Sentence examples for bronchial network from inspiring English sources

The bronchial network of the mammalian lung consists of millions of dichotomous branches arranged in a highly complex, space-filling tree.

In this review, we focus on three different theoretical approaches - geometric modeling, reaction-diffusion modeling, and continuum mechanical modeling - and discuss how, taken together, these models have identified the geometric principles necessary to build an efficient bronchial network, as well as the patterning mechanisms that specify airway geometry in the developing embryo.

In this review, we focus on three different theoretical approaches – geometric modeling, reaction-diffusion modeling, and continuum mechanical modeling – and discuss how, taken together, these models have identified the geometric principles necessary to build an efficient bronchial network, as well as the patterning mechanisms that specify airway geometry in the developing embryo.

Examples include the ramifying networks found in plants or mammalian cardiovascular or bronchial networks [ 2, 3].

Moreover, diffused thrombosis at the arterioles of the pulmonary circulation was observed and the microvessel structure was found to be made up of coarctate capillary network from the bronchial artery [ 19].

In the pulmonary arteries, the vasa vasorum is the microcirculatory network of the bronchial (systemic) circulation and, similar to its role in systemic vessels, is thought to contribute to vascular integrity through supply of oxygen and nutrients to the outer part of the vessel wall.

A pulmonary artery branch accompanies the bronchial tree and ends in capillary network within the alveolar wall [4].

After nebulization, uptake through the bronchial mucosa and distribution by a rich network of submucosal capillaries to other areas of the lung, could lead to adequate concentrations at various sites in the lung, as seen with inhaled antibiotics [ 53].

Figure 4(a) shows a correlation of the differential network backbone values derived from the bronchial and nasal samples in the in vivo dataset GSE16008 (smokers versus nonsmokers) computed using the NPA approach in the xenobiotic metabolism network model.

It was shown that the technique images the elastin network of the proximal and distal bronchial tree but does not give access to the collagen component of the ECM.

Figures 6(a) and 6(b) show the correlations, in bronchial and nasal samples, respectively, between the differential network backbone values generated from in vivo dataset to those generated from in vitro models.