Gaze entropy metrics for mental workload estimation are heterogenous during hands-off level 2 automation

As the level of vehicle automation increases, drivers are more likely to engage in non-driving related tasks which take their hands, eyes, and/or mind away from the driving task. Consequently, there has been increased interest in creating Driver Monitoring Systems (DMS) that are valid and reliable for detecting elements of driver state. Workload is one element of driver state that has remained elusive within the literature. Whilst there has been promising work in estimating mental workload using gaze-based metrics, the literature has placed too much emphasis on point estimate differences. Whilst these are useful for establishing whether effects exist, they ignore the inherent variability within individuals and between different drivers. The current work builds on this by using a Bayesian distributional modelling approach to quantify the within and between participants variability in Information Theoretical gaze metrics. Drivers (N = 38) undertook two experimental drives in hands-off Level 2 automation with their hands and feet away from operational controls. During both drives, their priority was to monitor the road before a critical takeover. During one drive participants had to complete a secondary cognitive task (2-back) during the hands-off Level 2 automation. Changes in Stationary Gaze Entropy and Gaze Transition Entropy were assessed for conditions with and without the 2-back to investigate whether consistent differences between workload conditions could be found across the sample. Stationary Gaze Entropy proved a reliable indicator of mental workload; 92 % of the population were predicted to show a decrease when completing 2-back during hands-off Level 2 automated driving. Conversely, Gaze Transition Entropy showed substantial heterogeneity; only 66 % of the population were predicted to have similar decreases. Furthermore, age was a strong predictor of the heterogeneity of the average causal effect that high mental workload had on eye movements. These results indicate that, whilst certain elements of Information Theoretic metrics can be used to estimate mental workload by DMS, future research needs to focus on the heterogeneity of these processes. Understanding this heterogeneity has important implications toward the design of future DMS and thus the safety of drivers using automated vehicle functions. It must be ensured that metrics used to detect mental workload are valid (accurately detecting a particular driver state) as well as reliable (consistently detecting this driver state across a population).

Refinement of paramagnetic bead-based digestion protocol for automatic sample preparation using an artificial neural network

Despite technological advances in the proteomics field, sample preparation still represents the main bottleneck in mass spectrometry (MS) analysis. Bead-based protein aggregation techniques have recently emerged as an efficient, reproducible, and high-throughput alternative for protein extraction and digestion. Here, a refined paramagnetic bead-based digestion protocol is described for Opentrons® OT-2 platform (OT-2) as a versatile, reproducible, and affordable alternative for the automatic sample preparation for MS analysis. For this purpose, an artificial neural network (ANN) was applied to maximize the number of peptides without missed cleavages identified in HeLa extract by combining factors such as the quantity (μg) of trypsin/Lys-C and beads (MagReSyn® Amine), % (w/v) SDS, % (v/v) acetonitrile, and time of digestion (h). ANN model predicted the optimal conditions for the digestion of 50 μg of HeLa extract, pointing to the use of 2.5% (w/v) SDS and 300 μg of beads for sample preparation and long-term digestion (16h) with 0.15 μg Lys-C and 2.5 μg trypsin (≈1:17 ratio). Based on the results of the ANN model, the manual protocol was automated in OT-2. The performance of the automatic protocol was evaluated with different sample types, including human plasma, Arabidopsis thaliana leaves, Escherichia coli cells, and mouse tissue cortex, showing great reproducibility and low sample-to-sample variability in all cases. In addition, we tested the performance of this method in the preparation of a challenging biological fluid such as rat bile, a proximal fluid that is rich in bile salts, bilirubin, cholesterol, and fatty acids, among other MS interferents. Compared to other protocols described in the literature for the extraction and digestion of bile proteins, the method described here allowed identify 385 unique proteins, thus contributing to improving the coverage of the bile proteome.

Investigation of the Formaldehyde-Catalyzed NNitrosation of Dialkyl Amines: An Automated Experimental and Kinetic Modelling Study Using Dibutylamine

The potential for drug substances and drug products to contain low levels of N-nitrosamines is of continued interest to the pharmaceutical industry and regulatory authorities. Acid-promoted nitrosation mechanisms in solution have been investigated widely in the literature and are supported by kinetic modelling studies. Carbonyl compounds, particularly formaldehyde, which may be present as impurities in excipients and drug product packaging components or introduced during drug substance manufacturing processes are also known to catalyze nitrosation, but their impact on the risk of N-nitrosamine formation has not been systematically investigated to date. In this study, we experimentally investigated the multivariate impact of formaldehyde, nitrite and pH on N-nitrosation in aqueous solution using dibutylamine as a model amine. We augmented a published kinetic model by adding formaldehyde-catalyzed nitrosation reactions. We validated the new kinetic model vs. the experimental data and then used the model to systematically investigate the impact of formaldehyde levels on N-nitrosamine formation. Simulations of aqueous solution systems show that at low formaldehyde levels the formaldehyde-catalyzed mechanisms are insignificant in comparison to other routes. However, formaldehyde-catalyzed mechanisms can become more significant at neutral and high pH under higher formaldehyde levels. Model-based sensitivity analysis demonstrated that under high nitrite levels and low formaldehyde levels (where the rate of formaldehyde-catalyzed nitrosation is low compared to the acid-promoted pathways) the model can be used with kinetic parameters for model amines in the literature without performing additional experiments to fit amine-specific parameters. For other combinations of reaction parameters containing formaldehyde, the formaldehyde-catalyzed kinetics are non-negligible, and thus it is advised that, under such conditions, additional experiments should be conducted to reliably use the model.

Automated vehicles for older adults with cognitive impairment: a survey study

As the population is ageing, the number of older adults with cognitive impairment (CI) is increasing. Automated vehicles (AVs) can improve independence and enhance the mobility of these individuals. This study aimed to: (1) understand the perception of older adults (with and without CI) and stakeholders providing services and supports regarding care and transportation about AVs, and (2) suggest potential solutions to improve the perception of AVs for older adults with mild or moderate CI. A survey was conducted with 435 older adults with and without CI and 188 stakeholders (e.g. caregivers). The results were analysed using partial least square - structural equation modelling and multiple correspondence analysis. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided recommendations to improve older adults' perception of AVs including education and adaptive driving simulation-based training.Practitioner summary: This study investigated the perception of older adults and other stakeholders regarding AVs. The findings suggested relationships between older adults' level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided guidelines to improve older adults' perception of AVs.

A smartphone serves as a data logger for a fully automated lab-constructed microfluidic system

Fluorescence is an innovative technique that has captivated scholars in recent years due to its superior sensitivity and selectivity. The development of microfluidic components has added to its appeal, particularly given the technology ability to control fluid using very small quantities (microliter range) and achieve high liquid throughput. We have combined these two technologies to develop a lab-constructed simple system for measuring fluorescence, notable for the following features:•The device constructed entirely in our lab and programmed for measuring the fluorescence of liquids using microfluidic technology, delivered excellent results. The regression coefficient R² (0.9995) was obtained five points between 0.001-0.01µg .ml-1. Moreover, the reproducibility standard deviation (%) of 0.008 µg .ml-1 fluorescein dye remained at zero, for ten repeated experiments.•The device was full automated using a smartphone as a data logger, and lab-constructed programs.•The results were satisfactory with a detection limit of 1 × 10-4 µg.ml-1. This proposed system can measure over 200 samples per hour making it highly efficient and eco-friendly due to the reduced use of reagents and lower waste production. The fully automated system can effectively be used to determine fluorescein dye concentrations. Another application (micro pump view) manages all actions required in this microfluidic system, such as operating the two lab-constructed peristaltic pumps.

Sequential Treatment Application Robot (STAR) for high-replication marine experimentation

Marine organisms are often subject to numerous anthropogenic stressors, resulting in widespread ecosystem degradation. Physiological responses to these stressors, however, are complicated by high biological variability, species-specific sensitivities, nonlinear relationships, and countless permutations of stressor combinations. Nevertheless, quantification of these relationships is paramount for parameterizing predictive tools and ultimately for effective management of marine resources. Multi-level, multi-stressor experimentation is therefore key, yet the high replication required has remained a logistical challenge and a financial barrier. To overcome these issues, we created an automated system for experimentation on marine organisms, the Sequential Treatment Application Robot (STAR). The system consists of a track-mounted robotic arm that sequentially applies precision treatments to independent aquaria via syringe and peristaltic pumps. The accuracy and precision were validated with dye and spectrophotometry, and stability was demonstrated by maintaining corals under treatment conditions for more than a month. The system is open source and scalable in that additional treatments and replicates may be added without incurring multiplicative costs. While STAR was designed for investigating the combined impacts of nutrients, warming, and disease on reef-building corals, it is highly customizable and may be used for experimentation involving a diverse array of treatments and species.

AST: An OpenSim-based tool for the automatic scaling of generic musculoskeletal models

BACKGROUND AND OBJECTIVES

The paper introduces a tool called Automatic Scaling Tool (AST) designed for improving and expediting musculoskeletal (MSK) simulations based on generic models in OpenSim. Scaling is a crucial initial step in MSK analyses, involving the correction of virtual marker locations on a model to align with actual experimental markers.

METHODS

The AST automates this process by iteratively adjusting virtual markers using scaling and inverse kinematics on a static trial. It evaluates the root mean square error (RMSE) and maximum marker error, implementing corrective actions until achieving the desired accuracy level. The tool determines whether to scale a segment with a marker-based or constant scaling factor based on checks on RMSE and segment scaling factors.

RESULTS

Testing on three generic MSK models demonstrated that the AST significantly outperformed manual scaling by an expert operator. The RMSE for static trials was one order of magnitude lower, and for gait tasks, it was five times lower (8.5 ± 0.76 mm vs. 44.5 ± 7.5 mm). The AST consistently achieved the desired level of accuracy in less than 100 iterations, providing reliable scaled MSK models within a relatively brief timeframe, ranging from minutes to hours depending on model complexity.

CONCLUSIONS

The paper concludes that AST can greatly benefit the biomechanical community by quickly and accurately scaling generic models, a critical first step in MSK analyses. Further validation through additional experimental datasets and generic models is proposed for future tests.

Cost analyses for malaria molecular diagnosis for research planners in India and beyond

BACKGROUND

Malaria elimination mandates early and accurate diagnosis of infection. Although malaria diagnosis is programmatically dependent on microscopy/RDTs, molecular diagnosis has much better diagnostic accuracy. Higher cost of molecular diagnoses is a recognized challenge for use at the point of care. Because funding is always a recognized constraint, we performed financial cost-analyses of available molecular platforms for better utilization of available budget.

METHODS

Two strategies were applied to deduce the cost per sample. Strategy 1 included recurring components (RC) in minimum pack size, and biologist's time whereas strategy 2 included only RC and non-recurring components and costs are calculated for sample sizes (1-1,000,000) to infer the sample size effect.

RESULTS

Spin column-based manual DNA extraction (US$ 3.93 per sample) is the lowest-cost method, followed by magnetic bead-based automated, semi-automated, and PCI-based manual method. Further, DNA extraction cost per sample via spin column-based manual method and semi-automated method decreases with an increase in sample size up to 10,000. Real-time PCRs are ~ 2-fold more economical than conventional PCR, regardless of sample size.

CONCLUSIONS

This study is the first for malaria to estimate systematic molecular diagnosis financial costs. Kit-based and automated methods may replace conventional DNA extraction and amplification methods for a frugal high-throughput diagnosis.

Practical deployment of automation to expedite aqueous two-phase extraction

The feasibility of bioprocess development relies heavily on the successful application of primary recovery and purification techniques. Aqueous two-phase extraction (ATPE) disrupts the definition of "unit operation" by serving as an integrative and intensive technique that combines different objectives such as the removal of biomass and integrated recovery and purification of the product of interest. The relative simplicity of processing large samples renders this technique an attractive alternative for industrial bioprocessing applications. However, process development is hindered by the lack of easily predictable partition behaviours, the elucidation of which necessitates a large number of experiments to be conducted. Liquid handling devices can assist to address this problem; however, they are configured to operate using low viscosity fluids such as water and water-based solutions as opposed to highly viscous polymeric solutions, which are typically required in ATPE. In this work, an automated high throughput ATPE process development framework is presented by constructing phase diagrams and identifying the binodal curves for PEG6000, PEG3000, and PEG2000. Models were built to determine viscosity- and volume-independent transfer parameters. The framework provided an appropriate strategy to develop a very precise and accurate operation by exploiting the relationship between different liquid transfer parameters and process error. Process accuracy, measured by mean absolute error, and device precision, evaluated by the coefficient of variation, were both shown to be affected by the mechanical properties, particularly viscosity, of the fluids employed. For PEG6000, the mean absolute error improved by six-fold (from 4.82% to 0.75%) and the coefficient of variation improved by three-fold (from 0.027 to 0.008) upon optimisation of the liquid transfer parameters accounting for the viscosity effect on the PEG-salt buffer utilising ATPE operations. As demonstrated here, automated liquid handling devices can serve to streamline process development for APTE enabling wide adoption of this technique in large scale bioprocess applications.

A fast and high precision multi-robot environment modeling based on M-BFSI: Bidirectional filtering and scene identification method

This article designs and implements a fast and high-precision multi-robot environment modeling method based on bidirectional filtering and scene identification. To solve the problem of feature tracking failure caused by large angle rotation, a bidirectional filtering mechanism is introduced to improve the error-matching elimination algorithm. A global key frame database for multiple robots is proposed based on a pretraining dictionary to convert images into a bag of words vectors. The images captured by different sub-robots are compared with the database for similarity score calculation, so as to realize fast identification and search of similar scenes. The coordinate transformation from local map to global map and the cooperative SLAM exploration of multiple robots is completed by the best matching image and the transformation matrix. The experimental results show that the proposed algorithm can effectively close the predicted trajectory of the sub-robot, thus achieving high-precision collaborative environment modeling.

A qualitative interview study to determine barriers and facilitators of implementing automated decision support tools for genomic data access

Data access committees (DAC) gatekeep access to secured genomic and related health datasets yet are challenged to keep pace with the rising volume and complexity of data generation. Automated decision support (ADS) systems have been shown to support consistency, compliance, and coordination of data access review decisions. However, we lack understanding of how DAC members perceive the value add of ADS, if any, on the quality and effectiveness of their reviews. In this qualitative study, we report findings from 13 semi-structured interviews with DAC members from around the world to identify relevant barriers and facilitators to implementing ADS for genomic data access management. Participants generally supported pilot studies that test ADS performance, for example in cataloging data types, verifying user credentials and tagging datasets for use terms. Concerns related to over-automation, lack of human oversight, low prioritization, and misalignment with institutional missions tempered enthusiasm for ADS among the DAC members we engaged. Tensions for change in institutional settings within which DACs operated was a powerful motivator for why DAC members considered the implementation of ADS into their access workflows, as well as perceptions of the relative advantage of ADS over the status quo. Future research is needed to build the evidence base around the comparative effectiveness and decisional outcomes of institutions that do/not use ADS into their workflows.

GMP production of [18F]FE-PE2I on a TRACERLab FX2 N synthesis module, a radiotracer for in vivo PET imaging of the dopamine transport

BACKGROUND

Parkinson's disease is a neurodegenerative disorder that is characterized by a degeneration of the dopaminergic system. Dopamine transporter (DAT) positron emission tomography (PET) imaging has emerged as a powerful and non-invasive method to quantify dopaminergic function in the living brain. The PET radioligand, [18F]FE-PE2I, a cocaine chemical derivative, has shown promising properties for in vivo PET imaging of DAT, including high affinity and selectivity for DAT, excellent brain permeability, and favorable metabolism. The aim of the current study was to scale up the production of [18F]FE-PE2I to fulfil the increasing clinical demand for this tracer.

RESULTS

Thus, a fully automated and GMP-compliant production procedure has been developed using a commercially available radiosynthesis module GE TRACERLab FX2 N. [18F]FE-PE2I was produced with a radiochemical yield of 39 ± 8% (n = 4, relative [18F]F- delivered to the module). The synthesis time was 70 min, and the molar activity was 925.3 ± 763 GBq/µmol (250 ± 20 Ci/µmol). The produced [18F]FE-PE2I was stable over 6 h at room temperature.

CONCLUSION

The protocol reliably provides a sterile and pyrogen-free GMP-compliant product.

Automatic center identification of electron diffraction with multi-scale transformer networks

Selected area electron diffraction (SAED) is a widely used technique for characterizing the structure and measuring lattice parameters of materials. An autonomous analytic method has become an urgent demand for the large-scale SAED data produced from in-situ experiments. In this work, we realize the automatic processing for center identification with a proposed deep segmentation model named the multi-scale Transformer (MS-Trans) network. This algorithm enables robust segmentation of the central spots by combining a novel gated axial-attention module and multi-scale feature fusion. The proposed MS-Trans model shows high precision and robustness, enabling autonomous processing of SAED patterns without any prior knowledge. The application on in-situ SAED data of the oxidation process of FeNi alloy demonstrates its capability of implementing autonomous quantitative processing. © 2017 Elsevier Inc. All rights reserved.

Should automated electronic hand-hygiene monitoring systems be implemented in routine patient care? Systematic review and appraisal with Medical Research Council Framework for Complex Interventions

Manual hand-hygiene audit is time-consuming, labour-intensive and inaccurate. Automated hand-hygiene monitoring systems (AHHMSs) offer advantages (generation of standardized data, avoidance of the Hawthorne effect). World Health Organization Guidelines for Hand Hygiene published in 2009 suggest that AHHMSs are a possible alternative. The objective of this review was to assess the current state of the literature for AHHMSs and offer recommendations for use in real-world settings. This was a systematic literature review, and publications included were from the time that PubMed commenced until 19th November 2023. Forty-three publications met the criteria. Using the Medical Research Council's Framework for Developing and Evaluating Complex Interventions, two were categorized as intervention development studies. Thirty-nine were evaluations. Two described implementation in real-world settings. Most were small scale and short duration. AHHMSs in conjunction with additional intervention (visual or auditory cue, performance feedback) could increase hand hygiene compliance in the short term. Impact on infection rates was difficult to determine. In the few publications where costs and resources were considered, time devoted to improving hand hygiene compliance increased when an AHHMS was in use. Health workers' opinions about AHHMSs were mixed. In conclusion, at present too little is known about the longer-term advantages of AHHMSs to recommend uptake in routine patient care. Until more longer-term accounts of implementation (over 12 months) become available, efforts should be made to improve direct observation of hand hygiene compliance to improve its accuracy and credibility. The Medical Research Council Framework could be used to categorize other complex interventions involving use of technology to prevent infection to help establish readiness for implementation.

Automated molecular detection of West Nile Virus in mosquito pools using the Panther Fusion system

West Nile Virus (WNV) is an arthropod-borne virus that is spread through mosquito vectors. WNV emerged in the US in 1999 and has since become endemic in the US, causing the most domestically acquired arboviral disease in the country. Mosquito surveillance for WNV is useful to monitor arboviral disease burden over time and across different locations. RT-qPCR is the preferred method for WNV surveillance, but these methods are labor-intensive. The Panther Fusion System has an Open Access feature that allows for laboratory-developed tests (LDTs) to run on a fully automated system for nucleic acid extraction, RT-qPCR, and result generation. This study demonstrates the successful optimization of a WNV multiplex LDT (assay targets: ENV and NS1 genes) for high-throughput environmental surveillance testing of mosquito pool homogenates on the Panther Fusion System. Analytical sensitivity of the assay was 186 and 744 copies/PCR reaction for the ENV and NS1 targets, respectively. To assess the performance of this assay, a total of 80 mosquito pools were tested, including 60 previously tested pools and 20 spiked negative mosquito pools. Among the 60 previously tested specimens, the Panther Fusion WNV LDT demonstrated 100% positive and negative agreement with the CDC West Nile RT-qPCR assay. The Panther Fusion WNV LDT also detected all 20 spiked specimens. The Panther Fusion WNV LDT assay was successfully developed and optimized for high throughput testing with similar performance to the previously used CDC West Nile RT-qPCR assay.

Handling of problematic ion chromatograms with the Automated Target Screening (ATS) workflow for unsupervised analysis of high-resolution mass spectrometry data

Liquid chromatography (LC) or gas chromatography (GC) coupled to high-resolution mass spectrometry (HRMS) is a versatile analytical method for the analysis of thousands of chemical pollutants that can be found in environmental and biological samples. While the tools for handling such complex datasets have improved, there are still no fully automated workflows for targeted screening analysis. Here we present an R-based workflow that is able to cope with challenging data like noisy ion chromatograms, retention time shifts, and multiple peak patterns. The workflow can be applied to batches of HRMS data recorded after GC with electron ionization (GC-EI) and LC coupled to electrospray ionization in both negative and positive mode (LC-ESIneg/LC-ESIpos) to perform peak annotation and quantitation fully unsupervised. We used Orbitrap HRMS data of surface water extracts to compare the Automated Target Screening (ATS) workflow with data evaluations performed with the vendor software TraceFinder and the established semi-automated analysis workflow in the MZmine software. The ATS approach increased the overall evaluation performance of the peak annotation compared to the established MZmine module without the need for any post-hoc corrections. The overall accuracy increased from 0.80 to 0.86 (LC-ESIpos), from 0.77 to 0.83 (LC-ESIneg), and from 0.67 to 0.76 (GC-EI). The mean average percentage errors for quantification of ATS were around 30% compared to the manual quantification with TraceFinder. The ATS workflow enables time-efficient analysis of GC- and LC-HRMS data and accelerates and improves the applicability of target screening in studies with a large number of analytes and sample sizes without the need for manual intervention.

[Artificial intelligence for pathology-how, where, and why?]

Artificial intelligence promises many innovations and simplifications in pathology, but also raises just as many questions and uncertainties. In this article, we provide a brief overview of the current status, the goals already achieved by existing algorithms, and the remaining challenges.

Unlocking human-like conversations: Scoping review of automation techniques for personalized healthcare interventions using conversational agents

BACKGROUND

Conversational agents (CAs) offer a sustainable approach to deliver personalized interventions and improve health outcomes.

OBJECTIVES

To review how human-like communication and automation techniques of CAs in personalized healthcare interventions have been implemented. It is intended for designers and developers, computational scientists, behavior scientists, and biomedical engineers who aim at developing CAs for healthcare interventions.

METHODOLOGY

A scoping review was conducted in accordance with PRISMA Extension for Scoping Review. A search was performed in May 2023 in Web of Science, Pubmed, Scopus and IEEE databases. Search results were extracted, duplicates removed, and the remaining results were screened. Studies that contained personalized and automated CAs within the healthcare domain were included. Information regarding study characterization, and human-like communication and automation techniques was extracted from articles that met the eligibility criteria.

RESULTS

Twenty-three studies were selected. These articles described the development of CAs designed for patients to either self-manage their diseases (such as diabetes, mental health issues, cancer, asthma, COVID-19, and other chronic conditions) or to enhance healthy habits. The human-like communication characteristics studied encompassed aspects like system flexibility, personalization, and affective characteristics. Seven studies used rule-based models, eleven applied retrieval-based techniques for content delivery, five used AI models, and six integrated affective computing.

CONCLUSIONS

The increasing interest in employing CAs for personalized healthcare interventions is noteworthy. The adaptability of dialogue structures and personalization features is still limited. Unlocking human-like conversations may encompass the use of affective computing and generative AI to help improve user engagement. Future research should focus on the integration of holistic methods to describe the end-user, and the safe use of generative models.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Radiosynthesis automation, non-human primate biodistribution and dosimetry of K+ channel tracer [11C]3MeO4AP

BACKGROUND

4-Aminopyridine (4AP) is a medication for the symptomatic treatment of multiple sclerosis. Several 4AP-based PET tracers have been developed for imaging demyelination. In preclinical studies, [11C]3MeO4AP has shown promise due to its high brain permeability, high metabolic stability, high plasma availability, and high in vivo binding affinity. To prepare for the translation to human studies, we developed a cGMP-compatible automated radiosynthesis protocol and evaluated the whole-body biodistribution and radiation dosimetry of [11C]3MeO4AP in non-human primates (NHPs).

METHODS

Automated radiosynthesis was carried out using a GE TRACERlab FX-C Pro synthesis module. One male and one female adult rhesus macaques were used in the study. A high-resolution CT from cranial vertex to knee was acquired. PET data were collected using a dynamic acquisition protocol with four bed positions and 13 passes over a total scan time of ~ 150 min. Based on the CT and PET images, volumes of interest (VOIs) were manually drawn for selected organs. Non-decay corrected time-activity curves (TACs) were extracted for each VOI. Radiation dosimetry and effective dose were calculated from the integrated TACs using OLINDA software.

RESULTS

Fully automated radiosynthesis of [11C]3MeO4AP was achieved with 7.3 ± 1.2% (n = 4) of non-decay corrected radiochemical yield within 38 min of synthesis and purification time. [11C]3MeO4AP distributed quickly throughout the body and into the brain. The organs with highest dose were the kidneys. The average effective dose of [11C]3MeO4AP was 4.0 ± 0.6 μSv/MBq. No significant changes in vital signs were observed during the scan.

CONCLUSION

A cGMP-compatible automated radiosynthesis of [11C]3MeO4AP was developed. The whole-body biodistribution and radiation dosimetry of [11C]3MeO4AP was successfully evaluated in NHPs. [11C]3MeO4AP shows lower average effective dose than [18F]3F4AP and similar average effective dose as other carbon-11 tracers.

Recent advances in the synthesis of extensive libraries of heparan sulfate oligosaccharides for structure-activity relationship studies

Heparan sulfate (HS) is a linear, sulfated and highly negatively-charged polysaccharide that plays important roles in many biological events. As a member of the glycosaminoglycan (GAG) family, HS is commonly found on mammalian cell surfaces and within the extracellular matrix. The structural complexities of natural HS polysaccharides have hampered the comprehension of their biological functions and structure-activity relationships (SARs). Although the sulfation patterns and backbone structures of HS can be major determinants of their biological activities, obtaining significant amounts of pure HS from natural sources for comprehensive SAR studies is challenging. Chemical and enzyme-based synthesis can aid in the production of structurally well-defined HS oligosaccharides. In this review, we discuss recent innovations enabling the syntheses of large libraries of HS and how these libraries can provide insights into the structural preferences of various HS binding proteins.

A novel automated multi-cycle magnetic solid-phase extraction coupled to LC-MS/MS to study the disorders of six functional B vitamins in patients with gastroenterology and hyperhomocysteinemia

B vitamins are essential for human life and their disorders can cause a variety of diseases. Solid-phase extraction (SPE) coupled to LC-MS/MS is a preferred technique for determining multiple B vitamins, however, their complexity in real biological matrices makes it hard to achieve satisfactory recovery and accuracy when simultaneous detection. In this study, a novel automated multi-cycle magnetic SPE (MSPE) coupled to the LC-MS/MS method was established using a mixed-mode anion exchange magnetic adsorbent for the simultaneous extraction of six functional B vitamins, including methylmalonic acid, riboflavin, pantothenic acid, 4-pyridoxic acid, folic acid, and 5-methyltetrahydrofolate. After three consecutive MSPE cycles, the recoveries of all analytes were between 51.5% and 89.6%. The method exhibited excellent sensitivity and linearity, with a dynamic range of 200-fold (R > 0.99 for all analytes), exceptional accuracy (ranging between 95.4% and 105.6%) and precision (with RSDs ≤ 6.2%) without significant matrix effects or interferences. Compared to manual SPE method, the automated multi-cycle MSPE method has better feasibility and greater vitamin coverage. It shows a high correlation with the manual method for the detection of 5-methyltetrahydrofolate and folate (R > 0.99). A study of patients from the gastroenterology department showed that those undergoing surgery and those with malignancies may be at risk of folate deficiency. In addition, patients with hyperhomocystinemia had higher levels of methylmalonic acid and lower levels of 5-methyltetrahydrofolate, which correlated with homocysteine levels (R = 0.404 and -0.311, respectively) and showed dose-response relationships. This method is highly automated and cost-effective, with minimal systematic error, making it suitable for the analysis of clinical samples.

Streamlining regular liquid chromatography with MALDI-TOF MS and NMR spectroscopy using automatic full-contact splitless spotting interface and flash-tap fractioning collection

BACKGROUND

High-resolution matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) and nuclear magnetic resonance (NMR) spectroscopy are powerful tools to identify unknown psychoactive substances. However, in complex matrices, trace levels of unknown substances usually require additional fractionation and concentration. Specialized liquid chromatography systems are necessary for both techniques. The small flow rate of nano LC, typically paired with MALDI-TOF MS, often results in prolonged fractionation times. Conversely, the larger flow rate of semi-preparative LC, used for NMR analysis, can be time-consuming and labor-intensive when concentrating samples. To address these issues, we developed an integrated automatic system that integrated to regular LC.

RESULT

Automatic spot collector (ASC) and automatic fraction collector (AFC) were present in this study. The ASC utilized in-line matrix mixing, full-contact spotting and real time heating (50 °C), achieving great capacity of 5 μL droplet on MALDI plate, high recovery (76-116%) and rapid evaporation in 2 min. The analytes were concentrated 4-8 times, forming even crystallization, reaching the detection limit at the concentration of 50 μg L-1 for 12 psychoactive substances in urine. The AFC utilizes flexible tubing which flash-tapped the microtube's upper rim (3 mm depth) instead of reaching the bottom. This method prevents sample loss and minimizes the robotic arm's movement, providing a high fractionating speed at 6 s 12 psychoactive compounds were fractionated in a single round analysis (recovery: 81%-114%). Methamphetamine and nitrazepam obtained from drug-laced coffee samples were successful analyzed with photodiode array (PDA) after one AFC round and NMR after five rounds.

SIGNIFICANCE

The ASC device employed real-time heating, in-line matrix mixing, and full-contact spotting to facilitate the samples spotting onto the MALDI target plate, thereby enhancing detection sensitivity in low-concentration and complex samples. The AFC device utilized the novel flash-tapping method to achieve rapid fractionation and high recovery rate. These devices were assembled using commercially available components, making them affordable (400 USD) for most laboratories while still meeting the required performance for advanced commercialized systems.

Phase Identification and Discovery of an Elusive Polymorph of Drug-Polymer Inclusion Complex Using Automated 3D Electron Diffraction

3D electron diffraction (3D ED) has shown great potential in crystal structure determination in materials, small organic molecules, and macromolecules. In this work, an automated, low-dose and low-bias 3D ED protocol has been implemented to identify six phases from a multiple-phase melt-crystallisation product of an active pharmaceutical ingredient, griseofulvin (GSF). Batch data collection under low-dose conditions using a widely available commercial software was combined with automated data analysis to collect and process over 230 datasets in three days. Accurate unit cell parameters obtained from 3D ED data allowed direct phase identification of GSF Forms III, I and the known GSF inclusion complex (IC) with polyethylene glycol (PEG) (GSF-PEG IC-I), as well as three minor phases, namely GSF Forms II, V and an elusive new phase, GSF-PEG IC-II. Their structures were then directly determined by 3D ED. Furthermore, we reveal how the stabilities of the two GSF-PEG IC polymorphs are closely related to their crystal structures. These results demonstrate the power of automated 3D ED for accurate phase identification and direct structure determination of complex, beam-sensitive crystallisation products, which is significant for drug development where solid form screening is crucial for the overall efficacy of the drug product.

Automatic procedure for modelling, calibration, and optimization of a three-component chromatographic separation

The current landscape of biopharmaceutical production necessitates an ever-growing set of tools to meet the demands for shorter development times and lower production costs. One path towards meeting these demands is the implementation of digital tools in the development stages. Mathematical modelling of process chromatography, one of the key unit operations in the biopharmaceutical downstream process, is one such tool. However, obtaining parameter values for such models is a time-consuming task that grows in complexity with the number of compounds in the mixture being purified. In this study, we tackle this issue by developing an automated model calibration procedure for purification of a multi-component mixture by linear gradient ion exchange chromatography. The procedure was implemented using the Orbit software (Lund University, Department of Chemical Engineering), which both generates a mathematical model structure and performs the experiments necessary to obtain data for model calibration. The procedure was extended to suggest operating points for the purification of one of the components in the mixture by means of multi-objective optimization using three different objectives. The procedure was tested on a three-component protein mixture and was able to generate a calibrated model capable of reproducing the experimental chromatograms to a satisfactory degree, using a total of six assays. An additional seventh experiment was performed to validate the model response under one of the suggested optimum conditions, respecting a 95 % purity requirement. All of the above was automated and set in motion by the push of a button. With these results, we have taken a step towards fully automating model calibration and thus accelerating digitalization in the development stages of new biopharmaceuticals.

Molecular architecture of the actin cytoskeleton: From single cells to whole organisms using cryo-electron tomography

Cryo-electron tomography (cryo-ET) has begun to provide intricate views of cellular architecture at unprecedented resolutions. Considerable efforts are being made to further optimize and automate the cryo-ET workflow, from sample preparation to data acquisition and analysis, to enable visual proteomics inside of cells. Here, we will discuss the latest advances in cryo-ET that go hand in hand with their application to the actin cytoskeleton. The development of deep learning tools for automated annotation of tomographic reconstructions and the serial lift-out sample preparation procedure will soon make it possible to perform high-resolution structural biology in a whole new range of samples, from multicellular organisms to organoids and tissues.

Multi-institutional evaluation of a Pareto navigation guided automated radiotherapy planning solution for prostate cancer

BACKGROUND

Current automated planning solutions are calibrated using trial and error or machine learning on historical datasets. Neither method allows for the intuitive exploration of differing trade-off options during calibration, which may aid in ensuring automated solutions align with clinical preference. Pareto navigation provides this functionality and offers a potential calibration alternative. The purpose of this study was to validate an automated radiotherapy planning solution with a novel multi-dimensional Pareto navigation calibration interface across two external institutions for prostate cancer.

METHODS

The implemented 'Pareto Guided Automated Planning' (PGAP) methodology was developed in RayStation using scripting and consisted of a Pareto navigation calibration interface built upon a 'Protocol Based Automatic Iterative Optimisation' planning framework. 30 previous patients were randomly selected by each institution (IA and IB), 10 for calibration and 20 for validation. Utilising the Pareto navigation interface automated protocols were calibrated to the institutions' clinical preferences. A single automated plan (VMATAuto) was generated for each validation patient with plan quality compared against the previously treated clinical plan (VMATClinical) both quantitatively, using a range of DVH metrics, and qualitatively through blind review at the external institution.

RESULTS

PGAP led to marked improvements across the majority of rectal dose metrics, with Dmean reduced by 3.7 Gy and 1.8 Gy for IA and IB respectively (p < 0.001). For bladder, results were mixed with low and intermediate dose metrics reduced for IB but increased for IA. Differences, whilst statistically significant (p < 0.05) were small and not considered clinically relevant. The reduction in rectum dose was not at the expense of PTV coverage (D98% was generally improved with VMATAuto), but was somewhat detrimental to PTV conformality. The prioritisation of rectum over conformality was however aligned with preferences expressed during calibration and was a key driver in both institutions demonstrating a clear preference towards VMATAuto, with 31/40 considered superior to VMATClinical upon blind review.

CONCLUSIONS

PGAP enabled intuitive adaptation of automated protocols to an institution's planning aims and yielded plans more congruent with the institution's clinical preference than the locally produced manual clinical plans.

Improved automated one-pot two-step radiosynthesis of (S)-[18F]FETrp, a radiotracer for PET imaging of indoleamine 2,3-dioxygenase 1 (IDO1)

BACKGROUND

(S)-[18F]FETrp is a promising PET radiotracer for imaging IDO1 activity, one of the main enzymes involved in the tryptophan metabolism that plays a key role in several diseases including cancers. To date, the radiosynthesis of this tryptophan analogue remains highly challenging due to partial racemization occurring during the nucleophilic radiofluorination step. This work aims to develop a short, epimerization-free and efficient automated procedure of (S)-[18F]FETrp from a corresponding enantiopure tosylate precursor.

RESULTS

Enantiomerically pure (S)- and (R)-FETrp references as well as tosylate precursors (S)- and (R)-3 were obtained from corresponding Na-Boc-(L and D)-tryptophan in 2 and 4 steps, respectively. Manual optimisation of the radiolabelling conditions resulted in > 90% radiochemical conversion with more than 99% enantiomeric purity. Based on these results, the (S)-[18F]FETrp radiosynthesis was fully automated on a SynChrom R&D EVOI module to produce the radiotracer in 55.2 ± 7.5% radiochemical yield, 99.9% radiochemical purity, 99.1 ± 0.5% enantiomeric excess, and molar activity of 53.2 ± 9.3 GBq/µmol (n = 3).

CONCLUSIONS

To avoid racemisation and complicated purification processes, currently encountered for the radiosynthesis of (S)-[18F]FETrp, we report herein significant improvements, including a versatile synthesis of enantiomerically pure tosylate precursor and reference compound and a convenient one-pot two-step automated procedure for the radiosynthesis of (S)-[18F]FETrp. This optimised and robust production method could facilitate further investigations of this relevant PET radiotracer for imaging IDO1 activity.

Rapid measurement of ageing by automated monitoring of movement of C. elegans populations

Finding new interventions that slow ageing and maintain human health is a huge challenge of our time. The nematode Caenorhabditis elegans offers a rapid in vivo method to determine whether a compound extends its 2 to 3-week lifespan. Measuring lifespan is the standard method to monitor ageing, but a compound that extends lifespan will not necessarily maintain health. Here, we describe the automated monitoring of C. elegans movement from early to mid-adulthood as a faster healthspan-based method to measure ageing. Using the WormGazer™ technology, multiple Petri dishes each containing several C. elegans worms are imaged simultaneously and non-invasively by an array of cameras that can be scaled easily. This approach demonstrates that most functional decline in C. elegans occurs during the first week of adulthood. We find 7 days of imaging is sufficient to measure the dose-dependent efficacy of sulfamethoxazole to slow ageing, compared to 40 days required for a parallel lifespan experiment. Understanding any negative consequences of interventions that slow ageing is important. We show that the long-lived mutant age-1(hx546) stays active for longer than the wild type but it moves slower in early adulthood. Thus, continuous analysis of movement can rapidly identify interventions that slow ageing while simultaneously revealing any negative effects on health.

Design and manufacturability data of automatic lotus fiber extractor

Lotus silk is a type of textile and is considered as one of the most expensive fabrics in the world. It is made by weaving lotus fibres which are popularly extracted from lotus stems in some Asian countries such as Vietnam, Myanmar, Cambodia, etc. This dataset consists of the design and manufacturability data for automatic lotus fiber extraction machine including three modules (1) workpiece feeding, (2) fibre pulling, and (3) fibre spinning. The diagram of the machine principle is built in AutoCad while the 3D model was designed in Solidworks and some parts were collected from open-source library such as MISUMI, GrabCAD, and AIRTAC. The connection diagram for the HMI is designed in AutoCad and the PLC program is constructed in TIA portal. In addition, the manufacturing cost is also provided for a purpose of reference if the other researchers are interested in developing this machine. This dataset consists of (1) diagram of principle, (2) the CAD file for the design, (3) the pdf file for the connection diagram of the HMI and the PLC program, and (4) the manufacturing cost excel file. This dataset has the potential to promote the future innovations that improves the productivity of lotus fibre extraction process and working environment of the labours.

Parallel purification of microscale libraries via automated solid phase extraction

High-throughput experimentation (HTE) has become more widely utilized in drug discovery for rapid reaction optimization and generation of large synthetic compound arrays. While this has accelerated medicinal chemistry design, make, test (DMT) iterations, the bottleneck of purification persists, consuming time and resources. Herein we describe a general parallel purification approach based on solid phase extraction (SPE) that provides a more efficient and sustainable workflow producing compound libraries with significantly upgraded purity. This robust, user-friendly workflow is fully automated and integrated with HTE library synthesis, as demonstrated by its application to a diverse parallel library compound array generated via amide-bond coupling in HTE microscale format.

Cutting-edge technology and automation in the pathology laboratory

One of the goals of pathology is to standardize laboratory practices to increase the precision and effectiveness of diagnostic testing, which will ultimately enhance patient care and results. Standardization is crucial in the domains of tissue processing, analysis, and reporting. To enhance diagnostic testing, innovative technologies are also being created and put into use. Furthermore, although problems like algorithm training and data privacy issues still need to be resolved, digital pathology and artificial intelligence are emerging in a structured manner. Overall, for the field of pathology to advance and for patient care to be improved, standard laboratory practices and innovative technologies must be adopted. In this paper, we describe the state-of-the-art of automation in pathology laboratories in order to lead technological progress and evolution. By anticipating laboratory needs and demands, the aim is to inspire innovation tools and processes as positively transformative support for operators, organizations, and patients.

Development and Operationalization of an Automated Workflow for Correlation of Knee MRI and Arthroscopy Findings

OBJECTIVE

In this study, we sought to establish and evaluate an automated workflow to prospectively capture and correlate knee MRI findings with surgical findings in a large medical center.

METHODS

This retrospective analysis included data from patients who had undergone knee MRI followed by arthroscopic knee surgery within 6 months during a 2-year period (2019-2020). Discrete data were automatically extracted from a structured knee MRI report template implementing pick lists. Operative findings were recorded discretely by surgeons using a custom-built web-based telephone application. MRI findings were classified as true-positive, true-negative, false-positive, or false-negative for medial meniscus (MM), lateral meniscus (LM), and anterior cruciate ligament (ACL) tears, with arthroscopy used as the reference standard. An automated dashboard displaying up-to-date concordance and individual and group accuracy was enabled for each radiologist. Manual correlation between MRI and operative reports was performed on a random sample of 10% of cases for comparison with automatically derived values.

RESULTS

Data from 3,187 patients (1,669 male; mean age, 47 years) were analyzed. Automatic correlation was available for 60% of cases, with an overall MRI diagnostic accuracy of 93% (MM, 92%; LM, 89%; ACL, 98%). In cases reviewed manually, the number of cases that could be correlated with surgery was higher (84%). Concordance between automated and manual review was 99% when both were available (MM, 98%; LM, 100%; ACL, 99%).

CONCLUSION

This automated system was able to accurately and continuously assess correlation between imaging and operative findings for a large number of MRI examinations.

Automated method for the solid phase extraction of tetracyclines in wastewater followed by fluorimetric determination

This work presents a multisyringe flow injection analysis (MSFIA) system for the automatic extraction and determination of tetracyclines in wastewater samples. The sample was adjusted with Na2EDTA buffer before solid-phase extraction with an Oasis HLB column used for the analyte preconcentration. The europium (Eu3+)-based and citrate-mediated method (using Tris-HCl buffer) was selected for the fluorimetric analysis (λexc/em = 400/612 nm). For fluorescence detection, a low-cost system consisting of an USB 2000 CCD detector and a 3D-printed support that holds a LED light source was used. Under optimized conditions, the proposed method provided low limits of detection (9.4 μg L-1) and quantification (31 μg L-1), and good values for intra-day (<4 %) and inter-day precisions (<6 %). Recoveries of spiked TCs in wastewater samples ranged from 87 to 106 %. The results of this work were in good agreement with the measurements obtained by liquid chromatography coupled to a fluorescence detector.

Establishing semi-automated infection surveillance in obstetrics and gynaecology

BACKGROUND

Surveillance is an acknowledged method to decrease nosocomial infections, such as surgical site infections (SSIs). Electronic healthcare records create the opportunity for automated surveillance. While approaches for different types of surgeries and indicators already exist, there are very few for obstetrics and gynaecology.

AIM

To analyse the sensitivity and workload reduction of semi-automated surveillance in obstetrics and gynaecology.

METHODS

In this retrospective, single-centre study at a 1438-bed tertiary care hospital in Germany, semi-automated SSI surveillance using the indicators 'antibiotic prescription', 'microbiological data' and 'administrative data' (diagnosis codes, readmission, post-hospitalization care) was compared with manual analysis and categorization of all patient files. Breast surgeries (BSs) conducted in 2018 and caesarean sections (CSs) that met the inclusion criteria between May 2013 and December 2019 were included. Indicators were analysed for sensitivity, number of analysed procedures needed to identify one case, and potential workload reduction in detecting SSIs in comparison with the control group.

FINDINGS

The reference standard showed nine SSIs in 416 BSs (2.2%). Sensitivities for the indicators 'antibiotic prescription', 'diagnosis code', 'microbiological sample taken', and the combination 'diagnosis code or microbiological sample' were 100%, 88.9%, 66.7% and 100%, respectively. The reference standard showed 54 SSIs in 3438 CSs (1.6%). Sensitivities for the indicators 'collection of microbiological samples', 'diagnosis codes', 'readmission/post-hospitalization care', and the combination of all indicators were 38.9%, 27.8%, 85.2% and 94.4%, respectively.

CONCLUSIONS

Semi-automated surveillance systems may reduce workload by maintaining high sensitivity depending on the type of surgery, local circumstances and thorough digitalization.

Excelzyme: A Swiss University-Industry Collaboration for Accelerated Biocatalyst Development

Excelzyme, an enzyme engineering platform located at the Zurich University of Applied Sciences, is dedicated to accelerating the development of tailored biocatalysts for large-scale industrial applications. Leveraging automation and advanced computational techniques, including machine learning, efficient biocatalysts can be generated in short timeframes. Toward this goal, Excelzyme systematically selects suitable protein scaffolds as the foundation for constructing complex enzyme libraries, thereby enhancing sequence and structural biocatalyst diversity. Here, we describe applied workflows and technologies as well as an industrial case study that exemplifies the successful application of the workflow.

Cost-effectiveness of Microsoft Academic Graph with machine learning for automated study identification in a living map of coronavirus disease 2019 (COVID-19) research

Background

Identifying new, eligible studies for integration into living systematic reviews and maps usually relies on conventional Boolean updating searches of multiple databases and manual processing of the updated results. Automated searches of one, comprehensive, continuously updated source, with adjunctive machine learning, could enable more efficient searching, selection and prioritisation workflows for updating (living) reviews and maps, though research is needed to establish this. Microsoft Academic Graph (MAG) is a potentially comprehensive single source which also contains metadata that can be used in machine learning to help efficiently identify eligible studies. This study sought to establish whether: (a) MAG was a sufficiently sensitive single source to maintain our living map of COVID-19 research; and (b) eligible records could be identified with an acceptably high level of specificity.

Methods

We conducted an eight-arm cost-effectiveness analysis to assess the costs, recall and precision of semi-automated workflows, incorporating MAG with adjunctive machine learning, for continually updating our living map. Resource use data (time use) were collected from information specialists and other researchers involved in map production. Our systematic review software, EPPI-Reviewer, was adapted to incorporate MAG and associated machine learning workflows, and also used to collect data on recall, precision, and manual screening workload.

Results

The semi-automated MAG-enabled workflow dominated conventional workflows in both the base case and sensitivity analyses. At one month our MAG-enabled workflow with machine learning, active learning and fixed screening targets identified 469 additional, eligible articles for inclusion in our living map, and cost £3,179 GBP per week less, compared with conventional methods relying on Boolean searches of Medline and Embase.

Conclusions

We were able to increase recall and coverage of a large living map, whilst reducing its production costs. This finding is likely to be transferrable to OpenAlex, MAG's successor database platform.

An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets

The increasing use of neuroimaging in clinical research has driven the creation of many large imaging datasets. However, these datasets often rely on inconsistent naming conventions in image file headers to describe acquisition, and time-consuming manual curation is necessary. Therefore, we sought to automate the process of classifying and organizing magnetic resonance imaging (MRI) data according to acquisition types common to the clinical routine, as well as automate the transformation of raw, unstructured images into Brain Imaging Data Structure (BIDS) datasets. To do this, we trained an XGBoost model to classify MRI acquisition types using relatively few acquisition parameters that are automatically stored by the MRI scanner in image file metadata, which are then mapped to the naming conventions prescribed by BIDS to transform the input images to the BIDS structure. The model recognizes MRI types with 99.475% accuracy, as well as a micro/macro-averaged precision of 0.9995/0.994, a micro/macro-averaged recall of 0.9995/0.989, and a micro/macro-averaged F1 of 0.9995/0.991. Our approach accurately and quickly classifies MRI types and transforms unstructured data into standardized structures with little-to-no user intervention, reducing the barrier of entry for clinical scientists and increasing the accessibility of existing neuroimaging data.

Piston-driven automated liquid handlers

Laboratory capacities are often limited by time-consuming manual repetitive procedures rather than analysis time itself. While modern instruments are typically equipped with an autosampler, sample preparation often follows manual procedures including many labor-intensive, monotonous tasks. Particularly, for a high number of samples, well plates, and low microliter pipetting, manual preparation is error-prone often requiring repeated experiments. Sampling and sample preparation can account for greater analytical variability than instrument analysis. Repetitive tasks such as liquid handling benefit strongly from technological advances and led to the increasing applications of various automated liquid handlers (ALHs). In this review, we discuss the considerations for ALHs in the microliter range and highlight advantages and challenges when transforming from manual to automated workflows. We strongly focused on differences in liquid handling and outlined advantages due to sensor-controlled pipetting. ALHs can substantially improve costs-effectiveness and laboratory capacity. This is a consequence of increased efficiency, and throughput of laboratories while simultaneously raising data quality. Additionally, ALHs can improve safety, documentation of data, and sustainability. While automation requires careful consideration and resource demanding implementation, we believe it offers numerous advantages and can help to transform modern laboratories.

Robotics-Driven Manufacturing of Cartilaginous Microtissues for Skeletal Tissue Engineering Applications

Automated technologies are attractive for enhancing the robust manufacturing of tissue-engineered products for clinical translation. In this work, we present an automation strategy using a robotics platform for media changes, and imaging of cartilaginous microtissues cultured in static microwell platforms. We use an automated image analysis pipeline to extract microtissue displacements and morphological features as noninvasive quality attributes. As a result, empty microwells were identified with a 96% accuracy, and dice coefficient of 0.84 for segmentation. Design of experiment are used for the optimization of liquid handling parameters to minimize empty microwells during long-term differentiation protocols. We found no significant effect of aspiration or dispension speeds at and beyond manual speed. Instead, repeated media changes and time in culture were the driving force or microtissue displacements. As the ovine model is the preclinical model of choice for large skeletal defects, we used ovine periosteum-derived cells to form cartilage-intermediate microtissues. Increased expression of COL2A1 confirms chondrogenic differentiation and RUNX2 shows no osteogenic specification. Histological analysis shows an increased secretion of cartilaginous extracellular matrix and glycosaminoglycans in larger microtissues. Furthermore, microtissue-based implants are capable of forming mineralized tissues and bone after 4 weeks of ectopic implantation in nude mice. We demonstrate the development of an integrated bioprocess for culturing and manipulation of cartilaginous microtissues and anticipate the progressive substitution of manual operations with automated solutions for the manufacturing of microtissue-based living implants.

Functionalized magnetic metal organic framework nanocomposites for high throughput automation extraction and sensitive detection of antipsychotic drugs in serum samples

Due to the complexity of biological sample matrix, the automated and high-throughput pretreatment technology is urgently needed for monitoring the antipsychotic drugs for mental patients. In this study, functionalized magnetic zirconium-based organic framework nanocomposites (Fe3O4@SiO2@Zr-MOFs) were successfully designed and synthesized by the layer-by-layer growth. Among them, Fe3O4@SiO2@UiO-67-COOH showed the best adsorption performance, and at the same time it exhibited excellent water dispersibility, high thermal stability, chemical stability and high hydrophobicity. Results of adsorption kinetics, isotherm and FT-IR showed that the adsorption process was dominated by chemical adsorption (hydrogen bond, electrostatic interaction, π-π interaction) and monolayer adsorption. Moreover, the smaller pore size improved the protein exclusion rate which reached 98.9-99.8%. Based on the above result, the synthesized magnetic nanoparticles were introduced to 96-well automatic extractor, antipsychotic drugs in 96 serum samples were automatically extracted within 9 min, which most greatly saved the time and labor costs and avoided artificial errors. By further integrating with ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), antipsychotic drugs can be detected in the range of 0.2-3.0 ng mL-1 with a quantitative limit of 0.06-0.9 ng mL-1. The recoveries of antipsychotic drugs and their metabolites in serum ranged from 95.7% to 112.3% within 1.4-6.5% of RSD. These features indicate that the proposed method is promising for high throughput and sensitively monitoring of drugs and other hazardous substances.

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.

Evolution of LC-MS/MS in clinical laboratories

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has attracted significant attention in clinical practice owing to its numerous advantages. However, the widespread adoption of this technique is hindered by certain limitations, such as inappropriate analyte selection, low levels of automation, and a lack of specific reference intervals and quality control programs. This review comprehensively summarizes the current challenges associated with LC-MS/MS and proposes potential resolutions. The principle of utility should guide the selection of biomarkers, prioritizing their practical value over sheer quantity. To achieve full-process automation, methodological innovation is crucial for developing high-throughput equipment. Establishing reference intervals for mass spectrometry-based assays across multiple centers and diverse populations is essential for accurate result interpretation. Additionally, the development of commercial quality control materials assumes pivotal importance in ensuring assay reliability and reproducibility. Harmonization and standardization efforts should focus on the development of reference methods and materials for the clinical use of LC-MS/MS. In the future, commercial assay kits and laboratory-developed tests (LDTs) are expected to coexist in clinical laboratories, each offering distinct advantages. The collaborative efforts of diverse professionals is vital for addressing the challenges associated with the clinical application of LC-MS/MS. The anticipated advancements include simplification, increased automation, intelligence, and the standardization of LC-MS/MS, ultimately facilitating its seamless integration into clinical routines for both technicians and clinicians.

Comparative study of drug release from electrospun nanofibers loaded with clotrimazole via two different approaches using a fully automated sequential injection system

The development of new drug delivery platforms including the use of nanotechnology has been found of great interest in recent years. Two different loading approaches of the model antimycotic drug clotrimazole into the nanofibrous polycaprolactone and polydioxanone structures including electrospinning of a drug-polymer blend and impregnation of nanofibers with drug have been tested. The final amount of clotrimazole in the nanofibrous materials was determined by HPLC analysis and Raman spectroscopy. The electrospinning of blend approach allowed the adsorption of clotrimazole in a quantity of up to 30 % using mixtures with polymer/clotrimazole ratios from 2:1 to 8:1 (w/w). Ethanolic clotrimazole solutions with concentrations from 2.5 to 3.5 mg L-1 were used for adsorbing clotrimazole in blank nanofibers for 1-3 h with final clotrimazole content ranging from 3.0 to 5.7 %. Furthermore, a comparative liberation study including comparison with commercially available creams was carried out in low pressure flow system. The results obtained confirmed well controlled release of clotrimazole from both types of nanofibers. Compared to commercial pharmaceutical formulations containing 1 % clotrimazole where first-order release kinetics was observed, nanofibrous materials provided linear controlled release (zero-order kinetics) in the tested 3 h period.

A streamlined, automated workflow to screen and triage large numbers of baculoviruses for protein expression

The baculovirus expression system is a powerful and widely used method to generate large quantities of recombinant protein. However, challenges exist in workflows utilizing either liquid baculovirus stocks or the Titerless Infected-Cells Preservation and Scale-Up (TIPS) method, including the time and effort to generate baculoviruses, screen for protein expression and store large numbers of baculovirus stocks. To mitigate these challenges, we have developed a streamlined, hybrid workflow which utilizes high titer liquid virus stocks for rapid plate-based protein expression screening, followed by a TIPS-based scale-up for larger protein production efforts. Additionally, we have automated each step in this screening workflow using a custom robotic system. With these process improvements, we have significantly reduced the time, effort and resources required to manage large baculovirus generation and expression screening campaigns.

Impacts of intelligent monitoring technology installation and additional modalities on hand hygiene compliance in a burn center: A quasi-experimental longitudinal trial

BACKGROUND

The increasing development of intelligent technologies for hand hygiene (HH) compliance audit has the potential to create an alternative to direct observation (DO), which is still considered the gold standard but has disadvantages such as lack of standardized monitoring practices, Hawthorne effect, insufficient sample size, and time/resource consumption. We aimed to share our preliminary results on the impacts of intelligent monitoring technology installation (IMTI) and additional modalities on healthcare workers' (HCWs') HH compliance in a Burn Center, according to the "5 Moments of HH" concept defined by the World Health Organization (WHO).

METHODS

A quasi-experimental longitudinal trial was conducted over eleven months.The first phase of the three-stage study evaluated basic HH compliances obtained by DO. The system-defined HH performances, which IMTI recorded, were assessed in the second phase. Finally, the effect of IMTI and additional modalities was determined in the third stage.

RESULTS

15202 HH events were performed by 41 HCWs, and a total of 20095 HH opportunities were observed. Four hundred fifty-five opportunities were in the preinstallation phase, and 19640 were during the total post-installation period. IMTIdefined performance rates in both Phase 2 (71.2%) and Phase 3 (80.5%) were generally considerably higher than HH compliances obtained from DO (58.5%). Nurses, physical therapy /anesthesia technicians, and housekeeping personnel showed significant increases, which was insignificant in physicians in phase 2. Meanwhile, a sustained increase was observed regarding IMTI and additional modalities of HH compliance of all HCWs in Phase 3.

CONCLUSION

IMTI has significantly increased HH performance rates. Furthermore, combining the IMTI with additional modalities as components of a multimodal strategy recommended by WHO appears to affect the sustainability of the increasing trend of HCWs' HH compliance.

Systematic scoping review of automated systems for the surveillance of healthcare-associated bloodstream infections related to intravascular catheters

INTRODUCTION

Intravascular catheters are crucial devices in medical practice that increase the risk of healthcare-associated infections (HAIs), and related health-economic adverse outcomes. This scoping review aims to provide a comprehensive overview of published automated algorithms for surveillance of catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI).

METHODS

We performed a scoping review based on a systematic search of the literature in PubMed and EMBASE from 1 January 2000 to 31 December 2021. Studies were included if they evaluated predictive performance of automated surveillance algorithms for CLABSI/CRBSI detection and used manually collected surveillance data as reference. We assessed the design of the automated systems, including the definitions used to develop algorithms (CLABSI versus CRBSI), the datasets and denominators used, and the algorithms evaluated in each of the studies.

RESULTS

We screened 586 studies based on title and abstract, and 99 were assessed based on full text. Nine studies were included in the scoping review. Most studies were monocentric (n = 5), and they identified CLABSI (n = 7) as an outcome. The majority of the studies used administrative and microbiological data (n = 9) and five studies included the presence of a vascular central line in their automated system. Six studies explained the denominator they selected, five of which chose central line-days. The most common rules and steps used in the algorithms were categorized as hospital-acquired rules, infection rules (infection versus contamination), deduplication, episode grouping, secondary BSI rules (secondary versus primary BSI), and catheter-associated rules.

CONCLUSION

The automated surveillance systems that we identified were heterogeneous in terms of definitions, datasets and denominators used, with a combination of rules in each algorithm. Further guidelines and studies are needed to develop and implement algorithms to detect CLABSI/CRBSI, with standardized definitions, appropriate data sources and suitable denominators.

Automated characterization of patient-ventilator interaction using surface electromyography

BACKGROUND

Characterizing patient-ventilator interaction in critically ill patients is time-consuming and requires trained staff to evaluate the behavior of the ventilated patient.

METHODS

In this study, we recorded surface electromyography ([Formula: see text]) signals from the diaphragm and intercostal muscles and esophageal pressure ([Formula: see text]) in mechanically ventilated patients with ARDS. The sEMG recordings were preprocessed, and two different algorithms (triangle algorithm and adaptive thresholding algorithm) were used to automatically detect inspiratory patient effort. Based on the detected inspirations, major asynchronies (ineffective, auto-, and double triggers and double efforts), delayed and synchronous triggers were computationally classified. Reverse triggers were not considered in this study. Subsequently, asynchrony indices were calculated. For the validation of detected efforts, two experts manually annotated inspiratory patient activity in [Formula: see text], blinded toward each other, the [Formula: see text] signals, and the algorithmic results. We also classified patient-ventilator interaction and calculated asynchrony indices with manually detected inspirations in [Formula: see text] as a reference for automated asynchrony classification and asynchrony index calculation.

RESULTS

Spontaneous breathing activity was recognized in 22 out of the 36 patients included in the study. Evaluation of the accuracy of the algorithms using 3057 inspiratory efforts in [Formula: see text] demonstrated reliable detection performance for both methods. Across all datasets, we found a high sensitivity (triangle algorithm/adaptive thresholding algorithm: 0.93/0.97) and a high positive predictive value (0.94/0.89) against expert annotations in [Formula: see text]. The average delay of automatically detected inspiratory onset to the [Formula: see text] reference was [Formula: see text]79 ms/29 ms for the two algorithms. Our findings also indicate that automatic asynchrony index prediction is reliable. For both algorithms, we found the same deviation of [Formula: see text] to the [Formula: see text]-based reference.

CONCLUSIONS

Our study demonstrates the feasibility of automating the quantification of patient-ventilator asynchrony in critically ill patients using noninvasive sEMG. This may facilitate more frequent diagnosis of asynchrony and support improving patient-ventilator interaction.

Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis

Automated chemistry platforms have been widely explored, but many focus on fixed tasks for chemical synthesis or analysis. However, a typical synthetic chemistry workflow utilizes both, such as kinetic measurements for reaction development and optimization. Due to their repetitive and time-consuming nature, kinetic measurements are often omitted, which limits the mechanistic investigation of reactions. Herein, we present a "Chemputer" platform with on-line analytics (UV/Vis, NMR) which automates routine kinetic measurements. The system's capabilities are showcased by exploring an inverse electron-demand Diels-Alder using initial rate measurements, a metal complexation using variable time normalization analysis (VTNA), and formation of a series of tosylamide derivatives using Hammett analysis. Over 60 individual experiments are presented which required minimal intervention, highlighting the significant time savings of automation. Owing to the modular design of the platform, which facilitates rapid integration of commercial analytical tools, our approach is widely accessible and adjustable to the reaction under investigation. The platform is operated using the chemical programming language, XDL, hence experimental procedures and results are stored in a precise, computer-readable format. We propose that widespread adoption of this reporting protocol in the chemical community could build a database of validated kinetic data beneficial for Machine Learning.

"High-throughput screening of catalytically active inclusion bodies using laboratory automation and Bayesian optimization"

BACKGROUND

In recent years, the production of inclusion bodies that retain substantial catalytic activity was demonstrated. These catalytically active inclusion bodies (CatIBs) are formed by genetic fusion of an aggregation-inducing tag to a gene of interest via short linker polypeptides. The resulting CatIBs are known for their easy and cost-efficient production, recyclability as well as their improved stability. Recent studies have outlined the cooperative effects of linker and aggregation-inducing tag on CatIB activities. However, no a priori prediction is possible so far to indicate the best combination thereof. Consequently, extensive screening is required to find the best performing CatIB variant.

RESULTS

In this work, a semi-automated cloning workflow was implemented and used for fast generation of 63 CatIB variants with glucose dehydrogenase of Bacillus subtilis (BsGDH). Furthermore, the variant BsGDH-PT-CBDCell was used to develop, optimize and validate an automated CatIB screening workflow, enhancing the analysis of many CatIB candidates in parallel. Compared to previous studies with CatIBs, important optimization steps include the exclusion of plate position effects in the BioLector by changing the cultivation temperature. For the overall workflow including strain construction, the manual workload could be reduced from 59 to 7 h for 48 variants (88%). After demonstration of high reproducibility with 1.9% relative standard deviation across 42 biological replicates, the workflow was performed in combination with a Bayesian process model and Thompson sampling. While the process model is crucial to derive key performance indicators of CatIBs, Thompson sampling serves as a strategy to balance exploitation and exploration in screening procedures. Our methodology allowed analysis of 63 BsGDH-CatIB variants within only three batch experiments. Because of the high likelihood of TDoT-PT-BsGDH being the best CatIB performer, it was selected in 50 biological replicates during the three screening rounds, much more than other, low-performing variants.

CONCLUSIONS

At the current state of knowledge, every new enzyme requires screening for different linker/aggregation-inducing tag combinations. For this purpose, the presented CatIB toolbox facilitates fast and simplified construction and screening procedures. The methodology thus assists in finding the best CatIB producer from large libraries in short time, rendering possible automated Design-Build-Test-Learn cycles to generate structure/function learnings.