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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".