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miR-146b-5p regulates bone marrow mesenchymal stem cell differentiation by SIAH2/PPARγ in aplastic anemia children and benzene-induced aplastic anemia mouse model

2021· dataset· en· W4394082887 on OpenAlexaff
Huanhuan Li, Xueju Xu, Dao Wen Wang, Li Zeng, Li Bai, Yuan Zhang, Shufang Su, Linlin Wei, Hongliang You, Yingqi Fang, Yingchao Wang, Yufeng Liu

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAplastic anemiaMesenchymal stem cellBone marrowStem cellCancer researchMedicineBiologyImmunologyCell biologyPathology

Abstract

fetched live from OpenAlex

This study aimed to reveal the mechanism of miR-146b-5p in the differentiation of bone marrow mesenchymal stem cells (BMSCs) derived from children with aplastic anemia (AA). Here, we found that miR-146b-5p was highly expressed in BMSCs from children with AA, and the BMSCs surface markers expressions in BMSCs derived from children with AA and the healthy controls exerted no significant differences. Besides, the overexpression of miR-146b-5p in normal human-derived BMSCs promoted the adipogenic differentiation of BMSCs. Furthermore, miR-146b-5p negatively regulated SIAH2 luciferase activity, and the interference with miR-146b-5p reduced the stability of PPARγ protein and inhibited SIAH2-mediated ubiquitination of PPARγ protein. Besides, the interference with miR-146b-5p was beneficial for ameliorating AA in a mouse model of AA. Overall, our results found that miR-146b-5p was highly expressed in BMSCs from children with AA, and our further studies indicated that miR-146b-5p improved AA via promoting SIAH2-mediated ubiquitination of PPARγ protein.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.055
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0550.017

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 head0.026
GPT teacher head0.262
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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