In situ and dynamic screening of extracellular vesicles as predictive biomarkers in immune-checkpoint inhibitor therapies
Bibliographic record
Abstract
Extracellular vesicles (EVs) is promising in predicting the efficacy of immune checkpoint inhibitor (ICI) therapies. But it is challenging to determine the level of circulating EVs due to their variations in spatial and temporal distribution. To address this, we developed an in situ EV detection platform integrating multiplex EV capture with microfluidic-generated immune-tumor spheroids. This platform enables in situ monitoring of EV secretion dynamics under ICI and chemotherapeutic treatments, capturing localized and temporal changes in EV release. Using predictive models, we identified EVs carrying programmed cell death ligand 1 (PD-L1) as the most robust predictors of spheroid viability during treatment. RNA sequencing further revealed that dynamic EV expression changes are driven by gene transcription, providing a temporal understanding of EV regulation. Our platform overcomes the limitations of traditional methods by offering a physiologically relevant system to study EV-mediated immune responses. By addressing the spatial and temporal heterogeneity of EVs, this work advances EV-based biomarker discovery and provides a foundation for optimizing personalized immunotherapies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".