Part I: Targeted Particles for Cancer Immunotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it