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Register a new userModeling of breath methane concentration profiles during exercise on an ergometer
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We develop a simple three compartment model based on mass balance equations which quantitatively describes the dynamics of breath methane concentration profiles during exercise on an ergometer. With the help of this model it is possible to estimate the endogenous production rate of methane in the large intestine by measuring breath gas concentrations of methane.
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In a recent paper we presented a simple two compartment model which describes the influence of inhaled concentrations on exhaled breath concentrations for volatile organic compounds (VOCs) with small Henry constants. In this paper we extend this investigation concerning the influence of inhaled concentrations on exhaled breath concentrations for VOCs with higher Henry constants. To this end we extend our model with an additional compartment which takes into account the influence of the upper airways on exhaled breath VOC concentrations.
In this paper we develop a simple two compartment model which extends the Farhi equation to the case when the inhaled concentration of a volatile organic compound (VOC) is not zero. The model connects the exhaled breath concentration of systemic VOCs with physiological parameters such as endogenous production rates and metabolic rates. Its validity is tested with data obtained for isoprene and inhaled deuterated isoprene-D5.
Isothermal rebreathing has been proposed as an experimental technique for estimating the alveolar levels of hydrophilic volatile organic compounds (VOCs) in exhaled breath. Using the prototypic test compound acetone we demonstrate that the end-tidal breath profiles of such substances during isothermal rebreathing show characteristics that contradict the conventional pulmonary inert gas elimination theory due to Farhi. On the other hand, these profiles can reliably be captured by virtue of a previously developed mathematical model for the general exhalation kinetics of highly soluble, blood-borne VOCs, which explicitly takes into account airway gas exchange as major determinant of the observable breath output. This model allows for a mechanistic analysis of various rebreathing protocols suggested in the literature. In particular, it clarifies the discrepancies between in vitro and in vivo blood-breath ratios of hydrophilic VOCs and yields further quantitative insights into the physiological components of isothermal rebreathing.
Recommended standardized procedures for determining exhaled lower respiratory nitric oxide and nasal nitric oxide have been developed by task forces of the European Respiratory Society and the American Thoracic Society. These recommendations have paved the way for the measurement of nitric oxide to become a diagnostic tool for specific clinical applications. It would be desirable to develop similar guidelines for the sampling of other trace gases in exhaled breath, especially volatile organic compounds (VOCs) which reflect ongoing metabolism. The concentrations of water-soluble, blood-borne substances in exhaled breath are influenced by: (i) breathing patterns affecting gas exchange in the conducting airways; (ii) the concentrations in the tracheo-bronchial lining fluid; (iii) the alveolar and systemic concentrations of the compound. The classical Farhi equation takes only the alveolar concentrations into account. Real-time measurements of acetone in end-tidal breath under an ergometer challenge show characteristics which cannot be explained within the Farhi setting. Here we develop a compartment model that reliably captures these profiles and is capable of relating breath to the systemic concentrations of acetone. By comparison with experimental data it is inferred that the major part of variability in breath acetone concentrations (e.g., in response to moderate exercise or altered breathing patterns) can be attributed to airway gas exchange, with minimal changes of the underlying blood and tissue concentrations. Moreover, it is deduced that measured end-tidal breath concentrations of acetone determined during resting conditions and free breathing will be rather poor indicators for endogenous levels. Particularly, the current formulation includes the classical Farhi and the Scheid series inhomogeneity model as special limiting cases.
The novel coronavirus disease, named COVID-19, emerged in China in December 2019, and has rapidly spread around the world. It is clearly urgent to fight COVID-19 at global scale. The development of methods for identifying drug uses based on phenotypic data can improve the efficiency of drug development. However, there are still many difficulties in identifying drug applications based on cell picture data. This work reported one state-of-the-art machine learning method to identify drug uses based on the cell image features of 1024 drugs generated in the LINCS program. Because the multi-dimensional features of the image are affected by non-experimental factors, the characteristics of similar drugs vary greatly, and the current sample number is not enough to use deep learning and other methods are used for learning optimization. As a consequence, this study is based on the supervised ITML algorithm to convert the characteristics of drugs. The results show that the characteristics of ITML conversion are more conducive to the recognition of drug functions. The analysis of feature conversion shows that different features play important roles in identifying different drug functions. For the current COVID-19, Chloroquine and Hydroxychloroquine achieve antiviral effects by inhibiting endocytosis, etc., and were classified to the same community. And Clomiphene in the same community inibited the entry of Ebola Virus, indicated a similar MoAs that could be reflected by cell image.
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