Exploring near-infrared absorbing nanocarriers to overcome cancer drug resistance
Bibliographic record
Abstract
One of the major obstacles of successful cancer therapy is cancer drug resistance. The unique tools and applications developed by nanomedicine provide new approaches to surmount this common limitation of current treatment regimens. Nanocarriers that absorb light in the near-infrared spectrum are particularly suitable for this purpose. These nanocarriers can produce heat, release drugs or stimulate the production of physiologically relevant compounds when illuminated with near-infrared light. The current review summarizes the causes contributing to cancer multidrug resistance. The major types of nanocarriers that have been developed in recent years to overcome these hurdles are described. We focus on nanoparticles that are responsive to near-infrared light and suitable to surmount cancer multidrug resistance. Our review concludes with the bottlenecks that currently restrict the use of nanocarriers in the clinic and an outlook on future directions.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".