Biomedical nanobubbles and opportunities for microfluidics
Bibliographic record
Abstract
The use of bulk nanobubbles in biomedicine is increasing in recent years, which is attributable to the array of therapeutic and diagnostic tools promised by developing bulk nanobubble technologies. From cancer drug delivery and ultrasound contrast enhancement to malaria detection and the diagnosis of acute donor tissue rejection, the potential applications of bulk nanobubbles are broad and diverse. Developing these technologies to the point of clinical use may significantly impact the quality of patient care. This review compiles and summarizes a representative collection of the current applications, fabrication techniques, and characterization methods of bulk nanobubbles in biomedicine. Current state-of-the-art generation methods are not designed to create nanobubbles of high concentration and low polydispersity, both characteristics of which are important for several bulk nanobubble applications. To date, microfluidics has not been widely considered as a tool for generating nanobubbles, even though the small-scale precision and real-time control offered by microfluidics may overcome the challenges mentioned above. We suggest possible uses of microfluidics for improving the quality of bulk nanobubble populations and propose ways of leveraging existing microfluidic technologies, such as organ-on-a-chip platforms, to expand the experimental toolbox of researchers working to develop biomedical nanobubbles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".