Hyaluronic Acid Engineered Nanomicelles Loaded with 3,4-Difluorobenzylidene Curcumin for Targeted Killing of CD44+ Stem-Like Pancreatic Cancer Cells
Bibliographic record
Abstract
Cancer stem-like cells (CSLCs) play a pivotal role in acquiring multidrug resistant (MDR) phenotypes. It has been established that pancreatic cancers overexpressing CD44 receptors (a target of hyaluronic acid; HA) is one of the major contributors for causing MDR. Therefore, targeted killing of CD44 expressing tumor cells using HA based active targeting strategies may be beneficial for eradicating MDR-pancreatic cancers. Here, we report the synthesis of a new HA conjugate of copoly(styrene maleic acid) (HA-SMA) that could be engineered to form nanomicelles with a potent anticancer agent, 3,4-difluorobenzylidene curcumin (CDF). The anticancer activity of CDF loaded nanomicelles against MiaPaCa-2 and AsPC-1 human pancreatic cancer cells revealed dose-dependent cell killing. Results of cellular internalization further confirmed better uptake of HA engineered nanomicelles in triple-marker positive (CD44+/CD133+/EpCAM+) pancreatic CSLCs compared with triple-marker negative (CD44-/CD133-/EpCAM-) counterparts. More importantly, HA-SMA-CDF exhibited superior anticancer response toward CD44+ pancreatic CSLCs. Results further confirmed that triple-marker positive cells treated with HA-SMA-CDF caused significant reduction in CD44 expression and marked inhibition of NF-κB that in-turn can mitigate their proliferative and invasive behavior. Conclusively, these results suggest that the newly developed CD44 targeted nanomicelles may have great implications in treating pancreatic cancers including the more aggressive pancreatic CSLCs.
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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".