Sarcopenia and adipose tissue evaluation by artificial intelligence predicts the overall survival after TAVI.

Sarcopenia is a serious systemic disease that reduces overall survival. TAVI is selectively performed in patients with severe aortic stenosis who are not indicated for open cardiac surgery due to severe polymorbidity. Artificial intelligence-assisted body composition assessment from available CT scans appears to be a simple tool to stratify these patients into low and high risk based on future estimates of all-cause mortality. Within our study, the segmentation of preprocedural CT scans at the level of the lumbar third vertebra in patients undergoing TAVI was performed using a neural network (AutoMATiCA). The obtained parameters (area and density of skeletal muscles and intramuscular, visceral, and subcutaneous adipose tissue) were analyzed using Cox univariate and multivariable models for continuous and categorical variables to assess the relation of selected variables with all-cause mortality. 866 patients were included (median(interquartile range)): age 79.7 (74.9-83.3) years; BMI 28.9 (25.9-32.6) kg/m2. Survival analysis was performed on all automatically obtained parameters of muscle and fat density and area. Skeletal muscle index (SMI in cm2/m2), visceral (VAT in HU) and subcutaneous adipose tissue (SAT in HU) density predicted the all-cause mortality in patients after TAVI expressed as hazard ratio (HR) with 95% confidence interval (CI): SMI HR 0.986, 95% CI (0.975-0.996); VAT 1.015 (1.002-1.028) and SAT 1.014 (1.004-1.023), all p < 0.05. Automatic body composition assessment can estimate higher all-cause mortality risk in patients after TAVI, which may be useful in preoperative clinical reasoning and stratification of patients.

Phenopacket-tools: Building and validating GA4GH Phenopackets.

The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.

GA4GH Phenopackets: A Practical Introduction.

The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases.

Novel variants in EDNRB gene in Waardenburg syndrome type II and SOX10 gene in PCWH syndrome.

Waardenburg syndrome (WS) is a clinically and genetically heterogeneous group of inherited disorders manifesting with sensorineural hearing loss and pigmentary anomalies. Here we present two Caucasian families with novel variants in EDNRB and SOX10 representing both sides of phenotype spectrum in WS. The c.521G>A variant in EDNRB identified in Family 1 leads to disruption of the cysteine disulfide bridge between extracellular segments of endothelin receptor type B and causes relatively mild phenotype of WS type II with low penetrance. The novel nonsense variant c.900C>A in SOX10 detected in Family 2 leads to PCWH syndrome and was found to be lethal.

Congenital hyperinsulinism and glycogenosis-like phenotype due to a novel HNF4A mutation.

AIM: Congenital hyperinsulinism (CHI) and glycogen storage disease (glycogenosis) are both causing hypoglycemia during infancy, but with different additional clinical features and therapeutic approach. We aimed to identify a genetic cause in a child with an ambiguous phenotype. METHODS AND RESULTS: We present a child with hyperinsulinemic hypoglycemia, physiological 3-OH butyrate, increased triglyceride serum levels, increased level of glycogen in erythrocytes, increased liver transaminases, and increased echogenicity on liver ultrasonography. As both parents of the proband were referred as healthy, we raised a clinical suspicion on glycogenosis with recessive inheritance. However, whole exome sequencing revealed no mutation in genes causing glycogenosis, but a novel heterozygous variant LRG_483t1: c.427-1G>A in the HNF4A gene was identified. Aberrant splicing resulting in in-frame deletion c.429_476del, p.(T144_I159del) was confirmed by sequencing of HNF4A transcripts reverse-transcribed from whole blood RNA. The same variant was found in five of eight tested family relatives (one of them already had diabetes, two had prediabetes). With regard to the results of DNA analysis, we added diazoxide to the therapy. Consequently, the frequency and severity of hypoglycemia in the proband decreased. We have also recommended sulfonylurea treatment after diabetes onset in adult mutation carriers. CONCLUSIONS: We have identified a novel HNF4A gene mutation in our patient with CHI and glycogenosis-like phenotype. The proband and her family members benefited from the genetic testing by WES method and consequently personalized therapy. Nevertheless, the HNF4A gene testing may be considered in selected CHI cases with glycogenosis-like phenotype prior WES analysis.