Myeloid Responses to Extracellular Vesicles in Health and Disease
Bibliographic record
Abstract
Extracellular vesicles are mediators of cell-cell communication playing a key role in both steady-state and disease conditions. Extracellular vesicles carry diverse donor-derived cargos, including DNA, RNA, proteins, and lipids that induce a complex network of signals in recipient cells. Due to their ability to capture particulate matter and/or capacity to polarize and orchestrate tissue responses, myeloid immune cells (e.g., dendritic cells, macrophages, etc.) rapidly respond to extracellular vesicles, driving local and systemic effects. In cancer, myeloid-extracellular vesicle communication contributes to chronic inflammation, self-tolerance, and therapeutic resistance while in autoimmune disease, extracellular vesicles support inflammation and tissue destruction. Here, we review cellular mechanisms by which extracellular vesicles modulate myeloid immunity in cancer and autoimmune disease, highlighting some contradictory results and outstanding questions. We will also summarize how understanding of extracellular vesicle biology is being utilized for novel therapeutic and diagnostic applications.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".