Segmentation-based blood flow parameter refinement in cerebrovascular structures using 4D arterial spin labeling MRA

Objective: Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and blood flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets. Methods: A previously published segmentation method, which uses blood flow data to improve its accuracy, is extended to estimate blood flow parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis. Results: The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated blood flow parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined blood flow estimation is more realistic compared to the raw hemodynamic parameters. Conclusion: The proposed method can provide accurate segmentations and blood flow parameter estimations in the cerebrovascular system using 4D ASL MRA datasets. Significance: The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively

Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system

The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico model of neurodegeneration of the visual system by equipping it with a mechanism to simulate neuroplasticity. Therefore, deep convolutional networks of multiple sizes were trained for object recognition tasks and progressively lesioned to simulate neurodegeneration of the visual cortex. More specifically, the injured parts of the network remained injured while we investigated how the added retraining steps were able to recover some of the model’s object recognition baseline performance. The results showed with retraining, model object recognition abilities are subject to a smoother and more gradual decline with increasing injury levels than without retraining and, therefore, more similar to the longitudinal cognition impairments of patients diagnosed with Alzheimer’s disease (AD). Moreover, with retraining, the injured model exhibits internal activation patterns similar to those of the healthy baseline model when compared to the injured model without retraining. Furthermore, we conducted this analysis on a network that had been extensively pruned, resulting in an optimized number of parameters or synapses. Our findings show that this network exhibited remarkably similar capability to recover task performance with decreasingly viable pathways through the network. In conclusion, adding a retraining step to the in-silico setup that simulates neuroplasticity improves the model’s biological feasibility considerably and could prove valuable to test different rehabilitation approaches in-silico

Issues in the Pharmacokinetics of Trichloroethylene and Its Metabolites

Much progress has been made in understanding the complex pharmacokinetics of trichloroethylene (TCE). Qualitatively, it is clear that TCE is metabolized to multiple metabolites either locally or into systemic circulation. Many of these metabolites are thought to have toxicologic importance. In addition, efforts to develop physiologically based pharmacokinetic (PBPK) models have led to a better quantitative assessment of the dosimetry of TCE and several of its metabolites. As part of a mini-monograph on key issues in the health risk assessment of TCE, this article is a review of a number of the current scientific issues in TCE pharmacokinetics and recent PBPK modeling efforts with a focus on literature published since 2000. Particular attention is paid to factors affecting PBPK modeling for application to risk assessment. Recent TCE PBPK modeling efforts, coupled with methodologic advances in characterizing uncertainty and variability, suggest that rigorous application of PBPK modeling to TCE risk assessment appears feasible at least for TCE and its major oxidative metabolites trichloroacetic acid and trichloroethanol. However, a number of basic structural hypotheses such as enterohepatic recirculation, plasma binding, and flow- or diffusion-limited treatment of tissue distribution require additional evaluation and analysis. Moreover, there are a number of metabolites of potential toxicologic interest, such as chloral, dichloroacetic acid, and those derived from glutathione conjugation, for which reliable pharmacokinetic data is sparse because of analytical difficulties or low concentrations in systemic circulation. It will be a challenge to develop reliable dosimetry for such cases

Skipping breakfast is associated with adiposity markers especially when sleep time is adequate in adolescents

Adolescence is a critical stage of development and has an important influence on energy balancerelated behaviours (EBRBs). When adolescents are associated with obesity it can lead to increased cardiometabolic risk. Here we assess if EBRBs adopted by adolescents included in a subsample are associated with markers of total and abdominal adiposity in a multicentre European study, Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA-CSS) and a Brazilian study, Brazilian Cardiovascular Adolescent Health (BRACAH study), and whether sleep duration influence the association between skipping breakfast, physical activity and sedentary behaviours, with total and abdominal obesity (AO). Multilevel linear regression models using fixed and random intercepts were used to analyse the association between markers of obesity and EBRBs. Skipping breakfast was the prevalent behaviour in association with obesity among European and Brazilian boys besides European girls, even after stratification by sleep time. Moreover, European boys who slept properly and skipped breakfast had an increased waist circumference (WC), while body mass index (BMI) increased in Brazilian boys. Among Brazilian boys less sleep was protective for total obesity (β = −0.93 kg/m2; 95% CI: −1.80; −0.07). European girls when they were more sedentary, showed an increase in WC, especially for those who reported they slept adequately. Skipping breakfast was associated with total and AO in adolescents independent of sleep duration.The HELENA-CSS took place with the financial support of the European Community Sixth RTD Framework Program (contract FOOD-CT-2005-007034). This study was also supported by a grant from the Spanish Ministry of Health: Maternal, Child Health and Development Network (numberRD08/0072) and grants from the Spanish Ministry of Education (EX-2008-0641) and the Swedish Heart-Lung Foundation (20090635). And supported by grants from the Spanish Ministry of Science and Innovation (JCI-2010-07055 and RYC-2010-05957). The authors thanked for the contribution of the HELENA Study Group. We are grateful for the financial support to the authors: Dr. Elsie C. O. Forkert was given a post-doctoral schoolarship from the Sao Paulo Research Foundation FAPESP (proc.2018/02887-6). Dr. Augusto César F. De Moraes was given a post-doctoral scholarship from the National Council for Scientific and Technological Development (CNPq: proc. 313772/2014-2) and the São Paulo Research Foundation FAPESP (proc. 2014/13367-2 and 2015/14319-4). Full Prof. Luis A. Moreno was given a scholarship of a visiting professor from São Paulo Research Foundation FAPESP (proc. 2015/11406-3). Dr Prof Heráclito B Carvalho is in receipt of an advanced scientist scholarship from the National Council for Scientific and Technological Development (CNPq: proc. 300951/2015-9)

Identification and functional characterisation of CRK12:CYC9, a novel cyclin-dependent kinase (CDK)-cyclin complex in Trypanosoma brucei

The protozoan parasite, Trypanosoma brucei, is spread by the tsetse fly and causes trypanosomiasis in humans and animals. Both the life cycle and cell cycle of the parasite are complex. Trypanosomes have eleven cdc2-related kinases (CRKs) and ten cyclins, an unusually large number for a single celled organism. To date, relatively little is known about the function of many of the CRKs and cyclins, and only CRK3 has previously been shown to be cyclin-dependent in vivo. Here we report the identification of a previously uncharacterised CRK:cyclin complex between CRK12 and the putative transcriptional cyclin, CYC9. CRK12:CYC9 interact to form an active protein kinase complex in procyclic and bloodstream T. brucei. Both CRK12 and CYC9 are essential for the proliferation of bloodstream trypanosomes in vitro, and we show that CRK12 is also essential for survival of T. brucei in a mouse model, providing genetic validation of CRK12:CYC9 as a novel drug target for trypanosomiasis. Further, functional characterisation of CRK12 and CYC9 using RNA interference reveals roles for these proteins in endocytosis and cytokinesis, respectively

Predicting Clinical Outcome of Stroke Patients with Tractographic Feature

The volume of stroke lesion is the gold standard for predicting the clinical outcome of stroke patients. However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients. In this paper, we introduce the tractographic feature to capture these potentially damaged regions and predict the modified Rankin Scale (mRS), which is a widely used outcome measure in stroke clinical trials. The tractographic feature is built from the stroke lesion and average connectome information from a group of normal subjects. The tractographic feature takes into account different functional regions that may be affected by the stroke, thus complementing the commonly used stroke volume features. The proposed tractographic feature is tested on a public stroke benchmark Ischemic Stroke Lesion Segmentation 2017 and achieves higher accuracy than the stroke volume and the state-of-the-art feature on predicting the mRS grades of stroke patients. In addition, the tractographic feature also yields a lower average absolute error than the commonly used stroke volume feature.Comment: 12 pages, 4 figures, 3 tables. Accepted by MICCAI-BrainLesion 2019 as an oral presentatio

Prediction of Thrombectomy Functional Outcomes using Multimodal Data

Recent randomised clinical trials have shown that patients with ischaemic stroke {due to occlusion of a large intracranial blood vessel} benefit from endovascular thrombectomy. However, predicting outcome of treatment in an individual patient remains a challenge. We propose a novel deep learning approach to directly exploit multimodal data (clinical metadata information, imaging data, and imaging biomarkers extracted from images) to estimate the success of endovascular treatment. We incorporate an attention mechanism in our architecture to model global feature inter-dependencies, both channel-wise and spatially. We perform comparative experiments using unimodal and multimodal data, to predict functional outcome (modified Rankin Scale score, mRS) and achieve 0.75 AUC for dichotomised mRS scores and 0.35 classification accuracy for individual mRS scores.Comment: Accepted at Medical Image Understanding and Analysis (MIUA) 202

Clara cell adhesion and migration to extracellular matrix

<p>Abstract</p> <p>Background</p> <p>Clara cells are the epithelial progenitor cell of the small airways, a location known to be important in many lung disorders. Although migration of alveolar type II and bronchiolar ciliated epithelial cells has been examined, the migratory response of Clara cells has received little attention.</p> <p>Methods</p> <p>Using a modification of existing procedures for Clara cell isolation, we examined mouse Clara cells and a mouse Clara-like cell line (C22) for adhesion to and migration toward matrix substrate gradients, to establish the nature and integrin dependence of migration in Clara cells.</p> <p>Results</p> <p>We observed that Clara cells adhere preferentially to fibronectin (Fn) and type I collagen (Col I) similar to previous reports. Migration of Clara cells can be directed by a fixed gradient of matrix substrates (haptotaxis). Migration of the C22 cell line was similar to the Clara cells so integrin dependence of migration was evaluated with this cell line. As determined by competition with an RGD containing-peptide, migration of C22 cells toward Fn and laminin (Lm) 511 (formerly laminin 10) was significantly RGD integrin dependent, but migration toward Col I was RGD integrin independent, suggesting that Clara cells utilize different receptors for these different matrices.</p> <p>Conclusion</p> <p>Thus, Clara cells resemble alveolar type II and bronchiolar ciliated epithelial cells by showing integrin mediated pro-migratory changes to extracellular matrix components that are present in tissues after injury.</p

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses.

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase ( www.facebase.org , https://doi.org/10.25550/3-HXMC ) and GitHub ( https://github.com/jaydevine/MusMorph )