Colon-Targeted Sustained-Release Combinatorial 5-Fluorouracil and Quercetin poly(lactic-<i>co</i>-glycolic) Acid (PLGA) Nanoparticles Show Enhanced Apoptosis and Minimal Tumor Drug Resistance for Their Potential Use in Colon Cancer
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Colorectal cancer (CRC) is the third most common cancer worldwide, acting as a significant public health problem. 5-Fluorouracil (5-FU) is a key chemotherapy for various types of cancer, due to its broad anticancer activity. However, the emergence of drug resistance is a considerable limitation in the clinical application of 5-FU. Quercetin (QC) is proposed as an adjuvant therapy to minimize drug resistance to chemotherapeutics and enhance their pharmacological efficacy. The oral delivery of 5-FU and QC is challenged by poor aqueous solubility of QC and poor cellular permeability of 5-FU. To solve this issue, novel polylactide- co -glycolide (PLGA) combinatorial nanoparticles loading 5-FU and QC were prepared to deliver them directly to the colon. These sustained-release combinatorial nanoparticles recorded a significant decrease in cancer cell proliferation, C-reactive protein (CRP) level, and Interleukin-8 (IL-8) expression by 30.08%, 40.7%, and 46.6%, respectively. The results revealed that this combination therapy may offer a new strategy for the targeted delivery of chemotherapeutics to the colon.
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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) | 0.000 | 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) | 0.000 | 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; 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".