Ehab I. Mohamed

The application of body cell mass index for studying muscle mass changes in health and disease conditions

Abstract.Body mass index (BMI) fails to detect altered nutritional state in the presence of overweight or obesity, since malnutrition can be present and masked by the abnormal amount of fat mass. Measuring body cell mass (BCM) contents for the evaluation of muscle mass and protein tissue states is well accepted. The aim of the present study was to apply body cell mass index (BCMI) to monitor the muscular mass changes of male and female Olympic athletes, renal dialysis patients, and anorexia nervosa patients in comparison with healthy subjects. The BCMI values of male subjects from the healthy group and Olympic athletes groups, but not the renal dialysis group, were significantly higher (p<0.0001) than those of female subjects from the same groups. In addition, subjects with normal or high BMI values may be malnourished as highlighted by a low BCMI. We believe the BCMI is more sensitive than the BMI for studying the nutritional status of the individual.

Evaluating body composition of Italian prostate cancer patients without metastases

Abstract.Prostate cancer (PCa) is becoming an increasingly important public health problem worldwide, and anthropometric studies have revealed that body composition is a potential risk factor for this type of cancer. The objective of the present study was to compare Italians with PCa and no history of androgen deprivation therapy or bone metastases with healthy controls in terms of body composition. We recruited 11 PCa patients [mean age (±SD), 68.67±4.93 years; body mass index (BMI), 28.42±2.96 kg/m2] and 11 healthy controls matched for age and BMI. Anthropometric and body composition parameters were measured for all participants using dual X-ray absorptiometry and bioelectric impedance analysis techniques. We found that lean body mass, but not fat mass, was significantly lower for PCa patients in comparison with healthy controls (50.24±5.03 vs. 53.63±5.61 kg, p<0.05). Thus, PCa patients with no history of androgen deprivation therapy and no bone metastasis suffer muscle mass depletion.

The capabilities of artificial neural networks in body composition research

Abstract.When estimating in vivo body composition or combining such estimates with other results, multiple variables must be taken into account (e. g. binary attributes such as gender or continuous attributes such as most biosignals). Standard statistical models, such as logistic regression and multivariate analysis, presume well-defined distributions (e. g. normal distribution); they also presume independence among all inputs and only linear relationships, yet rarely are these requirements met in real life. As an alternative to these models, artificial neural networks can be used. In the present work, we describe the pre-processing and multivariate analysis of data using neural network techniques, providing examples from the medical field and making comparisons with classic statistical approaches. We also address the criticisms raised regarding neural network techniques and discuss their potential improvement.

Body composition analysis for healthy Italian vegetarians

Abstract.The elementary nutritional needs of vegetarians are totally, or in great part, supplied by vegetarian food; thus the body composition of vegetarians could differ from that of omnivorous persons. The objective of the present study was to compare healthy Italian vegetarians to healthy omnivorous individuals in terms of body composition, determined using dual X-ray absorptiometry. The study population consisted of 20 vegetarians [mean age (±SD), 34.78±15.07 years; mean BMI, 22.41±2.15 kg/m2] and 10 omnivorous persons matched for age and BMI. We found no significant differences between the two groups in terms of fat mass, lean body mass, soft tissue, bone mineral content, or bone mineral density. These findings suggest that the vegetarian diet does not induce negative alterations in body composition.

Predicting bone mineral density of postmenopausal healthy and cirrhotic Italian women using age and body mass index.

Abstract.The objective of the present report was to develop mathematical prediction formulae for the lumbar spine, pelvis and total bone mineral density (BMD) based on the osteoporosis risk factors age and BMI in healthy and cirrhotic postmenopausal women. The study population comprised 20 postmenopausal cirrhotic women (late PM cirrhotic women), 20 postmenopausal healthy women matched for age and BMI (late PM healthy women), and 19 younger postmenopausal healthy women matched for BMI (early PM healthy women). Segmental and total bone mineral content and BMD, total bone-free lean body mass and total fat mass were measured for all women using dual X-ray absorptiometry (DXA). The prediction formulae for late PM cirrhotic women had higher cumulative correlation coefficients (r=0.71, p=0.05 for spine BMD, r=0.84, p=0.013 for pelvis BMD, and r=0.89, p=0.004 for total BMD) than those for early PM healthy women (r=0.64, p=0.015 for spine BMD, r=0.69, p=0.002 for pelvis BMD, and r=0.62, p=0.022 for total BMD) and late PM healthy women (r=0.29, p=NS for spine BMD, r=0.39, p=NS for pelvis BMD, and r=0.54, p=NS for total BMD). The mathematical formulae based on the variables age and BMI were capable of predicting lumbar spine BMD, pelvis BMD, and total BMD by DXA for the three groups of postmenopausal women.

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

ObjectivesBenzene 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 MethodsWe 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.ResultsThe 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/m3 (range: 4.69–260.86 μg/m3). 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.ConclusionsThe 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.

Artificial neural network analysis: a novel application for predicting site-specific bone mineral density

Abstract.Dual X-ray absorptiometry (DXA), which is the most commonly used method for the diagnosis and followup of human bone health, is known to produce accurate estimates of bone mineral density (BMD). However, high costs and problems with availability may prevent its use for mass screening. The objective of the present study was to estimate BMD values for healthy persons and those with conditions known to be associated with BMD, using artificial neural networks (ANN). An ANN was used to quantitatively estimate site-specific BMD values in comparison with reference values obtained by DXA (i. e. BMDspine, BMDpelvis, and BMDtotal). Anthropometric measurements (i. e. sex, age, weight, height, body mass index, waist-to-hip ratio, and the sum of four skinfold thicknesses) were fed to the ANN as independent input variables. The estimates based on four input variables were generated as output and were generally identical to the reference values for all studied groups. We believe the ANN is a promising approach for estimating and predicting site-specific BMD values using simple anthropometric measurements.

Effects of sleep disordered breathing on functional capacity and quality of life in chronic kidney disease Egyptian patients

PurposeWe assessed the prevalence of sleep disordered breathing (SDB) and investigated its effects on the muscle functional capacity and quality of life (QoL) among chronic kidney disease (CKD) Egyptian patients, either maintained or not maintained on hemodialysis (HD).MethodsThe study population comprised 100 CKD patients who were divided into patients maintained on HD (n = 60; M/F = 28:32) and patients not maintained on HD (n = 40; M/F = 24:16). Patients were observed overnight using the pulse-oximetry technique and further subdivided into patients with SDB and patients without SDB, according to their calculated oxygen desaturation index (cutoff 5). All patients were subjected also to estimation of Kt/V ratio (which is a measure for the efficiency of HD), body-composition analysis, biochemical analysis, muscle functional capacity, and QoL measurements using standard methods and questionnaires, respectively.ResultsPrimary outcomes were intergroup differences regarding physical capacity and muscle performance, QoL, and body-composition measurements. CKD patients in general, either maintained on HD or not, suffer from SDB, and the levels of urea and creatinine may increase the incidence of SDB in CKD patients not maintained on HD. CKD patients maintained on HD with SDB had poorer functional capacity, physical performance, and muscle composition, in comparison with those without SDB.ConclusionsOverall, SDB appears to partly contribute to the total diminished functional capacity of HD patients. Thus, CKD patients maintained on HD with SDB had significantly lower sleep quality and QoL as compared to those not maintained on HD with or without SDB.

Long-term follow-up of cellular proliferation as a predictive index for the relapse of nasal polyposis.

Background The objective of this study was to identify a reproducible clinical parameter for predicting relapses of nasal polyposis after surgery. Methods Ninety-three patients who had undergone ethmoidectomy of bilateral nasal polyps were followed for 3 years. Nasal polyps were obtained from all patients, and cellular suspensions were analyzed individually using flow cytometry. Based on the percentage of cells in the S phase, we identified three groups of patients (high, medium, and low percentages). Results A relapse of nasal polyposis was observed in 15.00, 70.00, and 100.00% of patients from the low-, medium-, and high-percentage groups, respectively. Patients suffering relapses had a high percentage of cells in the S phase. Conclusions We identified two cutoff values for the percentage of cells in the S phase; above the first cutoff, some of the patients suffered relapses; above the second cut-off, all patients suffered relapses.

Modeling combined transport of water and charged graded-size molecules across the glomerular capillary wall

Clearance studies using various probe molecules established that the passage of molecules/proteins across the glomerular capillary wall of mammalian kidneys is increasingly restricted as their size and net negative charge increase. An extended mathematical model, based on the Fiber Matrix theory, was developed to describe the dynamics of the size- and charge-selective functions of the glomerular capillary barrier using mainly its hemodynamic, morphometric, and electrostatic variables. The glomerular basement membrane was represented as a homogeneous three-dimensional network of fibers of uniform length (L(f)), radius (R(f)), and packing density (N(fv)) and characteristic Darcy permeability. The model was appropriate for simulating fractional clearance data of neutral and charged solutes from an experimental modeling exercise. We believe that the L(f) and R(f) best-fit numerical values may signify new insights for the diagnosis of some human nephropathies.