Electronic nose versus quadrupole mass spectrometry for identifying viral hepatitis C patients.

Hepatitis C is a leading cause of liver disease and transplantation and is a significant burden on public health worldwide. This study aimed to apply the Electronic Nose (E-Nose) and quadrupole Mass Spectrometry (MS/MS) technologies for screening blood samples from hepatitis C patients and healthy controls. We analysed volatile organic compounds (VOCs) in the headspace over blood samples to identify those VOCs characteristic for diagnosing hepatitis C patients. The study comprised 150 acute hepatitis C patients with age range: 24-59 years, and mean age ±SD: 41.5 ± 12.8 years and 150 age-matched healthy controls (age range: 24-51 and mean age: 40.11 ± 4.89 years) from the Hospital of the Medical Research Institute, Alexandria University, Alexandria, Egypt. Collected blood samples were analysed qualitatively and quantitatively using the E-Nose and MS/MS techniques, respectively. Principal component analysis of the E-Nose 10-sensor responses accurately classified blood samples from hepatitis C patients and healthy controls. The first two principal components explained over 98.35% of the variance in signals with no false-positive (healthy controls) or false-negative (hepatitis C patients) results. MS/MS showed two fragmentation ions at m/z of 104 and 151 Da with the positive electrospray ionization mode (ESI+) in blood samples for hepatitis C patients, but not for healthy controls or background water samples. We identified the two specific fragmentation ions at m/z 104 and m/z 151 Da as malonic acid (MF: C3 H4 O4 ; MW: 104.06 g/mol) and monosaccharide pentose (MF: C5 H10 O5 ; MW: 150.13 g/mol) in VOCs of the headspace over blood samples for hepatitis C patients. This provides a rationale for developing diagnostic tests for hepatitis C virus based on altered trace VOCs concentrations using the relatively inexpensive, easy-to-use, portable and non-invasive E-Nose technology.

Volatile organic compounds of biofluids for detecting lung cancer by an electronic nose based on artificial neural network.

Lung cancer (LC) incidence represents 11.5% of all new cancers, resulting in 1.72 million deaths worldwide in 2015. With the aim to investigate the capability of the electronic nose (e-nose) technology for detecting and differentiating complex mixtures of volatile organic compounds in biofluids ex-vivo, we enrolled 50 patients with suspected LC and 50 matching controls. Tissue biopsy was taken from suspicious lung mass for histopathological evaluation and blood, exhaled breath, and urine samples were collected from all participants and qualitatively processed using e-nose. Odor-print patterns were further analysed using the principal component analysis (PCA) and artificial neural network (ANN) analysis. Adenocarcinoma, non-small cell LC and squamous cell carcinoma were the predominant pathological types among LC patients. PCA cluster-plots showed a clear distinction between LC patients and controls for all biological samples; where the overall success ratios of classification for principal components #1 and #2 were: 95.46, 82.01, and 91.66% for blood, breath and urine samples, respectively. Moreover, ANN showed a better discrimination between LC patients and controls with success ratios of 95.74, 91.67 and 100% for blood, breath and urine samples, respectively. The e-nose is an easy noninvasive tool, capable of identifying LC patients from controls with great precision.

A Novel Morphological Analysis of DXA-DICOM Images by Artificial Neural Networks for Estimating Bone Mineral Density in Health and Disease.

One of the best methods for diagnosing bone disease in humans is site-specific and total bone mineral density (BMD) measurements by Dual-energy X-ray Absorptiometry (DXA) machines. The basic disadvantage of this technology is inconsistent BMD measurements among different DXA machines from different manufacturers due to different image analysis algorithms. The objective of the present study was to apply artificial neural networks (ANNs) to estimate total BMD for diagnosing a population of Egyptians with and without pathology, using extracted features from DXA-DICOM images based on the Histogram and Binary algorithms as compared to reference BMD measurements by DXA machine. The sample size comprised 3000 male and female participants with an age range 22-49 years, who were referred to us for diagnosis and/or treatment and for DXA total body scans in the period from January 2016 till December 2017. We constructed an entry computer data-logging visible unit, where we applied morphological operations to get a specific bone image, and used their extracted feature vectors as inputs to ANNs with cascade training, gathering, and testing for DXA-DICOM image processing. The multilayer feed-forward ANN set up its initial weights, carried out training and initiated the recall mode, and finally observed its decision and interaction based on estimated BMD. The ANN construction was carried out using a 3-layer architecture, with one hidden layer of 85 neurons. The input layer has neuron numbers equal to 256 for the Histogram and 77,365 for Binary algorithms, respectively. Total BMD estimation performance based on the Binary algorithm was capable of identifying all DXA-DICOM images with an accuracy of 100% for the training, cross-validation, and testing of the ANN phases. We believe this strategy will represent the means for standardizing bone measurements of all DXA machines, regardless of the manufacturer.

Comparative modeling of combined transport of water and graded-size molecules across the glomerular capillary wall.

Chronic kidney disease is a common and growing problem worldwide that necessitates recognition of individual risk and appropriate laboratory testing before its progression to end-stage renal failure, requiring dialysis or transplantation for survival. Clearance studies using various graded-size probe molecules established that the passage of molecules/proteins across the glomerular capillary barrier of mammalian kidneys is increasingly restricted as their size increase. Few mathematical models were developed to describe the dynamics of the size-selective functions of macromolecules across membranes and gelatins. In the present study, we compare the behavior of three mathematical descriptions for the Fiber Matrix theory, an Extended Fiber Matrix theory, and an Alternative Statistical Physics analysis to describe the size-selective function of the glomerular capillary barrier; using mainly its hemodynamic, morphometric and hydrodynamic variables; in two experimental rat models. The glomerular basement membrane was represented as a homogeneous three-dimensional network of fibers of uniform length (Lf), radius (Rf), total fractional solid volume of fibers (Vf) and characteristic Darcy permeability. The models were appropriate for simulating in vivo fractional clearance data of neutral Dextran and Ficoll macromolecules from two experimental rat models. We believe that the Lf, Rf and Vf best-fit numerical values may signify new insights for the diagnosis of human nephropathies.

Electronic noses for monitoring benzene occupational exposure in biological samples of Egyptian workers.

OBJECTIVES: Benzene is commonly emitted in several industries, leading to widespread environmental and occupational exposure hazards. While less toxic solvents have been substituted for benzene, it is still a component of petroleum products and is a trace impurity in industrial products resulting in continued higher occupational exposures in industrial settings in developing countries. MATERIALS AND METHODS: We investigated the potential use of an electronic nose (e-nose) to monitor the headspace volatiles in biological samples from benzene-exposed Egyptian workers and non-exposed controls. The study population comprised 150 non-smoking male workers exposed to benzene and an equal number of matching non-exposed controls. We determined biomarkers of benzene used to estimate exposure and risk including: benzene in exhaled air and blood; and its urinary metabolites such as phenol and muconic acid using gas chromatography technique and a portable e-nose. RESULTS: The average benzene concentration measured in the ambient air of the workplace of all studied industrial settings in Alexandria, Egypt; was 97.56 ± 88.12 µg/m(3) (range: 4.69-260.86 µg/m(3)). Levels of phenol and muconic acid were significantly (p < 0.001) higher in both blood and urine of benzene-exposed workers as compared to non-exposed controls. CONCLUSIONS: The e-nose technology has successfully classified and distinguished benzene-exposed workers from non-exposed controls for all measured samples of blood, urine and the exhaled air with a very high degree of precision. Thus, it will be a very useful tool for the low-cost mass screening and early detection of health hazards associated with the exposure to benzene in the industry.