Development of 3-in-1 nanotherapeutic strategies for ovarian cancer
Bibliographic record
Abstract
Among gynecological cancers, ovarian cancer causes the most fatality. Platin-based chemotherapy is the primary therapeutic option, but it is limited by a variety of drug resistance mechanisms. Ovarian cancer is a complex and challenging disease to treat, and combination approaches have shown stronger efficacy than a single drug alone. However, they still need to overcome challenges, such as the non-selective distribution of drugs, and side effects caused by each drug in the combination. To overcome these issues, here we explored a 3-in-1 combination nanotherapeutic approach containing cisplatin, olaparib, and metformin for ovarian cancer. To encapsulate hydrophilic cisplatin and metformin inside the nanoparticle (NP) core, we developed cisplatin polymer prodrugs and metformin derivatives. Our results showed successful development of 3-in-1 NPs containing cisplatin, olaparib, and metformin, and they are stable in the physiological conditions. In vitro evaluation showed each agent in the 3-in-1 NPs is active and exerts therapeutic effects, contributing to ovarian cancer cell killing at lower concentrations. These results provide insight into developing novel nanotherapeutic strategies for improving ovarian cancer treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".