Annals of biomedical engineering
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The objective of this study was to evaluate the delivery of inhaled pharmaceutical aerosols using an enhanced condensational growth (ECG) approach in an airway model extending from the oral cavity to the end of the tracheobronchial (TB) region. The geometry consisted of an elliptical mouth-throat (MT) model, the upper TB airways extending to bifurcation B3, and a subsequent individual path model entering the right lower lobe of the lung. Submicrometer monodisperse aerosols with diameters of 560 and 900 nm were delivered to the mouth inlet under control (25 °C with subsaturated air) or ECG (39 or 42 °C with saturated air) conditions. ⋯ This increase in aerosol size produced an order of magnitude increase in aerosol deposition within the TB airways compared with the controls, with TB deposition efficiencies of approximately 32-46% for ECG conditions. Estimates of downstream pulmonary deposition indicted near full lung retention of the aerosol during ECG delivery. Furthermore, targeting the region of TB deposition by controlling the inlet temperature conditions and initial aerosol size also appeared possible.
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Mechanical loading of the intervertebral disc (IVD) plays an important role in governing the function of nucleus pulposus (NP) cells. In situ, the loading environment of the NP is neither fully confined nor unconfined. To investigate the potential influences of these idealized loading modes, we utilized an alginate cell culture system to compare the effects of 1 h confined and unconfined compression on glycoprotein gene expression in mixed population NP cells. ⋯ We theorize that this pattern is indicative of regulation by fluid flow within loaded alginate disks. Calculations using the linear biphasic model suggest that spatial pressure gradients exist in static confined specimens for the majority of the loading duration. Using a parallel plate flow chamber, we report for the first time that NP cells are sensitive to fluid shear stress and confirm that lumican expression can be regulated by short durations of exposure to fluid shear.
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Automatic classification of the electrocardiogram (ECG) signals is an important subject for clinical diagnosis of heart disease. This study investigates the design of a high-efficient system to classify five types of ECG beat namely normal beats and four manifestations of heart arrhythmia, in twofold. First, we propose a system that includes two main modules: a feature extraction module and a classification module. ⋯ In this module, a genetic algorithm is used for optimization of the relevant parameters of system. These parameters are: wavelet filter type for feature extraction, wavelet decomposition level, and classifier's parameters. Experimental results show that optimization improves the recognition system, efficiently, and HIS is more superior to the system, which as constant parameters.