Predictive Value of Plasma Big Endothelin-1 in Adverse Events of Patients With Coronary Artery Restenosis and Diabetes Mellitus: Beyond Traditional and Angiographic Risk Factors

Ma, Yue; Tian, Tao; Wang, Tianjie; Wang, Juan; Guan, Hao; Yuan, Jiansong; Song, Lei; Yang, Wei; Qiao, Shubin

doi:10.3389/fcvm.2022.854107

Cited by 5 publications

(1 citation statement)

References 44 publications

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“…ET-1 has the ability to modulate ROS levels, thereby contributing to aberrant regulation of oxidative stress in endothelial cells . Diabetic patients exhibit elevated levels of ET-1, with its plasma level being indicative for predicting diabetes prognosis. , Upregulation of ET-1 and its receptor may be associated with vascular complications related to diabetes. However, an imbalance between the NO and ET-1 can further exacerbate endothelial cell injury.…”

Section: Biomarkers For Endothelial Dysfunction In Diabetesmentioning

confidence: 99%

Toward Ultrasound Molecular Imaging of Endothelial Dysfunction in Diabetes: Targets, Strategies, and Challenges

Liu,

Wang,

Qiu

et al. 2024

ACS Appl. Bio Mater.

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Diabetes vasculopathy is a significant complication of diabetes mellitus (DM), and early identification and timely intervention can effectively slow the progression. Accumulating studies have shown that diabetes causes vascular complications directly or indirectly through a variety of mechanisms. Direct imaging of the endothelial molecular changes not only identifies the early stage of diabetes vasculopathy but also sheds light on the precise treatment. Targeted ultrasound contrast agent (UCA)-based ultrasound molecular imaging (UMI) can noninvasively detect the expression status of molecular biomarkers overexpressed in the vasculature, thereby being a potential strategy for the diagnosis and treatment response evaluation of DM. Amounts of efforts have been focused on identification of the molecular targets expressed in the vasculature, manufacturing strategies of the targeted UCA, and the clinical translation for the diagnosis and evaluation of therapeutic efficacy in both micro-and macrovasculopathy in DM. This review summarizes the latest research progress on endothelium-targeted UCA and discusses their promising future and challenges in diabetes vasculopathy theranostics.

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Section: Biomarkers For Endothelial Dysfunction In Diabetesmentioning

confidence: 99%

Toward Ultrasound Molecular Imaging of Endothelial Dysfunction in Diabetes: Targets, Strategies, and Challenges

Liu,

Wang,

Qiu

et al. 2024

ACS Appl. Bio Mater.

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Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study

Wang,

Gao,

Zhang

et al. 2024

Intern Emerg Med

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Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsis outcomes early is vital. Despite machine learning’s (ML) use in medical research, local validation within the Medical Information Mart for Intensive Care IV (MIMIC-IV) database is lacking. We aimed to devise a prognostic model, leveraging MIMIC-IV data, to predict sepsis mortality and validate it in a Chinese teaching hospital. MIMIC-IV provided patient data, split into training and internal validation sets. Four ML models logistic regression (LR), support vector machine (SVM), deep neural networks (DNN), and extreme gradient boosting (XGBoost) were employed. Shapley additive interpretation offered early and interpretable mortality predictions. Area under the ROC curve (AUROC) gaged predictive performance. Results were cross verified in a Chinese teaching hospital. The study included 27,134 sepsis patients from MIMIC-IV and 487 from China. After comparing, 52 clinical indicators were selected for ML model development. All models exhibited excellent discriminative ability. XGBoost surpassed others, with AUROC of 0.873 internally and 0.844 externally. XGBoost outperformed other ML models (LR: 0.829; SVM: 0.830; DNN: 0.837) and clinical scores (Simplified Acute Physiology Score II: 0.728; Sequential Organ Failure Assessment: 0.728; Oxford Acute Severity of Illness Score: 0.738; Glasgow Coma Scale: 0.691). XGBoost’s hospital mortality prediction achieved AUROC 0.873, sensitivity 0.818, accuracy 0.777, specificity 0.768, and F1 score 0.551. We crafted an interpretable model for sepsis death risk prediction. ML algorithms surpassed traditional scores for sepsis mortality forecast. Validation in a Chinese teaching hospital echoed these findings.

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Association of plasma endothelin-1 levels with revascularization strategies and short-term clinical outcomes: Role of diabetes

Mayyas,

Ibrahim,

Ibdah

et al. 2024

Heliyon

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Predictive Value of Plasma Big Endothelin-1 in Adverse Events of Patients With Coronary Artery Restenosis and Diabetes Mellitus: Beyond Traditional and Angiographic Risk Factors

Cited by 5 publications

References 44 publications

Toward Ultrasound Molecular Imaging of Endothelial Dysfunction in Diabetes: Targets, Strategies, and Challenges

Toward Ultrasound Molecular Imaging of Endothelial Dysfunction in Diabetes: Targets, Strategies, and Challenges

Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study

Association of plasma endothelin-1 levels with revascularization strategies and short-term clinical outcomes: Role of diabetes

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