Delivery of rapamycin by PLGA nanoparticles enhances its suppressive activity on dendritic cells
Bibliographic record
Abstract
The purpose of this study was to evaluate the effect of rapamycin delivery by poly (D,L-lactic-co-glycolic acid) (PLGA) nanoparticles on the maturation of dendritic cells (DCs). DCs were generated from mouse bone marrow and exposed to particulate and soluble rapamycin without any additional treatment, or with pre- or posttreatment with lipopolysaccharide (LPS). Annexin V-FITC/PI staining was performed on DC cultures to assess the viability of DCs during study. Surface phenotype of DCs was characterized for the expression of maturation markers, that is, MHC class II, CD86, and CD40 by flow cytometry. Cell culture supernatants were analyzed for the production of TGF-beta, IL-12, and IL-10 cytokines using sandwich ELISA method. DCs from Balb/C mice were cocultured with T cells from C57BL/6 mice and allogenic mixed lymphocyte reaction was assessed by [3H]-Thymidine incorporation. Unlike free rapamycin that has shown little if any effect on the expression of maturation markers in immature DCs, PLGA encapsulated rapamycin decreased the expression of all maturation markers under the study, that is, MHC class II, CD86, and CD40, significantly. LPS pre- or posttreated DCs demonstrated decreased expression of MHC class II, CD86, and CD40 in the presence of soluble or encapsulated rapamycin. The cytokine secretion profiles revealed high levels of TGF-beta and very low levels of IL-10 and IL-12 production. Rapamycin in soluble or encapsulated form significantly inhibited mixed lymphocyte reaction in DCs. The inhibitory effect of rapamycin on the maturation of DCs with respect to DC phenotype, cytokine production, and functional effects on the proliferation of T cells was significantly increased by PLGA delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".