Extracellular vesicles in malignant and normal B lymphocyte growth and development
Bibliographic record
Abstract
Extracellular vesicles (EVs) are central mediators of intercellular communication in both healthy and malignant states. In normal B lymphocyte (cell) biology, EVs derived from B cells, mast cells, T cells, and mesenchymal stromal cells regulate maturation, antigen presentation, and activation. B cell-derived EVs can either suppress excessive activation to maintain immune homeostasis or amplify responses during an active immune response. Modulation of these responses often occurs via phosphoinositide 3-kinase signaling pathways in recipient cells. In B cell malignancies, such as leukemias, lymphomas, and multiple myeloma, EVs play pivotal roles in disease progression and therapy resistance. Tumor- and stromal-derived EVs can transfer pro-survival proteins, regulatory RNAs, and drug-resistance factors to directly promote tumor progression. In addition, EVs can shape the tumor microenvironment to indirectly promote tumor progression through macrophage polarization, stromal cell reprogramming, and suppression of anti-tumor immunity. Conversely, under certain conditions, B cell EVs can enhance immune surveillance by stimulating T cells and presenting antigen, highlighting their dual potential in cancer biology. Clinically, B cell-derived EVs represent promising liquid biopsy biomarkers: increases in EV abundance, expression of surface antigens, altered protein cargo, and distinct RNA signatures have been associated with disease stage, treatment response, and patient outcomes. Despite this potential, variability in EV isolation and analysis methods remains a barrier to clinical translation. Moving forward, identifying robust biomarker signatures across platforms and clarifying mechanisms of cargo selection and EV uptake will be critical for advancing diagnostic and therapeutic applications. Overall, B cell-derived EVs act as contextual regulators of immune function and malignancy, positioning them as both modulators of disease progression and promising clinical tools.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".