Fabricating and Labeling Microbubbles with Fluorescent and Radioactive Tracers
Bibliographic record
Abstract
Microbubbles are lipid-shelled, gas-filled particles that have evolved from vascular ultrasound contrast agents into revolutionary cancer therapy platforms. When combined with therapeutic focused ultrasound (FUS), they can safely and locally overcome physiological barriers (e.g., blood-brain barrier), deliver drugs to otherwise inaccessible cancers (e.g., glioblastoma and pancreatic cancer), and treat neurodegenerative diseases. The therapeutic arsenal of microbubble-FUS is advancing in new directions, including synergistic combination radiotherapy, multimodal imaging, and all-in-one drug loading and delivery from microbubble shells. Labeling microbubbles with radiotracers is key to establishing these expanded theranostic capabilities. However, existing microbubble radiolabeling strategies rely on purification methodologies known to perturb microbubble physicochemical properties, use short-lived radioisotopes, and do not always yield stable chelation. Collectively, this creates ambiguity surrounding the accuracy of microbubble radioimaging and the efficiency of tumor radioisotope delivery. This protocol describes a new one-pot, purification-free microbubble labeling methodology that preserves microbubble physicochemical properties while achieving >95% radioisotope chelation efficiency. It is versatile and can be applied successfully across custom and commercial microbubble formulations with differing acyl lipid chain length, charge, and chelator/probe (porphyrin, DTPA, DiI) composition. It can be adaptively applied during ground-up microbubble fabrication and to pre-made microbubble formulations with modular customizability of fluorescence and multimodal fluorescence/radioactive properties. Accordingly, this flexible method enables the production of tailored, traceable (radio, fluorescent, or radio/fluorescent active) multimodal microbubbles that are useful for advancing mechanistic, imaging, and therapeutic microbubble-FUS 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".