Focused Ultrasound Hyperthermia Mediated Drug Delivery Using Thermosensitive Liposomes and Visualized With <i>in vivo</i> Two-Photon Microscopy
Bibliographic record
Abstract
The future of nanomedicines in oncology requires leveraging more than just the passive drug accumulation in tumors through the enhanced permeability and retention effect. Promising results combining mild hyperthermia (HT) with lyso-thermosensitive liposomal doxorubicin (LTSL-DOX) has led to improved drug delivery and potent antitumor effects in pre-clinical studies. The ultimate patient benefit from these treatments can only be realized when robust methods of HT can be achieved clinically. One of the most promising methods of non-invasive HT is the use of focused ultrasound (FUS) with MRI thermometry for anatomical targeting and feedback. MRI-guided focused ultrasound (MRgFUS) is limited by respiratory motion and large blood vessel cooling. In order to translate exciting pre-clinical results to the clinic, novel heating approaches capable of overcoming the limitations on clinical MRgFUS+HT must be tested and evaluated on their ability to locally release drug from LTSL-DOX. Methods: In this work, a new system is described to integrate focused ultrasound (FUS) into a two-photon microscopy (2PM) setting to image the release of drug from LTSL-DOX in real-time during FUS+HT in vivo. A candidate scheme for overcoming the limitations of respiratory motion and large blood vessel cooling during MRgFUS+HT involves applying FUS+HT to 42C in short ~30s bursts. The spatiotemporal drug release pattern from LTSL-DOX as a result is quantified using 2PM and compared against continuous (3.5min and 20min at 42C) FUS+HT schemes and unheated controls. Results: It was observed for the first time in vivo that these short duration temperature elevations could produce substantial drug release from LTSL-DOX. Ten 30s bursts of FUS+HT was able to achieve almost half of the interstitial drug concentration as 20min of continuous FUS+HT. There was no significant difference between the intravascular area under the concentration-time curve for ten 30s bursts of FUS+HT and 3.5min of continuous FUS+HT.
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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) | 0.001 | 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".