Microfluidic Assembly of Monodisperse, Nanoparticle-Incorporated Perfluorocarbon Microbubbles for Medical Imaging and Therapy
Bibliographic record
Abstract
New medical imaging contrast agents that permit multiple imaging and therapy applications using a single agent can result in more accurate diagnosis and local treatment of diseased tissue. Solid nanoparticles (NPs) (5-150 nm in size) have emerged as promising imaging and therapy agents, as have micrometer-scale, perfluorocarbon gas-filled microbubbles (MBs) used in patients as intravascular ultrasound contrast agents. We propose that the modular combination of small, solid NPs and larger, highly compressible MBs into a single agent is an effective way to attain the desired complementary and hybrid properties of two very different agents. Presented here is a new strategy for the simple and robust incorporation of various medical NPs with monodisperse MBs based upon the controlled pH-based regulation of the electrostatic attraction between NPs and the MB shell. Using this simple approach, microfluidic-generated, protein-lipid-coated, perfluorobutane MBs (with size control down to 3 microm) were incorporated with silica-coated NPs, including CdSe/ZnS quantum dots, gold nanorods, iron oxide NPs, and Gd-loaded mesoporous silica NPs. The silica interface permits NP inclusion within MBs to be independent of NP composition, morphology, and size. Significantly, the NP-incorporated MBs (NP-MBs) diluted in saline were detectable using low-pressure ultrasound, and the monodisperse MB platform can be produced at high-throughput, sufficient for in vivo usage (10(6) MB/sec). The modular synthesis of a variety of NP-MBs can facilitate flexible, user-defined, multifunctional imaging and therapy agents tailored for specific applications and disease types.
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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) | 0.000 | 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".