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Objectives To introduce quantum computing technologies as a tool for biomedical research and highlight future applications within healthcare, focusing on its capabilities, benefits, and limitations. Target Audience Investigators seeking to explore quantum computing and create quantum-based applications for healthcare and biomedical research. Scope Quantum computing requires specialized hardware, known as quantum processing units, that use quantum bits (qubits) instead of classical bits to perform computations. This article will cover (1) proposed applications where quantum computing offers advantages to classical computing in biomedicine; (2) an introduction to how quantum computers operate, tailored for biomedical researchers; (3) recent progress that has expanded access to quantum computing; and (4) challenges, opportunities, and proposed solutions to integrate quantum computing in biomedical applications. [ABSTRACT FROM AUTHOR]

Background: The impact variant-specific immune evasion and waning protection have on declining COVID-19 vaccine effectiveness remains unclear. Using whole-genome-sequencing (WGS), we examined the contribution of these factors on the decline observed following the introduction of the Delta variant. Further, we evaluated the utility of calendar-period-based classification as an alternative to WGS.Methods: We conducted a test-negative-case-control study among people who received SARS-CoV-2 RT-PCR testing in the Yale New Haven Health System between April 1 and August 24, 2021. Variant classification was performed using WGS and calendar-period.Results: Overall, 2,029 cases (RT-PCR positive, sequenced samples [infections]) and 343,985 controls (negative RT-PCRs) were included. VE 14-89 days after 2nd dose was significantly higher against WGS-classified Alpha-infection (84.4%, CI: 75.6-90.0%) than Delta-infection (68.9%, CI: 58.0-77.1%, p-value = 0.013). The odds of WGS-classified Delta-infection were significantly higher 90-149 than 14-89 days after 2nd dose (p-value = 0.003). VE estimates against calendar-period-classified infections approximated estimates against WGS-classified infections, however, calendar-period-based classification was subject to outcome misclassification (35%: Alpha-period, 4%: Delta-period).Conclusions: Both waning protection and variant-specific immune evasion contributed to the lower effectiveness. While VE estimates against calendar-period-classified infections mirrored those against WGS-classified infections, our analysis highlights the need for WGS when variants are co-circulating and misclassification is likely. [ABSTRACT FROM AUTHOR]

Objective: The study sought to conduct an informatics analysis on the National Evaluation System for Health Technology Coordinating Center test case of cardiac ablation catheters and to demonstrate the role of informatics approaches in the feasibility assessment of capturing real-world data using unique device identifiers (UDIs) that are fit for purpose for label extensions for 2 cardiac ablation catheters from the electronic health records and other health information technology systems in a multicenter evaluation.Materials and Methods: We focused on data capture and transformation and data quality maturity model specified in the National Evaluation System for Health Technology Coordinating Center data quality framework. The informatics analysis included 4 elements: the use of UDIs for identifying device exposure data, the use of standardized codes for defining computable phenotypes, the use of natural language processing for capturing unstructured data elements from clinical data systems, and the use of common data models for standardizing data collection and analyses.Results: We found that, with the UDI implementation at 3 health systems, the target device exposure data could be effectively identified, particularly for brand-specific devices. Computable phenotypes for study outcomes could be defined using codes; however, ablation registries, natural language processing tools, and chart reviews were required for validating data quality of the phenotypes. The common data model implementation status varied across sites. The maturity level of the key informatics technologies was highly aligned with the data quality maturity model.Conclusions: We demonstrated that the informatics approaches can be feasibly used to capture safety and effectiveness outcomes in real-world data for use in medical device studies supporting label extensions. [ABSTRACT FROM AUTHOR]

Background: SARS-CoV-2 serologic assays are becoming increasingly available and may serve as a diagnostic aid in a multitude of settings relating to past infection status. However, there is limited literature detailing the longitudinal performance of EUA-cleared serologic assays in US populations, particularly in cohorts with a remote history of PCR-confirmed SARS-CoV-2 infection (e.g., >2 months). Methods: We evaluated the diagnostic sensitivities and specificities of the Elecsys® Anti-SARS-CoV-2 (anti-N) and Elecsys Anti-SARS-CoV-2 S (anti-S1-RBD) assays, using 174 residual clinical samples up to 267 days post-PCR diagnosis of SARS-CoV-2 infection (n = 154) and a subset of samples obtained prior to the COVID-19 pandemic as negative controls (n = 20). Results: The calculated diagnostic sensitivities for the anti-N and anti-S1-RBD assays were 89% and 93%, respectively. Of the 154 samples in the SARS-CoV-2-positive cohort, there were 6 discrepant results between the anti-N and anti-S1-RBD assays, 5 of which were specimens collected ≥200 days post-PCR positivity and only had detectable levels of anti-S1-RBD antibodies. When only considering specimens collected ≥100 days post-PCR positivity (n = 41), the sensitivities for the anti-N and anti-S1-RBD assays were 85% and 98%, respectively. Conclusions: The anti-S1-RBD assay demonstrated superior sensitivity at time points more remote to the PCR detection date, with 6 more specimens from the SARS-CoV-2-positive cohort detected, 5 of which were collected more than 200 days post-PCR positivity. While analytical differences and reagent lot-to-lot variability are possible, this may indicate that, in some instances, anti-S1-RBD antibodies may persist longer in vivo and may be a better target for detecting remote SARS-CoV-2 infection. [ABSTRACT FROM AUTHOR]

The authors describe a clinical pharmacogenetics program throughout their health system which includes academic and community-based inpatient facilities and affiliated outpatient providers to meet the demand for precision medicine. They talk about the number of patients that have been clinically tested with a panel-based genotyping approach since January 2019, the complexity of clinical genetic testing and delivery of actionable results, and the need for additional real-world deployments.

Next generation sequencing (NGS) has become a common technology in the clinical laboratory, particularly for the analysis of malignant neoplasms. However, most mutations identified by NGS are variants of unknown clinical significance (VOUS). Although the approach to define these variants differs by institution, software algorithms that predict variant effect on protein function may be used. However these algorithms commonly generate conflicting results, potentially adding uncertainty to interpretation. In this review, we examine several computational tools used to predict whether a variant has clinical significance. In addition to describing the role of these tools in clinical diagnostics, we assess their efficacy in analyzing known pathogenic and benign variants in hematologic malignancies. [ABSTRACT FROM AUTHOR]