Extracellular Vesicles and Their Applications in Tumor Diagnostics and Immunotherapy
Bibliographic record
Abstract
Extracellular vesicles (EVs) are cell-derived nanoparticles that have attracted significant attention in the investigation of human health and disease, including cancer biology and its clinical management. Concerning cancer, EVs have been shown to influence numerous aspects of oncogenesis, including tumor proliferation and metastasis. EVs can augment the immune system and have been implicated in virtually all aspects of innate and adaptive immunity. With immunotherapy changing the landscape of cancer treatment across multiple disease sites, it is paramount to understand their mechanisms of action and to further improve upon their efficacy. Despite a rapidly growing body of evidence supporting of the utility of EVs in cancer diagnostics and therapeutics, their application in clinical trials involving solid tumors and immunotherapy remains limited. To date, relatively few trials are known to incorporate EVs in this context, mainly employing them as biomarkers. To help address this gap, this review summarizes known applications of EVs in clinical trials and provides a brief overview of the roles that EVs play in cancer biology, immunology, and their proposed implications in immunotherapy. The impetus to leverage EVs in future clinical trials and correlative studies is crucial, as they are ideally positioned to synergize with advancements in multi-omics research to further therapeutic discovery and our understanding of cancer biology.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".