Controlled anti-cancer drug release through advanced nano-drug delivery systems: Static and dynamic targeting strategies
Bibliographic record
Abstract
Advances in nanomedicine, including early cancer detection, targeted drug delivery, and personalized approaches to cancer treatment are on the rise. For example, targeted drug delivery systems can improve intracellular delivery because of their multifunctionality. Novel endogenous-based and exogenous-based stimulus-responsive drug delivery systems have been proposed to prevent the cancer progression with proper drug delivery. To control effective dose loading and sustained release, targeted permeability and individual variability can now be described in more-complex ways, such as by combining internal and external stimuli. Despite these advances in release control, certain challenges remain and are identified in this research, which emphasizes the control of drug release and applications of nanoparticle-based drug delivery systems. Using a multiscale and multidisciplinary approach, this study investigates and analyzes drug delivery and release strategies in the nanoparticle-based treatment of cancer, both mathematically and clinically.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".