Performance Evaluation of a Commercial Automated Library Preparation System for Clinical Microbial Whole-Genome Sequencing Assays.

Next-generation sequencing (NGS) has proven clinical utility on disease management and serves as an important tool for genomic surveillance. Currently, hurdles surrounding its implementation, namely the complex and demanding analytical workflows, have impeded its widespread use in many laboratories. To address this challenge, the UCLA Molecular Microbiology and Pathogen Genomics Laboratory evaluated the performance of the Tecan MagicPrep NGS system, a commercial automated solution for library preparation for clinical whole-genome sequencing assays, against the Illumina Nextera DNA Flex Library Prep. Using 35 unique organisms (28 bacteria and 7 fungi) for various clinical applications, including microbial identification and genomic characterization, we compared the quantity and quality of the prepared libraries and the resulting sequences, and concordance of the overall results. We also assessed the impact of its implementation on laboratory workflow. The MagicPrep NGS produced higher library concentrations with smaller sizes, and correspondingly, higher molarity. Quality metrics of the sequences, however, demonstrated no significant impact on the overall results, producing 100% concordance with the reference method. Importantly, workflow analysis showed 5 hours less hands-on time per run with more flexibility. This evaluation study indicates that performance of the MagicPrep NGS is comparable to the Nextera DNA Flex with the added benefit of improving workflow efficiency and reducing labor for performing routine clinical microbial whole-genome sequencing tests.

Whole-Exome Sequencing Identifies Homozygote Nonsense Variants in LMOD2 Gene Causing Infantile Dilated Cardiomyopathy.

As an essential component of the sarcomere, actin thin filament stems from the Z-disk extend toward the middle of the sarcomere and overlaps with myosin thick filaments. Elongation of the cardiac thin filament is essential for normal sarcomere maturation and heart function. This process is regulated by the actin-binding proteins Leiomodins (LMODs), among which LMOD2 has recently been identified as a key regulator of thin filament elongation to reach a mature length. Few reports have implicated homozygous loss of function variants of LMOD2 in neonatal dilated cardiomyopathy (DCM) associated with thin filament shortening. We present the fifth case of DCM due to biallelic variants in the LMOD2 gene and the second case with the c.1193G>A (p.W398*) nonsense variant identified by whole-exome sequencing. The proband is a 4-month male infant of Hispanic descent with advanced heart failure. Consistent with previous reports, a myocardial biopsy exhibited remarkably short thin filaments. However, compared to other cases of identical or similar biallelic variants, the patient presented here has an unusually late onset of cardiomyopathy during infancy. Herein, we present the phenotypic and histological features of this variant, confirm the pathogenic impact on protein expression and sarcomere structure, and discuss the current knowledge of LMOD2-related cardiomyopathy.

Rethinking PICO in the Machine Learning Era: ML-PICO.

BACKGROUND: Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. OBJECTIVE: We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. CONCLUSION: The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.

The emerging role of clinical informatics fellows in service learning during the COVID-19 pandemic.

OBJECTIVE: The study sought to describe the contributions of clinical informatics (CI) fellows to their institutions' coronavirus disease 2019 (COVID-19) response. MATERIALS AND METHODS: We designed a survey to capture key domains of health informatics and perceptions regarding fellows' application of their CI skills. We also conducted detailed interviews with select fellows and described their specific projects in a brief case series. RESULTS: Forty-one of the 99 CI fellows responded to our survey. Seventy-five percent agreed that they were "able to apply clinical informatics training and interest to the COVID-19 response." The most common project types were telemedicine (63%), reporting and analytics (49%), and electronic health record builds and governance (32%). Telehealth projects included training providers on existing telehealth tools, building entirely new virtual clinics for video triage of COVID-19 patients, and pioneering workflows and implementation of brand-new emergency department and inpatient video visit types. Analytics projects included reports and dashboards for institutional leadership, as well as developing digital contact tracing tools. For electronic health record builds, fellows directly contributed to note templates with embedded screening and testing guidance, adding COVID-19 tests to order sets, and validating clinical triage workflows. DISCUSSION: Fellows were engaged in projects that span the breadth of the CI specialty and were able to make system-wide contributions in line with their educational milestones. CONCLUSIONS: CI fellows contributed meaningfully and rapidly to their institutions' response to the COVID-19 pandemic.