An Automated Electrochemical Flow Platform to Accelerate Library Synthesis and Reaction Optimization.

Automated batch and flow reactors are well-established for high throughput experimentation in both thermal chemistry and photochemistry. However, the development of automated electrochemical platforms is hindered by cell miniaturization challenges in batch and difficulties in designing effective single-pass flow systems. In order to address these issues, we have designed and implemented a new, slug-based automated electrochemical flow platform. This platform was successfully demonstrated for electrochemical C-N cross-couplings of E3 ligase binders with diverse amines (44 examples), which were subsequently transferred to a continuous-flow mode for confirmation and isolation, showing its applicability for medicinal chemistry purposes. To further validate the versatility of the platform, Design of Experiments (DoE) optimization was performed for an unsuccessful library target. This optimization process, fully automated by the platform, resulted in a remarkable 6-fold increase in reaction yield.

Enhancing Operational Efficiency and Service Delivery through a Robotic Dispensing System: A Case Study from a Retail Pharmacy in Brazil.

Drug dispensing in retail pharmacies typically involves several manual tasks that often lead to inefficiencies and errors. This is the first published quality improvement study in Latin America, specifically in Brazil, investigating the operational impacts of implementing a robotic dispensing system in a retail pharmacy. Through observational techniques, we measured the time required for the following pharmacy workflows before and after implementing the robotic dispensing system: customer service, receiving stock, stocking inventory, separation, invoicing, and packaging of online orders for delivery. Time savings were observed across all workflows within the pharmacy, notably in receiving stock and online order separation, which experienced 70% and 75% reductions in total time, respectively. Furthermore, customer service, stocking, invoicing, and packaging of online orders, also saw total time reductions from 36% to 53% after implementation of the robotic dispensing system. This study demonstrates an improvement in the pharmacy's operational efficiency post-implementation of the robotic dispensing system. These findings highlight the potential for such automated systems to streamline pharmacy operations, improve staff time efficiency, and enhance service delivery.

FAMS-A Targeted Fatty Acid Mass Spectrometry Method for Monitoring Free Fatty Acids from Polysorbate Hydrolysis.

Polysorbates are the predominant surfactants used to stabilize protein formulations. Unfortunately, polysorbates can undergo hydrolytic degradation, which releases fatty acids that can accumulate to form visible particles. The detection and quantitation of these fatty acid degradation products are critical for assessing the extent of polysorbate degradation and the associated risks of particle formation. We previously developed a user-friendly mass spectrometric method called Fatty Acids by Mass Spectrometry (FAMS) to quantify the free fatty acids. The FAMS method was validated according to ICH Q2 (R1) guidelines and is suitable for a wide range of products, buffers and protein concentrations. The end-to-end workflow can be automated from sample preparation to data analysis. To broaden method accessibility, the QDa detector selected for fatty acid measurement does not require specific mass spectrometry experience. We provide here a detailed procedure for both manual and automated sample preparation for high-throughput analysis. In addition, we highlight in this protocol the critical operational details, procedural watchouts and troubleshooting tips to support the successful execution of this method in another laboratory.

The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning.

Therapeutic antibodies such as monoclonal antibodies (mAbs), bispecific and multispecific antibodies are pivotal in therapeutic protein development and have transformed disease treatments across various therapeutic areas. The integrity of therapeutic antibodies, however, is compromised by sequence liabilities, notably deamidation, where asparagine (N) and glutamine (Q) residues undergo chemical degradations. Deamidation negatively impacts the efficacy, stability, and safety of diverse classes of antibodies, thus necessitating the critical need for the early and accurate identification of vulnerable sites. In this article, a comprehensive antibody deamidation-specific dataset (n = 2285) of varied modalities was created by using high-throughput automated peptide mapping followed by supervised machine learning to predict the deamidation propensities, as well as the extents, throughout the entire antibody sequences. We propose a novel chimeric deep learning model, integrating protein language model (pLM)-derived embeddings with local sequence information for enhanced deamidation predictions. Remarkably, this model requires only sequence inputs, eliminating the need for laborious feature engineering. Our approach demonstrates state-of-the-art performance, offering a streamlined workflow for high-throughput automated peptide mapping and deamidation prediction, with the potential of broader applicability to other antibody sequence liabilities.

Large language models can effectively extract stroke and reperfusion audit data from medical free-text discharge summaries.

INTRODUCTION: Audits are an integral part of effective modern healthcare. The collection of data for audits can be resource intensive. Large language models (LLM) may be able to assist. This pilot study aimed to assess the feasibility of using a LLM to extract stroke audit data from free-text medical documentation. METHOD: Discharge summaries from a one-month retrospective cohort of stroke admissions at a tertiary hospital were collected. A locally-deployed LLM, LLaMA3, was then used to extract a variety of routine stroke audit data from free-text discharge summaries. These data were compared to the previously collected human audit data in the statewide registry. Manual case note review was undertaken in cases of discordance. RESULTS: Overall, there was a total of 144 data points that were extracted (9 data points for each of the 16 patients). The LLM was correct in 135/144 (93.8%) of individual datapoints. This performance included binary categorical, multiple-option categorical, datetime, and free-text extraction fields. CONCLUSIONS: LLM may be able to assist with the efficient collection of stroke audit data. Such approaches may be pursued in other specialties. Future studies should seek to examine the most effective way to deploy such approaches in conjunction with human auditors and researchers.

Consumer-Centered Consent Automation in Health Information Exchange.

Considering consumer preferences, this paper proposes computer-readable policy expressions to govern data sharing and use in consent-based health information exchange. Consent automation is an essential component of digital health transformation efforts, enabling better consumer choices in using their sensitive data while unlocking new values through policy-aware workflow expressions. The approach is based on the HL7 FHIR standard while using the obligation and responsibility concepts formally defined in the ISO ODP Enterprise Language (ODP-EL) standard. These concepts were expressed using a domain specific language (DSL) to support code generation for integration with digital health applications. The approach enables improved interoperability, considering consent, privacy, security requirements, and the ability to adopt new business, regulative or jurisdictional policies. Additionally, it accommodates generative AI solutions for faster alignment with existing document-based policies, ensuring clear provenance. Finally, consent automation and a broader policy framework in the context of the latest Australian Government initiatives in digital health are discussed.

Diagnostic performance of an automated robot for MALDI target preparation in microbial identification.

The MBT Pathfinder is an automated colony-picking robot designed for efficient sample preparation in matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. This article presents results from three key experiments evaluating the instrument's performance in conjunction with MALDI Biotyper instrument. The method comparison experiment assessed its clinical performance, demonstrating comparable results with gram-positive, gram-negative, and anaerobic bacteria (scores larger than 2.00) and superior performance over simple direct yeast transfer (score: 1.80) when compared to samples prepared manually. The repeatability experiment confirmed consistent performance over multiple days and labs (average log score: 2.12, std. deviation: 0.59). The challenge panel experiment showcased its consistent and accurate performance across various samples and settings, yielding average scores between 1.76 and 2.19. These findings underline the MBT Pathfinder as a reliable and efficient tool for MALDI-TOF mass spectrometry sample preparation in clinical and research applications.

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring.

An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).

Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study.

BACKGROUND: Physicians spend approximately half of their time on administrative tasks, which is one of the leading causes of physician burnout and decreased work satisfaction. The implementation of natural language processing-assisted clinical documentation tools may provide a solution. OBJECTIVE: This study investigates the impact of a commercially available Dutch digital scribe system on clinical documentation efficiency and quality. METHODS: Medical students with experience in clinical practice and documentation (n=22) created a total of 430 summaries of mock consultations and recorded the time they spent on this task. The consultations were summarized using 3 methods: manual summaries, fully automated summaries, and automated summaries with manual editing. We then randomly reassigned the summaries and evaluated their quality using a modified version of the Physician Documentation Quality Instrument (PDQI-9). We compared the differences between the 3 methods in descriptive statistics, quantitative text metrics (word count and lexical diversity), the PDQI-9, Recall-Oriented Understudy for Gisting Evaluation scores, and BERTScore. RESULTS: The median time for manual summarization was 202 seconds against 186 seconds for editing an automatic summary. Without editing, the automatic summaries attained a poorer PDQI-9 score than manual summaries (median PDQI-9 score 25 vs 31, P<.001, ANOVA test). Automatic summaries were found to have higher word counts but lower lexical diversity than manual summaries (P<.001, independent t test). The study revealed variable impacts on PDQI-9 scores and summarization time across individuals. Generally, students viewed the digital scribe system as a potentially useful tool, noting its ease of use and time-saving potential, though some criticized the summaries for their greater length and rigid structure. CONCLUSIONS: This study highlights the potential of digital scribes in improving clinical documentation processes by offering a first summary draft for physicians to edit, thereby reducing documentation time without compromising the quality of patient records. Furthermore, digital scribes may be more beneficial to some physicians than to others and could play a role in improving the reusability of clinical documentation. Future studies should focus on the impact and quality of such a system when used by physicians in clinical practice.

GoFish: a foray into open-source, aquatic behavioral automation.

As the most species-rich vertebrate group, fish provide an array of opportunities to investigate the link between ecological interactions and the evolution of behavior and cognition, yet, as an animal model, they are relatively underutilized in studies of comparative cognition. To address this gap, we developed a fully automated platform for behavioral experiments in aquatic species, GoFish. GoFish includes closed-loop control of task contingencies using real-time video tracking, presentation of visual stimuli, automatic food reward dispensers, and built-in data acquisition. The hardware is relatively inexpensive and accessible, and all software components of the platform are open-source. GoFish facilitates experimental automation, allowing for customization of high-throughput protocols and the efficient acquisition of rich behavioral data. We hope this platform proves to be a useful tool for the research community, facilitating refined, reproducible behavioral experiments on aquatic species in comparative cognition, behavioral ecology, and neuroscience.

Design & automation of a small-scale towing tank for flow visualization.

Although the towing tank is a standard piece of equipment used to investigate fluid phenomena, it primarily exists as custom-built hardware that takes up a significant footprint. The size, cost, and custom-built nature have heretofore inhibited the production of this equipment in the authors' context, an African university. This paper presents a small-scale (1000 mm x 200 mm x 200 mm), low-cost (<$1,000) towing tank made using readily available components and basic digital fabrication tools. Other universities on the continent and beyond can hence create this foundational platform for fluid mechanics-related teaching and research. Leveraging an Arduino microcontroller loaded with the GRBL firmware, G-code is sent from the computer to stepper motors to execute movements in two axes. This allows for automation capabilities, controlled towing speeds, and consistent experimental conditions. Validation tests revealed motion accuracy within 1 %. A glitter-based flow visualization approach to measuring surface phenomena is demonstrated here. Experiments conducted successfully visualized relevant flow characteristics generated by bluff bodies being towed in the tank. As the Reynolds number increased within the operating range, wider wakes and larger, more distinct vortices were generated, as expected. This platform can be replicated widely in institutions that may otherwise forego experimentation in fluid mechanics.

An exploration of available methods and tools to improve the efficiency of systematic review production: a scoping review.

BACKGROUND: Systematic reviews (SRs) are time-consuming and labor-intensive to perform. With the growing number of scientific publications, the SR development process becomes even more laborious. This is problematic because timely SR evidence is essential for decision-making in evidence-based healthcare and policymaking. Numerous methods and tools that accelerate SR development have recently emerged. To date, no scoping review has been conducted to provide a comprehensive summary of methods and ready-to-use tools to improve efficiency in SR production. OBJECTIVE: To present an overview of primary studies that evaluated the use of ready-to-use applications of tools or review methods to improve efficiency in the review process. METHODS: We conducted a scoping review. An information specialist performed a systematic literature search in four databases, supplemented with citation-based and grey literature searching. We included studies reporting the performance of methods and ready-to-use tools for improving efficiency when producing or updating a SR in the health field. We performed dual, independent title and abstract screening, full-text selection, and data extraction. The results were analyzed descriptively and presented narratively. RESULTS: We included 103 studies: 51 studies reported on methods, 54 studies on tools, and 2 studies reported on both methods and tools to make SR production more efficient. A total of 72 studies evaluated the validity (n = 69) or usability (n = 3) of one method (n = 33) or tool (n = 39), and 31 studies performed comparative analyses of different methods (n = 15) or tools (n = 16). 20 studies conducted prospective evaluations in real-time workflows. Most studies evaluated methods or tools that aimed at screening titles and abstracts (n = 42) and literature searching (n = 24), while for other steps of the SR process, only a few studies were found. Regarding the outcomes included, most studies reported on validity outcomes (n = 84), while outcomes such as impact on results (n = 23), time-saving (n = 24), usability (n = 13), and cost-saving (n = 3) were less often evaluated. CONCLUSION: For title and abstract screening and literature searching, various evaluated methods and tools are available that aim at improving the efficiency of SR production. However, only few studies have addressed the influence of these methods and tools in real-world workflows. Few studies exist that evaluate methods or tools supporting the remaining tasks. Additionally, while validity outcomes are frequently reported, there is a lack of evaluation regarding other outcomes.

Closed-loop oxygen usage during invasive mechanical ventilation of pediatric patients (CLOUDIMPP): a randomized controlled cross-over study.

Background: The aim of this study is the evaluation of a closed-loop oxygen control system in pediatric patients undergoing invasive mechanical ventilation (IMV). Methods: Cross-over, multicenter, randomized, single-blind clinical trial. Patients between the ages of 1 month and 18 years who were undergoing IMV therapy for acute hypoxemic respiratory failure (AHRF) were assigned at random to either begin with a 2-hour period of closed-loop oxygen control or manual oxygen titrations. By using closed-loop oxygen control, the patients' SpO2 levels were maintained within a predetermined target range by the automated adjustment of the FiO2. During the manual oxygen titration phase of the trial, healthcare professionals at the bedside made manual changes to the FiO2, while maintaining the same target range for SpO2. Following either period, the patient transitioned to the alternative therapy. The outcomes were the percentage of time spent in predefined SpO2 ranges ±2% (primary), FiO2, total oxygen use, and the number of manual adjustments. Findings: The median age of included 33 patients was 17 (13-55.5) months. In contrast to manual oxygen titrations, patients spent a greater proportion of time within a predefined optimal SpO2 range when the closed-loop oxygen controller was enabled (95.7% [IQR 92.1-100%] vs. 65.6% [IQR 41.6-82.5%]), mean difference 33.4% [95%-CI 24.5-42%]; P < 0.001). Median FiO2 was lower (32.1% [IQR 23.9-54.1%] vs. 40.6% [IQR 31.1-62.8%]; P < 0.001) similar to total oxygen use (19.8 L/h [IQR 4.6-64.8] vs. 39.4 L/h [IQR 16.8-79]; P < 0.001); however, median SpO2/FiO2 was higher (329.4 [IQR 180-411.1] vs. 246.7 [IQR 151.1-320.5]; P < 0.001) with closed-loop oxygen control. With closed-loop oxygen control, the median number of manual adjustments reduced (0.0 [IQR 0.0-0.0] vs. 1 [IQR 0.0-2.2]; P < 0.001). Conclusion: Closed-loop oxygen control enhances oxygen therapy in pediatric patients undergoing IMV for AHRF, potentially leading to more efficient utilization of oxygen. This technology also decreases the necessity for manual adjustments, which could reduce the workloads of healthcare providers. Clinical Trial Registration: This research has been submitted to ClinicalTrials.gov (NCT05714527).

Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects.

The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.

Use of Web-Based Surveys to Collect Long-Term Pediatric Outcomes in Patients With Twin-Twin Transfusion Syndrome Treated With Fetoscopic Laser Photocoagulation: Observational Study.

Background: In the United States, patients with monochorionic diamniotic twins who undergo in utero fetoscopic laser photocoagulation (FLP) for twin-twin transfusion syndrome (TTTS) may travel great distances for care. After delivery, many parents cannot return to study sites for formal pediatric evaluation due to geographic location and cost. Objective: The aim of this study was to collect long-term pediatric outcomes in patients who underwent FLP for TTTS. Methods: We assessed the feasibility of using a web-based survey designed in REDCap (Research Electronic Data Capture; Vanderbilt University) to collect parent-reported outcomes in children treated for TTTS at a single center during 2011-2019. Patients with ≥1 neonatal survivor were invited via email to complete 5 possible questionnaires: the child status questionnaire (CSQ); fetal center questionnaire (FCQ); Ages & Stages Questionnaires, Third Edition (ASQ-3); Modified Checklist for Autism in Toddlers, Revised With Follow-Up (M-CHAT-R/F); and thank you questionnaire (TYQ). The R programming language (R Foundation for Statistical Computing) was used to automate survey distribution, scoring, and creation of customized reports. The survey was performed in 2019 and repeated after 12 months in the same study population in 2020. Results: A total of 389 patients in 26 different states and 2 international locations had an email address on file and received an invitation in 2019 to complete the survey (median pediatric age 48.9, IQR 1.0-93.6 months). Among surveyed mothers in 2019, the overall response rate was 37.3% (145/389), and the questionnaire completion rate was 98% (145/148), 87.8% (130/148), 71.1% (81/100), 86.4% (19/22), and 74.3% (110/148) for the CSQ, FCQ, ASQ-3, M-CHAT-R/F, and TYQ, respectively. In 2020, the overall response rate was 57.8% (56/97), and the questionnaire completion rate was 96.4% (54/56), 91.1% (51/56), 86.1% (31/36), 91.7% (11/12), and 80.4% (45/56) for the CSQ, FCQ, ASQ-3, M-CHAT-R/F, and TYQ, respectively. Conclusions: This is the first study to use both REDCap and computer automation to aid in the dissemination, collection, and reporting of surveys to collect long-term pediatric outcomes in the field of fetal medicine.

Integrating Health and Disability Data Into Academic Information Systems: Workflow Optimization Study.

Background: Integrating health information into university information systems holds significant potential for enhancing student support and well-being. Despite the growing body of research highlighting issues faced by university students, including stress, depression, and disability, little has been done in the informatics field to incorporate health technologies at the institutional level. Objective: This study aims to investigate the current state of health information integration within university systems and provide design recommendations to address existing gaps and opportunities. Methods: We used a user-centered approach to conduct interviews and focus group sessions with stakeholders to gather comprehensive insights and requirements for the system. The methodology involved data collection, analysis, and the development of a suggested workflow. Results: The findings of this study revealed the shortcomings in the current process of handling health and disability data within university information systems. In our results, we discuss some requirements identified for integrating health-related information into student information systems, such as privacy and confidentiality, timely communication, task automation, and disability resources. We propose a workflow that separates the process into 2 distinct components: a health and disability system and measures of quality of life and wellness. The proposed workflow highlights the vital role of academic advisors in facilitating support and enhancing coordination among stakeholders. Conclusions: To streamline the workflow, it is vital to have effective coordination among stakeholders and redesign the university information system. However, implementing the new system will require significant capital and resources. We strongly emphasize the importance of increased standardization and regulation to support the information system requirements for health and disability. Through the adoption of standardized practices and regulations, we can ensure the smooth and effective implementation of the required support system.

FruitSeg30_Segmentation dataset & mask annotations: A novel dataset for diverse fruit segmentation and classification.

Fruits are mature ovaries of flowering plants that are integral to human diets, providing essential nutrients such as vitamins, minerals, fiber and antioxidants that are crucial for health and disease prevention. Accurate classification and segmentation of fruits are crucial in the agricultural sector for enhancing the efficiency of sorting and quality control processes, which significantly benefit automated systems by reducing labor costs and improving product consistency. This paper introduces the "FruitSeg30_Segmentation Dataset & Mask Annotations", a novel dataset designed to advance the capability of deep learning models in fruit segmentation and classification. Comprising 1969 high-quality images across 30 distinct fruit classes, this dataset provides diverse visuals essential for a robust model. Utilizing a U-Net architecture, the model trained on this dataset achieved training accuracy of 94.72 %, validation accuracy of 92.57 %, precision of 94 %, recall of 91 %, f1-score of 92.5 %, IoU score of 86 %, and maximum dice score of 0.9472, demonstrating superior performance in segmentation tasks. The FruitSeg30 dataset fills a critical gap and sets new standards in dataset quality and diversity, enhancing agricultural technology and food industry applications.

A high-throughput system for drug screening based on the movement analysis of zebrafish.

Zebrafish is a highly advantageous model animal for drug screening and toxicity evaluation thanks to its amenability to optical imaging (i.e., transparency), possession of organ structures similar to humans, and the ease with which disease models can be established. However, current zebrafish drug screening technologies and devices suffer from limitations such as low level of automation and throughput, and low accuracy caused by the heterogeneity among individual zebrafish specimens. To address these issues, we herein develop a high-throughput zebrafish drug screening system. This system is capable of maintaining optimal culturing conditions and simultaneously monitoring and analyzing the movement of 288 zebrafish larvae under various external conditions, such as drug combinations. Moreover, to eliminate the effect of heterogeneity, locomotion of participating zebrafish is assessed and grouped before experiments. It is demonstrated that in contrast to the experimental results without pre-selection, which shows ∼20 % damaged motor function (i.e., degree of attenuation), the drug-induced variations among zebrafish with equivalent mobility reaches ∼80 %. Overall, our high-throughput zebrafish drug screening system overcomes current limitations by improving automation, throughput, and accuracy, resulting in enhanced detection of drug-induced variations in zebrafish motor function.

An Automated pre-Dilution Setup for Von Willebrand Factor Activity Assays.

Accurate quantification of von Willebrand factor ristocetin cofactor activity (VWF:RCo) is critical for the diagnosis and classification of von Willebrand disease, the most common hereditary and acquired bleeding disorder in humans. Moreover, it is important to accurately assess the function of von Willebrand factor (VWF) concentrates within the pharmaceutical industry to provide consistent and high-quality biopharmaceuticals. Although the performance of VWF:RCo assay has been improved by using coagulation analyzers, which are specialized devices for blood and blood plasma samples, scientists still report a high degree of intra- and inter-assay variation in clinical laboratories. Moreover, high, manual sample dilutions are required for VWF:RCo determination of VWF concentrates within the pharmaceutical industry, which are a major source for assay imprecision. For the first time, we present a precise and accurate method to determine VWF:RCo, where all critical pipetting and mixing steps are automated. A pre-dilution setup was established on CyBio FeliX (Analytik-Jena) liquid handling system, and an adapted VWF:RCo method on BCS-XP analyzer (Siemens) is used. The automated pre-dilution method was executed on three different, most frequently used coagulation analyzers and compared to manual pre-dilutions performed by an experienced operator. Comparative sample testing revealed a similar assay precision (coefficient of variation = 5.9% automated, 3.1% manual pre-dilution) and no significant differences between the automated approach and manual dilutions of an expert in this method. While no outliers were generated with the automated procedure, the manual pre-dilution resulted in an error rate of 8.3%. Overall, this operator-independent protocol enables standardization and offers an efficient way of fully automating VWF activity assays, while maintaining the precision and accuracy of an expert analyst. Key features • Automated pre-dilution setup for von Willebrand factor concentrates of various natures. • Combination of a liquid handling system (CyBio FeliX) with a coagulation analyzer (BCS-XP). • Simplifies method transfer to other laboratories. • Basic training for CyBio FeliX and BCS-XP is required. Graphical overview VWF:RCo assay principle and measurement setup. Platelets (yellow ellipsoids) with negative surface charge (- - -) are treated with formaldehyde, which partly denatures the cell surface and thus stabilizes platelets for use as assay reagents. Stabilized platelets (dark-yellow-framed yellow ellipsoids) are then brought in contact with ristocetin A (chemical structure shown; black dots), which binds to the platelet surface and facilitates binding of VWF (green circles). The graphs show an example of quantitative determination of platelet agglutination by measurement of light transmission, where increasing amounts of VWF increase light transmission over time. The photo in the left-bottom corner shows the CyBio FeliX setup for VWF sample dilution and the photo in the right-bottom corner displays the BCS-XP system, which is used for VWF:RCo measurements.