8. Examining the Effects of Peptide-Gold Nanoparticle Hybrids on the Activation of Myeloid Dendritic Cells
Bibliographic record
Abstract
Dendritic cells (DCs) are professional antigen-presenting cells that play an important role in innate and adaptive immunity. They have become a major target for immune modulation based therapeutic strategies. Gold nanoparticles (GNPs) are biocompatible materials. The surface of GNPs can be easily modified with peptides via thiol-gold linkage to alter their surface chemistry and to modulate their cellular uptake. The purpose of this present study was to investigate the ability of peptide-GNP hybrids to modulate the DC immune response in vitro. To assess the activation effects of peptide-GNP hybrids, DCs were differentiated from bone marrow (BM) cells of black B57BL/6 mice by culture with granulocyte macrophage colony stimulating factor (GM-CSF). At day 9 of culture, BM-derived DCs (BMDCs) were exposed to GNP hybrids with a diameter of approximately 10 nm. The purity of the resulting DCs was determined by the CD11c positive cell population measured by Flow Cytometry. We found that the peptide-GNP hybrids were internalized by immature BMDC via bright field microscope imaging. Furthermore, the addition of GNPs to immature BMDCs was found to influence both phenotype and function of immature BMDCs. Stimulation by peptide-GNP hybrids on the immature BMDCs led to an enhanced expression of co-stimulatory molecules on the cell surface, including CD40, CD80 and CD86, and a secretion of cytokines in the culture medium. This observation suggests that peptide-GNP stimulation can lead to the activation of BMDCs, and implies a potential application for strengthening vaccine delivery devices or cancer immune therapy.
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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".