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Record W2996366113 · doi:10.14348/molcells.2019.2283

Attenuation of Experimental Autoimmune Hepatitis in Mice with Bone Mesenchymal Stem Cell-Derived Exosomes Carrying MicroRNA-223-3p.

2019· article· en· W2996366113 on OpenAlexaff
Feng‐Bin Lu, Da‐Zhi Chen, Lu Chen, En-De Hu, Jinlu Wu, Hui Li, Yuewen Gong, Zhuo Lin, Xiaodong Wang, Ji Li, Xiaoya Jin, Lanman Xu, Yongping Chen

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrovesiclesMesenchymal stem cellAutoimmune hepatitismicroRNAMedicineImmunologyExosomeCancer researchHepatitisBiologyPathology

Abstract

fetched live from OpenAlex

MicroRNA-223-3p (miR-223-3p) is one of the potential microRNAs that have been shown to alleviate inflammatory responses in pre-clinical investigations and is highly encased in exosomes derived from bone mesenchymal stem cells (MSC-exosomes). MSC-exosomes are able to function as carriers to deliver microRNAs into cells. Autoimmune hepatitis is one of the challenging liver diseases with no effective treatment other than steroid hormones. Here, we examined whether MSC-exosomes can transfer miR-223-3p to treat autoimmune hepatitis in an experimental model. We found that MSC-exosomes were successfully incorporated with miR-223-3p and delivered miR-223-3p into macrophages. Moreover, there was no toxic effect of exosomes on the macrophages. Furthermore, treatments of either exosomes or exosomes with miR-223-3p successfully attenuated inflammatory responses in the liver of autoimmune hepatitis and inflammatory cytokine release in both the liver and macrophages. The mechanism may be related to the regulation of miR-223-3p level and STAT3 expression in the liver and macrophages. These results suggest that MSC-exosomes can be used to deliver miR-223-3p for the treatment of autoimmune hepatitis.

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)0.000
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 score0.104
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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

Citations73
Published2019
Admission routes1
Has abstractyes

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