Exhaled air analysis as a potential fast method for early diagnosis of dengue disease

ABSTRACTDengue fever, caused by infections with the dengue virus (DENV), affects nearly 400 million people globally every year. Early diagnosis and management can reduce the morbidity and mortality rates of severe forms of dengue disease as well as decrease the risk of wider outbreaks. Although the early diagnosis of dengue can be achieved using a number of commercial NS1 detection kits, none of these can differentiate among the four dengue virus serotypes. In this study, we developed an enzyme-linked immunosorbent assay (ELISA) for the detection of dengue virus (DENV) NS1 by pairing a serotype-cross-reactive monoclonal antibody (MAb) with one of four serotype-specific MAbs in order to facilitate the rapid detection of NS1 antigens and the simultaneous differentiation of DENV serotypes. A total of 146 serum samples obtained from patients suspected to be in the acute phase of DENV infection were used to evaluate the clinical application of our novel test for the detection and serotyping of DENV. The overall sensitivity rate of our test was 84.85%, and the sensitivity rates for serotyping were as follows: 88.2% (15/17) for DENV serotype 1 (DENV1), 94.7% (18/19) for DENV2, 75% (12/16) for DENV3, and 66.6% (6/9) for DENV4. Moreover, there was no cross-reactivity among serotypes, and no cross-reactivity was observed in sera from nondengue patients. Thus, our test not only enables the rapid detection of the dengue virus but also can distinguish among the specific serotypes during the early stages of infection. These results indicate that our ELISA for DENV NS1 is a convenient tool that may help elucidate the epidemiology of DENV outbreaks and facilitate the clinical management of DENV infections.

Download Full-text

Exhaled Air Analysis in Patients with Different Lung Diseases Using Artificial Odour Sensors

Proceedings of the Latvian Academy of Sciences Section B Natural Exact and Applied Sciences ◽

10.2478/v10046-009-0043-4 ◽

2009 ◽

Vol 63 (4-5) ◽

pp. 210-213

Author(s):

Immanuels Taivans ◽

Normunds Jurka ◽

Līga Balode ◽

Māris Bukovskis ◽

Uldis Kopeika ◽

...

Keyword(s):

Lung Cancer ◽

Lung Diseases ◽

Clinical Medicine ◽

Chronic Obstructive ◽

Exhaled Breath ◽

Air Pollution Control ◽

Air Analysis ◽

Artificial Nose ◽

Chronic Lung Diseases ◽

Exhaled Air

Exhaled Air Analysis in Patients with Different Lung Diseases Using Artificial Odour Sensors Sniffing breath to diagnose a disease has been practiced by doctors since ancient times. Nowadays, electronic noses are successfully used in the food, textile and perfume industry as well as for air pollution control. The aim of this study was to test whether exhaled breath analysed by an artificial nose could identify and discriminate between different lung diseases. A total of 76 individuals were tested: 25 bronchial asthma, 19 lung cancer, 10 pneumonia, 6 chronic obstructive pulmonary disease (COPD) patients and 16 healthy volunteers. Exhaled air was collected in plastic bags and immediately analysed using an electronic nose instrument (9185, Nordic Sensors AB) containing 14 different odour sensors. Multifactor logistic regression analysis was used to determine correlation between the amplitudes of sensor responses and the clinical diagnoses of patients and to calculate sensitivity and specificity of the method for each diagnosis. For diagnostics of asthma the sensitivity was found to be 84% and specificity — 86%. For lung cancer, the sensitivity was 74% and specificity, 95%; for pneumonia 90% and 98%, but for COPD, 33% and 97%, respectively. We conclude that an artificial nose is able to discriminate among different lung diseases with sufficiently good accuracy. This method may be further developed to implement it in clinical medicine for express diagnostics of acute and chronic lung diseases.

Download Full-text

Detection of Asymptomatic Nigrostriatal Dopaminergic Lesion in Rats by Exhaled Air Analysis Using Carbon Nanotube Sensors

ACS Chemical Neuroscience ◽

10.1021/cn200093r ◽

2011 ◽

Vol 3 (3) ◽

pp. 161-166 ◽

Cited By ~ 37

Author(s):

Ulrike Tisch ◽

Yuval Aluf ◽

Radu Ionescu ◽

Morad Nakhleh ◽

Rana Bassal ◽

...

Keyword(s):

Carbon Nanotube ◽

Air Analysis ◽

Exhaled Air

Download Full-text

Early Diagnostics Model for Dengue Disease Using Decision Tree-Based Approaches

Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention - Advances in Medical Diagnosis, Treatment, and Care ◽

10.4018/978-1-5225-7131-5.ch003 ◽

2019 ◽

pp. 69-87 ◽

Cited By ~ 1

Author(s):

Shalini Gambhir ◽

Yugal Kumar ◽

Sanjay Malik ◽

Geeta Yadav ◽

Amita Malik

Keyword(s):

Logistic Regression ◽

Early Diagnosis ◽

Decision Tree ◽

Treatment Planning ◽

Real World ◽

Diagnostic System ◽

Early Diagnostics ◽

Classification Schemes ◽

Dengue Disease ◽

Statistical Metrics

Classification schemes have been applied in the medical arena to explore patients' data and extract a predictive model.This model helps doctors to improve their prognosis, diagnosis, or treatment planning processes.The aim of this work is to utilize and compare different decision tree classifiers for early diagnosis of Dengue. Six approaches, mainly J48 tree, random tree, REP tree, SOM, logistic regression, and naïve Bayes, have been utilized to study real-world Dengue data collected from different hospitals in the Delhi, India region during 2015-2016. Standard statistical metrics are used to assess the efficiency of the proposed Dengue disease diagnostic system, and the outcomes showed that REP tree is best among these classifiers with 82.7% efficient in supplying an exact diagnosis.

Download Full-text