ST-elevation myocardial infarction, pulmonary embolism, and cerebral ischemic stroke in a patient with critically low levels of natural anticoagulants

Reznik, E. V.; Щербакова, Е. С.; Borisovskaya, S. V.; Gavrilov, Yurij Valerevich; Pajeva, Tatyana Mikhailovna; Lepkov, S.; Миронков, А. Б.; Джобава, Э. М.; Никитин, И. Г.

doi:10.1016/j.jccase.2019.11.003

Cited by 7 publications

(1 citation statement)

References 9 publications

(7 reference statements)

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“…The chosen genes show great promise as molecular signatures, suggesting that more validation is necessary to confirm their use as non-invasive diagnostic or prognostic markers. [31][32][33] Clinical Validation of Biomarkers:…”

Section: Prioritized Biomarker Genesmentioning

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: Prioritized Biomarker Genesmentioning

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

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Bromisoval/caffeine/calcium-gluconate/papaverine/phenobarbital/oxcarbazepine

2020

Reactions Weekly

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Thromboses and Hemostasis Disorders Associated with COVID-19: The Possible Causal Role of Cross-Reactivity and Immunological Imprinting

Kanduc

2021

Glob Med Genet

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By examining the issue of the thromboses and hemostasis disorders associated with severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) through the lens of cross-reactivity, it was found that 60 pentapeptides are shared by SARS-CoV-2 spike glycoprotein (gp) and human proteins that— when altered, mutated, deficient or, however, improperly functioning— cause vascular diseases, thromboembolic complications, venous thrombosis, thrombocytopenia, coagulopathies, and bleeding, inter alia. The peptide commonality has a relevant immunological potential as almost all of the shared sequences are present in experimentally validated SARS-CoV-2 spike gp-derived epitopes, thus supporting the possibility of cross-reactions between the viral gp and the thromboses-related human proteins. Moreover, many of the shared peptide sequences are also present in pathogens to which individuals have previously been exposed following natural infection or vaccinal routes, and of which the immune system has stored imprint. Such an immunological memory might rapidly trigger anamnestic secondary cross-reactive responses of extreme affinity and avidity, in this way explaining the thromboembolic adverse events that can associate with SARS-CoV-2 infection or active immunization.

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ST-elevation myocardial infarction, pulmonary embolism, and cerebral ischemic stroke in a patient with critically low levels of natural anticoagulants

Cited by 7 publications

References 9 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

Bromisoval/caffeine/calcium-gluconate/papaverine/phenobarbital/oxcarbazepine

Thromboses and Hemostasis Disorders Associated with COVID-19: The Possible Causal Role of Cross-Reactivity and Immunological Imprinting

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