Immunomodulation of human T cells by microbubble-mediated focused ultrasound
Bibliographic record
Abstract
While met with initial and ground-breaking success targeting blood borne cancers, cellular immunotherapy remains significantly hindered in the context of solid tumors by the tumor microenvironment. Focused ultrasound, in conjunction with microbubbles, has found tremendous potential as a targeted and local drug/gene delivery technique for cancer therapy. The specific immunomodulating effects of this technique on immune cells, including T-cells, remain unexplored. Here, with freshly isolated human immune cells, we examine how focused ultrasound can viably modulate immune cell membrane permeability and influence the secretion of over 90 cytokines, chemokines and other analytes relevant to a potent immune response against cancer. We determine that microbubble-mediated focused ultrasound modulates the immune cell secretome in a time-dependent manner – ranging in ~0.1-3.6-fold changes in the concentration of a given cytokine compared to sham controls over 48 hours post-treatment (e.g. IL-1β, TNF-α, CX3CL1, CCL21). Further, we determine the general trend of a negative correlation between secreted cytokine concentration and viable ultrasound-assisted membrane permeability with negligible loss of cell viability. Taken together, the data presented here highlights the potential of microbubble-mediated focused ultrasound to viably enhance T-cell permeability and modulate key pro-immune pathways, offering a novel approach to augment targeted cellular therapies for solid tumors.
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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".