Automation in Flow Cytometry.

Automation in clinical flow cytometry has the potential to revolutionize the field by improving processes and enhancing efficiency and accuracy. Integrating advanced robotics and artificial intelligence, these technologies can streamline sample preparation, data acquisition, and analysis. Automated sample handling reduces human error and increases throughput, allowing laboratories to handle larger volumes with consistent precision. Intelligent algorithms contribute to rapid data interpretation, aiding in the identification of cellular markers for disease diagnosis and monitoring. This automation not only accelerates turnaround times but also ensures reproducibility, making clinical flow cytometry a reliable tool in the realm of personalized medicine and diagnostics.

Heparin Induced Thrombocytopenia Testing.

This article provides a comprehensive overview of Heparin-Induced Thrombocytopenia (HIT) with an emphasis on laboratory testing and advantages of automation. HIT is a critical condition arising from heparin exposure, leading to a contradictory combination of thrombocytopenia with an increased thrombosis risk. The article discusses HIT's history, clinical presentation, laboratory diagnosis, and management strategies. It highlights the importance of interdisciplinary collaboration for effective diagnosis and treatment, underscoring advancements in technology and targeted therapies that are shaping future approaches to HIT management.

Evaluation of a real-life experience with a digital fluid balance monitoring technology.

BACKGROUND: Innovations in healthcare technologies have the potential to address challenges, including the monitoring of fluid balance. OBJECTIVE: This study aims to evaluate the functionality and accuracy of a digital technology compared to standard manual documentation in a real-life setting. METHODS: The digital technology, LICENSE, was designed to calculate fluid balance using data collected from devices measuring urine, oral and intravenous fluids. Participating patients were connected to the LICENSE system, which transmitted data wirelessly to a database. These data were compared to the nursing staff's manual measurements documented in the electronic patient record according to their usual practice. RESULTS: We included 55 patients in the Urology Department needing fluid balance charting and observed them for an average of 22.9 hours. We found a mean difference of -44.2 ml in total fluid balance between the two methods. Differences ranged from -2230 ml to 2695 ml, with a divergence exceeding 500 ml in 57.4% of cases. The primary source of error was inaccurate or omitted manual documentation. However, errors were also identified in the oral LICENSE device. CONCLUSIONS: When used correctly, the LICENSE system performs satisfactorily in measuring urine and intravenous fluids, although the oral device requires revision due to identified errors.

Using old laboratory equipment with modern Web-of-Things standards: a smart laboratory with LabThings Retro.

There has been an increasing, and welcome, open hardware trend towards science teams building and sharing their designs for new instruments. These devices, often built upon low-cost microprocessors and microcontrollers, can be readily connected to enable complex, automated and smart experiments. When designed to use open communication web standards, devices from different laboratories and manufacturers can be controlled using a single protocol and even communicate with each other. However, science labs still have a majority of old, perfectly functional equipment which tends to use older, and sometimes proprietary, standards for communications. In order to encourage the continued and integrated use of this equipment in modern automated experiments, we develop and demonstrate LabThings Retro. This allows us to retrofit old instruments to use modern Web-of-Things standards, which we demonstrate with closed-loop feedback involving an optical microscope, digital imaging and fluid pumping.

Automated multifactorial design of experiment and Bayesian optimisation algorithm approaches to method development for the green analysis by supercritical fluid chromatography of a pharmaceutical ingredient.

During drug development, chromatography is frequently used for purity and stability testing of both drug substance and drug product. Reversed phase liquid chromatography (RPLC) is one of the most widely used methodologies due to its wide scope of application. In the later stages of drug development, the specified impurities and degradation products that define the critical quality attribute of the final API, also known as Key Predictive Sample Set (KPSS), are usually well defined and controlled. At this point, a method review enables selecting the most appropriate technique which should be the one providing optimal robustness (ICH-Q14[1]), with the support of Quality by Design (QbD) approaches. Supercritical Fluid Chromatography (SFC) is a preferred technique for its proven diversity in selectivity. The adoption of a technique which presents the most favourable environmental impact, such as, but not limited to, SFC, is also becoming increasingly important as laboratories strive to reduce carbon footprint. Re-developing a method requires high resource-demands in terms of staff, materials, and time. Any step of the process that can be automated can facilitate this approach, speeding up the delivery of the method whilst preserving robustness. In this article we describe how an SFC method was developed for the purity profiling of a late-stage oncology candidate, taking advantage of the superior selectivity of SFC towards structurally similar analytes, owed to the high orthogonality with R2 as low as 0.014 towards the KPSS. We describe two approaches to automate the method development. Firstly, a multifactorial design of experiments (DoE) and secondly, an optimization via a Bayesian algorithm, which was completed in one night, highlighting the potential and limitations, with an insight into the robustness. Both methods achieved baseline separation with varying levels of automation embedded into the process and a large reduction of the resource demands when compared to traditional optimisation methods. Finally, we describe the beneficial environmental impact that implementing SFC methods can yield, with a calculated green score reduced to a value between 17 and 30 % compared to RPLC, depending on the number of runs per sequence.

Neuroimaging article reexecution and reproduction assessment system.

The value of research articles is increasingly contingent on complex data analysis results which substantiate their claims. Compared to data production, data analysis more readily lends itself to a higher standard of transparency and repeated operator-independent execution. This higher standard can be approached via fully reexecutable research outputs, which contain the entire instruction set for automatic end-to-end generation of an entire article from the earliest feasible provenance point. In this study, we make use of a peer-reviewed neuroimaging article which provides complete but fragile reexecution instructions, as a starting point to draft a new reexecution system which is both robust and portable. We render this system modular as a core design aspect, so that reexecutable article code, data, and environment specifications could potentially be substituted or adapted. In conjunction with this system, which forms the demonstrative product of this study, we detail the core challenges with full article reexecution and specify a number of best practices which permitted us to mitigate them. We further show how the capabilities of our system can subsequently be used to provide reproducibility assessments, both via simple statistical metrics and by visually highlighting divergent elements for human inspection. We argue that fully reexecutable articles are thus a feasible best practice, which can greatly enhance the understanding of data analysis variability and the trust in results. Lastly, we comment at length on the outlook for reexecutable research outputs and encourage re-use and derivation of the system produced herein.

Assessing Patient Trust in Automation in Health Care Systems: Within-Subjects Experimental Study.

BACKGROUND: Health care technology has the ability to change patient outcomes for the betterment when designed appropriately. Automation is becoming smarter and is increasingly being integrated into health care work systems. OBJECTIVE: This study focuses on investigating trust between patients and an automated cardiac risk assessment tool (CRAT) in a simulated emergency department setting. METHODS: A within-subjects experimental study was performed to investigate differences in automation modes for the CRAT: (1) no automation, (2) automation only, and (3) semiautomation. Participants were asked to enter their simulated symptoms for each scenario into the CRAT as instructed by the experimenter, and they would automatically be classified as high, medium, or low risk depending on the symptoms entered. Participants were asked to provide their trust ratings for each combination of risk classification and automation mode on a scale of 1 to 10 (1=absolutely no trust and 10=complete trust). RESULTS: Results from this study indicate that the participants significantly trusted the semiautomation condition more compared to the automation-only condition (P=.002), and they trusted the no automation condition significantly more than the automation-only condition (P=.03). Additionally, participants significantly trusted the CRAT more in the high-severity scenario compared to the medium-severity scenario (P=.004). CONCLUSIONS: The findings from this study emphasize the importance of the human component of automation when designing automated technology in health care systems. Automation and artificially intelligent systems are becoming more prevalent in health care systems, and this work emphasizes the need to consider the human element when designing automation into care delivery.

Decision support improves information integration and criterion placement in a multi-cue signal-identification task.

Identifying contacts in a military context can require operators to integrate multiple cues and to adjust response criteria to event base rates. The current experiment tested whether support from a decision aid would improve these processes. Participants performed a signal identification task that required them to integrate cues displayed as visual scale readings. In a static condition, participants saw a single set of readings each trial. In dynamic conditions, readings were updated over time. Base rates of signal categories were unequal, requiring participants to adopt biased response criteria to maximise response accuracy. Participants worked with or without an aid that combined cues and base rate information in an ideal manner. Support from the aid pushed participants' response criteria towards optimal and improved integration of dynamic cues. Decision aids may be especially useful when task demands require biased response criteria and when cues are sampled over time.

Applied decision making often requires operators to gather and integrate multiple probabilistic cues. An experiment examined the information processing steps in multiple-cue decision tasks that could be improved by an automated decision aid. Statistically ideal aids improved operators' response bias and information integration, although operator performance remained suboptimal.

The future of pharmacy work: How pharmacists are adapting to and preparing for technology infusion.

Background: The pharmacy sector is rapidly evolving due to technological advancements, presenting challenges and opportunities for pharmacists. However, limited literature exists on the future of pharmacy work, especially concerning technology adoption. Objective: This exploratory study investigates pharmacists' perspectives on the impact of technologies on the profession - including career security, role evolution, adjustments to changes - and the impact of the COVID-19 pandemic on technology implementation and the broader future of work in pharmacy. Method: A cross-sectional survey design was used, targeting all registered pharmacists in New Zealand. A questionnaire, adapted from Future of Work literature, was piloted and distributed to 3037 pharmacists. Data were analyzed using descriptive statistics, two-step hierarchical analysis, and content and thematic analysis. Ethics approval was obtained. Results: 177 responses met the inclusion criteria, yielding a 5.82% response rate. Respondent demographics included a lower proportion of community pharmacists and individuals of Asian ethnicity, but a higher proportion of males and hospital pharmacists compared to the national workforce. Most respondents were aged between 30 and 59 years, representing all District Health Board locations.Qualitative analysis identified two themes: 1) Factors affecting technology adoption across macro, meso and micro levels, including COVID-19's impact on work efficiency, regulatory gaps, fragmented IT and organizational infrastructures, patient safety, and attitudes at workforce and individual levels; 2) Career impacts, highlighting role expansion, job replacement fears, and the need for adaptation and upskilling. Quantitative findings indicate that early technology adopters are more prepared to learn new skills and plan their careers. Technology impact positively correlates with career planning, while job loss concerns negatively affect skill development readiness. Conclusion: The study underscores the importance of early technological adoption for readiness to acquire new skills and career planning in pharmacy. Embracing technological change, supported by regulatory and policy frameworks, is crucial for advancing the profession.

Towards robotic laboratory automation plug & play: Reference architecture model for robot integration.

Supportive robotic solutions take over mundane, but essential tasks from human workforce in biomedical research and development laboratories. The newest technologies in collaborative and mobile robotics enable the utilization in the human-centered and low-structured environment. Their adaptability, however, is hindered by the additional complexity that they introduce. In our paper we aim to entangle the convoluted laboratory robot integration architectures. We begin by hierarchically decomposing the laboratory workflows, and mapping the activity representations to layers and components of the automation control architecture. We elaborate the framework in detail on the example of pick-and-place labware transportation - a crucial supportive step, which we identified as the number one area of interest among experts of the field. Our concept proposal serves as a reference architecture model, the key principles of which were used in reference implementations, and are in line with international standardization efforts.

Evaluation of the Accuracy of Estimated Endpoint Titer of NOVA View in Indirect Immunofluorescent Antinuclear Antibody Testing.

For antinuclear antibody (ANA) screening, the gold standard method is an indirect immunofluorescence assay (IIFA) using HEp-2 cells, and a serial dilution test is needed to determine the endpoint titer. We aimed to evaluate the accuracy of the estimated endpoint titer (eEPT) by the NOVA View system, by comparing it with the EPT by the serial dilution method (dEPT). The endpoint titers of a total of 1518 ANA positive cases with five major patterns including speckled, homogeneous, centromere, nucleolar, and nuclear dots patterns were determined using both the estimation function and the serial dilution method by the NOVA View system. A significant correlation between the light intensity unit (LIU) values and dEPTs was identified in all five patterns with high ρ values, ranging from 0.666 to 0.832. However, the overall exact match rate between dEPT and eEPT was 22.1% (336/1518), with the ±one-titer match rate being highest in the centromere pattern (62.8%, 81/129), and lowest in the homogeneous pattern (37.6%, 200/532). This suggests that while LIU values correlate well with dEPT, there are discrepancies in numerical agreement. Most cases that did not show an exact match, showed one-to-three-titer overestimations by eEPT. Therefore, adjusting eEPT downward significantly improved the concordance rates with dEPTs. Further investigation for an appropriate cutoff of LIU values for determining eEPT should be performed for clinical application and contribution to the standardization of the ANA titer.

Benchmarking automation-aided performance in a forensic face matching task.

Carragher and Hancock (2023) investigated how individuals performed in a one-to-one face matching task when assisted by an Automated Facial Recognition System (AFRS). Across five pre-registered experiments they found evidence of suboptimal aided performance, with AFRS-assisted individuals consistently failing to reach the level of performance the AFRS achieved alone. The current study reanalyses these data (Carragher and Hancock, 2023), to benchmark automation-aided performance against a series of statistical models of collaborative decision making, spanning a range of efficiency levels. Analyses using a Bayesian hierarchical signal detection model revealed that collaborative performance was highly inefficient, falling closest to the most suboptimal models of automation dependence tested. This pattern of results generalises previous reports of suboptimal human-automation interaction across a range of visual search, target detection, sensory discrimination, and numeric estimation decision-making tasks. The current study is the first to provide benchmarks of automation-aided performance in the one-to-one face matching task.

The Impact of Information Relevancy and Interactivity on Intensivists' Trust in a Machine Learning-Based Bacteremia Prediction System: Simulation Study.

Background: The exponential growth in computing power and the increasing digitization of information have substantially advanced the machine learning (ML) research field. However, ML algorithms are often considered "black boxes," and this fosters distrust. In medical domains, in which mistakes can result in fatal outcomes, practitioners may be especially reluctant to trust ML algorithms. Objective: The aim of this study is to explore the effect of user-interface design features on intensivists' trust in an ML-based clinical decision support system. Methods: A total of 47 physicians from critical care specialties were presented with 3 patient cases of bacteremia in the setting of an ML-based simulation system. Three conditions of the simulation were tested according to combinations of information relevancy and interactivity. Participants' trust in the system was assessed by their agreement with the system's prediction and a postexperiment questionnaire. Linear regression models were applied to measure the effects. Results: Participants' agreement with the system's prediction did not differ according to the experimental conditions. However, in the postexperiment questionnaire, higher information relevancy ratings and interactivity ratings were associated with higher perceived trust in the system (P<.001 for both). The explicit visual presentation of the features of the ML algorithm on the user interface resulted in lower trust among the participants (P=.05). Conclusions: Information relevancy and interactivity features should be considered in the design of the user interface of ML-based clinical decision support systems to enhance intensivists' trust. This study sheds light on the connection between information relevancy, interactivity, and trust in human-ML interaction, specifically in the intensive care unit environment.

SPOT-RASTR-A cryo-EM specimen preparation technique that overcomes problems with preferred orientation and the air/water interface.

In cryogenic electron microscopy (cryo-EM), specimen preparation remains a bottleneck despite recent advancements. Classical plunge freezing methods often result in issues like aggregation and preferred orientations at the air/water interface. Many alternative methods have been proposed, but there remains a lack a universal solution, and multiple techniques are often required for challenging samples. Here, we demonstrate the use of lipid nanotubes with nickel NTA headgroups as a platform for cryo-EM sample preparation. His-tagged specimens of interest are added to the tubules, and they can be frozen by conventional plunge freezing. We show that the nanotubes protect samples from the air/water interface and promote a wider range of orientations. The reconstruction of average subtracted tubular regions (RASTR) method allows for the removal of the nanotubule signal from the cryo-EM images resulting in isolated images of specimens of interest. Testing with ß-galactosidase validates the method's ability to capture particles at lower concentrations, overcome preferred orientations, and achieve near-atomic resolution reconstructions. Since the nanotubules can be identified and targeted automatically at low magnification, the method enables fully automated data collection. Furthermore, the particles on the tubes can be automatically identified and centered using 2D classification enabling particle picking without requiring prior information. Altogether, our approach that we call specimen preparation on a tube RASTR holds promise for overcoming air-water interface and preferred orientation challenges and offers the potential for fully automated cryo-EM data collection and structure determination.

Neuro Receptor Signal Detecting and Monitoring Smart Devices for Biological Changes in Cognitive Health Conditions.

Background: Currently, wearable sensors significantly impact health care through continuous monitoring and event prediction. The types and clinical applications of wearable technology for the prevention of mental illnesses, as well as associated health authority rules, are covered in the current review. Summary: The technologies behind wearable ECG monitors, biosensors, electronic skin patches, neural interfaces, retinal prosthesis, and smart contact lenses were discussed. We described how sensors will examine neuronal impulses using verified machine-learning algorithms running in real-time. These sensors will closely monitor body signals and demonstrate continuous sensing with wireless functionality. The wearable applications in the following medical fields were covered in our review: sleep, neurology, mental health, anxiety, depression, Parkinson's disease, epilepsy, seizures, and schizophrenia. These mental health conditions can cause serious issues, even death. Inflammation brought on by mental health problems can worsen hypothalamic-pituitary-adrenal axis dysfunction and interfere with certain neuroregulatory systems such as the neural peptide Y, serotonergic, and cholinergic systems. Severe depressive disorder symptoms are correlated with elevated Interleukin (IL-6) levels. On the basis of previous and present data collected utilizing a variety of sensory modalities, researchers are currently investigating ways to identify or detect the current mental state. Key message: This review explores the potential of various mental health monitoring technologies. The types and clinical uses of wearable technology, such as ECG monitors, biosensors, electronic skin patches, brain interfaces, retinal prostheses, and smart contact lenses, were covered in the current review will be beneficial for patients with mental health problems like Alzheimer, epilepsy, dementia. The sensors will closely monitor bodily signals with wireless functionality while using machine learning algorithms to analyse neural impulses in real time.

Enhancing Autonomous Truck Navigation with Ultra-Wideband Technology in Industrial Environments.

The integration of autonomous vehicles in industrial settings necessitates advanced positioning and navigation systems to ensure operational safety and efficiency. This study rigorously evaluates the application of Ultra-Wideband (UWB) technology in autonomous industrial trucks and compares its effectiveness with conventional systems such as Light Detection and Ranging (LiDAR), Global Positioning System (GPS), and cameras. Through comprehensive experiments conducted in a real factory environment, this study meticulously assesses the accuracy and reliability of UWB technology across various reference distances and under diverse environmental conditions. The findings reveal that UWB technology consistently achieves positioning accuracy within 0.2 cm 99% of the time, significantly surpassing the 10 cm and 5 cm accuracies of GPS and LiDAR, respectively. The exceptional performance of UWB, especially in environments afflicted by high metallic interference and non-line-of-sight conditions-where GPS and LiDAR's efficacy decreased by 40% and 25%, respectively-highlights its potential to revolutionize the operational capabilities of autonomous trucks in industrial applications. This study underscores the robustness of UWB in maintaining high accuracy even in adverse conditions and illustrates its low power consumption and efficiency in multi-user scenarios without signal interference. This study not only confirms the superior capabilities of UWB technology but also contributes to the broader field of autonomous vehicle technology by highlighting the practical benefits and integration potential of UWB systems in complex and dynamic environments.

Technology for Automated Production of High-Performance Building Compounds for 3D Printing.

Three-dimensional printing technology in construction is a rapidly growing field that offers innovative opportunities for design and construction execution. A key component of this process is the automated production of high-performance construction mixtures that meet specific requirements for strength, fluidity, and setting speed. This overview article outlines the history and development of 3D printing technology in the construction industry, describes various printing technologies, and discusses the properties and requirements for construction mixes. Special attention is given to automated systems for batching and mixing ingredients, which increase the precision and efficiency of production. The different types of construction mixes used in 3D printing and the main technical and operational challenges associated with their application are also presented. The article's conclusions highlight the potential of this technology to revolutionize the construction industry by improving efficiency and reducing costs and project lead times.

GOLDBAR: A Framework for Combinatorial Biological Design.

With the rise of new DNA part libraries and technologies for assembling DNA, synthetic biologists are increasingly constructing and screening combinatorial libraries to optimize their biological designs. As combinatorial libraries are used to generate data on design performance, new rules for composing biological designs will emerge. Most formal frameworks for combinatorial design, however, do not yet support formal comparison of design composition, which is needed to facilitate automated analysis and machine learning in massive biological design spaces. To address this need, we introduce a combinatorial design framework called GOLDBAR. Compared with existing frameworks, GOLDBAR enables synthetic biologists to intersect and merge the rules for entire classes of biological designs to extract common design motifs and infer new ones. Here, we demonstrate the application of GOLDBAR to refine/validate design spaces for TetR-homologue transcriptional logic circuits, verify the assembly of a partial nif gene cluster, and infer novel gene clusters for the biosynthesis of rebeccamycin. We also discuss how GOLDBAR could be used to facilitate grammar-based machine learning in synthetic biology.

Automated Bio-AFM Generation of Large Mechanome Data Set and Their Analysis by Machine Learning to Classify Cancerous Cell Lines.

Mechanobiological measurements have the potential to discriminate healthy cells from pathological cells. However, a technology frequently used to measure these properties, i.e., atomic force microscopy (AFM), suffers from its low output and lack of standardization. In this work, we have optimized AFM mechanical measurement on cell populations and developed a technology combining cell patterning and AFM automation that has the potential to record data on hundreds of cells (956 cells measured for publication). On each cell, 16 force curves (FCs) and seven features/FC, constituting the mechanome, were calculated. All of the FCs were then classified using machine learning tools with a statistical approach based on a fuzzy logic algorithm, trained to discriminate between nonmalignant and cancerous cells (training base, up to 120 cells/cell line). The proof of concept was first made on prostate nonmalignant (RWPE-1) and cancerous cell lines (PC3-GFP), then on nonmalignant (Hs 895.Sk) and cancerous (Hs 895.T) skin fibroblast cell lines, and demonstrated the ability of our method to classify correctly 73% of the cells (194 cells in the database/cell line) despite the very high degree of similarity of the whole set of measurements (79-100% similarity).