Antitumor Effects of Carrier-Free Functionalized Lignin Materials on Human Hepatocellular Carcinoma (HepG2) Cells
Bibliographic record
Abstract
Lignin, as an abundant aromatic biopolymer in plants, has great potential for medical applications due to its active sites, antioxidant activity, low biotoxicity, and good biocompatibility. In this work, a simple and ecofriendly approach for lignin fractionation and modification was developed to improve the antitumor activity of lignin. The lignin fraction KL-3 obtained by the lignin gradient acid precipitation at pH = 9–13 showed good cytotoxicity. Furthermore, the cell-feeding lignin after additional structural modifications such as demethylation (DKL-3), sulfonation (SL-3), and demethylsulfonation (DSKL-3) could exhibit higher glutathione responsiveness in the tumor microenvironment, resulting in reactive oxygen species accumulation and mitochondrial damage and eventually leading to apoptosis in HepG2 cells with minimal damage to normal cells. The IC 50 values for KL-3, SL-3, and DSKL-3 were 0.71, 0.57, and 0.41 mg/mL, respectively, which were superior to those of other biomass extractives or unmodified lignin. Importantly, in vivo experiments conducted in nude mouse models demonstrated good biosafety and effective tumor destruction. This work provides a promising example of constructing carrier-free functionalized lignin antitumor materials with different structures for inhibiting the growth of human hepatocellular carcinoma (HepG2) cells, which is expected to improve cancer therapy outcomes.
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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) | 0.001 | 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".