Custom-designed Laser-based Heating Apparatus for Triggered Release of Cisplatin from Thermosensitive Liposomes with Magnetic Resonance Image Guidance
Bibliographic record
Abstract
Liposomes have been employed as drug delivery systems to target solid tumors through exploitation of the enhanced permeability and retention (EPR) effect resulting in significant reductions in systemic toxicity. Nonetheless, insufficient release of encapsulated drug from liposomes has limited their clinical efficacy. Temperature-sensitive liposomes have been engineered to provide site-specific release of drug in order to overcome the problem of limited tumor drug bioavailability. Our lab has designed and developed a heat-activated thermosensitive liposome formulation of cisplatin (CDDP), known as HTLC, to provide triggered release of CDDP at solid tumors. Heat-activated delivery in vivo was achieved in murine models using a custom-built laser-based heating apparatus that provides a conformal heating pattern at the tumor site as confirmed by MR thermometry (MRT). A fiber optic temperature monitoring device was used to measure the temperature in real-time during the entire heating period with online adjustment of heat delivery by alternating the laser power. Drug delivery was optimized under magnetic resonance (MR) image guidance by co-encapsulation of an MR contrast agent (i.e., gadoteridol) along with CDDP into the thermosensitive liposomes as a means to validate the heating protocol and to assess tumor accumulation. The heating protocol consisted of a preheating period of 5 min prior to administration of HTLC and 20 min heating post-injection. This heating protocol resulted in effective release of the encapsulated agents with the highest MR signal change observed in the heated tumor in comparison to the unheated tumor and muscle. This study demonstrated the successful application of the laser-based heating apparatus for preclinical thermosensitive liposome development and the importance of MR-guided validation of the heating protocol for optimization of drug delivery.
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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".