Enhanced efficacy of curcumin with phosphatidylserine-decorated nanoparticles in the treatment of hepatic fibrosis
Bibliographic record
Abstract
Hepatic macrophages have been considered as a therapeutic target for liver fibrosis treatment, and phosphatidylserine (PS)-containing nanoparticles are commonly used to mimic apoptotic cells that can specifically regulate macrophage functions, resulting in anti-inflammatory effects. This study was designed to test the efficacy of PS-modified nanostructured lipid carriers (mNLCs) containing curcumin (Cur) (Cur-mNLCs) in the treatment of liver fibrosis in a rat model. Carbon tetrachloride-induced liver fibrosis in rats was used as an experimental model, and the severity of the disease was examined by both biochemical and histological methods. Here, we showed that mNLCs were spherical nanoparticles with decreased negative zeta potentials due to PS decoration, and significantly increased both mean residence time and area under the curve of Cur. In the rats with liver fibrosis, PS-modification of NLCs enhanced the nanoparticles targeting to the diseased liver, which was evidenced by their highest accumulation in the liver. As compared to all the controls, Cur-mNLCs were significantly more effective at reducing the liver damage and fibrosis, which were indicated by in Cur-mNLCs-treated rats the least increase in liver enzymes and pro-inflammatory cytokines in the circulation, along with the least increase in collagen fibers and alpha smooth muscle actin and the most increased hepatocyte growth factors (HGF) and matrix metalloprotease (MMP) two in the livers. In conclusion, PS-modified NLCs nanoparticles prolonged the retention time of Cur, and enhanced its bioavailability and delivery efficiency to the livers, resulting in reduced liver fibrosis and up-regulating hepatic expression of HGF and MMP-2.
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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".