In silico modeling of asthma

The incidence of asthma is increasing throughout the world, especially among children, to the extent that it has become a medical issue of serious global concern. Appropriately, numerous pharmacologic drugs and clinical protocols for the treatment and prophylaxis of the disease have been reported. From a scientific perspective, a review of the literature suggests that the targeted delivery of an aerosol would, in a real sense, enhance the efficacy of an inhaled medicine. Therefore, in accordance with published data we have developed a mathematical description of disease-induced effects of disease on airway morphology. A morphological algorithm defining the heterogeneity of asthma has been integrated with a computer code that formulates the behavior and fate of inhaled drugs. In this work, predicted drug particle deposition patterns have been compared with SPECT images from experiments with healthy human subjects (controls) and asthmatic patients. The asthma drug delivery model simulations agree with observations from human testing. The results indicate that in silico modeling provides a technical foundation for addressing effects of disease on the administration of aerosolized drugs, and suggest that modeling should be used in a complementary manner with future inhalation therapy protocols

A computer model of lung morphology to analyze SPECT images

Measurement of the spatial distribution of aerosol deposition in human lungs can be performed using single photon emission computed tomography (SPECT). To relate deposition patterns to real lung structures, a computer model of the airway network has been developed. Computer simulations are presented that are compatible with the analysis of SPECT images. Computational techniques that are consistent with clinical procedures are used to analyze airways by type and number within transverse slices of the lung volume. The computer models serve as customized templates, which when analyzed alongside gamma scintigraphy images, can assist in the interpretation of human test data

A novel cell-selection optimization handover for long-term evolution (LTE) macrocellusing fuzzy TOPSIS

To satisfy the demand for higher data rate while maintaining the quality of service, a dense long-term evolution (LTE) cells environment is required. This imposes a big challenge to the network when it comes to performing handover (HO). Cell selection has an important influence on network performance, to achieve seamless handover. Although a successful handover is accomplished, it might be to a wrong cell when the selected cell is not an optimal one in terms of signal quality and bandwidth. This may cause significant interference with other cells, handover failure (HOF), or handover ping-pong (HOPP), consequently degrading the cell throughput. To address this issue, we propose a multiple-criteria decision-making method. In this method, we use an integrated fuzzy technique for order preference by using similarity to ideal solution (TOPSIS) on S-criterion, availability of resource blocks (RBs), and uplink signal-to-interference-plus-noise ratio. The conventional cell selection in LTE is based on S-criterion, which is inadequate since it only relies on downlink signal quality. A novel method called fuzzy multiple-criteria cell selection (FMCCS) is proposed in this paper. FMCCS considers RBs utilization and user equipment uplink condition in addition to S-criterion. System analysis demonstrates that FMCCS managed to reduce handover ping-pong and handover failure significantly. This improvement stems from the highly reliable cell-selection technique that leads to increased throughput of the cell with a successful handover. The simulation results show that FMCCS outperforms the conventional and cell selection scheme (CSS) methods