Part I: Targeted Particles for Cancer Immunotherapy
Bibliographic record
Abstract
Dendritic cells (DCs) are the key antigen presenting cells that link innate and adaptive immunity. In the periphery, DCs capture antigens, process them and migrate into the regional lymph nodes where they could initiate antigen specific T cell immune responses. Immunotherapeutic strategies that aim to deliver tumor antigens specifically to DCs could not only boost anti-tumor immune responses but also could alleviate non-specific immune activation and/or unwanted side effects. Nano-sized particulate delivery systems are efficient modalities that can deliver tumor antigens to DCs in a targeted and specific manner. This review will provide general information on the rationale behind targeting antigens to DCs and the crucial role of DCs in initiating antigen specific T cell responses. Different strategies that have been employed in delivering antigens to DCs will be also discussed. A special emphasis will be put on specific targeting of cancer vaccine formulations to DC-specific receptors (e.g. CD11c, CD40, Fcγ, CCR6, pathogenic recognition receptors such as Toll-like receptors (TLRs) and C-type lectin receptors (CLRs)). Keywords: Cancer vaccines, dendritic cells, immunotherapy, nanotechnology, targeting, malignant cells, tumour antigens, tolerogenic DCs, immunostimulators, lymphoid organs, T cell repertoire, dendrites, peptides, viral vectors
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".