Nanoparticles for Immune Stimulation Against Infection, Cancer, and Autoimmunity
Bibliographic record
Abstract
Vaccination using nanocarrier-based delivery systems has recently emerged as a promising approach for meeting the continued challenge posed by infectious diseases and cancer. A diverse portfolio of nanocarriers of various sizes, compositions, and physical parameters have now been developed, and this diversity provides an opportunity for the rational design of vaccines that can mediate targeted delivery of various antigens and adjuvants or immune regulatory agents in ways unachievable with classical vaccination approaches. This flexibility allows control over the characteristics of vaccine-elicited immune responses such that they can be tailored to be effective in circumstances where classical vaccines have failed. Furthermore, the utility of nanocarrier-based immune modulation extends to the treatment of autoimmune disease where precisely targeted inhibition of immune responses is desirable. Clearly, the selection of appropriate nanocarriers, antigens, adjuvants, and other components underpins the efficacy of these nanoimmune interventions. Herein, we provide an overview of currently available nanocarriers of various types and their physical and pharmacological properties with the goal of providing a resource for researchers exploring nanomaterial-based approaches for immune modulation and identify some information gaps and unexplored questions to help guide future investigation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".