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.

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.

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.

Streamlining the Generation of Advanced Polymer Materials through the Marriage of Automation and Multiblock Copolymer Synthesis in Emulsion

Synthetic polymers are of paramount importance - an incredibly wide range of polymeric materials possessing an impressive variety of properties have been developed to date. The recent emergence of artificial intelligence and automation presents a great opportunity to significantly speed up discovery and development of the next generation of advanced polymeric materials. We have focused on the high-throughput automated synthesis of multiblock copolymers, which are defined as three or more distinct polymer segments comprising different monomer compositions covalently bonded together in linear sequence. The present work has exploited automation to prepare high molecular weight multiblock copolymers (typically > 100,000 g mol-1) using RAFT polymerization in aqueous emulsions. A variety of original multiblock copolymers have been synthesised via a Chemspeed robot, exemplified by a high molecular weight multiblock copolymer comprising thirteen blocks. Libraries of copolymers of randomized monomer compositions, block orders, and block lengths were generated. One multiblock copolymer contained all four monomer families listed in the pool, which is unprecedented in the literature. The present work demonstrates that automation has the power to render complex and laborious syntheses of such unprecedented materials not just possible, but facile and straightforward, representing the way forward to the next generation of macromolecular architectures.

Development and implementation of medication-related clinical rules for obstetrics, gynaecology, and paediatric outpatients

OBJECTIVES

Prescription errors can cause serious adverse drug events. Clinical decision support systems prevent prescription errors; however, real-time clinical rules in obstetrics, gynaecology, and paediatric outpatients remain unexplored. We evaluated the effects of localised, real-time clinical rules on alert rates and acceptance rates compared with manual prescription review.

METHODS

We developed real-time clinical rules that incorporate information systems to obtain characteristic information and laboratory values. We conducted a retrospective cohort study to compare the alert and recommendation acceptance rates of all prescription error types before and after clinical rule implementation in obstetrics, gynaecology, and paediatrics. Clinical rules, prescription error types, and alerts were determined by a prescribing review committee comprising physicians, pharmacists, nurses, and administrators. The difference in alert and acceptance rates between the groups was analysed using relative risk.

RESULTS

The number of alerts increased after clinical rules implementation; the number of on-duty pharmacists for review decreased from 10 to 2. Compared with those with manual review, the alert rates for paediatrics and obstetrics and gynaecology increased with the clinical rules by 3.97- and 11.26-fold, respectively, and the alert rates for drug-drug interactions (DDIs) and combined medication errors in obstetrics and gynaecology increased with the clinical rules by 26.10- and 26.54-fold, respectively. In paediatrics, the alert rate for all prescription error types was higher with the clinical rules review than with the manual review; the alert rates for DDI, dosage, and combination medication errors were significantly different between the clinical rules and the manual review. However, there was no difference in the recommendation acceptance rate between the manual review and the clinical rules.

CONCLUSIONS

Clinical rules can identify prescription errors that manual review cannot detect and ensure real-time review efficiency in high-volume outpatient prescription settings. The high acceptance rate and modification of prescriptions may be relevant to highly customised and localised clinical rules.

A flow-batch lab-in-syringe foam microextraction platform for the simultaneous preconcentration and in situ membraneless gas-liquid separation of mercury prior to cold vapor atomic absorption spectrometry

Herein, the proof-of-concept of a novel lab-in-syringe (LIS) foam microextraction platform is presented as a front-end to cold vapor atomic absorption spectrometry (CVAAS) for the simultaneous preconcentration and membraneless gas-liquid separation (GLS) of inorganic mercury in biological samples. The proposed method is based on the on-line formation of the ammonium pyrrolidine dithiocarbamate complex with mercury that was retained in the pores of polyurethane foam immobilized on the piston of the LIS system. Metal complex elution and in situ mercury vapor generation are accomplished inside the microsyringe in a flow-batch format, while the separation of vapor species is achieved via the membraneless GLS found at the top of the syringe's barrel. Under optimized operation conditions, for 90 s preconcentration time, the limit of detection was 0.02 μg L-1 and the repeatability (RSD) was 3.8% (at the 0.5 μg L-1 concentration level), within a working range extending up to 4.0 μg L-1. The practicality of the novel manifold was demonstrated using the Blue Applicability Grade Index, while the accuracy of the method was evaluated using certified reference materials and spiked samples.

Automated sepsis detection with vancomycin- and allantoin-polydopamine magnetic nanoparticles

Rapid and accurate identification of the bacteria responsible for sepsis is paramount for effective patient care. Molecular diagnostic methods, such as polymerase chain reaction (PCR), encounter challenges in sepsis due to inhibitory compounds in the blood, necessitating their removal for precise analysis. In this study we present an innovative approach that utilizes vancomycin (Van) and allantoin (Al)-conjugated polydopamine (PDA)-coated magnetic nanoparticles (MNPs) for the rapid and automated enrichment of bacteria and their DNA extraction from blood without inducing clumping and aggregation of blood. Al/Van-PDA-MNPs, facilitated by IMS, eliminate the need for preliminary sample treatments, providing a swift and efficient method for bacterial concentration and DNA extraction within an hour. Employing Al/Van-PDA-MNPs within an automated framework has markedly improved our ability to pre-concentrate various Gram-negative and Gram-positive bacteria directly from blood samples. This advancement has effectively reduced the detection threshold to 102 colony-forming unit/mL by both PCR and quantitative PCR. The method's expedited processing time, combined with its precision, positions it as a feasible diagnostic tool for diverse healthcare settings, ranging from small clinics to large hospitals. Furthermore, the innovative application of nanoparticles for DNA extraction holds promising potential for advancing sepsis diagnostics, enabling earlier interventions and improving patient outcomes.

Dashing Growth Curves: a web application for rapid and interactive analysis of microbial growth curves

BACKGROUND

Recording and analyzing microbial growth is a routine task in the life sciences. Microplate readers that record dozens to hundreds of growth curves simultaneously are increasingly used for this task raising the demand for their rapid and reliable analysis.

RESULTS

Here, we present Dashing Growth Curves, an interactive web application ( http://dashing-growth-curves.ethz.ch/ ) that enables researchers to quickly visualize and analyze growth curves without the requirement for coding knowledge and independent of operating system. Growth curves can be fitted with parametric and non-parametric models or manually. The application extracts maximum growth rates as well as other features such as lag time, length of exponential growth phase and maximum population size among others. Furthermore, Dashing Growth Curves automatically groups replicate samples and generates downloadable summary plots for of all growth parameters.

CONCLUSIONS

Dashing Growth Curves is an open-source web application that reduces the time required to analyze microbial growth curves from hours to minutes.

Control of mean arterial pressure using a closed-loop system for norepinephrine infusion in severe brain injury patients: the COMAT randomized controlled trial

Brain injury patients require precise blood pressure (BP) management to maintain cerebral perfusion pressure (CPP) and avoid intracranial hypertension. Nurses have many tasks and norepinephrine titration has been shown to be suboptimal. This can lead to limited BP control in patients that are in critical need of cerebral perfusion optimization. We have designed a closed-loop vasopressor (CLV) system capable of maintaining mean arterial pressure (MAP) in a narrow range and we aimed to assess its performance when treating severe brain injury patients. Within the first 48 h of intensive care unit (ICU) admission, 18 patients with a severe brain injury underwent either CLV or manual norepinephrine titration. In both groups, the objective was to maintain MAP in target (within ± 5 mmHg of a predefined target MAP) to achieve optimal CPP. Fluid administration was standardized in the two groups. The primary objective was the percentage of time patients were in target. Secondary outcomes included time spent over and under target. Over the four-hour study period, the mean percentage of time with MAP in target was greater in the CLV group than in the control group (95.8 ± 2.2% vs. 42.5 ± 27.0%, p < 0.001). Severe undershooting, defined as MAP < 10 mmHg of target value was lower in the CLV group (0.2 ± 0.3% vs. 7.4 ± 14.2%, p < 0.001) as was severe overshooting defined as MAP > 10 mmHg of target (0.0 ± 0.0% vs. 22.0 ± 29.0%, p < 0.001). The CLV system can maintain MAP in target better than nurses caring for severe brain injury patients.

ANDeS: An automated nanoliter droplet selection and collection device

The Droplet Microarray (DMA) has emerged as a tool for high-throughput biological and chemical applications by enabling miniaturization and parallelization of experimental processes. Due to its ability to hold hundreds of nanoliter droplets, the DMA enables simple screening and analysis of samples such as cells and biomolecules. However, handling of nanoliter volumes poses a challenge, as manual recovery of nanoliter volumes is not feasible, and traditional laboratory equipment is not suited to work with such low volumes, and small array formats. To tackle this challenge, we developed the Automated Nanoliter Droplet Selection device (ANDeS), a robotic system for automated collection and transfer of nanoliter samples from DMA. ANDeS can automatically collect volumes from 50 to 350 nL from the flat surface of DMA with a movement accuracy of ±30 µm using fused silica capillaries. The system can automatically collect and transfer the droplets from DMA chip into other platforms, such as microtiter plates, conical tubes or another DMA. In addition, to ensure high throughput and multiple droplet collection, the uptake of multiple droplets within a single capillary, separated by air gaps to avoid mixing of the samples within the capillary, was optimized and demonstrated. This study shows the potential of ANDeS in laboratory applications by using it for the collection and transfer of biological samples, contained in nanoliter droplets, for subsequent analysis. The experimental results demonstrate the ability of ANDeS to increase the versatility of the DMA platform by allowing for automated retrieval of nanoliter samples from DMA, which was not possible manually on the level of individual droplets. Therefore, it widens the variety of analytical techniques that can be used for the analysis of content of individual droplets and experiments performed using DMA. Thus, ANDeS opens up opportunities to expand the development of miniaturized assays in such fields as cell screening, omics analysis and combinatorial chemistry.

The buccal micronucleus cytome assay: New horizons for its implementation in human studies

In this report we provide a summary of the presentations and discussion of the latest knowledge regarding the buccal micronucleus (MN) cytome assay. This information was presented at the HUMN workshop held in Malaga, Spain, in connection with the 2023 European, Environmental Mutagenesis and Genomics conference. The presentations covered the most salient topics relevant to the buccal MN cytome assay including (i) the biology of the buccal mucosa, (ii) its application in human studies relating to DNA damage caused by environmental exposure to genotoxins, (iii) the association of buccal MN with cancer and a wide range of reproductive, metabolic, immunological, neurodegenerative and other age-related diseases, (iv) the impact of nutrition and lifestyle on buccal MN cytome assay biomarkers; (v) its potential for application to studies of DNA damage in children and obesity, and (vi) the growing prospects of enhancing the clinical utility by automated scoring of the buccal MN cytome assay biomarkers by image recognition software developed using artificial intelligence. The most important knowledge gap is the need of prospective studies to test whether the buccal MN cytome assay biomarkers predict health and disease.

Tight control of mean arterial pressure using a closed loop system for norepinephrine infusion after high-risk abdominal surgery: a randomized controlled trial

Intensive care unit (ICU) nurses frequently manually titrate norepinephrine to maintain a predefined mean arterial pressure (MAP) target after high-risk surgery. However, achieving this task is often suboptimal. We have developed a closed-loop vasopressor (CLV) controller to better maintain MAP within a narrow range. After ethical committee approval, fifty-three patients admitted to the ICU following high-risk abdominal surgery were randomized to CLV or manual norepinephrine titration. In both groups, the aim was to maintain MAP in the predefined target of 80-90 mmHg. Fluid administration was standardized in the two groups using an advanced hemodynamic monitoring device. The primary outcome of our study was the percentage of time patients were in the MAP target. Over the 2-hour study period, the percentage of time with MAP in target was greater in the CLV group than in the control group (median: IQR25-75: 80 [68-88]% vs. 42 [22-65]%), difference 37.2, 95% CI (23.0-49.2); p < 0.001). Percentage time with MAP under 80 mmHg (1 [0-5]% vs. 26 [16-75]%, p < 0.001) and MAP under 65 mmHg (0 [0-0]% vs. 0 [0-4]%, p = 0.017) were both lower in the CLV group than in the control group. The percentage of time with a MAP > 90 mmHg was not statistically different between groups. In patients admitted to the ICU after high-risk abdominal surgery, closed-loop control of norepinephrine infusion better maintained a MAP target of 80 to 90 mmHg and significantly decreased postoperative hypotensive when compared to manual norepinephrine titration.

Automated measurement of transepithelial electrical resistance (TEER) in 96-well transwells using ECIS TEER96: Single and multiple time point assessments

Transepithelial electrical resistance (TEER) is a widely used technique for quantifying the permeability of epithelial and endothelial cell layers. However, traditional methods of measuring TEER are limited to single timepoint measurements and can subject cells to an altered environment during the measurement. Here, we assessed the validity of TEER measurements by the ECIS TEER96 device, which is designed to take continuous TEER measurements of a cell culture system in a standard laboratory incubator. We found that the instrument accurately measures TEER across TEER values ranging from 10 to 2050 Ω*cm2 and is more accurate than the manual epithelial voltohmmeter electrode at high TEER values. Furthermore, the high-resolution measurements provided by the device allowed for a unique insight into the mechanisms and kinetics of cells in vitro. To demonstrate the continuous measurement capability of the device, we tracked the formation of an MDCKI cell monolayer until TEER plateaued. Furthermore, we treated Caco-2 monolayers with different concentrations of DMSO and the antimicrobial and surfactant compound benzethonium chloride to measure disruptions to barrier integrity. Treatment of both compounds resulted in concentration-dependent loss of barrier integrity. Our results suggest that the ECIS TEER96 device is a reliable and convenient option for measuring TEER in cell cultures and can provide valuable insights into the behavior of cells in vitro. This technology will be especially useful for increasing throughput of drug permeability assays, inflammation studies, and gaining better understanding of disease states in a cell culture system.

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.Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety.• Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance.• AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated.

Automated method development in high-pressure liquid chromatography

Method development in liquid chromatography is a crucial step in the optimization of analytical separations for various applications. However, it is often a challenging endeavour due to its time-consuming, resource intensive and costly nature, which is further hampered by its complexity requiring highly skilled and experienced scientists. This review presents an examination of the methods that are required for a completely automated method development procedure in liquid chromatography, aimed at taking the human out of the decision loop. Some of the presented approaches have recently witnessed an important increase in interest as they offer the promise to facilitate, streamline and speed up the method development process. The review first discusses the mathematical description of the separation problem by means of multi-criteria optimization functions. Two different strategies to resolve this optimization are then presented; an experimental and a model-based approach. Additionally, methods for automated peak detection and peak tracking are reviewed, which, upon integration in an instrument, allow for a completely closed-loop method development process. For each of these approaches, various currently applied methods are presented, recent trends and approaches discussed, short-comings pointed out, and future prospects highlighted.

A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology

BACKGROUND

Tablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science.

OBJECTIVES

The primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes.

METHODS

This review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques.

RESULTS

Recent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process.

CONCLUSION

The integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.

A multicenter evaluation of Copan's Colibrí™, an automated instrument for MALDI TOF MS target application for bacterial identification

The Colibrí™ is a new instrument that automates picking and placement of colonies on target plates for MALDI identification. This study compared the performance of the Colibrí™ to standard manual spotting using the VITEK® MS for bacterial identification. Colonies were selected from cultures of urine, wound, respiratory, and positive blood cultures. The Colibrí™ sampled the colonies, transferred them to a MALDI target slide, and overlayed each spot with matrix. Manual spotting was then performed using the same or similar colonies. A total of 444 bacteria were compared. Identification was achieved with both methods for 432 organisms with only 2 discrepant results, overall agreement of 99.54%. Twelve organisms (2.70%) gave no identification using Colibrí™. The Colibrí™ provides automation to a manual process with a high accuracy. Use of the Colibrí™ instrumentation provides an opportunity to reallocate technologist time to more complicated tasks and provides complete traceability from plating to organism identification.