scholarly journals Detection of Disease-Specific Volatile Organic Compounds Using Infrared Spectroscopy

2021 ◽  
Vol 8 (1) ◽  
pp. 15
Author(s):  
Kiran Sankar Maiti ◽  
Susmita Roy ◽  
Renée Lampe ◽  
Alexander Apolonski
Keyword(s):  
Infrared Spectroscopy ◽  
Early Stage ◽  
Sample Collection ◽  
Early Detection Of Disease ◽  
Noninvasive Diagnostics ◽  
Volatile Organic ◽  
Acute Gastritis ◽  
Life Threatening ◽  
Asymptomatic Stage ◽  
Disease Specific

Many life-threatening diseases at an early stage remain unrecognized due to a lack of pronounced symptoms. It is also accepted that the early detection of disease is a key ingredient for saving many lives. Unfortunately, in most of the cases, diagnostics implies an invasive sample collection, being problematic at the asymptomatic stage. Infrared spectroscopy of breath offers reliable noninvasive diagnostics at every stage and has already been tested for several diseases. This approach offers not only the detection of specific metabolites, but also the analysis of their imbalance and transportation. In this article, the power of infrared spectroscopy is demonstrated for diabetes, cerebral palsy, acute gastritis caused by bacterial infection, and prostate cancer.

scholarly journals Fast-Onset Diffuse Interstitial Lung Disease in Anti-MDA5 Antibodies-Associated Amyopathic Dermatomyositis

2021 ◽  
Vol 11 (2) ◽  
pp. 235-240
Author(s):  
Houari Aissaoui ◽  
Kinan Drak Alsibai ◽  
Naji Khayath
Keyword(s):  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Distress Syndrome ◽  
Early Stage ◽  
Immunosuppressive Treatment ◽  
Amyopathic Dermatomyositis ◽  
Pulmonary Manifestations ◽  
Life Threatening ◽  
Adequate Management ◽  
Chest Ct Scan

Anti-MDA5 antibodies-associated amyopathic dermatomyositisis a rare autoimmune disease that involve polyarthritis, cutaneous and pulmonary manifestations. The development of rapidly progressing interstitial lung disease is a life-threatening complication. We report the case of a 45-year-old woman without medical history, who was addressed to the Pulmonary Department for a polyarthritis with dry cough and hypoxemic dyspnea. Initially there was neither cutaneous manifestation nor interstitial lung disease on chest CT scan. After a few days, the patient developed fatal acute respiratory failure with diffuse ground glass opacities. Identification of anti-MDA5 antibodies allowed establishing diagnosis, despite the fact that the first immunological assessment was negative. Corticosteroid bolus of 1 g for three days and immunosuppressive treatment by cyclophosphamide was only initiated at the acute respiratory distress syndrome stage. Given the rapidly unfavorable prognosis of this entity of amyopathic dermatomyositis, the testing for anti-MDA5 antibodies should be recommended in case of progressive pulmonary symptoms associated with joint signs in order to identify this disease at an early stage and to begin rapid and adequate management.

scholarly journals Analysis of Torque Ripples of an Induction Motor Taking into Account a Inter-Turn Short-Circuit in a Stator Winding

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3626 ◽  
Author(s):  
Wojciech Pietrowski ◽  
Konrad Górny
Keyword(s):  
Early Stage ◽  
Short Circuit ◽  
Torque Ripple ◽  
Drive System ◽  
Electrical Machines ◽  
Stator Winding ◽  
Permanent Magnet Synchronous Machines ◽  
Noninvasive Diagnostics ◽  
Drive Systems ◽  
Commercial Applications

Despite the increasing popularity of permanent magnet synchronous machines, induction motors (IM) are still the most frequently used electrical machines in commercial applications. Ensuring a failure-free operation of IM motivates research aimed at the development of effective methods of monitoring and diagnostic of electrical machines. The presented paper deals with diagnostics of an IM with failure of an inter-turn short-circuit in a stator winding. As this type of failure commonly does not lead immediately to exclusion of a drive system, an early stage diagnosis of inter-turn short-circuit enables preventive maintenance and reduce the costs of a whole drive system failure. In the proposed approach, the early diagnostics of IM with the inter-turn short-circuit is based on the analysis of an electromagnetic torque waveform. The research is based on an elaborated numerical field–circuit model of IM. In the presented model, the inter-turn short-circuit in the selected winding has been accounted for. As the short-circuit between the turns can occur in different locations in coils of winding, computations were carried out for various quantity of shorted turns in the winding. The performed analysis of impact of inter-turn short-circuit on torque waveforms allowed to find the correlation between the quantity of shorted turns and torque ripple level. This correlation can be used as input into the first layer of an artificial neural network in early and noninvasive diagnostics of drive systems.

scholarly journals Genomic Profile of Early Stage Node-Negative Luminal Breast Cancer Associated with Short Disease-Specific Survival

2014 ◽  
Vol 25 ◽  
pp. iv95
Author(s):  
A. Durigova ◽  
P. Tsantoulis ◽  
R. Lyle ◽  
C. Borel ◽  
G. Fioretta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Luminal Breast Cancer ◽  
Genomic Profile ◽  
Disease Specific Survival ◽  
Node Negative ◽  
Disease Specific

scholarly journals Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rosa Alba Sola Martínez ◽  
José María Pastor Hernández ◽  
Gema Lozano Terol ◽  
Julia Gallego-Jara ◽  
Luis García-Marcos ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Data Preprocessing ◽  
Exhaled Breath ◽  
Sample Collection ◽  
Clinical Tool ◽  
Chronic Respiratory Diseases ◽  
Current State ◽  
Exhaled Breath Analysis ◽  
High Prevalence ◽  
Volatile Organic

AbstractThe noninvasive diagnosis and monitoring of high prevalence diseases such as cardiovascular diseases, cancers and chronic respiratory diseases are currently priority objectives in the area of health. In this regard, the analysis of volatile organic compounds (VOCs) has been identified as a potential noninvasive tool for the diagnosis and surveillance of several diseases. Despite the advantages of this strategy, it is not yet a routine clinical tool. The lack of reproducible protocols for each step of the biomarker discovery phase is an obstacle of the current state. Specifically, this issue is present at the data preprocessing step. Thus, an open source workflow for preprocessing the data obtained by the analysis of exhaled breath samples using gas chromatography coupled with single quadrupole mass spectrometry (GC/MS) is presented in this paper. This workflow is based on the connection of two approaches to transform raw data into a useful matrix for statistical analysis. Moreover, this workflow includes matching compounds from breath samples with a spectral library. Three free packages (xcms, cliqueMS and eRah) written in the language R are used for this purpose. Furthermore, this paper presents a suitable protocol for exhaled breath sample collection from infants under 2 years of age for GC/MS.

scholarly journals DFT Study on Adsorption of Volatile Organic Compounds on Silicene

10.29007/bcpg ◽  
2020 ◽  
Author(s):  
Trong Lam Pham ◽  
Van On Vo ◽  
Van An Dinh
Keyword(s):  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Potential Energy Surfaces ◽  
Early Stage ◽  
Volume Ratio ◽  
Simulation Method ◽  
Early Diagnosis Of Cancer ◽  
Volatile Organic ◽  
Energy Surfaces ◽  
New Sensors

Cancer can be regarded as a rising threat to modern societies. Detecting cancer at an early stage significantly improves the durability of the disease; unfortunately, currently available methods for early diagnosis of cancer are scarce and inefficient. In fact, the concentration of Volatile Organic Compounds (VOCs) in cancer patients in the breath is different from that in normal people. Therefore, the development of new sensors that can detect VOCs with low concentrations at the early stage of cancer, is desirable. 2D materials are expected as attractive materials for these sensors due to their large surface area to volume ratio. In this work, we investigated the adsorption mechanism of some small-to-medium VOCs on the surface of silicene by the quantum simulation method. The images of the potential energy surfaces for different positions of the adsorbate on the silicene surface were explored by Computational DFT-based Nanoscope for the determination of the most stable configurations and diffusion possibilities. The adsorption energy profiles were calculated by three approximations of van der Waals interaction: revPBE-vdW, optPBE-vdW, and vdW-DF2. It is found that the adsorption energies of the VOCs in question vary in the range of 0.6-1.0 eV, which indicates that silicene is considerably sensitive with these VOCs. The charge transfer between the substrate and VOCs was also addressed.

scholarly journals Role of Machine Learning Approach for Detection and Classification of Diseases in Cotton Plant

2021 ◽  
Vol 12 (5) ◽  
pp. 810-817
Author(s):  
Sandhya N. dhage, Dr. Vijay Kumar Garg
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Cotton Plant ◽  
Early Stage ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Early Detection Of Disease ◽  
Cotton Crop ◽  
Machine Learning Approach ◽  
Crop Diseases

Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires  regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming  too late for diseases to be cured.  Lack of early detection of diseases causes the diseases to be spread in nearby crops in the field and also spraying of pesticides is done on entire field for minimizing the infection of disease. The main goal of proposed research topic is to find the solution to the agriculture problem which involves detecting disease in cotton plant at early stage and classify the disease based on symptoms. Early detection of disease at an early stage prevent it from spreading to another area and preventive measures can be taken by farmers by spraying pesticides to control its growth which helps to increase the cotton yield production. Automatic identification of the different diseases affecting cotton crop will give many benefits to the farmers so that time, money will be saved and also gives healthy life to the crop. The contribution of this paper is to present the machine learning approach used for cotton crop disease diagnosis and classification.

scholarly journals Analysis of infrared spectra of blood serum of patients with multiple myeloma

2021 ◽  
Vol 2103 (1) ◽  
pp. 012052
Author(s):  
D A Chernyshev ◽  
E S Mikhailets ◽  
E A Telnaya ◽  
L V Plotnikova ◽  
A D Garifullin ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Blood Serum ◽  
Infrared Spectra ◽  
Early Stage ◽  
Principal Component ◽  
Healthy Donors ◽  
Components Analysis ◽  
Serious Disease

Abstract Multiple myeloma (MM) is a serious disease that is difficult to diagnose especially at early stage. Infrared spectroscopy is a promising approach for diagnosing MM. The principal component analysis (PCA) allows us to reduce the dimension of the data and keep only the important variables. In this study, we apply principal components analysis to infrared (IR) spectra of blood serum from healthy donors and multiple myeloma patients. As a result of the analysis by PCA, it was possible to visualize the separation of patient’s and donor’s samples into two clusters. The result indicates that this method is potentially applicable for diagnosis of multiple myeloma.

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