scholarly journals Applications of Electronic-Nose Technologies for Noninvasive Early Detection of Plant, Animal and Human Diseases

Chemosensors ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 45 ◽  
Author(s):  
Alphus Dan Wilson
Keyword(s):  
Electronic Nose ◽  
Gas Sensing ◽  
Sensor Arrays ◽  
Human Diseases ◽  
Detection Methods ◽  
Research Progress ◽  
Disease Detection ◽  
New Information ◽  
Disease Diagnostics ◽  
Diagnostic Applications

The development of electronic-nose (e-nose) technologies for disease diagnostics was initiated in the biomedical field for detection of biotic (microbial) causes of human diseases during the mid-1980s. The use of e-nose devices for disease-diagnostic applications subsequently was extended to plant and animal hosts through the invention of new gas-sensing instrument types and disease-detection methods with sensor arrays developed and adapted for additional host types and chemical classes of volatile organic compounds (VOCs) closely associated with individual diseases. Considerable progress in animal disease detection using e-noses in combination with metabolomics has been accomplished in the field of veterinary medicine with new important discoveries of biomarker metabolites and aroma profiles for major infectious diseases of livestock, wildlife, and fish from both terrestrial and aquaculture pathology research. Progress in the discovery of new e-nose technologies developed for biomedical applications has exploded with new information and methods for diagnostic sampling and disease detection, identification of key chemical disease biomarkers, improvements in sensor designs, algorithms for discriminant analysis, and greater, more widespread testing of efficacy in clinical trials. This review summarizes progressive advancements in utilizing these specialized gas-sensing devices for numerous diagnostic applications involving noninvasive early detections of plant, animal, and human diseases.

scholarly journals Development of Electronic-Nose Technologies for Early Disease Detection Based on Microbial Dysbiosis

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 32 ◽  
Author(s):  
Alphus Dan Wilson ◽  
Lisa Beth Forse
Keyword(s):  
Electronic Nose ◽  
Sensor Arrays ◽  
The Body ◽  
Disease Detection ◽  
Disease Development ◽  
Human Immune System ◽  
Early Disease ◽  
Microbial Dysbiosis ◽  
Early Disease Detection ◽  
Metabolic Activities

A new frontier in clinical disease diagnostics was quietly launched by a series of recent discoveries of dysbiosis-related phenomena. These developments make important connections between the metabolic activities of resident microbes and human diseases. Numerous studies have demonstrated that biochemical mechanisms leading to disease development involve not only pathogenesis, but also interactions between microbiota in the oral cavity, lungs, and gut, as well as the microbial metabolites they produce, and the human immune system. Microbial dysbiosis (MD) or changes in commensal microbiota diversity and composition, often modulate disease development by at least two different mechanisms, including disease-induced dysbiosis and alterations in gut microbiota (GM), caused by abiotic and exogenous factors (diet, drug use, and environment). This paper summarizes recent evidence demonstrating how electronic-nose (e-nose) technologies with multi-sensor arrays and chemical-analysis capabilities could potentially be used for early diagnosis of certain diseases by identifying a new category of VOC-biomarker metabolites, called dysbiosis-related disease biomarkers (DRDBs). DRDBs are produced in specific locations of the body due to dysbiosis associated with specific diseases. Recent advances in e-nose technologies offer new tools for exploiting the common occurrence of MD for noninvasive early disease detection.

Behavior assessment and applications for BRD diagnosis: preweaned dairy calves

2020 ◽  
pp. 1-4
Author(s):  
Catie Cramer ◽  
Theresa L. Ollivett
Keyword(s):  
Clinical Signs ◽  
Sickness Behavior ◽  
Behavioral Changes ◽  
Dairy Calves ◽  
Detection Methods ◽  
Disease Detection ◽  
Test Characteristics ◽  
Behavioral Studies ◽  
Detection Program ◽  
Behavioral Assessments

Abstract Bovine respiratory disease (BRD) is an important disease in dairy calves due to its long-lasting effects. Early identification results in better outcomes for the animal, but producers struggle to identify all calves with BRD. Sickness behavior, or the behavioral changes that accompany illness, has been investigated for its usefulness as a disease detection tool. Behavioral changes associated with BRD include decreased milk intake and drinking speed, depressed attitude, and less likelihood of approaching a novel object or stationary human. Behavioral measurements are useful, as they can be collected automatically or with little financial input. However, one limitation of many BRD behavioral studies includes the use of either lung auscultation or clinical signs as reference methods, which are imperfect. Additionally, external factors may influence the expression of sickness behavior, which can affect if and when behavior can be used to identify calves with BRD. Behavioral measures available to detect BRD lack adequate sensitivity and specificity to be the sole means of disease detection, especially when detection tools, such as calf lung ultrasound, have better test characteristics. However, using behavioral assessments in addition to other detection methods can allow for a robust BRD detection program that can ameliorate the consequences of BRD.

scholarly journals Bimetallic Nanocrystals: Structure, Controllable Synthesis and Applications in Catalysis, Energy and Sensing

Nanomaterials ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1926
Author(s):  
Gaojie Li ◽  
Wenshuang Zhang ◽  
Na Luo ◽  
Zhenggang Xue ◽  
Qingmin Hu ◽  
...  
Keyword(s):  
Gas Sensing ◽  
Chemical Properties ◽  
Research Progress ◽  
Synthesis Methods ◽  
Controlled Synthesis ◽  
Controllable Synthesis ◽  
Composition And Structure ◽  
Bimetallic Nanocrystals ◽  
Physical And Chemical ◽  
And Chemical Properties

In recent years, bimetallic nanocrystals have attracted great interest from many researchers. Bimetallic nanocrystals are expected to exhibit improved physical and chemical properties due to the synergistic effect between the two metals, not just a combination of two monometallic properties. More importantly, the properties of bimetallic nanocrystals are significantly affected by their morphology, structure, and atomic arrangement. Reasonable regulation of these parameters of nanocrystals can effectively control their properties and enhance their practicality in a given application. This review summarizes some recent research progress in the controlled synthesis of shape, composition and structure, as well as some important applications of bimetallic nanocrystals. We first give a brief introduction to the development of bimetals, followed by the architectural diversity of bimetallic nanocrystals. The most commonly used and typical synthesis methods are also summarized, and the possible morphologies under different conditions are also discussed. Finally, we discuss the composition-dependent and shape-dependent properties of bimetals in terms of highlighting applications such as catalysis, energy conversion, gas sensing and bio-detection applications.

scholarly journals Spread and Increase of Ratoon Stunting Disease of Sugarcane and Comparison of Disease Detection Methods

Plant Disease ◽  
1999 ◽  
Vol 83 (12) ◽  
pp. 1170-1175 ◽  
Author(s):  
J. W. Hoy ◽  
M. P. Grisham ◽  
K. E. Damann
Keyword(s):  
Yield Loss ◽  
Detection Method ◽  
Xylem Sap ◽  
Disease Spread ◽  
Detection Methods ◽  
Disease Detection ◽  
Correct Identification ◽  
Dot Blot ◽  
Ratoon Stunting Disease ◽  
Enzyme Immunoassays

The spread and increase of ratoon stunting disease (RSD) resulting from two mechanical harvests were compared in eight sugarcane cultivars at two locations. RSD spread and increase were detected in the ratoon crops grown after each harvest and varied among cultivars and locations. Disease spread and increase were greater in plants grown from stalks collected at the first harvest than in the first ratoon growth from the harvested field. RSD infection was determined using five disease detection methods: alkaline-induced metaxylem autofluorescence; microscopic examination of xylem sap; and dot blot, evaporative-binding, and tissue blot enzyme immunoassays. The tissue blot enzyme immunoassay was the most accurate RSD detection method. The dot blot and evaporative-binding enzyme immunoassays were the least sensitive for detection of RSD-infected stalks, and alkaline-induced metaxylem autofluorescence was least accurate for correct identification of noninfected stalks. The results indicate that disease spread and increase are variable even among cultivars susceptible to yield loss due to RSD, and the greatest threat of disease spread and increase occurs at planting.

scholarly journals Curating Metal-Organic Frameworks to Compose Robust Gas Sensor Arrays in Dilute Conditions

2019 ◽  
Author(s):  
Arni Sturluson ◽  
Rachel Sousa ◽  
Yujing Zhang ◽  
Melanie T. Huynh ◽  
Caleb Laird ◽  
...  
Keyword(s):  
Sensor Array ◽  
Gas Sensing ◽  
Metal Organic Frameworks ◽  
Sensor Arrays ◽  
Gas Composition ◽  
Composition Space ◽  
The Matrix ◽  
Metal Organic ◽  
Analyte Detection ◽  
Value Decomposition

Metal-organic frameworks (MOFs)-- tunable, nano-porous materials-- are alluring recognition elements for gas sensing. Mimicking human olfaction, an array of cross-sensitive, MOF-based sensors could enable analyte detection in complex, variable gas mixtures containing confounding gas species. Herein, we address the question: given a set of MOF candidates and their adsorption properties, how do we select the optimal subset to compose a sensor array that accurately and robustly predicts the gas composition via monitoring the adsorbed mass in each MOF? We first mathematically formulate the MOF-based sensor array problem under dilute conditions. Instructively, the sensor array can be viewed as a linear map from <i>gas composition space</i> to <i>sensor array response space</i> defined by the matrix <b>H</b> of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of <b>H </b>is a useful tool for evaluating MOF subsets for sensor arrays, as it determines the sensitivity of the predicted gas composition to measurement error, quantifies the magnitude of the response to changes in composition, and recovers which direction in gas composition space elicits the largest/smallest response. To illustrate, on the basis of experimental adsorption data, we curate MOFs for a sensor array with the objective of determining the concentration of CO<sub>2</sub> and SO<sub>2</sub> in the gas phase.

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

THE USE OF GAS-SENSOR ARRAYS TO DIAGNOSE URINARY TRACT INFECTIONS

2005 ◽  
Vol 15 (05) ◽  
pp. 363-376 ◽  
Author(s):  
VASSILIS KODOGIANNIS ◽  
EDMUND WADGE
Keyword(s):  
Urinary Tract ◽  
Urinary Tract Infections ◽  
Electronic Nose ◽  
Radial Basis Function Network ◽  
Sensor Arrays ◽  
Test Tube ◽  
Classification Rate ◽  
Investigation Methods ◽  
Tract Infections

Sensorial analysis based on the utilisation of human senses, is one of the most important and straightforward investigation methods in food and chemical analysis. An electronic nose has been used to detect in vivo Urinary Tract Infections from 45 suspected cases that were sent for analysis in a UK Health Laboratory environment. These samples were analysed by incubation in a volatile generation test tube system for 4–5 h. The volatile production patterns were then analysed using an electronic nose system with 14 conducting polymer sensors. An intelligent model consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on learning techniques has been considered. The implementation of an Extended Normalised Radial Basis Function network with advanced features for determining its size and parameters and the concept of fusion of multiple classifiers dedicated to specific feature parameters has been also adopted in this study. The proposed scheme achieved a very high classification rate of the testing dataset, demonstrating in this way the efficiency of the proposed scheme compared with other approaches. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology.

scholarly journals Polymeric Nanowires for Diagnostic Applications

Micromachines ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 225 ◽  
Author(s):  
Hendrik Hubbe ◽  
Eduardo Mendes ◽  
Pouyan Boukany
Keyword(s):  
Electrical Signal ◽  
Considerable Progress ◽  
Detection Methods ◽  
Chemical Modifications ◽  
Single Wire ◽  
Related Research ◽  
Disordered Structures ◽  
Functional Part ◽  
Diagnostic Applications ◽  
Polymer Nanowires

Polymer nanowire-related research has shown considerable progress over the last decade. The wide variety of materials and the multitude of well-established chemical modifications have made polymer nanowires interesting as a functional part of a diagnostic biosensing device. This review provides an overview of relevant publications addressing the needs for a nanowire-based sensor for biomolecules. Working our way towards the detection methods itself, we review different nanowire fabrication methods and materials. Especially for an electrical signal read-out, the nanowire should persist in a single-wire configuration with well-defined positioning. Thus, the possibility of the alignment of nanowires is discussed. While some fabrication methods immanently yield an aligned single wire, other methods result in disordered structures and have to be manipulated into the desired configuration.

scholarly journals Correction: Disease-specific protein corona sensor arrays may have disease detection capacity

2020 ◽  
Vol 5 (2) ◽  
pp. 372-372
Author(s):  
Giulio Caracciolo ◽  
Reihaneh Safavi-Sohi ◽  
Reza Malekzadeh ◽  
Hossein Poustchi ◽  
Mahdi Vasighi ◽  
...  
Keyword(s):  
Sensor Arrays ◽  
Protein Corona ◽  
Specific Protein ◽  
Disease Detection ◽  
Disease Specific

Correction for ‘Disease-specific protein corona sensor arrays may have disease detection capacity’ by Giulio Caracciolo et al., Nanoscale Horiz., 2019, 4, 1063–1076.

scholarly journals Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 685 ◽  
Author(s):  
Han Fan ◽  
Victor Hernandez Bennetts ◽  
Erik Schaffernicht ◽  
Achim Lilienthal
Keyword(s):  
Emergency Response ◽  
Electronic Nose ◽  
Gas Sensing ◽  
Practical Significance ◽  
Computationally Efficient ◽  
Urban Search And Rescue ◽  
Efficient Manner ◽  
Distribution Maps ◽  
Gas Identification ◽  
Sampling Procedures

Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.

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