scholarly journals Magnetic Particles for Advanced Molecular Diagnosis

Materials ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2158 ◽  
Author(s):  
Cristina Chircov ◽  
Alexandru Mihai Grumezescu ◽  
Alina Maria Holban
Keyword(s):  
Gene Expression ◽  
Infectious Disease ◽  
Nucleic Acid ◽  
Sample Preparation ◽  
Molecular Diagnosis ◽  
Magnetic Particles ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Laboratory Medicine ◽  
Clinical Reality

Molecular diagnosis is the field that aims to develop nucleic-acid-based analytical methods for biological markers and gene expression assessments by combining laboratory medicine and molecular genetics. As it gradually becomes a clinical reality, molecular diagnosis could benefit from improvements resulting from thorough studies that could enhance the accuracy of these methods. The application of magnetic particles in molecular diagnosis tools has led to tremendous breakthroughs in terms of specificity, sensitivity, and discrimination in bioassays. Therefore, the aim of this review is to highlight the principles involved in the implementation of magnetic particles for sample preparation and targeted analyte isolation, purification, and extraction. Furthermore, the most recent advancements in the area of cancer and infectious disease diagnosis are presented, with an emphasis on screening and early stage detection.

A fully-integrated and automated testing device for PCR-free viral nucleic acid detection in whole blood

Lab on a Chip ◽  
2018 ◽  
Vol 18 (13) ◽  
pp. 1928-1935 ◽  
Author(s):  
Wenhan Liu ◽  
Jagotamoy Das ◽  
Adam H. Mepham ◽  
Carine R. Nemr ◽  
Edward H. Sargent ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Nucleic Acid ◽  
Disease Diagnosis ◽  
Automated Testing ◽  
Nucleic Acid Detection ◽  
Nucleic Acid Testing ◽  
Testing Device ◽  
Viral Nucleic Acid ◽  
Fully Integrated ◽  
Integrated Devices

Integrated devices for automated nucleic acid testing (NAT) are critical for infectious disease diagnosis to be performed outside of centralized laboratories.

Magnetic particles for in vitro molecular diagnosis: From sample preparation to integration into microsystems

2017 ◽  
Vol 158 ◽  
pp. 1-8 ◽  
Author(s):  
Tienrat Tangchaikeeree ◽  
Duangporn Polpanich ◽  
Abdelhamid Elaissari ◽  
Kulachart Jangpatarapongsa
Keyword(s):  
Sample Preparation ◽  
Molecular Diagnosis ◽  
Magnetic Particles

scholarly journals Paper microfluidics for nucleic acid amplification testing (NAAT) of infectious diseases

Lab on a Chip ◽  
2017 ◽  
Vol 17 (14) ◽  
pp. 2347-2371 ◽  
Author(s):  
Laura Magro ◽  
Camille Escadafal ◽  
Pierre Garneret ◽  
Béatrice Jacquelin ◽  
Aurélia Kwasiborski ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Nucleic Acid ◽  
Sample Preparation ◽  
Infectious Diseases ◽  
Nucleic Acid Amplification ◽  
Paper Microfluidics ◽  
Nucleic Acid Amplification Testing ◽  
Disease Diagnostics ◽  
Infectious Disease Diagnostics

On-field infectious disease diagnostics can be performed with paper microfluidics through sample preparation and nucleic acid amplification.

ECG Analysis: A Brief Review

2019 ◽  
Vol 12 ◽  
Author(s):  
Saumendra Kumar Mohapatra ◽  
Mihir Narayan Mohanty
Keyword(s):  
Technology Development ◽  
Accurate Result ◽  
Early Stage ◽  
Heart Diseases ◽  
Short Review ◽  
Disease Diagnosis ◽  
Accurate Analysis ◽  
Ecg Analysis ◽  
Proper Analysis ◽  
Living Being

Background: In recent years cardiac problems found proportional to technology development. Cardiac signal (Electrocardiogram) relates to the electrical activity of the heart of a living being and it is an important tool for diagnosis of heart diseases. Method: Accurate analysis of ECG signal can provide support for detection, classification, and diagnosis. Physicians can detect the disease and start the diagnosis at an early stage. Apart from cardiac disease diagnosis ECG can be used for emotion recognition, heart rate detection, and biometric identification. Objective: The objective of this paper is to provide a short review of earlier techniques used for ECG analysis. It can provide support to the researchers in a new direction. The review is based on preprocessing, feature extraction, classification, and different measuring parameters for accuracy proof. Also, different data sources for getting the cardiac signal is presented and various application area of the ECG analysis is presented. It explains the work from 2008 to 2018. Conclusion: Proper analysis of the cardiac signal is essential for better diagnosis. In automated ECG analysis, it is essential to get an accurate result. Numerous techniques were addressed by the researchers for the analysis of ECG. It is important to know different steps related to ECG analysis. A review is done based on different stages of ECG analysis and its applications in society.

An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

2019 ◽  
Vol 21 (9) ◽  
pp. 631-645 ◽  
Author(s):  
Saeed Ahmed ◽  
Muhammad Kabir ◽  
Zakir Ali ◽  
Muhammad Arif ◽  
Farman Ali ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Accuracy ◽  
Early Stage ◽  
Small Sample Size ◽  
Feature Selection Method ◽  
Small Sample ◽  
Expression Data ◽  
Base Function

Aim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.

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