NanoConstruct: A web application builder of ellipsoidal nanoparticles for the investigation of their crystal growth, stability, and the calculation of atomistic descriptors

NanoConstruct is a state-of-the-art computational tool that enables a) the digital construction of ellipsoidal neutral energy minimized nanoparticles (NPs) in vacuum through its graphical user-friendly interface, and b) the calculation of NPs atomistic descriptors. It allows the user to select NP's shape and size by inserting its ellipsoidal axes and rotation angle while the NP material is selected by uploading its Crystallography Information File (CIF). To investigate the stability of materials not yet synthesised, NanoConstruct allows the substitution of the chemical elements of an already synthesized material with chemical elements that belong into the same group and neighbouring rows of the periodic table. The process is divided into three stages: 1) digital construction of the unit cell, 2) digital construction of NP using geometry rules and keeping its stoichiometry and 3) energy minimization of the geometrically constructed NP and calculation of its atomistic descriptors. In this study, NanoConstruct was applied for the investigation of the crystal growth of Zirconia (ZrO2) NPs when in the rutile form. The most stable configuration and the crystal growth route were identified, showing a preferential direction for the crystal growth of ZrO2 in its rutile form. NanoConstruct is freely available through the Enalos Cloud Platform (https://enaloscloud.novamechanics.com/riskgone/nanoconstruct/).

Automated online solid-phase extraction-tandem mass spectrometry detection for simultaneous analysis of acidic and alkaline catecholamines and their metabolites in human urine

Metabolic dysregulation of catecholamines (CAs) is implicated in various human diseases. Simultaneously analyzing these acidic and alkaline CAs and their metabolites poses a significant challenge for clinical detection. This study introduces an efficient method employing automated online solid-phase extraction coupled with tandem mass spectrometry (aoSPE-MS/MS). The method employs weak cation exchange (WCX) and mixed-mode anion exchange (MAX) adsorbents to fabricate an on-line solid-phase extraction (SPE) column, along with an automated injection and multi-valve switching capabilities. The setup allows for automated extraction and analysis of urine samples in 15 minutes while retaining a wide range of acidic and basic CAs and their metabolites. The applicability of this method was demonstrated by optimising the adsorbent dosage volume, extraction solvent, and extraction rate. The limits of detection (LODs) and limits of quantitation (LOQs) for the 8 CAs and their metabolites were determined using the aoSPE-MS/MS approach, with ranges of 0.0625 ∼ 62.5 ng/mL and 0.125 ∼ 125 ng/mL, respectively. Additionally, assessments were made on the linearity, accuracy, and precision within and between batches, as well as matrix and ionic effects, and spiked recoveries. The study discovered that the aoSPE-MS/MS technique simplifies operation, increases efficiency, saves time, and has low detection and quantification limits when detecting a wide range of acid and alkaline CAs and their metabolites in urine. The study successfully demonstrated the high-throughput and automated detection of the 8 CAs and their metabolites with varying acidity and alkalinity in human urine samples. This method is expected to be a potential powerful tool for clinical detection.

Integrated decentralized blood culture incubation: A step towards a 24/7 microbiology service?

To have an impact on the mortality of bloodstream infections, microbiological diagnostics of blood cultures (BC) should provide first results within 12 h. Here, we show how a decentralized BC incubation connected to the central BC incubators via a browser-based application significantly reduces turnaround times.

Comprehensive wheat coccinellid detection dataset: Essential resource for digital entomology

Wheat (Triticum aestivum) is a major cereal crop planted in the Southern Great Plains. This crop faces diverse pests that can affect their development and reduce yield productivity. For example, aphids are a significant pest in wheat, and their management relies on pesticides, which affect the sustainability and biodiversity of natural predators that prey on aphids. Coccinellids, commonly named lady beetles, are the most abundant natural predators of wheat. These natural enemies contribute to the natural predation of aphids, which can reduce the use of excessive pesticides for aphid management. Usually, visual observations of these natural enemies are performed during pest sampling; however, it is time-consuming and requires manual labor, which can be expensive. An automation system or detection models based on machine learning approaches that can detect these insects is needed to reduce unnecessary pesticide applications and manual labor costs. However, developing an automation system or computer vision models that automatically detect these natural enemies requires imagery to train and validate this cutting-edge technology. To solve this research problem, we collected this dataset, which includes images and label annotations to help researchers and students develop this technology that can benefit wheat growers and science to understand the capabilities of automation in Entomology. We collected a dataset using mobile devices, which included a diverse range of coccinellids on wheat images. The dataset consists of 2,133 images with a standard size of 640 × 640 pixels, which can be used to train and develop detection models for machine learning purposes. In addition, the dataset includes annotated labels that can be used for training models within the YOLO family or others, which have been proven to detect small insects in crops. Our dataset will increase the understanding of machine learning capabilities in entomology, precision agriculture, education, and crop pest management decisions.

The AutoSpawner system - Automated ex situ spawning and fertilisation of corals for reef restoration

The restoration of reefs damaged by global and local pressures remains constrained by the scale of intervention currently feasible. Traditional methods for ex situ sexual propagation of corals produce limited materials, typically of limited genetic diversity and only sufficient for small field trials. The development and validation of new technologies to upscale and automate coral propagation is required to achieve logistically and financially feasible reef restoration at ecologically relevant scales. To address the need for upscaled production of genetically diverse material for use in reef restoration we designed an automated system (the AutoSpawner) for harvesting, fertilising and washing gametes from tropical broadcast-spawning corals. The system includes a novel high density dynamic fertilisation process, which enables the production of large numbers of fertilised coral eggs (>7 million per night for highly fecund species) without any downstream negative effects on larval quality. The functionality of the system and the quality of the produced larvae was assessed using multiple species from two coral families (Acroporidae and Merulinidae) across a range of spawning and gamete characteristics. We present the schematics and protocols required for automated sexual propagation of high-quality coral larvae using this novel system; and demonstrate that the time demands, and labour costs, associated with traditional manual-based sexual propagation of corals can be reduced by up to 113-fold using the AutoSpawner.

A qualitative, multi-centre approach to the current state of digitalisation and automation of surveillance in infection prevention and control in German hospitals

BACKGROUND

Healthcare associated infections (HAI) pose a major threat to healthcare systems resulting in an increased burden of disease. Surveillance plays a key role in rapidly identifying these infections and preventing further transmissions. Alas, in German hospitals, the majority of surveillance efforts have been heavily relying on labour intensive processes like manual chart review. In order to be able to identify further starting points for future digital tools and interventions to aid the surveillance of HAI we aimed to gain an understanding of the current state of digitalisation in the context of the general surveillance organisation in German clinics across all care-levels. The end user perspective of infection prevention and control (IPC) professionals was chosen to identify digital interventions that have the biggest impact on the daily surveillance work routines of IPC professionals. Perceived impediments in the advancement of surveillance digitalisation should be explored.

METHODS

Following the development of an interview guideline, eight IPC professionals from seven German hospitals of different care levels were questioned in semi- structured interviews between December 2022 and January 2023. These included questions about general surveillance organisation, access to digital data sources, software to aid the surveillance process as well as current issues in the surveillance process and implementation of software systems. Subsequently, after full transcription, the interview sections were categorized in code categories (first deductive then inductive coding) and analysed qualitatively.

RESULTS

Results were characterised by high heterogeneity in terms of general surveillance organisation and access to digital data sources. Software configuration of hospital and laboratory information systems (HIS/LIS) as well as patient data management systems (PDMS) varied not only between hospitals of different care levels but also between hospitals of the same care level. Outside research projects, neither fully automatic software nor solutions utilising artificial intelligence have currently been implemented in clinical routine in any of the hospitals.

CONCLUSIONS

Access to digital data sources and software is increasingly available to aid surveillance of HAI. Nevertheless, surveillance processes in hospitals analysed in this study still heavily rely on manual processes. In the analysed hospitals, there is an implementation and funding gap of (semi-) automatic surveillance solutions in clinical practice, especially in healthcare facilities of lower care levels.

Automation of ePROMs in radiation oncology and its impact on patient response and bias

PURPOSE

This study evaluates the impact of integrating a novel, in-house developed electronic Patient-Reported Outcome Measures (ePROMs) tool with a commercial Oncology Information System (OIS) on patient response rates and potential biases in real-world data science applications.

MATERIALS AND METHODS

We designed an ePROMs tool using the NodeJS web application framework, automatically sending e-mail questionnaires to patients based on their treatment schedules in the OIS. The tool is used across various treatment sites to collect PROMs data in a real-world setting. This research examined the effects of increasing automation levels on both recruitment and response rates, as well as potential biases across different patient cohorts. Automation was implemented in three escalating levels, from telephone reminders for missing reports to minimal intervention from study nurses.

RESULTS

From August 2020 to December 2023, 1,944 patients participated in the PROMs study. Our findings indicate that automating the workflows substantially reduced the patient management workload. However, higher levels of automation led to lower response rates, particularly in collecting late-phase symptoms in breast and head-and-neck cancer cohorts. Additionally, email-based PROMs introduced an age bias when recruiting new patients for the ePROMs study. Nevertheless, age was not a significant predictor of early dropout or missing symptom reports among patients participating. Notably, increased automation was significantly correlated with lower response rates in breast (p = 0.026) and head-and-neck cancer patients (p < 0.001).

CONCLUSION

Integrating ePROMs within the OIS can significantly reduce workload and personnel resources. However, this efficiency may compromise patient responses in certain groups. A balance must be achieved between workload, resource allocation, and the sensitivity needed to detect clinically significant effects. This may necessitate customized automation levels tailored to specific cancer groups, highlighting a fundamental trade-off between operational efficiency and data quality.

Development of a Rapid and Fully Automated Factor VIII-Inhibitor Assay, Insensitive for Emicizumab, and a Lowest Level of Quantification of 0.2BU/mL

BACKGROUND

Factor VIII inhibitors are measured using labour and resource expensive Nijmegen or Bethesda assays, which lack sensitivity for low-titre-inhibitors, and show high variations in quality surveys, mainly because of manual assay procedures.

METHODS

A new rapid, fully automated, factor VIII inhibitor assay is presented, the core of which is use of full-length recombinant FVIII (rFVIII) (Kovaltry®) as inhibitor substrate instead of plasma FVIII, resulting in rapid binding of inhibitors to rFVIII due to absence of VWF. Dramatic shortening of incubation time facilitated full automation on an analyser capable of three subsequent sample dilution steps and three reagent additions. Equal volume mixtures of sample and rFVIII (1.0 U/mL) were incubated 10 minutes/37°C, whereafter remaining FVIII-activity was analysed with a kinetic chromogenic assay, allowing inhibitor-activity calculation without preceding FVIII-activity calibration, using a Ceveron s100 analyser.

RESULTS

Mean titre in 60 non-haemophiliacs was 0.0BU/mL (SD 0.1), yielding a Limit-of-Blank of 0.1BU/mL and Lower-Limit-of-Quantification of 0.2BU/mL. Analyses were performed with the new method and a Nijmegen Assay in 28 inhibitor-positive clinical samples, 14 containing emicizumab and 14 without. Correlation coefficient in emicizumab-free type-I-inhibitor samples was r=1.0. Emicizumab dependency of the method was excluded in spiking experiments with inhibitor-positive-samples. Reproducibility was tested by analysing seven samples in three laboratories on five days, twice daily; CV of all samples were <15%.

CONCLUSION

We present development data of a sensitive and specific, rapid, automated FVIII inhibitor assay generating results within 20 minutes, is less resource intensive than standard assays, with potential to improve assay variability.

Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed

BACKGROUND

The demand for high-quality systematic literature reviews (SRs) for evidence-based medical decision-making is growing. SRs are costly and require the scarce resource of highly skilled reviewers. Automation technology has been proposed to save workload and expedite the SR workflow. We aimed to provide a comprehensive overview of SR automation studies indexed in PubMed, focusing on the applicability of these technologies in real world practice.

METHODS

In November 2022, we extracted, combined, and ran an integrated PubMed search for SRs on SR automation. Full-text English peer-reviewed articles were included if they reported studies on SR automation methods (SSAM), or automated SRs (ASR). Bibliographic analyses and knowledge-discovery studies were excluded. Record screening was performed by single reviewers, and the selection of full text papers was performed in duplicate. We summarized the publication details, automated review stages, automation goals, applied tools, data sources, methods, results, and Google Scholar citations of SR automation studies.

RESULTS

From 5321 records screened by title and abstract, we included 123 full text articles, of which 108 were SSAM and 15 ASR. Automation was applied for search (19/123, 15.4%), record screening (89/123, 72.4%), full-text selection (6/123, 4.9%), data extraction (13/123, 10.6%), risk of bias assessment (9/123, 7.3%), evidence synthesis (2/123, 1.6%), assessment of evidence quality (2/123, 1.6%), and reporting (2/123, 1.6%). Multiple SR stages were automated by 11 (8.9%) studies. The performance of automated record screening varied largely across SR topics. In published ASR, we found examples of automated search, record screening, full-text selection, and data extraction. In some ASRs, automation fully complemented manual reviews to increase sensitivity rather than to save workload. Reporting of automation details was often incomplete in ASRs.

CONCLUSIONS

Automation techniques are being developed for all SR stages, but with limited real-world adoption. Most SR automation tools target single SR stages, with modest time savings for the entire SR process and varying sensitivity and specificity across studies. Therefore, the real-world benefits of SR automation remain uncertain. Standardizing the terminology, reporting, and metrics of study reports could enhance the adoption of SR automation techniques in real-world practice.

Comparative assessment of the diagnostic performances of particle-based multianalyte technology and commercial ELISA for antiphospholipid autoantibody testing

BACKGROUND

Diagnosing Antiphospholipid Syndrome (APS) relies heavily on laboratory findings, particularly the detection of specific antibodies like lupus anticoagulant (LA), IgG and/or IgM anti-cardiolipin (aCL), and IgG and/or IgM anti-β2 glycoprotein 1 (aB2GP1). Although ELISA is widely used in the US for this purpose, standardization between different assay methodologies remains challenging, leading to significant variability across laboratories. Particle-based multi-analyte technology (PMAT) offers a streamlined one-step detection for all six antiphospholipid (aPL) autoantibodies, covering aCL and aB2GP1 of IgA, IgG, and IgM isotypes.

METHODS

In this study involving 224 subjects, including 34 clinically diagnosed with APS, alongside 160 non-APS patients and 30 healthy donors, PMAT's performance was evaluated against commercial ELISA in detecting aPL antibodies.

RESULTS

At the manufacturer's suggested cutoff, PMAT exhibited sensitivity comparable to ELISA, albeit with a low to moderate decrease in specificity for certain antibodies. With anti-CL IgM alone, PMAT displayed a 17.7% decrease in sensitivity, accompanied by a corresponding 31.1% increase in specificity compared to ELISA. However, applying a stricter cutoff (88-90% specificity), IgA and IgM antibodies yielded 5.9-17.6% higher sensitivities with PMAT, and IgG antibodies displayed similar sensitivity.

CONCLUSIONS

In this study cohort, PMAT demonstrated higher or comparable sensitivity to that of commercial ELISA for all six aPL antibodies at a specificity cutoff near 90%. Notably, PMAT demonstrated superior sensitivity and specificity overall in detecting IgA aCL and aB2GP1 antibodies. This study highlights the potential of automated PMAT for detecting aPL antibodies in APS evaluation.

POStoperative INTELLiVENT-adaptive support VEntilation in cardiac surgery patients (POSITiVE) II-study protocol of a randomized clinical trial

BACKGROUND

One single-center randomized clinical trial showed that INTELLiVENT-adaptive support ventilation (ASV) is superior to conventional ventilation with respect to the quality of ventilation in post-cardiac surgery patients. Other studies showed that this automated ventilation mode reduces the number of manual interventions at the ventilator in various types of critically ill patients. In this multicenter study in patients post-cardiac surgery, we test the hypothesis that INTELLiVENT-ASV is superior to conventional ventilation with respect to the quality of ventilation.

METHODS

"POStoperative INTELLiVENT-adaptive support VEntilation in cardiac surgery patients II (POSITiVE II)" is an international, multicenter, two-group randomized clinical superiority trial. In total, 328 cardiac surgery patients will be randomized. Investigators screen patients aged > 18 years of age, scheduled for elective cardiac surgery, and expected to receive postoperative ventilation in the ICU for longer than 2 h. Patients either receive automated ventilation by means of INTELLiVENT-ASV or ventilation that is not automated by means of a conventional ventilation mode. The primary endpoint is quality of ventilation, defined as the proportion of postoperative ventilation time characterized by exposure to predefined optimal, acceptable, and critical (injurious) ventilatory parameters in the first two postoperative hours. One major secondary endpoint is ICU team staff workload, captured by the ventilator software collecting manual settings on alarms. Patient-centered endpoints include duration of postoperative ventilation and length of stay in ICU.

DISCUSSION

POSITiVE II is the first international, multicenter, randomized clinical trial designed to confirm that POStoperative INTELLiVENT-ASV is superior to non-automated conventional ventilation and secondary to determine if this closed-loop ventilation mode reduces ICU team staff workload. The results of POSITiVE II will support intensive care teams in their choices regarding the use of automated ventilation in postoperative care of uncomplicated cardiac surgery patients.

TRIAL REGISTRATION

Clinicaltrials.gov NCT06178510 . Registered on December 4, 2023.

Early evaluation of a natural language processing tool to improve access to educational resources for surgical patients

PURPOSE

Accessible patient information sources are vital in educating patients about the benefits and risks of spinal surgery, which is crucial for obtaining informed consent. We aim to assess the effectiveness of a natural language processing (NLP) pipeline in recognizing surgical procedures from clinic letters and linking this with educational resources.

METHODS

Retrospective examination of letters from patients seeking surgery for degenerative spinal disease at a single neurosurgical center. We utilized MedCAT, a named entity recognition and linking NLP, integrated into the electronic health record (EHR), which extracts concepts and links them to systematized nomenclature of medicine-clinical terms (SNOMED-CT). Investigators reviewed clinic letters, identifying words or phrases that described or identified operations and recording the SNOMED-CT terms as ground truth. This was compared to SNOMED-CT terms identified by the model, untrained on our dataset. A pipeline linking clinic letters to patient-specific educational resources was established, and precision, recall, and F1 scores were calculated.

RESULTS

Across 199 letters the model identified 582 surgical procedures, and the overall pipeline after adding rules a total of 784 procedures (precision = 0.94, recall = 0.86, F1 = 0.91). Across 187 letters with identified SNOMED-CT terms the integrated pipeline linking education resources directly to the EHR was successful in 157 (78%) patients (precision = 0.99, recall = 0.87, F1 = 0.92).

CONCLUSIONS

NLP accurately identifies surgical procedures in pre-operative clinic letters within an untrained subspecialty. Performance varies among letter authors and depends on the language used by clinicians. The identified procedures can be linked to patient education resources, potentially improving patients' understanding of surgical procedures.

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.

Automated analyses of dried blood spots collected by volumetric microsampling devices

BACKGROUND

Dried blood spot (DBS) sampling on cellulose cards suffers from varying blood haematocrit levels and from chromatographic effects, which have a direct impact on quantitative DBS analyses. Commercial volumetric microsampling devices were, therefore, introduced to mitigate these effects, however, these devices are not compatible with automated DBS processing systems and must be processed manually.

RESULTS

Capillary electrophoresis (CE) instruments use fused-silica (FS) capillaries for precise and accurate liquid handling as well as for injection, separation, and quantitative analyses of liquid samples. These inherent features of an Agilent 7100 CE instrument were employed for the automated processing (elution and homogenization) of DBSs collected by hemaPEN® volumetric devices (2.74 μL of capillary blood per spot). The hemaPEN® samples were processed directly in CE vials by consecutive transfers of 56 μL of methanol and 14 μL of deionized water through the FS capillary in a sequence of 39 DBSs with repeatability of the liquid transfers better than 1.4 %. The resulting DBS eluates were homogenized by a quick air flush through the capillary and analyzed by the same capillary and CE instrument. Creatinine was selected as a clinically relevant model analyte and its endogenous concentrations in DBSs were determined by CE with capacitively coupled contactless conductivity detection (CE-C4D) in a background electrolyte solution consisting of 50 mM acetic acid and 0.1 % (v/v) Tween 20 (pH 3.0). The overall repeatability of the automated DBS processing and CE-C4D analyses of 39 DBSs was ≤7.1 % (peak areas) and ≤0.6 % (migration times), the calibration curve was linear in the 25-500 μM range (R2 = 0.9993) and covered all endogenous blood creatinine levels, the limit of detection was 5.0 μM, and sample throughput was >12 DBSs per hour. DBS ageing for 60 days and varying blood haematocrit levels (20-70 %) did not affect creatinine quantitative results (≤6.9 % for peak areas). Inter-capillary and inter-instrument repeatability was ≤7.7 % (peak areas) and ≤3.4 % (migration times) and demonstrated an excellent transferability of the proposed analytical concept among laboratories.

SIGNIFICANCE AND NOVELTY

This contribution is the first-ever report on the use of a single off-the-shelf analytical instrument for fully automated analyses of DBSs collected by commercial volumetric microsampling devices and holds great promise for future unmanned quantitative DBS analyses.

cGMP compliant one-step, one-pot automated [18F]FBnTP production for clinical imaging of mitochondrial activity

BACKGROUND

4-[18F]fluorobenzyl-triphenylphosphonium ([18F]FBnTP) is a lipophilic cation PET tracer. The cellular uptake of [18F]FBnTP is correlated with oxidative phosphorylation by mitochondria, which has been associated with multiple critical diseases. To date, [18F]FBnTP has been successfully applied for imaging myocardial perfusion, assessment of severity of coronary artery stenosis, delineation of the ischemic area after transient coronary occlusion, and detection/quantification of apoptosis in various animal models. Recent preclinical and clinical studies have also expanded the possibilities of using [18F]FBnTP in oncological diagnosis and therapeutic monitoring. However, [18F]FBnTP is typically prepared through a tediously lengthy four-step, three-pot reaction and required multiple synthesizer modules; Thus, such an approach remains a challenge for this promising radiopharmaceutical to be implemented for routine clinical studies. Herein, we report an optimized one-step, one-pot automated approach to produce [18F]FBnTP through a single standard commercially-available radiosynthesizer that enables centralized production for clinical use.

RESULTS

The fully automated production of [18F]FBnTP took less than 55 min with radiochemical yields ranging from 28.33 ± 13.92% (non-decay corrected), apparent molar activity of 69.23 ± 45.62 GBq/µmol, and radiochemical purities of 99.79 ± 0.41%. The formulated [18F]FBnTP solution was determined to be sterile and colorless with a pH of 4.0-6.0. Our data has indicated no observable radiolysis after 8 h from the time of final product formulation and maximum assay of 7.88 GBq.

CONCLUSIONS

A simplified and cGMP-compliant radiosynthesis of [18F]FBnTP has been established on the commercially available synthesizer in high activity concentration and radiochemical purity. While the preclinical and clinical studies using [18F]FBnTP PET are currently underway, the automated approaches reported herein facilitate clinical adoption of this radiotracer and warrant centralized production of [18F]FBnTP for imaging multiple patients.

Development and transfer of automated methods in neuroscience: The DADTA

In the second half of the 20th century, neuroscientists across North America developed automated systems for use in their research laboratories. Their decisions to do so were complex and contingent, partly a result of global reasons, such as the need to increase efficiency and flexibility, and partly a result of local reasons, such as the need to amend perceived biases of earlier research methodologies. Automated methods were advancements but raised several challenges. Transferring a system from one location to another required that certain components of the system be standardized, such as the hardware, software, and programming language. This proved difficult as commercial manufacturers lacked incentives to create standardized products for the few neuroscientists working towards automation. Additionally, investing in automated systems required massive amounts of time, labor, funding, and computer expertise. Moreover, neuroscientists did not agree on the value of automation. My brief history investigates Karl Pribram's decisions to expand his newly created automated system by standardizing equipment, programming, and protocols. Although he was an eminent Stanford neuroscientist with strong institutional support and computer know-how, the development and transfer of his automated behavioral testing system was riddled with challenges. For Pribram and neuroscience more generally, automation was not so automatic.

Automated radiosynthesis and preclinical evaluation of two new PSMA-617 derivatives radiolabelled via [18F]AlF2+ method

BACKGROUND

In the last decade the development of new PSMA-ligand based radiopharmaceuticals for the imaging and therapy of prostate cancer has been a highly active and important area of research. The most promising derivative in terms of interaction with the antigen and clinical properties has been found to be "PSMA-617", and its lutetium-177 radiolabelled version has recently been approved by EU and USA regulatory agencies for therapeutic purposes. For the above reasons, the development of new derivatives of PSMA-617 radiolabelled with fluorine-18 may still be of great interest. This paper proposes the comparison of two different PSMA-617 derivatives functionalized with NODA and RESCA chelators, respectively, radiolabelled via [18F]AlF2+ complexation.

RESULTS

The organic synthesis of two PSMA-617 derivatives and their radiolabelling via [18F]AlF2+ complexation resulted to proceed efficiently and successfully. Moreover, stability in solution and in plasma has been evaluated. The whole radiosynthesis procedure has been fully automated, and the final products have been obtained with radiochemical yield and purity potentially suitable for clinical studies. The biodistribution of the two derivatives was performed both in prostate cancer and glioma tumour models. Compared with the reference [18F]F-PSMA-1007 and [18F]F-PSMA-617-RESCA, [18F]F-PSMA-617-NODA derivative showed a higher uptake in both tumors, faster clearance in non-target organs, and lower uptake in salivary glands.

CONCLUSION

PSMA-617 NODA and RESCA derivatives were radiolabelled successfully via [18F]AlF2+ chelation, the former being more stable in solution and human plasma. Moreover, preclinical biodistribution studies showed that [18F]F-PSMA-617-NODA might be of potential interest for clinical applications.

Analysis of production time and capacity for manual and robotic compounding scenarios for parenteral hazardous drugs

BACKGROUND

The increasing amount of hazardous preparations in combination with shortages leads to a call for more efficient compounding methods. This research aims to evaluate the required amount of time, production capacity and direct labour costs of the manual, manual software-supported and robotic compounding of parenteral hazardous drugs.

METHODS

This multicentre study was conducted at the clinical pharmacy departments of three Dutch hospitals with different compounding methods: St Antonius hospital (manual software-supported compounding), Amsterdam University Medical Centre (Amsterdam UMC) (both robotic compounding and manual compounding without software support) and OLVG (robotic compounding). Time measurements of individual hazardous drugs were performed in all three hospitals. At Amsterdam UMC and St Antonius hospital, the times per compounding phase, the production capacity and the direct labour costs per preparation were also determined. To reflect real-world situations, the combination of robotic and manual compounding was also studied.

RESULTS

The total compounding process, including the actions before compounding and the release-time and cleaning time, lasted 6:44 min with robotic compounding and was faster than manual compounding with and without software support (6:48 and 9:48 min, respectively). The production capacity of one full-time equivalent (FTE) on 1 day (P1FTE1day) was 15 preparations per FTE per day with manual compounding with and without software support, and 57 preparations per FTE per day with only robotic compounding. If manual and robotic compounding were combined, the production capacity was 30 preparations per FTE per day. In this setting, the direct labour costs per preparation were €5.21, while these costs were €13.18 with only manual compounding.

CONCLUSION

Compared with manual compounding, robotic compounding was faster over the total compounding process. A combination of manual compounding and robotic compounding could lead to 100% more preparations per FTE and 2.5 times lower direct labour costs compared with manual compounding.

A multi-label dataset and its evaluation for automated scoring system for cleanliness assessment in video capsule endoscopy

An automated scoring system for cleanliness assessment during video capsule endoscopy (VCE) is presently lacking. The present study focused on developing an approach to automatically assess the cleanliness in VCE frames as per the latest scoring i.e., Korea-Canada (KODA). Initially, an easy-to-use mobile application called artificial intelligence-KODA (AI-KODA) score was developed to collect a multi-label image dataset of twenty-eight patient capsule videos. Three readers (gastroenterology fellows), who had been trained in reading VCE, rated this dataset in a duplicate manner. The labels were saved automatically in real-time. Inter-rater and intra-rater reliability were checked. The developed dataset was then randomly split into train:validate:test ratio of 70:20:10 and 60:20:20. It was followed by a comprehensive benchmarking and evaluation of three multi-label classification tasks using ten machine learning and two deep learning algorithms. Reliability estimation was found to be overall good among the three readers. Overall, random forest classifier achieved the best evaluation metrics, followed by Adaboost, KNeighbours, and Gaussian naive bayes in the machine learning-based classification tasks. Deep learning algorithms outperformed the machine learning-based classification tasks for only VM labels. Thorough analysis indicates that the proposed approach has the potential to save time in cleanliness assessment and is user-friendly for research and clinical use. Further research is required for the improvement of intra-rater reliability of KODA, and the development of automated multi-task classification in this field.

Multi role ChatGPT framework for transforming medical data analysis

The application of ChatGPTin the medical field has sparked debate regarding its accuracy. To address this issue, we present a Multi-Role ChatGPT Framework (MRCF), designed to improve ChatGPT's performance in medical data analysis by optimizing prompt words, integrating real-world data, and implementing quality control protocols. Compared to the singular ChatGPT model, MRCF significantly outperforms traditional manual analysis in interpreting medical data, exhibiting fewer random errors, higher accuracy, and better identification of incorrect information. Notably, MRCF is over 600 times more time-efficient than conventional manual annotation methods and costs only one-tenth as much. Leveraging MRCF, we have established two user-friendly databases for efficient and straightforward drug repositioning analysis. This research not only enhances the accuracy and efficiency of ChatGPT in medical data science applications but also offers valuable insights for data analysis models across various professional domains.

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.

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.

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.