Releasing genetic biomarkers from cells and tissues with ultrasound
Bibliographic record
Abstract
Ultrasound sonoporation has long been investigated as a means of enhancing therapeutic delivery into cells. More recently, however, ultrasound has been explored as means to liberate biomarkers out of cells. I will discuss our past efforts to enhance ultrasound biomarker release using microbubbles and nanodroplets both in vivo and ex vivo. Our recent in vivo work has demonstrated that ultrasound and nanodroplets can enhance extracellular vesicles and tumor DNA/RNA in the blood by 100-1000-fold. Because we can compare post- sonication blood biomarker levels to a pre-sonication blood draw baseline, and because any increase in biomarkers must be due to ultrasound treatment, this approach is a powerful alternative to biopies with much less tissue damage. In a different paradigm, we are exploring contrast-agent-free ablative acoustic mechanisms on a micro-scale to enhance biomarker release even further. Our group is further exploring novel avenues to detect circulating tumor cells in blood samples, where ultrasound treatment is applied to a separated blood fraction. By detecting biomarkers released from circulating tumor cells but primarily absent in blood cells, our approach is sensitive to single cells. Ongoing work in prostate cancer patients suggest promise for sensitive and specific detection of clinically significant prostate cancer. Ultrasound for biomarker release is a promising avenue of research which could lead to many important clinical applications given additional work and validation.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".