A Nanoparticle Delivery of Plasmid Encoding Hepatocyte Growth Factor for Gene Therapy of Silicosis in Mice
Bibliographic record
Abstract
PURPOSE: Silicosis is a serious occupational disease that is characterized by pulmonary infiltrates and fibrosis and is often refractory to current treatments. New therapeutic strategies for silicosis are needed. Hepatocyte growth factor (HGF) is a latent anti-inflammatory and anti-fibrotic growth factor. METHODS: We prepared a polyethyleneimine-polyethylene glycol/pHGF/hyaluronic acid (PEG-PEI/pHGF/HA) nanomaterials loaded with plasmid DNA encoding HGF gene to increase its transfection efficiency. The characterization, including DNA entrapment efficiency, morphology, particle size, and zeta-potential of PEG-PEI/pHGF/HA was studied. And a PEG-PEI/pHGF/HA (N/P=30:1) nanoparticle with low toxicity and high transfection efficiency was used in treatment for silicosis in mice. RESULTS: The results showed that the human HGF expression in the lungs of the mice was increased, and the inflammatory cell infiltration and fibrous collagen deposition was significantly reduced. CONCLUSION: Therefore, PEG-PEI/pHGF/HA nanoparticle warrant further investigation and may be a potential therapeutic strategy for silicosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".