Nanovaccine‐Based Strategies to Overcome Challenges in the Whole Vaccination Cascade for Tumor Immunotherapy
Bibliographic record
Abstract
Nanovaccine-based immunotherapy (NBI) has received greater attention recently for its potential to prime tumor-specific immunity and establish a long-term immune memory that prevents tumor recurrence. Despite encouraging results in the recent studies, there are still numerous challenges to be tackled for eliciting potent antitumor immunity using NBI strategies. Based on the principles that govern immune response, here it is proposed that these challenges need to be addressed at the five critical cascading events: Loading tumor-specific antigens by nanoscale drug delivery systems (L); Draining tumor antigens to lymph nodes (D); Internalization by dendritic cells (DCs) (I); Maturation of DCs by costimulatory signaling (M); and Presenting tumor-peptide-major histocompatibility complexes to T cells (P) (LDIMP cascade in short). This review provides a detailed and objective overview of emerging NBI strategies to improve the efficacy of nanovaccines in each step of the LDIMP cascade. It is concluded that the balance between each step must be optimized by delicate designing and modification of nanovaccines and by combining with complementary approaches to provide a synergistic immunity in the fight against cancer.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".