In vivo tracking of CAR-T cells in tumors via nanobubble-based contrast enhanced ultrasound
Bibliographic record
Abstract
Summary CAR-T cell therapy has led to remarkable advances in the outcomes of patients with acute lymphoblastic leukemia (ALL), B cell lymphomas, and multiple myeloma. Given these successes in hematologic malignancies, extensive efforts are now focused on developing CAR-T cell therapies for the treatment of solid tumors. The treatment of solid tumors poses significant hurdles with cell trafficking that is necessary to achieve efficacy and minimize off-tumor side effects. The development of simple, safe and inexpensive modalities to track CAR-T cell distribution in humans could provide critical insights to facilitate the development of improved CAR-T products for solid tumors. Here we demonstrate a strategy to monitor CAR-T cells in vivo using ultrasound imaging and nanobubble (NB) labeled cells. NBs are ultrasound contrast agents composed of a lipid shell and a C 4 F 10 gas core that can be efficiently internalized into cells. This approach enables us to image the CAR-T cells using nonlinear contrast-enhanced ultrasound (CEUS). Utilizing this method, we found that CAR-T cells can be visualized after injection into both tumor bearing and non-tumor bearing mice. In summary, our ultrasound-based tracking approach can effectively monitor the trafficking of CAR-T cells in vivo , offering a valuable new strategy that can further enable the development of new CAR-T products and strategies to modulate cell trafficking.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".