Novel amphiphilic rhamnogalacturonnan I‐based nanomicelles for targeted delivery of curcumin to hepatocellular carcinoma cells
Bibliographic record
Abstract
Abstract Curcumin (Cur) is a bioactive nutraceutical with great potential in biological, nutritional, and medical applications. However, these applications are limited by various factors such as insufficient ingestion, low aqueous solubility, and relatively high toxicity to normal cells. To tackle these obstacles, we synthesized a novel Cur‐modified‐rhamnogalacturonan (RG‐C) nano‐micelle carrier to target‐deliver Cur to hepatocellular carcinoma HepG2 cells via specific recognition of RG‐I by the overexpressed surface galactin‐3 receptor. Fourier transfer infrared, UV–vis, and 1H NMR analyses confirmed the conjugation between RG and Cur RG‐C loaded with Cur (RG‐CC) was formed via self‐assembly in an aqueous solution with a drug loading efficiency of 12.2%. RG‐CC micelle was ellipsoidal or cubic with a size ranging between 100 and 200 nm by scanning electron microscopy observation. Cur release from RG‐CC exhibited a controlled and pH‐dependent manner with 50% at pH 5.0 in contrast to 5% at pH 7.4 after 24 h exposure. RG‐CC possessed more potent anti‐proliferative activity against HepG2 cells than normal embryonic kidney 293T cells. Compared to free Cur, both the anti‐proliferative effect and uptake of RG‐CC were significantly higher in HepG2 cells as revealed from laser confocal microscopy and flow cytometry analyses. RG‐CC was a promising anticancer candidate and deserves further preclinical and clinical investigations.
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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".