Development of a Lipid-encapsulated TGFβRI-siRNA Drug for Liver Fibrosis Induced by <i>Schistosoma mansoni</i>
Bibliographic record
Abstract
Schistosoma mansoni infection leads to chronic schistosomiasis and severe hepatic fibrosis. We designed a liver-targeted lipid nanoparticle (LNP) carrying siRNA against type I TGF-β receptor (TGFβRI) mRNA to treat schistosomiasis-induced liver fibrosis in BALB/c mice. Knockdown of TGFβRI by LNP-siTGFβRI reduced LX-2 cell activation in vitro and alleviated liver fibrosis in S. mansoni-infected mice. αSMA and Col1a1 fibrotic markers in the liver tissues of infected mice were significantly suppressed in the treatment groups. In the serum of the LNP-siTGFβRI-treated groups, cytokines IFNγ, IL-1α, IL-6, IL-12, RANTES (CCL5), and TNFα increased, while GM-CSF, IL-2, IL-4, IL-10, IL-13, and KC (CXCL1) decreased compared to the control. Cell proportions were significantly altered in S. mansoni-infected mice, with increased CD56d NK cells and decreased CD19+ B cells and CD4+ T cells compared to naïve mice. Following LNP-siTGFβRI treatment, CD56d NK cells were downregulated, while B and memory Th cell populations were upregulated. The density of fibrotic regions significantly decreased with LNP-siTGFβRI treatment in a dose-dependent manner, and no systemic toxicity was observed in the major organs. This targeted siRNA delivery strategy effectively reduced granulomatous lesions in schistosomiasis-induced liver fibrosis without detectable side effects.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".