On the Acoustic Response of Ultrasonically - stimulated microbubbles and Enhanced Intracellular Uptake of a Fluorescent Molecule
Bibliographic record
Abstract
Effectiveness of ultrasound-and-microbubble mediated therapy depends on the acoustic response of microbubbles. In this study, the acoustic response of microbubbles in the absence and presence of cells is measured using integrated cavitation dose (ICD) over harmonics/ultraharmonics/broadband, and correlated with intracellular uptake of a fluorescent marker for varying peak-negative-pressures (PNPs). The ICD was independent of presence of cells. A PNP-threshold of 0.64 MPa was observed for microbubble’s inertial cavitation; stable cavitation (PNPs<0.64MPa) and inertial cavitation (PNPs≥0.64MPa) regimes were identified. Within inertial cavitation regime, a stronger correlation (R2>0.9) was observed between the ICD and FITC-positive cells, whereas, a weaker correlation, ranging from R2=0.59 at 3rd ultra-harmonic to R2 = 0.85 at 2nd ultra-harmonic, was observed under stable cavitation regime. The intracellular uptake, ICD and their correlation is dependent on microbubbles cavitation regime, indicating that ICD shows potential to predict bio-effects induced not only by inertial cavitation but also by stable cavitation of MBs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".