We investigated the feasibility of telemonitoring services for patients with severe respiratory illness. In the first phase of the study, patients were observed and treated using face-to-face medical visits for 12 months. In the second phase of the study, the patients were monitored at home for 12 months, during which time determinations of arterial oxygen saturation and heart rate were performed twice a week, and the data were automatically transmitted to the hospital's processing centre via a normal telephone line. Thirty patients on long-term oxygen therapy were enrolled in the study; 23 completed the 12 months of home telemonitoring. The numbers of hospital admissions and of acute home exacerbations during the telemonitoring phase of the study decreased by 50% and 55%, respectively, in comparison with the first phase. Estimates of hospitalization costs for the patients during the second phase were approximately 17% lower than those for the first phase. Patients were satisfied with the quality of the personal telemonitoring process in 96% of cases. We believe that telemedicine can enable the provision of high-quality home care for patients with severe respiratory illness.

Bioelectric impedance analysis (BIA) is commonly used in clinical settings and field studies for estimating total, extracellular, and intracellular water compartments. The objective of the present study was to carry out a meta-analysis of published reports in which total body water (TBW) was estimated using BIA techniques and comparisons were made with reference values. We identified 16 reports conducted among healthy and obese adults and individuals with chronic renal failure. Based on the weighted mean difference, we found that those studies using only multi-frequency BIA did not significantly overestimate the TBW compared with the reference values. Thus, among BIA techniques, multi-frequency BIA seems to be a more accurate method for estimating the TBW compartment for healthy and obese adults and for those with chronic renal failure.

Although the effects of sex, ageing, height, race, and current and past health on pulmonary function tests have been described, only non-significant associations have been observed between body weight and lung function among healthy persons after having accounted for the effects of age and height. However, few studies have considered the influence of body compartments (e. g. lean and fat masses and their distribution) on lung function and respiratory gas exchange. The present work consists of a review of the literature on the effects of body weight components and body composition measurements on lung function. One of the important findings of this review was that the central (or upper body) pattern of fat distribution is negatively associated with airway function and that increases in body muscular mass result in linear increases for all spirometric variables in healthy persons. Nonetheless, the role that body composition plays in lung function still needs to be clarified.

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.

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Many studies have investigated lung function in relation to age and height among Caucasians, however, most of these studies did not consider the individual components of body weight. The objective of the present study was to study the effect of body weight components [bone-free lean body mass (BF-LBM), bone mineral content (BMC), and fat mass (FM)] measured by dual x-ray absorptiometry (DXA) on the lung-function variables (FVC, FEV1, and PEF) and to derive prediction equations for these variables in healthy adult Italians. Dynamic spirometric tests and body composition analysis by DXA were performed on 58 nonsmoking males, mean age ( +/-SE) 26.72 +/- 1.98 years and BMI 25.51 +/- 0.64 kg/m2, and 60 nonsmoking females matched for age and BMI (29.61 +/- 1.65 years and 26.45 +/- 1.05 kg/m2, respectively). Bivariate linear regression analysis showed the variables age, height, BF-LBM, BMC, and the interaction term BF-LBM*Height, but not weight and FM, to correlate significantly with lung-function variables for males and for females separately. Multiple linear regression analysis showed that sex, age, height, and BF-LBM*Height were significantly associated with FVC, FEV1, and PEF. The prediction equations developed for FVC, FEV1, and PEF on the basis of the independent variables i.e. sex, age (y), height (m), and BF-LBM*Height (kg. m) had a significantly higher cumulative correlation coefficient and a lower SEE compared with those based on age and height only. The present report suggests that the BF-LBM, expressed independently from height, can be considered for predicting lung-function variables.

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.

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