In vivo monitoring of tissue pharmacokinetics of liposome/drug using MRI: Illustration of targeted delivery
Bibliographic record
Abstract
The purpose of this study was to determine if MnSO(4)/doxorubicin (DOX) loaded liposomes could be used for in vivo monitoring of liposome concentration distribution and drug release using MRI. In vitro results show that T(1) shortening correlates with MnSO(4) concentration. Using a temperature-sensitive liposome formulation, it was found that MnSO(4) release significantly shortened T(1). This feature, therefore, suggests that content release can also be measured with these MnSO(4)-loaded liposomes. The feasibility of monitoring this drug delivery and release-imaging agent was shown in a murine tumor model. Upon tumor heating, nonthermally sensitive liposomes selectively but heterogeneously accumulated in the tumor region. The thermally sensitive liposomes showed a clear pattern of accumulation at the periphery of the tumor, concordant with the release temperature of this formulation (39-40 degrees C). This liposome contrast agent has potential for use with hyperthermia by providing individualized monitoring of tissue drug concentration distribution during or after treatment. This would allow for: 1) modification of treatment variables to improve the uniformity of drug delivery, and 2) provide a means to select patients most likely to benefit from this liposomal drug treatment. Additionally, the drug-loading method used for this liposome is applicable to a wide range of drugs, thereby broadening its applicability. The method is also applicable to other liposomal formulations with triggered release mechanisms.
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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 | 0.001 | 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".