Superradiative Plasmonic Nanoantenna Biosensors Enable Sensitive Immunoassay Using Naked Eyes

Nanoscale ◽  
2021 ◽  
Author(s):  
Yang Xiong ◽  
Tong Fu ◽  
Daxiao Zhang ◽  
Shunping Zhang ◽  
Hongxing Xu
Keyword(s):  
Disease Prediction ◽  
Biomarker Detection

Easy-to-use and sensitive quantification of biomarkers has great significance in disease prediction, diagnosis, and monitoring. Here, we report a biosensor for simple and sensitive biomarker detection based on the strong...

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

scholarly journals Single Step In Situ Detection of Surface Protein and MicroRNA in Clustered Extracellular Vesicles Using Flow Cytometry

2021 ◽  
Vol 10 (2) ◽  
pp. 319
Author(s):  
Hee Cheol Yang ◽  
Won Jong Rhee
Keyword(s):  
Extracellular Vesicles ◽  
Liquid Biopsy ◽  
Molecular Beacon ◽  
Disease Diagnosis ◽  
Single Step ◽  
Flow Cytometer ◽  
Surface Proteins ◽  
Biomarker Detection ◽  
In Situ Detection

Because cancers are heterogeneous, it is evident that multiplexed detection is required to achieve disease diagnosis with high accuracy and specificity. Extracellular vesicles (EVs) have been a subject of great interest as sources of novel biomarkers for cancer liquid biopsy. However, EVs are nano-sized particles that are difficult to handle; thus, it is necessary to develop a method that enables efficient and straightforward EV biomarker detection. In the present study, we developed a method for single step in situ detection of EV surface proteins and inner miRNAs simultaneously using a flow cytometer. CD63 antibody and molecular beacon-21 were investigated for multiplexed biomarker detection in normal and cancer EVs. A phospholipid-polymer-phospholipid conjugate was introduced to induce clustering of the EVs analyzed using nanoparticle tracking analysis, which enhanced the detection signals. As a result, the method could detect and distinguish cancer cell-derived EVs using a flow cytometer. Thus, single step in situ detection of multiple EV biomarkers using a flow cytometer can be applied as a simple, labor- and time-saving, non-invasive liquid biopsy for the diagnosis of various diseases, including cancer.

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