Faculty Opinions recommendation of Cross-presentation of epitopes on virus-like particles via the MHC I receptor recycling pathway.
Bibliographic record
Abstract
Effective vaccines and immunotherapies against cancer require professional antigen-presenting cells to cross-present exogenous antigen to initiate cytotoxic T-cell responses to destroy tumors. Virus-like particles (VLPs), containing tumor antigens, which can immunize against cancers, are cross-presented by dendritic cell (DC) but the mechanism by which this occurs is not fully understood. Here, we used VLPs, derived from rabbit hemorrhagic disease virus (RHDV) with both murine and human DCs, to elucidate these pathways. We have employed inhibitors to demonstrate that these VLPs are taken up by clathrin-dependent macropinocytosis and phagocytosis before being degraded in acidic lysosomal compartments. VLP-derived peptides are loaded onto major histocompatibility complex I that have been recycled from the cell surface. Antigen-coupled VLPs and murine ovalbumin-specific and human melanoma-associated antigen recognized by T cells (MART-1)-specific CD8(+) T cells were used to demonstrate cross-presentation via this alternate, receptor recycling pathway, which operated independently of the proteasome and the transporter-associated with antigen presentation. Finally, we found that cross-presentation of VLPs in vivo was not confined to CD8α(+) DC subsets. These data define the cross-presentation pathway for RHDV VLPs and may lead to improved cancer immunotherapies. PMID: 21221122
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.263 | 0.175 |
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".