Recent advances in drug delivery strategies for treatment of ovarian cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Ovarian cancer is associated with the highest mortality rate of all gynecological malignancies, due in part to inadequate treatment strategies and the asymptomatic nature of the disease. Current standard of care includes surgery and systemic chemotherapy. However, this approach can result in toxicities and eventual disease relapse, due to the emergence of multidrug resistance. Drug delivery systems (DDS) have shown promise in overcoming many of the limitations facing conventional treatment regimens. AREAS COVERED: This review provides an overview of recent advances in DDS strategies for the treatment ovarian cancers. Nano-sized systems, including nanoparticles, micelles, liposomes and drug conjugates; microspheres; implants and injectable depots are discussed. The advantages, limitations and clinical potential of these strategies are also outlined. EXPERT OPINION: Nano-sized DDS enable passive targeting to tumors due to their size, and further improvements in tumor localization can be made using targeting moieties. Microspheres, implants and injectable depots have been investigated for peritoneal localized and sustained therapy. Overall, the benefits of using DDS for ovarian cancer therapy include higher drug levels at the diseased site, circumvention of drug resistance mechanisms, minimization of non-specific toxicities, improvements in solubility of poorly soluble drugs and elimination of toxicities associated with conventionally used pharmaceutical excipients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it