Sesquiterpene-Loaded Co-Polymer Hybrid Nanoparticle Effects on Human Mast Cell Surface Receptor Expression, Granule Contents, and Degranulation
Bibliographic record
Abstract
Biodegradable polymeric nanoparticles (NPs) such as poly(lactic-co-glycolic acid) (PLGA) and polyvinyl alcohol (PVA) have been used as drug delivery systems for natural and synthetic compounds and are designed to control the loading and release of biodegradable materials to target cells, tissues, and organs. Eremophilane-type sesquiterpenes have anti-inflammatory properties but are lipophilic, cytotoxic, and not biocompatible with many cells. To determine whether biodegradable PLGA/PVA could improve the biocompatibility of sesquiterpenes, sesquiterpene-loaded NPs were synthesized and their effects on human mast cells (LAD2), the major effector cells of allergic inflammation, were determined. NPs composed of PLGA/PVA and two types of sesquiterpenes (fukinone, PLGA/PVA-21 and 10βH-8α,12-epidioxyeremophil-7(11)-en-8β-ol, PLGA/PVA-22) were produced using a microfluidic synthesis method. The NPs’ size distribution and morphology were evaluated by dynamic light scattering and cryogenic transmission electron microscopy (TEM). PLGA/PVA-21 and PLGA/PVA-22 were 60 to 70 nm and were readily internalized by LAD2 as shown by flow cytometry, fluorescence microscopy, and TEM. While unencapsulated sesquiterpenes decreased LAD2 cell viability by 20%, PLGA/PVA-21 and PLGA/PVA-22 did not alter LAD2 viability, showing that encapsulation improved the biocompatibility of the sesquiterpenes. PLGA/PVA-21 and PLGA/PVA-22 decreased the expression of genes encoding the subunits of the high affinity immunoglobulin E receptor (FcεR1α, FcεR1β, FcεR1γ) and the stem cell factor receptor (Kit,), suggesting that hybrid NPs could alter mast cell responses to antigens and shift their maturation. Similarly, PLGA/PVA-21 and PLGA/PVA-22 inhibited tryptase expression but had no effect on chymase expression, thereby promoting a shift to the tryptase-positive phenotype (MCT). Lastly, PLGA/PVA-21 and PLGA/PVA-22 inhibited mast cell degranulation when the LAD2 cells were activated by IgE crosslinking and FcεRI. Overall, our results suggest that PLGA/PVA-21 and PLGA/PVA-22 alter human mast cell phenotype and activation without modifying viability, making them a more biocompatible approach than treating cells with sesquiterpenes alone.
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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".