Ultrasound and microbubble mediated T cell modulation in peripheral blood mononuclear cells
Bibliographic record
Abstract
Ultrasound (US)-stimulated microbubbles are emerging as a revolutionary therapeutic technique, notably within the field of cancer immunotherapy. While this approach has been shown to modify cell permeability and to trigger local anti-tumor effects, the interactions between vibrating microbubbles and T-cells is not well understood. Here, we explore the biophysical impact of microbubbles on T-cell permeability and viability, setting the stage for potential advancements in cellular immunotherapy. Following the activation of fresh human peripheral blood mononuclear cells (PBMCs), cells were incubated with FITC-dextran (10kDa)—used here as a surrogate drug– and exposed in the presence of DefinityTM(1:217–cell:bubble ratio) US (1MHz, N = 1000, PRF = 5 ms for 2 minutes) over a range of peak-negative pressures (208-563kPa). Viability was assessed immediately and post-treatment concurrently with FITC macromolecule uptake using flow cytometry. T-cell population was identified using CD3 and CD4 antibodies (60% CD3 + and 20% CD4+). All conditions examined resulted > 95% viability. Results indicated a significant increase in viably permeated PBMCs with higher acoustic pressures, ranging from 0% to 30% (sham-corrected). When analyzed separately both CD4 + and CD4- T-cells exhibited similar permeability rates (0%–37.5%). The 563 kPa condition yielded the highest permeability with minimal viability loss. These findings open avenues for enhancing the efficacy of cellular immunotherapy 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".