatment of autoimmune disorders. We demonstrate that VD3-NPs could successfully be generated and that they significantly inhibit secretion of IL-6, IL-10, IL-23, and TNFα in human whole blood cultures. We observed that poly(lactic-co-glycolic acid) (PLGA) NPs are efficiently taken up by neutrophils, monocytes and B cells, prompting further investigation into the effect of VD3-NPs on these subsets. Investigation into each of the immune cell subsets demonstrated that the VD3-NPs were able inhibit cytokine secretion by both monocytes and neutrophils. Moreover, VD3-NPs induced a tolerogenic phenotype in monocytes. In B cells, we observed that VD3-NPs impaired in vitro plasma B cell differentiation and suppressed antibody production. Together, our results validate for the first time in primary human cells the therapeutic potential of VD3 encapsulated in PLGA NPs, posing an attractive strategy for the treatment of autoimmune diseases.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".