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Record W3207848291 · doi:10.18433/jpps32218

A Nanoparticle Delivery of Plasmid Encoding Hepatocyte Growth Factor for Gene Therapy of Silicosis in Mice

2021· article· en· W3207848291 on OpenAlexvenueno aff
Haiying Duan, Peng Gao, Xiaochen Cheng, Yuxin Lu, Chunsheng Hu, Xuefeng Zhu, Xiaoying Wang, Dujuan Li, Fengjun Xiao, Li Du, Yunmei Liu, Qinglin Zhang

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsnot available
Fundersnot available
KeywordsSilicosisTransfectionHepatocyte growth factorHyaluronic acidGene deliveryPEG ratioGenetic enhancementFibrosisHepatocyteMedicineMolecular biologyPharmacologyChemistryPathologyGeneBiologyBiochemistryInternal medicineIn vitroAnatomy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.140
GPT teacher head0.411
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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