scholarly journals Correction to: Clinical and Molecular Features of 5 European Multigenerational Families With Moyamoya Angiopathy

Stroke ◽  
2021 ◽  
Vol 52 (1) ◽  
Stroke ◽  
2019 ◽  
Vol 50 (4) ◽  
pp. 789-796 ◽  
Author(s):  
Lou Grangeon ◽  
Stéphanie Guey ◽  
Jan Claudius Schwitalla ◽  
Françoise Bergametti ◽  
Minh Arnould ◽  
...  

Background and Purpose Moyamoya angiopathy (MMA) is a rare cerebral vasculopathy outside of Asia. In Japanese patients, a vast majority of patients carry the founder p.R4810K variant in the RNF213 gene, and familial cases are around 10%. In European patients, data about familial occurrence are limited. The aim of this study was to characterize the clinical and molecular features of several European families with a parent-to-child transmission of MMA. Methods Out of 126 MMA probands referred, we identified 113 sporadic probands and 13 familial probands. Segregation analysis showed a vertical parent-to-child pattern of inheritance in the families of 5 of these probands. All 5 families were of German or Dutch ancestry. We investigated the clinical features of affected members and used whole-exome sequencing to screen RNF213 and 13 genes involved in Mendelian MMA and to identify genes recurrently mutated in these families. Results Twelve affected MMA patients were identified, including 9 females and 3 males. Age at clinical onset ranged from 11 to 65 years. In 3 of 5 families, associated livedo racemosa was found. We did not detect any deleterious variants in the 13 known MMA genes. RNF213 rare missense variants predicted to be pathogenic were detected in all affected members of 2 of these families, as well as 2 candidate variants of the PALD1 gene. Conclusions Nonsyndromic MMA was identified in 5 European families, including 2 to 3 clinically affected cases segregating with a parent-to-child pattern of inheritance in each family. Molecular screening detected rare deleterious variants within RNF213 and PALD1 in all affected members of 2 of these 5 families, as well as in some clinically unaffected members. Altogether these data raise the difficult and, to date unanswered, question of the medical indication of presymptomatic screening.


2020 ◽  
Author(s):  
Depanjan Sarkar ◽  
Drupad Trivedi ◽  
Eleanor Sinclair ◽  
Sze Hway Lim ◽  
Caitlin Walton-Doyle ◽  
...  

Parkinson’s disease (PD) is the second most common neurodegenerative disorder for which identification of robust biomarkers to complement clinical PD diagnosis would accelerate treatment options and help to stratify disease progression. Here we demonstrate the use of paper spray ionisation coupled with ion mobility mass spectrometry (PSI IM-MS) to determine diagnostic molecular features of PD in sebum. PSI IM-MS was performed directly from skin swabs, collected from 34 people with PD and 30 matched control subjects as a training set and a further 91 samples from 5 different collection sites as a validation set. PSI IM-MS elucidates ~ 4200 features from each individual and we report two classes of lipids (namely phosphatidylcholine and cardiolipin) that differ significantly in the sebum of people with PD. Putative metabolite annotations are obtained using tandem mass spectrometry experiments combined with accurate mass measurements. Sample preparation and PSI IM-MS analysis and diagnosis can be performed ~5 minutes per sample offering a new route to for rapid and inexpensive confirmatory diagnosis of this disease.


2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>


2019 ◽  
Vol 47 (2) ◽  
pp. 167-180
Author(s):  
H B. Wang ◽  
Q. Zhang ◽  
Z H. Lin ◽  
J. Y. Li ◽  
S X. Lin ◽  
...  

Author(s):  
I. O. Marinkin ◽  
E. S. Lisova ◽  
V. V. Evchenko

The features of biomechanisms of endometrial hyperplasia in subjects exposed to reproductive toxicants were inflammation and oxidative stress. An association of Ki67 expression with 8-hydroxydeoxyguanosine, length of service, CD34 expression with 8-isoprostane and both Ki67 and CD34 expression with transforming growth factor B1 and lead exposure established.


Author(s):  
Apilak Worachartcheewan ◽  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Reny Pratiwi ◽  
Virapong Prachayasittikul ◽  
...  

Background: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+ -dependent histone deacetylases which play important functional roles in removal of the acetyl group of acetyl-lysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. Objective: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. Method: Simplified molecular input line entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The data set was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration and external sets. Results: Statistical indices for the evaluation of QSAR models suggested good statistical quality for models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e. promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved Sirt1 and Sirt2 inhibitors. Conclusion: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.


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