Complex Workflows Research Articles

Optimizing Colorectal Screening in Portugal with Process Mining

Abstract Background Efficient healthcare screening programs management influences patient outcomes. Process mining has emerged as a powerful tool to enhance these programs by refining complex workflows and pinpointing operational deviations. Objectives This study utilizes process mining techniques to analyze the colorectal cancer screening program in Northern Portugal, focusing on detecting deviations and performance bottlenecks in process models. Methods This screening program started in March 2018, but process mining was only used on data posterior to 2020. Over 1 million case observations from administrative data were included in the process model analysis, involving more than six key process activities and numerous resources. We used new software and statistical tools for process mining to rapidly compute observations and filter for different operational needs and monitor interventions. Results The discovery analysis identified deviations, such as unexpected performance issues and irregular activity sequences. Although over 100 traces were identified, fewer than five traces accounted for more than 90% of all case paths, indicating the most common traces that have a true impact in participation rates, referral rates, and treatment rates. Notable differences in resource performance during specific periods, such as COVID-19 or seasonal events, were also observed. These findings provided insights for adjusting resource allocation and improving coordination, with impact in minimizing seasonal phenomena or peak production periods. Lessons Process mining offers actionable insights for effective health program coordination, enabling the monitoring and timely response to process deviations. With key areas for action identified in a more timely and precise manner, we can enhance efficiency, and expand population coverage and treatment referral. Future initiatives will focus on further process optimization and national coverage expansion using these management tools. Key messages • Process mining revolutionizes healthcare by enabling the strategic management of complex oncological screening program workflows. • Comprehensive data analysis across the patient pathway enhances program efficiency and expands treatment access.

AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. We present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflow simplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflow for three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. AnVILWorkflow lowers the barrier to utilizing AnVIL’s resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

Using Laboratory Documentation to Extend a Large Language Model to Serve as a Laboratory Assistant

Abstract Demographic and structural changes in the workforce are resulting in ever smaller cohorts of laboratory medical professionals while medical advancements and an aging population has increased demand for laboratory testing. Furthermore, the inexorable aging of our technologists has resulted in a workforce with a high variation in experience. The need for new tools to help bridge the experience gap and increase productivity are readily apparent. Recently, large language models (LLMs) have emerged as powerful tools in various industries, including healthcare. However, numerous concerns have been raised regarding data security particularly with respect to proprietary and patient data. Furthermore, these models must be adapted to function effectively within the unique context of laboratories. To this end, we present a proof of concept project that uses laboratory documentation to extend a locally run LLM to serve as a laboratory assistant. To develop this tool, we collected and analyzed job aids, SOPs, multidisciplinary guidelines, and classification systems relevant to a particular laboratory at our institution. We then used retrieval augmented generation (RAG) on these documents to locally extend open source large language models to understand laboratory-specific terminology, procedures, and contexts. Preliminary results suggest that the language model can accurately process and generate responses related to specific lab tasks, including sample handling, testing protocols, and data interpretation. For example, it can suggest possible differential diagnoses based on test results and generate suggested follow-up tests even for complex workflows such as leukemia/lymphoma flow cytometry. By using existing specific laboratory documentation as training data, we have demonstrated that it is possible to securely adapt large language models to function effectively within the context of laboratories. This represents an essential step towards integrating advanced technologies into the laboratory. Future work includes evaluating the language model’s performance in different lab settings and expanding its functionality. We envision that this project could evolve into an integral component of laboratories’ digital infrastructure, hopefully enhancing training, knowledge sharing, and operational efficiency.

SKYQUANT 3D: Quantifying Vascular Anatomy With an Open-Source Workflow for Comprehensive Analysis of Volumetric Optoacoustic Angiography Data.

Efficient visualization of the vascular system is of key importance in biomedical research into tumor angiogenesis, cerebrovascular alterations, and other angiopathies. Optoacoustic (OA) angiography offers a promising solution combining molecular optical contrast with high resolution and deep penetration of ultrasound. However, its hybrid nature implies complex data collection and processing workflows, with significant variability in methodologies across developers and users. To streamline interoperability, we introduce SKYQUANT 3D, a Python-based set of instructions for the Thermo Fisher Scientific Amira/Avizo 3D Visualization & Analysis Software. Our workflow simplifies the batch processing of volumetric optoacoustic angiography images, extracting meaningful quantitative information while also providing statistical analysis and graphical representation of the results. Quantification performance of SKYQUANT 3D is demonstrated using functional preclinical and clinical in vivo 3D OA angiographic tests involving ambient temperature variations and repositioning of the imaged limb.

Developing interoperable, accessible software via the atomic, molecular, and optical sciences gateway: A case study of the B-spline atomic R-matrix code graphical user interface.

The Atomic, Molecular, and Optical Science (AMOS) Gateway is a comprehensive cyberinfrastructure for research and educational activities in computational AMO science. The B-Spline atomic R-Matrix (BSR) suite of programs is one of several computer programs currently available on the gateway. It is an excellent example of the gateway's potential to increase the scientific productivity of AMOS users. While the suite is available to be used in batch mode, its complexity does not make it well-suited to the approach taken in the gateway's default setup. The complexity originates from the need to execute many different computations and to construct generally complex workflows, requiring numerous input files that must be used in a specific sequence. The BSR graphical user interface described in this paper was developed to considerably simplify employing the BSR codes on the gateway, making BSR available to a large group of researchers and students interested in AMO science.

Optimizing Serverless Function Orchestration for Complex Workflows in Cloud Platforms

Purpose: This paper aims to investigate ways of increasing the efficiency of various workloads of serverless function orchestration in cloud systems while decreasing latency and optimizing resources. Materials and Methods: The report suggests adaptive learning systems and a feedback loop framework for managing the orchestrating serverless environment. Findings: It has also been affirmed that case studies help validate the framework rather than presenting the practical implications of the proposed techniques. Implications to Theory, Practice and Policy: The paper recommends focusing on managing performance, dependencies, and expenses in serverless function orchestration by employing adaptive learning systems and feedback loop frameworks.

Mimicking a Cellular Crowding Environment for Enzyme-Free Paper-Based Nucleic Acid Tests at the Point of Care.

Point of care (PoC) nucleic acid amplification tests (NAATs) are a cornerstone of public health, providing the earliest and most accurate diagnostic method for many communicable diseases in the same location where the patient receives treatment. Communicable diseases, such as human immunodeficiency virus (HIV), disproportionately impact low-resource communities where NAATs are often unobtainable due to the resource-intensive enzymes that drive the tests. Enzyme-free nucleic acid detection methods, such as hybridization chain reaction (HCR), use DNA secondary structures for self-driven amplification schemes, producing large DNA nanostructures, capable of single-molecule detection in cellulo. These thermodynamically driven DNA-based tests have struggled to penetrate the PoC diagnostic field due to their inadequate limits of detection or complex workflows. Here, we present a proof-of-concept NAAT that combines HCR-based amplification of a target nucleic acid sequence with paper-based nucleic acid filtration and enrichment capable of detecting sub-pM levels of synthetic DNA. We reconstruct the favorable hybridization conditions of an in cellulo reaction in vitro by incubating HCR in an evaporating, microvolume environment containing poly(ethylene glycol) as a crowding agent. We demonstrate that the kinetics and thermodynamics of DNA-DNA and DNA-RNA hybridization is enhanced by the dynamic evaporating environment and inclusion of crowding agents, bringing HCR closer to meeting PoC NAAT needs.

End-to-end AI pipeline optimization: Benchmarking and performance enhancement techniques for recommendation systems

End-to-end AI pipeline optimization is critical for improving the efficiency and performance of recommendation systems, which play a pivotal role in personalizing user experiences across various domains. This review explores benchmarking and performance enhancement techniques tailored to recommendation systems within AI pipelines. The objective is to streamline the processes involved in data ingestion, feature engineering, model training, and deployment to achieve optimal system performance and user satisfaction. Recommendation systems typically involve complex workflows that require continuous optimization. Benchmarking serves as a foundational step, enabling the identification of bottlenecks and inefficiencies within the pipeline. By establishing clear performance metrics, such as precision, recall, and latency, benchmarking allows for the comparative analysis of different algorithms, data processing methods, and system configurations. These metrics guide the selection of the most suitable models and techniques, thereby enhancing overall system effectiveness. Performance enhancement techniques are then applied to various stages of the AI pipeline. Advanced methods in feature engineering, such as automated feature selection and dimensionality reduction, can significantly improve model accuracy while reducing computational overhead. In the model training phase, techniques like hyperparameter tuning, gradient-based optimization, and distributed training are employed to accelerate convergence and improve model generalization. Additionally, optimization strategies at the deployment stage, including model compression, quantization, and the use of specialized hardware, are crucial for minimizing latency and resource consumption. This review also highlights the importance of continuous monitoring and feedback loops to maintain the effectiveness of recommendation systems in dynamic environments. By integrating real-time analytics and adaptive algorithms, systems can adjust to changing user behaviors and preferences, ensuring sustained performance improvements. In conclusion, optimizing end-to-end AI pipelines for recommendation systems involves a multifaceted approach that includes benchmarking, feature engineering, model training, and deployment enhancements. These efforts collectively contribute to more efficient, scalable, and accurate recommendation systems, ultimately leading to better user experiences and operational efficiencies. This paper will focus on optimizing AI pipelines for recommendation systems, covering the entire process from data extraction and feature engineering to model deployment and performance benchmarking. It will discuss techniques for identifying and mitigating performance bottlenecks in different computing environments, providing valuable insights for enhancing the efficiency of recommendation systems, which are crucial for various applications.

A Demonstration of the Risk-Informed NEI 18-04 Design Evaluation Model for the Modular High-Temperature Gas Reactor

Several advanced reactor designs are now under active development in the United States and elsewhere, promising sustainable solutions to the growing world energy needs. The designs currently being considered are quite diverse and different from the more established light water reactor technology that has dominated the operating commercial nuclear landscape. While advanced reactor concepts were first explored in the dawn of the nuclear age, they are now being reconsidered under the light of modern needs, and specifically, for their flexible operating conditions and inherent safety characteristics. In response, the U.S. Nuclear Regulatory Commission staff is moving forward with development of 10 CFR Part 53 rulemaking, which is a more risk-informed, technology-agnostic framework for licensing and regulating such new designs. The nuclear industry response to this regulatory initiative resulted in the technical report by the Nuclear Energy Institute, NEI 18-04 Revision 1, which provides an implementation roadmap of the risk-informed approach when defining the safety case for a new plant design. The implementation of this safety case may be a nontrivial exercise for an actual reactor design. This paper provides a demonstration of performing such an analysis for a representative advanced reactor. Public information from the General Atomics high-temperature gas reactor design was considered in this demonstration. The analysis workflow was facilitated with the FPoliSolutions’ proprietary Risk-Informed System Engineering (RISE) digital platform, a product that was presented in previous publications. RISE is one application of FPoli’s enterprise digital platform, which was created to facilitate orchestration of complex workflows leveraging recent technologies developed at national laboratories, such as Idaho National Laboratory’s RAVEN and EMRALD frameworks. The analysis described in the paper includes the selection and classifications of events, the integration of probabilistic risk analysis artifacts, and event modeling simulations for consequence evaluations. The results are then used for system, structures, and components safety classification and a synthesis of the safety case for the design in line with the frequency-consequence targets presented in NEI 18-04. The purpose of the analysis, as framed in RISE, is to readily produce outputs and views that can aid users and regulators in making risk-informed decisions to demonstrate their plant safety case.

Towards a data platform for multimodal 4D mechanics of material microstructures

This paper presents advances in the data management strategy applied to 4D multimodal mechanics of material sample microstructures. Guidelines to build a data platform allowing for complex workflows, involving several high-throughput experimental and numerical techniques, and complying with FAIR data management principles, are discussed. Next, their implementation within the open-source Python package Pymicro is presented, offering a high-level interface to build complex datasets through multimodal methodologies. Its capability to enable and automate complex workflows by building a digital twin of a commercially pure titanium sample under tension are then demonstrated. The digital twin contains microstructural and mechanical data for thousands of grains gathered on the same sample through synchrotron DCT and in-situ SEM experiments, as well as full-field numerical simulation. Finally, a local and statistical comparison between simulation and measurements of plastic slip and crystal rotation in hundreds of grains is shown, as an example of the contribution of this platform to multimodal data convergence and its importance for the development of a new generation of material models.

Pilot Federated Analysis Projects

The Cambrian explosion of Trusted Research Environments (TREs) will inevitably lead to increased data siloing and potentially decrease achievable data science. Federated access to data is a mitigation against this risk. The presentation will cover two major infrastructure demonstrator projects to address this need and start the UK journey towards interconnected TRE’s. TELEPORT was an infrastructure to connect TREs and allow all the data assets to be seen through a single pane of glass while leaving them in situ; the analysis can be performed on each data asset, as well as combining data from multiple TREs to derive knowledge. The analysis is performed within the construct of a pop-up TRE, which is a temporary project-specific TRE. The data governance model is operated in an as-is state, with all egress controls being applied upon extraction of research output. This method has been termed Federated Data. TREFX is a set of infrastructure based around international GA4GH standards and the RO-CRATE concept to provide a platform to send complex workflows to the TRE’s to be performed upon the data assets, with the output post egress checking to be re-packed into the inbound RO-CRATE prior to being returned to the researcher. This method addresses both federated analysis and method reproducibility. This project was extended to cover ADDI workloads for dementia. Both solutions were jointly developed by Swansea University / SeRP and share common elements. Future work will see both these products being combined into a hybrid approach as well as spin-out products and services.

Metapipeline-DNA: A Comprehensive Germline & Somatic Genomics Nextflow Pipeline.

DNA sequencing is becoming more affordable and faster through advances in high-throughput technologies. This rise in data availability has contributed to the development of novel algorithms to elucidate previously obscure features and led to an increased reliance on complex workflows to integrate such tools into analyses pipelines. To facilitate the analysis of DNA sequencing data, we created metapipeline-DNA, a highly configurable and extensible pipeline. It encompasses a broad range of processing including raw sequencing read alignment and recalibration, variant calling, quality control and subclonal reconstruction. Metapipeline-DNA also contains configuration options to select and tune analyses while being robust to failures. This standardizes and simplifies the ability to analyze large DNA sequencing in both clinical and research settings. Metapipeline-DNA is an open-source Nextflow pipeline under the GPLv2 license and is freely available at https://github.com/uclahs-cds/metapipeline-DNA.

Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification

Workflows orchestrate a collection of computing tasks to form a complex workflow logic. Different from the traditional monolithic workflow management systems, modern workflow systems often manifest high throughput, concurrency and scalability. As service-based systems, execution time monitoring is an important part of maintaining the performance for those systems. We developed a trace profiling approach that leverages quantitative verification (also known as probabilistic model checking) to analyse complex time metrics for workflow traces. The strength of probabilistic model checking lies in the ability of expressing various temporal properties for a stochastic system model and performing automated quantitative verification. We employ semi-Makrov chains (SMCs) as the formal model and consider the first passage times (FPT) measures in the SMCs. Our approach maintains simple mergeable data summaries of the workflow executions and computes the moment parameters for FPT efficiently. We describe an application of our approach to AWS Step Functions, a notable workflow web service. An empirical evaluation shows that our approach is efficient for computer high-order FPT moments for sizeable workflows in practice. It can compute up to the fourth moment for a large workflow model with 10,000 states within 70 s.

Prescription Digital Therapeutics for Substance Use Disorder in Primary Care: Mixed Methods Evaluation of a Pilot Implementation Study.

Delivering prescription digital therapeutics (ie, evidence-based interventions designed to treat, manage, or prevent disorders via websites or smartphone apps) in primary care could increase patient access to substance use disorder (SUD) treatments. However, the optimal approach to implementing prescription digital therapeutics in primary care remains unknown. This pilot study is a precursor to a larger trial designed to test whether implementation strategies (practice facilitation [PF] and health coaching [HC]) improve the delivery of prescription digital therapeutics for SUDs in primary care. This mixed methods study describes outcomes among patients in the 2 pilot clinics and presents qualitative findings on implementation. From February 10 to August 6, 2021, a total of 3 mental health specialists embedded in 2 primary care practices of the same integrated health system were tasked with offering app-based prescription digital therapeutics to patients with SUD. In the first half of the pilot, implementation activities included training and supportive tools. PF (at 1 clinic) and HC (at 2 clinics) were added in the second half. All study analyses relied on secondary data, including electronic health records and digital therapeutic vendor data. Primary outcomes were the proportion of patients reached by the prescription digital therapeutics and fidelity related to ideal use. We used qualitative methods to assess the adherence to planned activities and the barriers and facilitators to implementing prescription digital therapeutics. Of all 18 patients prescribed the apps, 10 (56%) downloaded the app and activated their prescription, and 8 (44%) completed at least 1 module of content. Patients who activated the app completed 1 module per week on average. Ideal use (fidelity) was defined as completing 4 modules per week and having a monthly SUD-related visit; 1 (6%) patient met these criteria for 10 weeks (of the 12-week prescription period). A total of 5 (28%) patients had prescriptions while HC was available, 2 (11%) were successfully contacted, and both declined coaching. Clinicians reported competing clinical priorities, technical challenges, and logistically complex workflows in part because the apps required a prescription. Some pilot activities were impacted by staff turnover that coincided with the COVID-19 pandemic. The facilitators to implementation were high engagement and the perception that the apps could meet patient needs. The pilot study encountered the barriers to implementing prescription digital therapeutics in a real-world primary care setting, especially staffing shortages, turnover, and competing priorities for clinic teams. The larger randomized trial will clarify the extent to which PF and HC improve the implementation of digital therapeutics. ClinicalTrials.gov NCT04907045; https://clinicaltrials.gov/study/NCT04907045.

Artificial intelligence-based motion tracking in cancer radiotherapy: A review.

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Artificial intelligence (AI) has recently demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges, including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review presents the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provides a literature summary on the topic. We will also discuss the limitations of these AI-based studies and propose potential improvements.

Digital: accelerating the pathway.

The Spherical Tokamak for Energy Production (STEP) programme is an ambitious but challenging endeavour to design and deliver a prototype fusion power plant. It is a rapid, fast-moving programme, designing a first of a kind device in a Volatile, Uncertain, Complex and Ambiguous (VUCA) environment, and digital tools play a pivotal role in managing and navigating this space. Digital helps manage the complexity and sheer volume of information. Advanced modelling and simulation techniques provide a platform for designers to explore various scenarios and iteratively refine designs, providing insights into the intricate interplay of requirements, constraints and design factors across physics, technology and engineering domains and aiding informed decision-making amidst uncertainties. It also provides a means of building confidence in the new scientific, technological and engineering solutions, given that a full-scale-integrated precursor test is not feasible, almost by definition. The digital strategy for STEP is built around a vision of a digital twin of the whole plant. This will evolve from the current digital shadow formed by system architecting codes and complex workflows and will be underpinned by developing capabilities in plasma, materials and engineering simulation, data management, advanced control, industrial cybersecurity, regulation, digital technologies and related digital disciplines. These capabilities will help address the key challenges of managing the complexity and quantity of information, improving the reliability and robustness of the current digital shadow and developing an understanding of its validity and performance.This article is part of the theme issue 'Delivering Fusion Energy - The Spherical Tokamak for Energy Production (STEP)'.

Optimizing scientific workflow scheduling in cloud computing: a multi-level approach using whale optimization algorithm

Cloud computing has evolved into an indispensable tool for facilitating scientific research due to its ability to efficiently distribute and process workloads in a virtual environment. Scientific tasks that involve complicated task dependencies and user-defined constraints related to quality of service (QoS) and time constraints require the efficient use of cloud resources. Planning these scientific workflow tasks represents an NP-complete problem, prompting researchers to explore various solutions, including conventional planners and evolutionary optimization algorithms. In this study, we present a novel, multistage algorithm specifically designed to schedule scientific workflows in cloud computing contexts. This approach addresses the challenges of efficiently mapping complex workflows onto distributed cloud resources while considering factors like resource heterogeneity, dynamic workloads, and stringent performance requirements. The algorithm uses the whale optimization algorithm (WOA) with a two-phase approach to shorten execution time, minimize financial costs, and effectively maintain load balancing.

Designing Digital Twin with IoT and AI in Warehouse to Support Optimization and Safety in Engineer-to-Order Manufacturing Process for Prefabricated Building Products

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This paper outlines a methodology encompassing users’ requirements and the design to support the development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and artificial intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi’s production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM-, IoT-, and AI-powered digital twin for engineer-to-order manufacturers. This research, developed within the IRIS project, serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in the construction industry.

Performance of a Rapid Digital PCR Test for the Detection of Non-Small Cell Lung Cancer (NSCLC) Variants.

Next-generation sequencing is widely used for comprehensive molecular profiling for many cancers including lung cancer. However, the complex workflows and long turnaround times limit its access and utility. ChromaCode's High Definition PCR Non-Small Cell Lung Cancer Panel (HDPCR™ NSCLC Panel) is a low-cost, rapid turnaround, digital polymerase chain reaction assay that is designed to detect variants in nine NSCLC genes listed in National Comprehensive Cancer Network guidelines. This assay uses TaqMan® probe limiting chemistry and proprietary analysis software to enable multi-target detection within a single-color channel. We compared the performance of the HDPCR™ NSCLC Panel against an in-house, laboratory-developed, targeted next-generation sequencing panel used in the Molecular Diagnostics Laboratory at the University of Minnesota Medical Center to detect biomarkers for NSCLC. The overall accuracy of the HDPCR panel was 99.48% (95% confidence interval 99.01-99.76) with a sensitivity of 95.35% (95% confidence interval 88.52-98.72) and a specificity of 99.69% (95% confidence interval 99.29-99.90). The HDPCR wet lab workflow was 4 h, and the time to generate variant calls from raw data using the ChromaCode Cloud was 2 minutes. We demonstrated that the HDPCR™ NSCLC Panel provides timely, comprehensive, and sensitive mutation detection in NSCLC samples with results in less than 24 h.

A chemoenzymatic method for simultaneous profiling N- and O-glycans on glycoproteins using one-pot format

Glycosylation is generally characterized and controlled as a critical quality attribute for therapeutic glycoproteins because glycans can impact protein drug-product efficacy, half-life, stability, and safety. Analytical procedures to characterize N-glycans are relatively well established, but the characterization of O-glycans is challenging due to the complex workflows and lack of enzymatic tools. Here, we present a simplified chemoenzymatic method to simultaneously profile N- and O-glycans from the same sample using a one-pot format by mass spectrometry (MS). N-glycans were first released by PNGase F, followed by O-glycopeptide generation by proteinase K, selective N-glycan reduction, and O-glycan release by β-elimination during permethylation of both N- and O-glycans. Glycan structural assignments and determination of N- to O-glycan ratio was obtained from the one-pot mass spectra. The streamlined, one-pot method is a reliable approach that will facilitate advanced characterizations for quality assessments of therapeutic glycoproteins.