Nanoparticle-Based Immunotherapy for Cancer
Bibliographic record
Abstract
The design of nanovaccines capable of triggering effective antitumor immunity requires an understanding of how the immune system senses and responds to threats, including pathogens and tumors. Equally important is an understanding of the mechanisms employed by tumor cells to evade immunity and an appreciation of the deleterious effects that antitumor immune responses can have on tumor growth, such as by skewing tumor cell composition toward immunologically silent tumor cell variants. The immune system and tumors engage in a tug-of-war driven by competition where promoting antitumor immunity or tumor cell death alone may be therapeutically insufficient. Nanotechnology affords a unique opportunity to develop therapeutic compounds than can simultaneously tackle both aspects, favoring tumor eradication. Here, we review the current status of nanoparticle-based immunotherapeutic strategies for the treatment of cancer, ranging from antigen/adjuvant delivery vehicles (to professional antigen-presenting cell types of the immune system) to direct tumor antigen-specific T-lymphocyte-targeting compounds and their combinations thereof.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".