Liposome Formation from Acoustically Cavitated Microbubbles for Local Molecular Capture
Bibliographic record
Abstract
Microbubble cavitation in tumour vasculature has been investigated as a means to release biomarkers into the bloodstream for personalized therapy. However, even using this method, certain tumour subtypes result in biomarker release too low for detection. To overcome this limitation, this thesis proposes to use phospholipids from microbubbles after cavitation as a means to capture biomarkers, here, fluorescent molecules (i.e. Dsred2 protein and fluorescein) present in the local environment, for later isolation. For this evaluation, new microbubbles, composed of 3-14 mg/mL of DPPA/DPPC, were designed and synthesized. The concentration of liposomes formed was found to increase with phospholipid concentration and acoustic pressure (from 1.97x106 to 4.78x107 /mL). Microscopy showed colocalization of particles, DsRed2, and fluorescein, indicating molecular capture. This capture suggests a new means for isolating biomarkers in the bloodstream for future applications in personalized cancer treatment.
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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) | 0.002 | 0.001 |
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".