A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Mahajan, Sahil
- Objective Monitoring of Cardiovascular Biomarkers Using Artificial Intelligence (AI)
Authors
1 METs Institute of Pharmacy, Bhujbal Knowledge City, Affiliated to SPPU Pune University, Nashik, Maharashtra, IN
2 IEDC SVKM’s NMIMS University, Shirpur, Dist. Dhule, Maharashtra, IN
3 Department of Pharmaceutical Chemistry, Dadasaheb Balpande College of Pharmacy, Nagpur, Maharashtra, IN
Source
Asian Journal of Pharmaceutical Research, Vol 12, No 3 (2022), Pagination: 229 - 234Abstract
Different CVDs (CVD) are the leading wreak of mortality and disability worldwide. The pathology of CVD is complex; multiple biological pathways have been involved. Biomarkers act as a measure of usual or pathogenic biological processes. They play a significant part in the definition, prognostication, and decision-making with respect to the treatment of cardiovascular events. Inthis article, we had summarized key biomarkers which are essential to predict CVDs. We had studied prevalence, pattern of expression of biomarkers (salivary, inflammatory, oxidative stress, chemokines, antioxidants, genetic, etc.), its measurable impact, benefits of early detection and its scope. A Considerable number of deaths due to cardiovascular diseases (CVDs) can be attributed to tobacco smoking and it rises the precarious of deathfrom coronary heart disease and cerebrovascular diseases. Cytokines which is categorized into pro inflammatory and anti-inflammatory take part in as biomarkers in CHD, MI, HF. Troponin, growth differentiation factor-15(GDF-15), C-reactive protein, fibrinogen, uric acid diagnose MI and CAD. Matrix Metalloproteins, Cell Adhesion Molecules, Myeloperoxidase, Oxidative stress biomarkers, Incendiary biomarkers are useful to predict the risk of UA, MI, and HF. Increased Endothelin-1, Natriuretic peptides, copeptin, ST-2, Galectin-3, mid-regional-pro-adrenomedullin, catecholamines are used to prognosticate Heart failure. Modern technologies like Artificial Intelligence (AI), Biosensor and high-speed data communication made it possible to collect the high-resolution data in real time. The high-resolution data can be analyzed with advance Machine Learning (ML) algorithms, it will not only help to discover the disease patterns but also an real-time and objective monitoring of bio-signals can help to discover the unknown patterns linked with CVD.
Keywords
CVDs, Biomarkers, Artificial Intelligence, Machine Learning, Personalized medicine, Monitoring
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