scholarly journals CPT1A: the future of heart disease detection and personalized medicine?

2014 ◽  
Vol 9 (1) ◽  
pp. 9-12 ◽  
Author(s):  
Marguerite Ryan Irvin ◽  
Stella Aslibekyan ◽  
Bertha Hidalgo ◽  
Donna K Arnett
Keyword(s):  
Heart Disease ◽  
Personalized Medicine ◽  
Disease Detection ◽  
The Future ◽  
And Personalized Medicine

The future of glioma treatment: stem cells, nanotechnology and personalized medicine

2012 ◽  
Vol 8 (9) ◽  
pp. 1149-1156 ◽  
Author(s):  
Maryam Rahman ◽  
Brian Hoh ◽  
Nathan Kohler ◽  
Erin M Dunbar ◽  
Gregory JA Murad
Keyword(s):  
Stem Cells ◽  
Personalized Medicine ◽  
The Future ◽  
And Personalized Medicine

scholarly journals The Future State of Newborn Stem Cell Banking

2019 ◽  
Vol 8 (1) ◽  
pp. 117 ◽  
Author(s):  
Katherine Brown ◽  
Mahendra Rao ◽  
Heather Brown
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Personalized Medicine ◽  
Blood Banks ◽  
Cell Banking ◽  
The Future ◽  
Future Potential ◽  
Stem Cell Banking ◽  
Cord Blood Banks ◽  
And Personalized Medicine

Newborn stem cell banking began with the establishment of cord blood banks more than 25 years ago. Over the course of nearly three decades, there has been considerable evolution in the clinical application of stem cells isolated from newborn tissues. The industry now finds itself at an inflection point as personalized medicine and regenerative medicine continue to advance. In this review, we summarize our perspective on newborn stem cell banking in the context of the future potential that stem cells from perinatal tissues are likely to play in nascent applications. Specifically, we describe the relevance of newborn stem cell banking and how the cells stored can be utilized as starting material for the next generation of advanced cellular therapies and personalized medicine.

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

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