Targeted delivery of BMP4-siRNA to hepatic stellate cells for treatment of liver fibrosis
Bibliographic record
Abstract
Hepatic fibrosis is a serious health problem in many parts of the world. However, its treatment remains severely limited because of inadequate target specificity. HSC are the largest reservoir of vitamin A in the body. They are also the principal players responsible for the pathogenesis of liver fibrosis. Targeting HSC is an effective strategy for treatment of liver fibrosis. The specific association of BMP4 with various liver diseases including liver fibrosis makes it an ideal candidate for targeting HSC cells using siRNA. The objective of this study is to develop and characterize vitamin A (VA)-coupled liposomes for the targeted delivery of BMP4-siRNA to cultured HSC. DOTAP/DOPE liposomes surfaces were prepared by thin film hydration and their surfaces were decorated with VA (1:2 mol/mol). Particle size and zeta potential were determined using ZetaPALS. In addition, the siRNA binding efficiency was determined by ultra-centrifugation and fluorescence assays. The cytotoxicity of VA-conjugated liposomes was evaluated by the WST-1 cytotoxicity assay. Inhibition of BMP4 and α-SMA was determined by real time PCR and ELISA. Their average particle size was in the range of 100-120 nm and they exhibited zeta potential around +45 mV. VA-coated liposomes were mixed with BMP4-siRNA, forming lipoplexes with particle sizes less than 200 nm and zeta potential around +25 mV. The presence of VA did not alter the siRNA binding efficiency, it also had no effect on cytotoxicity, but resulted in enhanced cellular uptake of siRNA as shown by flow cytometry. There was a significant reduction in BMP4 mRNA with VA-coupled liposomes carrying BMP4-siRNA. Moreover, BMP4 gene silencing was accompanied by a significantly reduced the expression of the potent fibrinogenic α-SMA at mRNA and protein levels. In conclusion, VA-coated liposomes were successfully able to target and deliver BMP4-siRNA to HSC. This could offer an interesting perspective for the treatment of liver fibrosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".