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Implementation of rapid genomic sequencing in safety-net neonatal intensive care units: protocol for the VIrtual GenOme CenteR (VIGOR) proof-of-concept study

Por: D'Gama · A. M. · Hills · S. · Douglas · J. · Young · V. · Genetti · C. A. · Wojcik · M. H. · Feldman · H. A. · Yu · T. W. · G Parker · M. · Agrawal · P. B. · VIGOR Network · Agrawal · Allcroft · Bhandari · Cantu · DGama · Douglas · Feldman · Genetti · Hills · Honrubia · Kritzer · Parke
Introduction

Rapid genomic sequencing (rGS) in critically ill infants with suspected genetic disorders has high diagnostic and clinical utility. However, rGS has primarily been available at large referral centres with the resources and expertise to offer state-of-the-art genomic care. Critically ill infants from racial and ethnic minority and/or low-income populations disproportionately receive care in safety-net and/or community settings lacking access to state-of-the-art genomic care, contributing to unacceptable health equity gaps. VIrtual GenOme CenteR is a ‘proof-of-concept’ implementation science study of an innovative delivery model for genomic care in safety-net neonatal intensive care units (NICUs).

Methods and analysis

We developed a virtual genome centre at a referral centre to remotely support safety-net NICU sites predominantly serving racial and ethnic minority and/or low-income populations and have limited to no access to rGS. Neonatal providers at each site receive basic education about genomic medicine from the study team and identify eligible infants. The study team enrols eligible infants (goal n of 250) and their parents and follows families for 12 months. Enrolled infants receive rGS, the study team creates clinical interpretive reports to guide neonatal providers on interpreting results, and neonatal providers return results to families. Data is collected via (1) medical record abstraction, (2) surveys, interviews and focus groups with neonatal providers and (3) surveys and interviews with families. We aim to examine comprehensive implementation outcomes based on the Proctor Implementation Framework using a mixed methods approach.

Ethics and dissemination

This study is approved by the institutional review board of Boston Children’s Hospital (IRB-P00040496) and participating sites. Participating families are required to provide electronic written informed consent and neonatal provider consent is implied through the completion of surveys. The results will be disseminated via peer-reviewed publications and data will be made accessible per National Institutes of Health (NIH) policies.

Trial registration number

NCT05205356/clinicaltrials.gov.

Ultrasound tomography enhancement by signal feature extraction with modular machine learning method

by Bartłomiej Baran, Dariusz Majerek, Piotr Szyszka, Dariusz Wójcik, Tomasz Rymarczyk

Robust and reliable diagnostic methods are desired in various types of industries. This article presents a novel approach to object detection in industrial or general ultrasound tomography. The key idea is to analyze the time-dependent ultrasonic signal recorded by three independent transducers of an experimental system. It focuses on finding common or related characteristics of these signals using custom-designed deep neural network models. In principle, models use convolution layers to extract common features of signals, which are passed to dense layers responsible for predicting the number of objects or their locations and sizes. Predicting the number and properties of objects are characterized by a high value of the coefficient of determination R2 = 99.8% and R2 = 98.4%, respectively. The proposed solution can result in a reliable and low-cost method of object detection for various industry sectors.
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