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Record W4229056936 · doi:10.1139/bcb-2021-0267

IL-6 induced enhanced clearance of proANP and ANP by insulin-degrading enzyme in T1DM mice

2021· article· en· W4229056936 on OpenAlexvenueno aff
Caiyun Li, Yao Wang, Guozhen Zhu, Yaxian Shang, Jiefang Kang, Jia Li

Bibliographic record

VenueBiochemistry and Cell Biology · 2021
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsInternal medicineEndocrinologyAtrial natriuretic peptideEnzymeMAPK/ERK pathwayMedicineDiabetes mellitusSecretionCytokineNatriuretic peptidePathophysiologySignal transductionHeart failureBiologyBiochemistry

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) is a prevalent cause of morbidity and mortality in type I diabetes mellitus (T1DM). However, the pathophysiological mechanisms underlying the relationship between CVD, CVD risk factors, and T1DM have not yet been sufficiently explored. Here, we report that insulin-degrading enzyme (IDE) effectively degrades the precursor of atrial natriuretic peptide (proANP) in HEK293T cells. The pro-inflammatory cytokine IL-6 elicited a significant dose-dependent increase in IDE protein expression. Inhibition of the ERK/MAPK signaling pathway with selumetinib abolished the IL-6-stimulated increase in IDE protein levels and decreased ANP secretion in H9C2 cells. Importantly, the T1DM mouse model displayed lower proANP in the heart and ANP in serum, due to increased IDE expression and activity. Our results suggest a novel role of IL-6 in ANP metabolism via IDE and provide possibilities for new potential therapeutic strategies for diabetes-related cardiovascular complications.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.011
GPT teacher head0.245
Teacher spread0.234 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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