Modeling and analysis of transport in the mammary glands

Quezada, Ana; Vafai, Kambiz

doi:10.1088/1478-3975/11/4/045004

Cited by 12 publications

(7 citation statements)

References 73 publications

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“…The third term, toxin transport, is specific to breast cancer. It is suggested that increased toxin presence in the mammary tissue is a pre-disposing factor to breast cancer [23,28].…”

Section: Resultsmentioning

confidence: 99%

forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction

Kong

2020

Bioinformatics

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A unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This "n p" property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating external gene network information such as the graph-embedded deep feedforward network (GEDFN) [19] model has been a solution to this issue. However, such methods require an existing feature graph, and potential mis-specification of the feature graph can be harmful on classification and feature selection. To address this limitation and develop a robust classification model without relying on external knowledge, we propose a forest graph-embedded deep feedforward network (forgeNet) model, to integrate the GEDFN architecture with a forest feature graph extractor, so that the feature graph can be learned in a supervised manner and specifically constructed for a given prediction task. To validate the method's capability, we experimented the forgeNet model with both synthetic and real datasets. The resulting high classification accuracy suggests that the method is a valuable addition to sparse deep learning models for omics data.

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“…The third term, toxin transport, is specific to breast cancer. It is suggested that increased toxin presence in the mammary tissue is a pre-disposing factor to breast cancer [23,28].…”

Section: Resultsmentioning

confidence: 99%

forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction

Kong

2020

Bioinformatics

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“…A multilayer model that focused more on the properties of biologic membranes than molecular descriptors was developed to predict M/P ratios . It takes into account drug particle size and diffusion coefficient as well as a number of membrane factors.…”

Section: Prediction Of the M/p Ratiomentioning

confidence: 99%

“…A multilayer model that focused more on the properties of biologic membranes than molecular descriptors was developed to predict M/P ratios. 55 It takes into account drug particle size and diffusion coefficient as well as a number of membrane factors. This very complex model was successful in predicting the M/P ratios of three drugs that are excreted into milk by different mechanisms, including active transport with cimetidine.…”

Section: Qsar Prediction Of the M/p Ratiomentioning

confidence: 99%

Modeling drug passage into human milk

Anderson

Sauberan

2016

Clin Pharma and Therapeutics

109

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Breastfeeding has positive health consequences for both the breastfed infant and the nursing mother.(1,2) Although information on drug use during lactation is available through sites such as LactMed,(3) available information is often incomplete. Unlike pregnancy, in which large numbers of pregnant women need to be studied to assure safety, measurement of drug concentrations in breastmilk in a relatively few subjects can provide valuable information to assess drug safety. This article reviews methods of measuring and predicting drug passage into breastmilk.

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“…These models have emanated from the substantial body of work on mass transport in biological tissue which has featured heavily in the literature over the past few decades. We refer the interested reader to the review by Khaled and Vafai [4] for background reading and to the recent work by the same group [5] on mass transport in mammary glands which is an excellent exemplar of a different application of mass transport in biological tissue.…”

Section: Introductionmentioning

confidence: 99%

Does anisotropy promote spatial uniformity of stent-delivered drug distribution in arterial tissue?

McGinty

Wheel

McKee

et al. 2015

International Journal of Heat and Mass Transfer

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In this article we investigate the role of anisotropic diffusion on the resulting arterial wall drug distribution following stent-based delivery. The arterial wall is known to exhibit anisotropic diffusive properties, yet many authors neglect this, and it is unclear what effect this simplification has on the resulting arterial wall drug concentrations. Firstly, we explore the justification for neglecting the curvature of the cylindrical arterial wall in favour of using a Cartesian coordinate system. We then proceed to consider three separate transport regimes (convection dominated, diffusion dominated, reaction dominated) based on the range of parameter values available in the literature. By comparing the results of a simple one-dimensional model with those of a fully three-dimensional numerical model, we demonstrate, perhaps surprisingly, that the anisotropic diffusion can promote the spatial uniformity of drug concentrations, and furthermore, that the simple analytical one-dimensional model is an excellent predictor of the three-dimensional numerical results. However, the level of uniformity and the time taken to reach a uniform concentration profile depends on the particular regime considered. Furthermore, the more uniform the profile, the better the agreement between the one-dimensional and three-dimensional models. We discuss the potential implications in clinical practice and in stent design

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Modeling and analysis of transport in the mammary glands

Cited by 12 publications

References 73 publications

forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction

forgeNet: a graph deep neural network model using tree-based ensemble classifiers for feature graph construction

Modeling drug passage into human milk

Does anisotropy promote spatial uniformity of stent-delivered drug distribution in arterial tissue?

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