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Jingjing Zhang

Researcher at Lanzhou University

Publications -  7
Citations -  155

Jingjing Zhang is an academic researcher from Lanzhou University. The author has contributed to research in topics: Angelica sinensis & Percutaneous coronary intervention. The author has an hindex of 5, co-authored 6 publications receiving 92 citations.

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Weighted Gene Co-Expression Network Analysis Identifies Critical Genes in the Development of Heart Failure After Acute Myocardial Infarction.

TL;DR: By using the WGCNA, new insights are provided into the underlying molecular mechanism and molecular markers correlated with HF development following an AMI, which may serve to improve risk stratification, therapeutic decisions, and prognosis prediction in AMI patients.
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Effect of intracoronary agents on the no-reflow phenomenon during primary percutaneous coronary intervention in patients with ST-elevation myocardial infarction: a network meta-analysis

TL;DR: The intracoronary administration of anisodamine appears to improve myocardial reperfusion, cardiac function, and clinical outcomes in patients with STEMI undergoing PPCI.
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Network pharmacology-based identification of major component of Angelica sinensis and its action mechanism for the treatment of acute myocardial infarction

TL;DR: Experimental data indicate that ASP protects the heart against ischemic injury by activating ATF6 to ameliorate the detrimental ER stress, and the associations between A. sinensis and AMI through multiple targets and several key signaling pathways are revealed.
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Polysaccharide from Angelica sinensis protects H9c2 cells against oxidative injury and endoplasmic reticulum stress by activating the ATF6 pathway

TL;DR: Findings indicate that ASP, a major water-soluble component of A. sinensis, exerts protective effects against H2O2-induced injury in H9c2 cells by activating the ATF6 pathway, thus ameliorating ER and oxidative stress.
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Risk stratification based on components of the complete blood count in patients with acute coronary syndrome: A classification and regression tree analysis.

TL;DR: Patients with ACS can be readily stratified into distinct prognostic categories using the C ART risk stratification tool on the basis of CBC components, and the combined use of the CART risk categories and GRACE scores yielded a more accurate predictive value for MACEs.