Bibliographic record
Abstract
INTRODUCTION: Specific delivery of a drug to a target site is a major goal of drug delivery research. Using temperature-sensitive liposomes (TSLs) is one way to achieve this; the liposome acts as a protective carrier, allowing increased drug to flow through the bloodstream by minimizing clearance and non-specific uptake. On reaching microvessels within a heated tumor, the drug is released and quickly penetrates. A major advance in the field is ThermoDox® (Celsion), demonstrating significant improvements to the drug release rates and drug uptake in heated tumors (∼ 41°C). Most recently, magnetic resonance-guided focused ultrasound (MRgFUS) has been combined with TSL drug delivery to provide localized chemotherapy with simultaneous quantification of drug release within the tumor. AREAS COVERED: In this article the field of hyperthermia-induced drug delivery is discussed, with an emphasis on the development of TSLs and their combination with hyperthermia (both mild and ablative) in cancer therapy. State-of-the-art image-guided heating technologies used with this combination strategy will also be presented, with examples of real-time monitoring of drug delivery and prediction of efficacy. EXPERT OPINION: The specific delivery of drugs by combining hyperthermia with TSLs is showing great promise in the clinic and its potential will be even greater as the use of image-guided focused ultrasound becomes more widespread - a technique capable of penetrating deep within the body to heat a specific area with improved control. In conjunction with this, it is anticipated that multifunctional TSLs will be a major topic of study in this field.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".