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.

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.

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.

[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.

HPLC-Based Automated Synthesis and Purification of Carbohydrates

Reported herein is a new HPLC-based automated synthesizer (HPLC-A) capable of a temperature-controlled synthesis and purification of carbohydrates. The developed platform allows to perform various protecting group manipulations as well as the synthesis of O- and N-glycosides. A fully automated synthesis and purification was showcased in application to different carbohydrate derivatives including glycosides, oligosaccharides, glycopeptides, glycolipids, and nucleosides.

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.

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.

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.

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.

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.