Effect of ultrasound-microbubble exposure on acute myeloid leukemia cancer cell proteome
Bibliographic record
Abstract
The application of ultrasound in combination with microbubbles (USMB) induces a broad spectrum of bioeffects on cells including plasma membrane disruption, cytoskeleton rearrangement, influx/efflux of cytosolic/extracellular molecules, metabolic stress and signalling pathways. This study investigated the effect of USMB on cell proteomics in human acute myeloid leukemia cells. Cells (OCI-AML-5) in suspension were exposed to ultrasound (f = 1 MHz, PD = 20 μs and PRP = 1 ms for 120 s) with and without microbubbles (Definity at 1.7% v/v) at varying acoustic pressures. Following exposure, cells were analysed using flow cytometry, mass spectrometry and immunoblotting. USMB enhanced cell membrane permeability (~ 37%), as expected, and induced significant changes in the cell proteome. Of 6825 proteins, 78 (~ 1.14%) were statistically significant and had a fold-change (≤ 0.8 or ≥ 1.2) compared to untreated control, referred to as a differentially expressed protein (DEP). The highest fold change of ~ 4–5 was induced in HMOX1 protein. The protein expression depended on acoustic pressure, presence of microbubbles and elapsed-time following exposure to ultrasound. Western blotting analysis confirmed the effect of USMB on three proteins (HMOX1, BACH1 and ANXA1) with R 2 values > 0.9. In addition, the gene ontology analysis based on DAVID bioinformatics indicated that the DEPs are associated with GO cellular components such as plasma membrane and exosomes, which can be affected by USMB. This study suggests that targeted changes in the cell proteome may be induced with USMB to potentially optimize existing therapies and develop new cancer therapeutic targets.
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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".