Subharmonic behavior of targeted and untargeted lipid encapsulated microbubbles at high ultrasound frequencies.
Bibliographic record
Abstract
Molecular imaging with ultrasound contrast agents (microbubbles) has recently gained interest as a feasible technique for disease-specific imaging, with applications ranging from intravascular ultrasound to small animal imaging. The attachment of targeting ligands to the microbubble shell enables a selective accumulation of bound microbubbles around a target site. The ability, however, to differentiate between the nonlinear signal from bound microbubbles and from unbound, circulating agent still remains a challenge. This study conducts a size-per-size comparison of the acoustic nonlinear response of individual streptavidin-coated MicroMarker microbubbles either bound (BMM) or adjacent (UBMM) to a compliant agarose/biotin gel surface. Bubbles were optically sized and insonified at 25 MHz over a range of pressures and pulse bandwidths. The subharmonic (nonlinear) response between unbound (n = 24) and bound (n = 29) bubbles was found to differ significantly, with UBMM bubbles having a higher propensity to initiate non-destructive subharmonics, in addition to lower onset threshold pressures and a smaller preferentially active diameter than BMM bubbles. In summary, this variability in the nonlinear response of the same bubble type between targeted and untargeted states can have implications for detection strategies, agent fabrication, and contrast imaging quantification for high frequency molecular imaging applications.
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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 | 0.001 |
| 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".