Protective effect of breviscapine in acute pulmonary embolism rats via regulation of<scp>MCP</scp>‐1 and<scp>IL</scp>‐13

Li, Zhenkun; Lin, Hui‐Yi; Dong, Zhaoxing; Zhao, Xiaoyuan; Li, Ling

doi:10.1111/jcmm.13871

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Following model fitting, each GO word feature's Gini significance scores were obtained. [21]The reduction in node impurity-as determined by the Gini index-that results from splitting each feature throughout the whole forest's tree population is reflected in the Gini significance. Greater contribution to class separability and prediction is correlated with higher Gini scores.…”

Section: Predictive Gene Ontology Termsmentioning

confidence: 99%

Predicting biomarker for the acute pulmonary embolism by using gene ontology and machine learning

Zhou,

Duan,

Chen

et al. 2023

Preprint

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Key PointsEarly and Accurate Diagnosis Essential:Acute pulmonary embolism (PE) is a critical condition that demands prompt and precise diagnosis for effective treatment.Limitations of Current Diagnostics:Existing diagnostic methods like Computed Tomography Pulmonary Angiography (CTPA) have certain limitations, leading to the exploration of alternative approaches.Potential of Blood-Based Biomarkers:A recent study focused on identifying blood-based biomarkers for PE. This involved using gene ontology analysis and machine learning methods to analyze gene expression data from both PE patients and healthy controls.Gene Selection and Analysis:The study selected 20 genes for detailed analysis. These included various coagulation factors, fibrinolytic genes, and inflammation markers. Gene Ontology enrichment analysis was performed to understand the biological processes and molecular functions of these genes.Machine Learning for Diagnosis:Supervised machine learning algorithms were utilized to create classification models using the expression levels of these 20 genes. The models demonstrated promising results in distinguishing PE patients from healthy individuals.Acute pulmonary embolism (PE) is a life-threatening condition requiring early and accurate diagnosis. Current diagnostic methods like CTPA have limitations, and a study aimed to identify potential blood-based biomarkers for PE using gene ontology analysis and machine learning methods. Gene expression data of PE patients and healthy controls were obtained from the Gene Expression Omnibus database. A total of 20 genes were selected for further analysis, including coagulation factors F7, F10, F12, fibrinolytic genes PLAT, SERPINE1 and SERPINE2, and inflammation markers SELE, VCAM1 and ICAM. Gene Ontology enrichment analysis was performed to identify biological processes and molecular functions overrepresented among the candidate genes. Supervised machine learning algorithms were applied to build classification models using the expression levels of the 20 genes as features. Nested cross-validation was employed to assess model performance. The RF model achieved the highest area under the receiver operating characteristic curve of 0.89, indicating excellent discrimination between PE patients and controls based on the gene expression signature. Validation in larger cohorts is warranted to clinically translate these findings into a non-invasive diagnostic test for PE.

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Section: Predictive Gene Ontology Termsmentioning

confidence: 99%

Predicting biomarker for the acute pulmonary embolism by using gene ontology and machine learning

Zhou,

Duan,

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Design and evaluation of inhalable nanocrystals embedded microparticles with enhanced redispersibility and bioavailability for breviscapine

Chen

Gui²,

Luo

et al. 2021

Powder Technology

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Oxidative stress in acute pulmonary embolism: emerging roles and therapeutic implications

Yang,

Xu,

et al. 2024

Thrombosis J

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Oxidative stress is an imbalance between the body’s reactive oxygen species and antioxidant defense mechanisms. Oxidative stress is involved in the development of several cardiovascular diseases, such as pulmonary hypertension, atherosclerosis, and diabetes mellitus. A growing number of studies have suggested the potential role of oxidative stress in the pathogenesis of pulmonary embolism. Biomarkers of oxidative stress in pulmonary embolism have also been explored, such as matrix metalloproteinases, asymmetric dimethylarginine, and neutrophil/lymphocyte ratio. Here, we comprehensively summarize some oxidative stress mechanisms and biomarkers in the development of acute pulmonary embolism and summarize related treatments based on antioxidant stress to explore effective treatment strategies for acute pulmonary embolism.

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Protective effect of breviscapine in acute pulmonary embolism rats via regulation ofMCP‐1 andIL‐13

Cited by 3 publications

References 6 publications

Predicting biomarker for the acute pulmonary embolism by using gene ontology and machine learning

Predicting biomarker for the acute pulmonary embolism by using gene ontology and machine learning

Design and evaluation of inhalable nanocrystals embedded microparticles with enhanced redispersibility and bioavailability for breviscapine

Oxidative stress in acute pulmonary embolism: emerging roles and therapeutic implications

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