Gas Flow in Occluded Respiratory Tree: A New Matrix-Based Approach

Studies suggest that both the size of airways and the number of bifurcations of the respiratory tree provide the best structural design to accomplish its function. However, constrictions and occlusions due to inflammation and pulmonary edema of the airways can inhibit normal air flowing through the respiratory tree, affecting gas exchange. It results in heterogeneity in gas exchange (and pulmonary perfusion) with adverse risk factors. In this study, we propose a methodology based on the airway tree admittance (reciprocal of impedance) to study this problem. This methodology is distinct from the traditional quantification, based on overall impedance using lump parameter models, and applies to a matrix formed by admittances of each airway of the entire conducting part of the bronchial tree. The generated system admittance matrix is highly sparse in nature, and thus to solve the same system, a modified block-based LU decomposition method is proposed to improve the space-time trade-off. Our approach enables the determination of the local ventilation pattern and reduces the mis-evaluation, mainly in the cases that characterize the early-stage obstructive disorders. The key finding of the present study is to show that how the position and intensity of local obstruction in an airway can affect the overall as well as regional ventilation which can lead to impaired gas exchange.

Organosilica-Modified Multiblock Copolymers for Membrane Gas Separation

Organosubstituted silica derivatives were synthesized and investigated as modifiers of block copolymers based on macroinitiator and 2,4-toluene diisocyanate. A peculiarity of the modified block copolymers is the existence in their structure of coplanar rigid polyisocyanate blocks of acetal nature (O-polyisocyanates). Organosubstituted silica derivatives have a non-additive effect on high-temperature relaxation and α-transitions of modified polymers and exhibit the ability to influence the supramolecular structure of block copolymers. The use of the developed modifiers leads to a change in the gas transport properties of block copolymers. The increase of the permeability coefficients is due to the increase of the diffusion coefficients. At the same time, the gas solubility coefficients do not change. An increase in the ideal selectivity for a number of gas pairs is observed. An increase in the selectivity for the CO2/N2 gas pair (from 25 to 39) by 1.5 times demonstrates the promising use of this material for flue gases separation.

Polylactic acid block copolymer grafted temozolomide targeted nano delivery in the treatment of glioma

Gliomas are tumors of the central nervous system; they can be invasive and are commonly treated by surgical resection, chemo, and radiotherapy. Removal of a glioma is dangerous and not always successful. Temozolomide (TMZ), the first-line chemotherapy drug for gliomas, inhibit tumor recurrence, and metastasis to some extent. TMZ is often accompanied by side effects such as anemia, fever, constipation, and more. Here using a double-targeted Kunitz domain Angiopep-2 (ANG) modified block copolymer poly(lactic acid)-polyethylene glycol (PEG-PLA) coated with TMZ, we constructed a nano-delivery system (ANG@PLA-PEG/TMZ) that crosses the blood-brain barrier (BBB) to treat gliomas.

NUMERICAL SOLUTION OF MIXED LINEAR VOLTERRA-FREDHOLM INTEGRAL EQUATIONS BY MODIFIED BLOCK PULSE FUNCTIONS

A numerical method based on modified block pulse functions is proposed for solving the mixed linear Volterra-Fredholm integral equations. We obtain an integration operational matrix of modified block pulse functions on interval [0,T). A modified block pulse functions and their operational matrix of integration, the mixed linear Volterra-Fredholm integral equations can be reduced to a linear system of algebraic equations. The rate of convergence is O(h) and error analysis of the proposed method are discussed. Some examples are provided to show that the proposed method have a good degree of accuracy.

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Fetal ECG extraction from abdominal ECG is critical task for telemonitoring of fetus which require lot of understanding to the subject. Conventional source separation methods are not efficient enough to separate FECG from huge multichannel ECG. Thus use of compression technique is needed to compress and reconstruct ECG signal without any significant losses in quality of signal. Compressed sensing shows promising results for such tasks. However, current compressed sensing theory is not so far that successful due to the non-sparsity and strong noise contamination present in ECG signal. The proposed work explores the concept of block compressed sensing to reconstruct non-sparse FECG signal using GFOCUSS algorithm. The main objective of this paper is not only to successfully reconstruct the ECG signal but to efficiently separate FECG from abdominal ECG. The proposed algorithm is explained in very extensive manner for all experiments. The key feature of proposed method is, that it doesn’t affect the interdependence relation between multichannel ECG. The useof walsh sensing matrix made it possible to achieve high compression ratio. Experimental results shows that even at very high compression ratio, successful FECG reconstruction from raw ECG is possible. These results are validated using PSNR, SINR, and MSE. This shows the framework, compared to other algorithms such as current blocking CS algorithms, rackness CS algorithm and wavelet algorithms, can greatly reduce code execution time during data compression stage and achieve better reconstruction in terms of MSE, PSNR and SINR.