scholarly journals Recent Trends in Exhaled Breath Diagnosis Using an Artificial Olfactory System

Biosensors ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 337
Author(s):  
Chuntae Kim ◽  
Iruthayapandi Selestin Raja ◽  
Jong-Min Lee ◽  
Jong Ho Lee ◽  
Moon Sung Kang ◽  
...  
Keyword(s):  
Real Time ◽  
Technology Development ◽  
Early Stage ◽  
Development Trend ◽  
Disease Diagnosis ◽  
Health Conditions ◽  
Exhaled Breath ◽  
Human Breath ◽  
Gas Sensor Array ◽  
Olfactory Systems

Artificial olfactory systems are needed in various fields that require real-time monitoring, such as healthcare. This review introduces cases of detection of specific volatile organic compounds (VOCs) in a patient’s exhaled breath and discusses trends in disease diagnosis technology development using artificial olfactory technology that analyzes exhaled human breath. We briefly introduce algorithms that classify patterns of odors (VOC profiles) and describe artificial olfactory systems based on nanosensors. On the basis of recently published research results, we describe the development trend of artificial olfactory systems based on the pattern-recognition gas sensor array technology and the prospects of application of this technology to disease diagnostic devices. Medical technologies that enable early monitoring of health conditions and early diagnosis of diseases are crucial in modern healthcare. By regularly monitoring health status, diseases can be prevented or treated at an early stage, thus increasing the human survival rate and reducing the overall treatment costs. This review introduces several promising technical fields with the aim of developing technologies that can monitor health conditions and diagnose diseases early by analyzing exhaled human breath in real time.

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.

scholarly journals Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on AWS DeepLens

2020 ◽  
Author(s):  
Asim Khan ◽  
Umair Nawaz ◽  
Anwaar Ulhaq ◽  
Randall W. Robinson
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Fruits And Vegetables ◽  
Early Stage ◽  
Economic Loss ◽  
Disease Diagnosis ◽  
Data Set ◽  
Plant Leaf ◽  
Leaf Disease

In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on A.W.S. DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using A.W.S. DeepLens on average took 0.349s, providing disease information to the user in less than a second.

scholarly journals Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on A.W.S. DeepLens

2020 ◽  
Author(s):  
Asim Khan ◽  
Umair Nawaz ◽  
Anwaar Ulhaq ◽  
Randall W. Robinson
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Fruits And Vegetables ◽  
Early Stage ◽  
Economic Loss ◽  
Disease Diagnosis ◽  
Data Set ◽  
Plant Leaf ◽  
Leaf Disease

In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (D.C.D.M.) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on A.W.S. DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using A.W.S. DeepLens on average took 0.349s, providing disease information to the user in less than a second.

Highly stable Pd/HNb3O8-based flexible humidity sensor for perdurable wireless wearable applications

2021 ◽  
Author(s):  
Yuyao Lu ◽  
Kaichen Xu ◽  
Min-Quan Yang ◽  
Shin-Yi Tang ◽  
Tzu-Yi Yang ◽  
...  
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Humidity Sensor ◽  
Early Stage ◽  
Patient Data ◽  
Health Conditions

Real-time, daily health monitoring can provide large amounts of patient data, which may greatly improve the likelihood of diagnosing health conditions in the early stage. One potential sensor is a...

scholarly journals Real-Time Plant Health Assessment via implementing Cloud-Based Scalable Transfer Learning on AWS DeepLens

2020 ◽  
Author(s):  
Asim Khan ◽  
Umair Nawaz ◽  
Anwaar Ulhaq ◽  
Randall W. Robinson
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Fruits And Vegetables ◽  
Early Stage ◽  
Economic Loss ◽  
Disease Diagnosis ◽  
Data Set ◽  
Plant Leaf ◽  
Leaf Disease

In the Agriculture sector, control of plant leaf diseases is crucial as it influences the quality and production of plant species with an impact on the economy of any country. Therefore, automated identification and classification of plant leaf disease at an early stage is essential to reduce economic loss and to conserve the specific species. Previously, to detect and classify plant leaf disease, various Machine Learning models have been proposed; however, they lack usability due to hardware incompatibility, limited scalability and inefficiency in practical usage. Our proposed DeepLens Classification and Detection Model (DCDM) approach deal with such limitations by introducing automated detection and classification of the leaf diseases in fruits (apple, grapes, peach and strawberry) and vegetables (potato and tomato) via scalable transfer learning on A.W.S. SageMaker and importing it on AWS DeepLens for real-time practical usability. Cloud integration provides scalability and ubiquitous access to our approach. Our experiments on extensive image data set of healthy and unhealthy leaves of fruits and vegetables showed an accuracy of 98.78% with a real-time diagnosis of plant leaves diseases. We used forty thousand images for the training of deep learning model and then evaluated it on ten thousand images. The process of testing an image for disease diagnosis and classification using AWS DeepLens on average took 0.349s, providing disease information to the user in less than a second.

A Proof for the Possibility of Ce-Oxide from CMP Residuals in Si-Wafers by Analytical TEM

2012 ◽  
Author(s):  
Wayne Zhao ◽  
Liem Do Thanh ◽  
Michael Gribelyuk ◽  
Mary-Ann Zaitz ◽  
Wing Lai
Keyword(s):  
Technology Development ◽  
Early Stage ◽  
Chemical Mechanical Planarization ◽  
Particle Cluster ◽  
Limited Region ◽  
Physical Evidence ◽  
Analytical Tem ◽  
Transmission Electron ◽  
Oxide Particles

Abstract Inclusion of cerium (Ce) oxide particles as an abrasive into chemical mechanical planarization (CMP) slurries has become popular for wafer fabs below the 45nm technology node due to better polishing quality and improved CMP selectivity. Transmission electron microscopy (TEM) has difficulties finding and identifying Ce-oxide residuals due to the limited region of analysis unless dedicated efforts to search for them are employed. This article presents a case study that proved the concept in which physical evidence of Ce-rich particles was directly identified by analytical TEM during a CMP tool qualification in the early stage of 20nm node technology development. This justifies the need to setup in-fab monitoring for trace amounts of CMP residuals in Si-based wafer foundries. The fact that Cr resided right above the Ce-O particle cluster, further proved that the Ce-O particles were from the wafer and not introduced during the sample preparation.

scholarly journals Photonic Crystal Nanobeam Cavities for Nanoscale Optical Sensing: A Review

Micromachines ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 72 ◽  
Author(s):  
Da-Quan Yang ◽  
Bing Duan ◽  
Xiao Liu ◽  
Ai-Qiang Wang ◽  
Xiao-Gang Li ◽  
...  
Keyword(s):  
Photonic Crystal ◽  
Optical Waveguides ◽  
Optical Sensing ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Label Free ◽  
Quality Factors ◽  
Integrated Sensor ◽  
Early Stage Disease ◽  
Wide Range

The ability to detect nanoscale objects is particular crucial for a wide range of applications, such as environmental protection, early-stage disease diagnosis and drug discovery. Photonic crystal nanobeam cavity (PCNC) sensors have attracted great attention due to high-quality factors and small-mode volumes (Q/V) and good on-chip integrability with optical waveguides/circuits. In this review, we focus on nanoscale optical sensing based on PCNC sensors, including ultrahigh figure of merit (FOM) sensing, single nanoparticle trapping, label-free molecule detection and an integrated sensor array for multiplexed sensing. We believe that the PCNC sensors featuring ultracompact footprint, high monolithic integration capability, fast response and ultrahigh sensitivity sensing ability, etc., will provide a promising platform for further developing lab-on-a-chip devices for biosensing and other functionalities.

scholarly journals Real Time Breath Analysis Using Portable Gas Chromatography for Adult Asthma Phenotypes

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 265
Author(s):  
Ruchi Sharma ◽  
Wenzhe Zang ◽  
Menglian Zhou ◽  
Nicole Schafer ◽  
Lesa A. Begley ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Real Time ◽  
Rapid Assessment ◽  
Principal Component ◽  
Corticosteroid Treatment ◽  
Breath Analysis ◽  
Blood Eosinophil ◽  
Exhaled Breath ◽  
Respiratory Illnesses ◽  
Linear Discriminant

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

scholarly journals Testing the Mig-HealthCare Algorithm in Greece

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
E Riza ◽  
P Karnaki ◽  
D Zota ◽  
A Linos
Keyword(s):  
Health Care ◽  
Early Stage ◽  
Health Care Systems ◽  
Health Conditions ◽  
Health Issues ◽  
Host Countries ◽  
Local Hospital ◽  
Raising Awareness ◽  
Significant Burden ◽  
Care Systems

Abstract The Mig-HealthCare Algorithm is a tool, comprising a set of questions developed with the aim to (a) guide the user on how to access all the categories and tools that are available through the Roadmap & Toolbox (b) help the user identify the health issues of importance when providing care to a specific migrant/refugee. At the end of a series of questions, a brief report summarizing the main outcomes is generated. The algorithm was tested in Greece in two mainland reception centres and a local hospital in an area serving migrants/refugees. Results discuss the usefulness of the algorithm for improving the delivery of appropriate health services to migrants/refugees and its importance in raising awareness about the health conditions which are crucial for migrants/refugees and are expected to pose a significant burden on the health care systems of host countries unless dealt with adequately at an early stage.

scholarly journals Holistic Metabolomic Laboratory-Developed Test (LDT): Development and Use for the Diagnosis of Early-Stage Parkinson’s Disease

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 14
Author(s):  
Petr G. Lokhov ◽  
Dmitry L. Maslov ◽  
Steven Lichtenberg ◽  
Oxana P. Trifonova ◽  
Elena E. Balashova
Keyword(s):  
Parkinson’S Disease ◽  
Molecular Weight ◽  
Parkinson's Disease ◽  
High Resolution Mass Spectrometry ◽  
Early Stage ◽  
Disease Diagnosis ◽  
The Body ◽  
Low Molecular Weight ◽  
Low Molecular Weight Compounds

A laboratory-developed test (LDT) is a type of in vitro diagnostic test that is developed and used within a single laboratory. The holistic metabolomic LDT integrating the currently available data on human metabolic pathways, changes in the concentrations of low-molecular-weight compounds in the human blood during diseases and other conditions, and their prevalent location in the body was developed. That is, the LDT uses all of the accumulated metabolic data relevant for disease diagnosis and high-resolution mass spectrometry with data processing by in-house software. In this study, the LDT was applied to diagnose early-stage Parkinson’s disease (PD), which currently lacks available laboratory tests. The use of the LDT for blood plasma samples confirmed its ability for such diagnostics with 73% accuracy. The diagnosis was based on relevant data, such as the detection of overrepresented metabolite sets associated with PD and other neurodegenerative diseases. Additionally, the ability of the LDT to detect normal composition of low-molecular-weight compounds in blood was demonstrated, thus providing a definition of healthy at the molecular level. This LDT approach as a screening tool can be used for the further widespread testing for other diseases, since ‘omics’ tests, to which the metabolomic LDT belongs, cover a variety of them.

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