Hyaluronic Acid Engineered Nanomicelles Loaded with 3,4-Difluorobenzylidene Curcumin for Targeted Killing of CD44+ Stem-Like Pancreatic Cancer Cells
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it