Chemotherapy-Associated Extracellular Vesicles Modulate T Cells Activity and Cytokine Release
Bibliographic record
Abstract
Colorectal cancer (CRC) remains one of the most widely diagnosed cancers worldwide. Despite the advances in medical research, there is still a lot to be explored between cancer cells and the tumor microenvironment, namely immune cells. Extracellular vesicles (EVs) have been shown to mediate communication between cells and can modulate the activity of immune cells. External stimuli such as stress and chemotherapy can influence the activity of the released EVs. Nevertheless, the relationship between chemotherapy, EVs and immune cells has yet to be fully explored. In this study, we aimed to elucidate the immune-related functional mechanisms of EVs isolated from pre- and post- FOLFOX chemotherapy from CRC patients. The EVs were isolated from the serum of matched patients and characterized via dynamic light scattering. The EVs were then co-incubated with primary CD8 T cells isolated from healthy donors and Jurkat cells. The apoptosis, cell cycle profile, gene expression and cytokines were evaluated. Upon treatment with EVs, the T cells underwent apoptosis however no differences were seen in the cell cycle phases. Gene expression related to cytokine release was also differentially expressed namely IRF4. The level of cytokines that were released also differed between the two groups. Our study has shown that there are some minor differences in the activity of the EVs after induction with chemotherapy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".