Abstract 280: Sub-30-nm capsules for drug delivery
Bibliographic record
Abstract
Abstract Systemic treatment of cancer using long-circulating nanomedicines is promising due to their passive tumor targeting ability to achieve higher and more selective accumulation in tumors with irregular vascularization, a phenomenon known as extended permeation and retention (EPR) effect.1,2 Clinical use of nanometer-sized carriers, such as Doxil and Abraxane, to deliver chemotherapeutics to solid tumors is proven effective in highly vascularized tumors such as breast cancer, ovarian cancer, multiple myeloma, and Kaposi's sarcoma.3-5 Most nanomedicines that are being developed or approved so far have a diameter of around 100-200 nm for prolonged retention in highly angiogenic and densely vascularized tumors,6 however, they suffer from limited accumulation and poor penetration to the inner core of avascular or hypovascular tumors (such as prostate and pancreatic cancer),7-9 therefore nanomedicines small than 100 nm are more preferred for improved tumor penetration.10,11 Here we present our strategy to form cyclodextrin-based sub-30-nm nanocarriers, which allows easy drug encapsulation, and successful delivery of therapeutics to human tumor xenografts with significantly reduced tumor growth rates and improved survival rates. Citation Format: Xiaowei Ma, Ping Zhang, Chao Cui, Chang-Chun Ling, Lina Cui. Sub-30-nm capsules for drug delivery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 280.
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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) | 0.000 | 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) | 0.008 | 0.005 |
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".