Part II: Targeted Particles for Imaging of Anticancer Immune Responses
Bibliographic record
Abstract
The interaction of dendritic cells (DCs) and T cells has been the cornerstone of approaches to cancer immunotherapy. Antitumoral immune responses can be elicited by delivering cancer antigens to DCs. As antigen presenting cells, these DCs activate cancer antigen specific T cells. Whereas the first part of the review discusses methods for delivery of cancer vaccines to DCs, in this part the focus is on the potential role of nanoscopic devices for molecular imaging of these immune responses. Nanoscopic devices could potentially deliver tracking molecules to DCs, enabling monitoring of DCs and/or T cell activation and tumoricidal activity during immunotherapy, using non-invasive imaging modalities such as nuclear imaging (single photon emission computed tomography (SPECT), positron emission tomography (PET)), magnetic resonance imaging (MRI) and optical imaging. Keywords: Cancer vaccines, dendritic cells, immunomonitoring, nanotechnology, targeting, Antitumoral immune, nanoscopic devices, tomography, endoscopy, mediastinoscopy, laparoscopy
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.007 | 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".