Role of Whole-exome Sequencing in Phenotype Classification and Clinical Treatment of Pediatric Restrictive Cardiomyopathy

Background： Restrictive cardiomyopathy （RCM） is the least common cardiomyopathy in which the walls are rigid and the heart is restricted from stretching and filling properly. Cardiac troponin I （cTnI） mutation-caused myofibril Ca2＋ hypersensitivity has been shown to be associated with impaired diastolic function. This study aimed to investigate the linkage between the genotype and clinical therapy of RCM. Methods： Five sporadic pediatric RCM patients confirmed by echocardiography were enrolled in this study.Whole-exome sequencing （WES） was performed for the cohort to find out candidate causative gene variants. Sanger sequencing confirmed the WES-identified variants. Results： TNNI3 variants were found in all of the five patients. R192H mutation was shared in four patients while R204H mutation was found only in one patient. Structure investigation showed that the C terminus of TNNI3 was flexible and mutation on the C terminus was possible to cause the RCM. Catechins were prescribed for the five patients once genotype was confirmed. Ventricular diastolic function was improved in three patients during the follow-up. Conclusions： Our data demonstrated that TNNI3 mutation-induced RCM1 is the most common type of pediatric RCM in this study. In addition, WES is a reliable approach to identify likely pathogenic genes of RCM and might be useful for the guidance of clinical treatment scheme.

Cross Entropy Based Sparse Logistic Regression to Identify Phenotype-Related Mutations in Methicillin-Resistant <i>Staphylococcus aureus</i>

Emergence of drug resistant bacteria is one of the serious problems in today’s public health. However, the relationship between genomic mutation of bacteria and the phenotypic difference of them is still unclear. In this paper, based on the mutation information in whole genome sequences of 96 MRSA strains, two kinds of phenotypes (pathogenicity and drug resistance) were learnt and predicted by machine learning algorithms. As a result of effective feature selection by cross entropy based sparse logistic regression, these phenotypes could be predicted in sufficiently high accuracy (100% and 97.87%, respectively) with less than 10 features. It means that we could develop a novel rapid test method in the future for checking MRSA phenotypes.

Palmprint Phenotype Feature Extraction and Classification Based on Deep Learning

Palmprints are of long practical and cultural interest.Palmprint principal lines,also called primary palmar lines,are one of the most dominant palmprint features and do not change over the lifespan.The existing methods utilize filters and edge detection operators to get the principal lines from the palm region of interest(ROI),but can not distinguish the principal lines from fine wrinkles.This paper proposes a novel deep-learning architecture to extract palmprint principal lines,which could greatly reduce the influence of fine wrinkles,and classify palmprint phenotypes further from 2D palmprint images.This architecture includes three modules,ROI extraction module(REM)using pre-trained hand key point location model,principal line extraction module(PLEM)using deep edge detection model,and phenotype classifier(PC)based on ResNet34 network.Compared with the current ROI extraction method,our extraction is competitive with a success rate of 95.2%.For principal line extraction,the similarity score between our extracted lines and ground truth palmprint lines achieves 0.813.And the proposed architecture achieves a phenotype classification accuracy of 95.7%based on our self-built palmprint dataset CAS_Palm.