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The Food and Drug Administration's recent decision to eliminate mandatory animal testing for drug approval marks a significant shift to alternative methods. Similarly, the European Parliament is advocating for a faster transition, reflecting public preference for animal‐free research practices. In vitro tissue models are increasingly recognized as valuable tools for regulatory assessments before clinical trials, in line with the 3R principles (Replace, Reduce, Refine). Despite their potential, barriers such as the need for standardization, availability, and cost hinder their widespread adoption. To address these challenges, the Robotic Enabled Biological Automation (ReBiA) system is developed. This system uses a dual‐arm robot capable of standardizing laboratory processes within a closed automated environment, translating manual processes into automated ones. This reduces the need for process‐specific developments, making in vitro tissue models more consistent and cost‐effective. ReBiA's performance is demonstrated through producing human reconstructed epidermis, human airway epithelial models, and human intestinal organoids. Analyses confirm that these models match the morphology and protein expression of manually prepared and native tissues, with similar cell viability. These successes highlight ReBiA's potential to lower barriers to broader adoption of in vitro tissue models, supporting a shift toward more ethical and advanced research methods. [ABSTRACT FROM AUTHOR]

This Paper guides a review platform which allows to evaluate Artificial intelligence assisted diagnosis for blood cancer using machine learning. Advanced medical and technology-based research has fuelled the adoption of latest technologies for the sake of advancement in medical science application and overall improvement in detection, diagnosis, prevention and treatment of diseases. AI technology is being used widely in medicine, economy and daily life; in medicine, artificial intelligence is used for mainly treatment, diagnosis and prediction of disease prognosis. This review effectively highlights the wide-ranging applications of AI in medicine, with a specific focus on its contribution to treatment, diagnosis, prognosis and prediction. [ABSTRACT FROM AUTHOR]

Organs-on-a-chip (OoCs) have proven to mimic the basic physiological behavior of organs and the influence of therapeutics on them in greater detail than conventional models, resulting in enormous projected market growth rates. However, the breakthrough to profitable commercialization of that technology has not yet been achieved, partly because the production process chain is characterized by a high proportion of manual laboratory work. The present work addresses this point. Utilizing affordable components, a demonstrator was developed that can be integrated into an existing 3D-bioprinting system and enables the automated production of perfusion-ready OoC devices starting from pre-fabricated injection-molded microfluidic chips. To this end, a corresponding process chain was first defined, and an expandable, configurable algorithm was developed and validated in the form of a finite state machine (FSM). This algorithm controls a modified 4-axis robot arm that covers the steps upstream and downstream of the printing process in the manufacturing process and achieves success rates of up to 100 %. A virtual interface between the robot and printer enables mutual communication and full integration of the algorithm into the process chain. Steps that pose a challenge for the automation of the process chain and appropriate countermeasures and optimizations were identified. This lays the foundation for scaling and standardizing the automated production of OoCs.

Lab Automation facilitates high-throughput processes and improves reproducibility and efficiency while removing human action, primary source of contaminating particles. Handling poses a risk of contamination due to close contact with the objects. We propose a novel gripper (CrocoGrip) relying on compliant mechanisms to reduce the amount of contaminating particles generated by the gripper rather than preventing their emission, the latter being the common approach in current grippers. Our novel gripper is actuated by linear solenoids and purely relies on deformation for its motion. As a result, abrasive behavior and, therefore, the generation of particles is reduced without the need for additional sealing. We experimentally proved that only particles smaller than 3.0µm are emitted by the gripper, with a large proportion of the particles being generated by the actuation. The CrocoGrip fulfills the demands of ISO14644 class 5. The gripping relies on the deformation energy of the compliant mechanism, making the gripping energy-efficient and safe. The maximum gripping force achieved by the CrocoGrip was 5.5N. Because the force transmitted to the handling object depends on the design of the gripping jaws, which are interchangeable, the force can be reduced for more sensible handling objects. Using three different sets of jaws, CrocoGrip was able to handle a microplate in SBS-standard, a 50mL Falcon tube, and a Ø60mm Petri dish using a robotic arm. Due to the monolithic design of the CrocoGrip and, as a result, the need for few components, we achieve a simplicity of design, making cleaning, sterilization and maintenance easy, even for nonexperts. The CrocoGrip exploits the advantages of compliant mechanisms, especially for applications requiring clean-room environments. This approach of compliant-mechanism-based grippers enables an increase in the cleanliness of handling processes without an increase in system complexity of the gripper to facilitate the lab automation of highly sensible processes, such as in tissue engineering.

Abstract Background Common beans (Phaseolus vulgaris L.) provide important protein and calories globally. Anthracnose (Colletotrichum lindemuthianum (Sacc. & Magnus) Briosi & Cavara, 1889) is a major disease in common bean and causes significant yield losses in bean production areas. Screening for markers linked to known disease resistance genes provides useful information for plant breeders to develop improved common bean varieties. The Kompetitive Allele Specific PCR (KASP) assay is an affordable genetic screening technique that can be used to accelerate breeding programs, but manual DNA extraction and KASP assay preparation are time-consuming. Several KASP markers have been developed for genes involved in resistance to bean anthracnose, which can reduce yield by up to 100%, but their usefulness is hindered by the labor required to screen a significant number of bean lines. Our research objective was to develop publicly available protocols for DNA extraction and KASP assaying using a liquid handling robot (LHR) which would facilitate high-throughput genetic screening with less active human time required. Anthracnose resistance markers were used to compare manual and automated results. Results The 12 bean anthracnose differential cultivars were screened for four anthracnose KASP markers linked to the resistance genes Co-1, Co-3 and Co-4 2 both by hand and with the use of an LHR. A protocol was written for DNA extraction and KASP assay thermocycling to implement the LHR. The LHR protocol reduced the active human screening time of 24 samples from 3h44 to 1h23. KASP calls were consistent across replicates but not always accurate for their known linked resistance genes, suggesting more specific markers still need to be developed. Using an LHR, information from KASP assays can be accumulated with little active human time. Conclusion Results suggest that LHRs can be used to expedite time-consuming and tedious lab work such as DNA extraction or PCR plate filling. Notably, LHRs can be used to prepare KASP assays for large sample sizes, facilitating higher throughput use of genetic marker screening tools.

Protocol standardization and sharing are crucial for reproducibility in life sciences. In spite of numerous efforts for standardized protocol description, adherence to these standards in literature remains largely inconsistent. Curation of protocols are especially challenging due to the labor intensive process, requiring expert domain knowledge of each experimental procedure. Recent advancements in Large Language Models (LLMs) offer a promising solution to interpret and curate knowledge from complex scientific literature. In this work, we develop ProtoCode, a tool leveraging fine-tune LLMs to curate protocols into intermediate representation formats which can be interpretable by both human and machine interfaces. Our proof-of-concept, focused on polymerase chain reaction (PCR) protocols, retrieves information from PCR protocols at an accuracy ranging 69–100 % depending on the information content. In all tested protocols, we demonstrate that ProtoCode successfully converts literature-based protocols into correct operational files for multiple thermal cycler systems. In conclusion, ProtoCode can alleviate labor intensive curation and standardization of life science protocols to enhance research reproducibility by providing a reliable, automated means to process and standardize protocols. ProtoCode is freely available as a web server at https://curation.taxila.io/ProtoCode/.

Dengue fever has remained a continuing global medical threat that impacts half of the world's population. Developing a highly effective dengue vaccine, with live-attenuated tetravalent vaccines as leading candidates, remains essential in preventing this disease. For the development of live virus vaccines (LVVs), potency measurements play a vital role in quantifying the active components of vaccine drug substance as well as drug product during various stages of research, development, and post-licensure evaluations. Traditional plaque-based assays are one of the most common potency test methods, but they generally take up to weeks to complete. Less labor and time-intensive potency assays are thus called for to aid in the acceleration of vaccine development, especially for multivalent LVVs. Here, we introduce a fully automated, 96-well format µPlaque assay that has been optimized as a high-throughput tool to evaluate process and formulation development of a live-attenuated tetravalent dengue vaccine. To the best of our knowledge, this is the first report of a miniaturized viral plaque method for dengue with full automation via an integrated robotic system. Compared to the traditional manual plaque assay, this newly developed method substantially reduces testing time by approximately half and allows for the evaluation of over ten times more samples per run. The fully automated workflow, from cell culture to plaque counting, significantly minimizes analyst hands-on time and improves assay repeatability. The study presents a pioneering solution for the rapid measurement of LVV viral titers, offering promising prospects for advancing vaccine development through high-throughput analytics. [ABSTRACT FROM AUTHOR]

Dengue fever has remained a continuing global medical threat that impacts half of the world’s population. Developing a highly effective dengue vaccine, with live-attenuated tetravalent vaccines as leading candidates, remains essential in preventing this disease. For the development of live virus vaccines (LVVs), potency measurements play a vital role in quantifying the active components of vaccine drug substance as well as drug product during various stages of research, development, and post-licensure evaluations. Traditional plaquebased assays are one of the most common potency test methods, but they generally take up to weeks to complete. Less labor and time-intensive potency assays are thus called for to aid in the acceleration of vaccine development, especially for multivalent LVVs. Here, we introduce a fully automated, 96-well format µPlaque assay that has been optimized as a high-throughput tool to evaluate process and formulation development of a live-attenuated tetravalent dengue vaccine. To the best of our knowledge, this is the first report of a miniaturized viral plaque method for dengue with full automation via an integrated robotic system. Compared to the traditional manual plaque assay, this newly developed method substantially reduces testing time by approximately half and allows for the evaluation of over ten times more samples per run. The fully automated workflow, from cell culture to plaque counting, significantly minimizes analyst hands-on time and improves assay repeatability. The study presents a pioneering solution for the rapid measurement of LVV viral titers, offering promising prospects for advancing vaccine development through highthroughput analytics. [ABSTRACT FROM AUTHOR]

Background: Common beans (Phaseolus vulgaris L.) provide important protein and calories globally. Anthracnose (Colletotrichum lindemuthianum (Sacc. & Magnus) Briosi & Cavara, 1889) is a major disease in common bean and causes significant yield losses in bean production areas. Screening for markers linked to known disease resistance genes provides useful information for plant breeders to develop improved common bean varieties. The Kompetitive Allele Specific PCR (KASP) assay is an affordable genetic screening technique that can be used to accelerate breeding programs, but manual DNA extraction and KASP assay preparation are time-consuming. Several KASP markers have been developed for genes involved in resistance to bean anthracnose, which can reduce yield by up to 100%, but their usefulness is hindered by the labor required to screen a significant number of bean lines. Our research objective was to develop publicly available protocols for DNA extraction and KASP assaying using a liquid handling robot (LHR) which would facilitate high-throughput genetic screening with less active human time required. Anthracnose resistance markers were used to compare manual and automated results. Results: The 12 bean anthracnose differential cultivars were screened for four anthracnose KASP markers linked to the resistance genes Co-1, Co-3 and Co-42 both by hand and with the use of an LHR. A protocol was written for DNA extraction and KASP assay thermocycling to implement the LHR. The LHR protocol reduced the active human screening time of 24 samples from 3h44 to 1h23. KASP calls were consistent across replicates but not always accurate for their known linked resistance genes, suggesting more specific markers still need to be developed. Using an LHR, information from KASP assays can be accumulated with little active human time. Conclusion: Results suggest that LHRs can be used to expedite time-consuming and tedious lab work such as DNA extraction or PCR plate filling. Notably, LHRs can be used to prepare KASP assays for large sample sizes, facilitating higher throughput use of genetic marker screening tools. [ABSTRACT FROM AUTHOR]

Background: Advance technology and diagnostic approach to disease has increased workload burden on laboratory and timely reporting as well as reliable results provision to physicians is another challenge of laboratory.Objective: To compare pre-analytical errors, peak volume, flow rate and turn around time before and after total lab automation in clinical laboratory.Study type,settings& duration:A cross-sectional study was carried out at Shifa International Hospital, Islamabad from January 2018 to December 2020.Methodology:Present study assesses the key performance indicators based on the Total Lab Automation (TLA) work flow. Frequency and percentage was used to calculate increase in test volume before and after TLA implementation while one way ANOVA was used to test the difference in Turnaround Time (TAT).Results:Prior TLA average turnaround time(TAT) for routine chemistry was 4 hours while immunoassay TAT average reported was 6-7 hours.Post TLA average TAT for routine chemistry is 1-2 hour while for immunoassay it 3-4 hours. There is 5-10% increase in test volume after the TLA implementation. TAT shows significant mean difference before and after TLA installation (pvalue=0.00).Conclusion:In current study, findings prove that overall efficiency, turnaround time overall workload and optimize workflow occurred after implementation of TLA.Monitoring of key metrics help in continuous process improvement. Advance technology in laboratory makes work easier which was previously laborious.

Induced pluripotent stem cells (iPSCs) have great potential as physiological disease models for human disorders where access to primary cells is difficult, such as neurons. In recent years, many protocols have been developed for the generation of iPSCs and the differentiation into specialised cell subtypes of interest. More recently, these models have been modified to allow large-scale phenotyping and high-content screening of small molecule compounds in iPSC-derived neuronal cells. Here, we describe the automated seeding of day 11 ventral midbrain progenitor cells into 96-well plates, administration of compounds, automated staining for immunofluorescence, the acquisition of images on a high-content screening platform and workflows for image analysis.

To know phase diagrams is a time saving approach for developing novel materials. To efficiently construct phase diagrams, a machine learning technique was developed using uncertainty sampling, which is called as PDC (Phase Diagram Construction) package [K. Terayama et al. Phys. Rev. Mater. 3, 033802 (2019).]. In this method, the most uncertain point in the phase diagram was suggested as the next experimental condition. However, owing to recent progress in lab automation techniques and robotics, high-throughput batch experiments can be performed. To benefit from such a high-throughput nature, multiple conditions must be selected simultaneously to effectively construct a phase diagram using a machine learning technique. In this study, we consider some strategies to do so, and their performances were compared when exploring ternary isothermal sections (two-dimensional) and temperature-dependent ternary phase diagrams (three-dimensional). We show that even if the suggestions are explored several instead of one at a time, the performance did not change drastically. Thus, we conclude that PDC with multiple suggestions is suitable for high-throughput batch experiments and can be expected to play an active role in next-generation automated material development.

Variation in methylcytosine is perhaps the most well-studied epigenetic mechanism of gene regulation. Methods that have been developed and implemented for assessing DNA methylation require sample DNA to be extracted, purified and chemically-processed through bisulfite conversion before downstream analysis. While some automated solutions exist for each of these individual process steps, a fully integrated solution for accomplishing the entire process in a high-throughput manner has yet to be demonstrated. Thus, sample processing methods still require numerous manual steps that may reduce sample throughput and precision, while increasing the risk of contamination and human error. In this work, we present an integrated, automated solution for performing the entire sample preparation process, including DNA extraction, purification, bisulfite conversion and PCR plate preparation within in an enclosed environment. The method employs silica-coated magnetic particles that eliminate the need for a centrifuge or vacuum manifold, thereby reducing the complexity and cost of the required automation platform. Toward this end, we also compare commercial DNA extraction and bisulfite conversion kits to identify a protocol suitable for automation to significantly improve genomic and bisulfite-treated DNA yields over manufacturer protocols. Overall, this research demonstrated development of an automated protocol that offers the ability to generate high-quality, bisulfite-treated DNA samples in a high-throughput and clean environment with minimal user intervention and comparable yields to manual processing.

Abstract Background Currently, the generation of genetic diversity for microbial cell factories outpaces the screening of strain variants with omics-based phenotyping methods. Especially isotopic labeling experiments, which constitute techniques aimed at elucidating cellular phenotypes and supporting rational strain design by growing microorganisms on substrates enriched with heavy isotopes, suffer from comparably low throughput and the high cost of labeled substrates. Results We present a miniaturized, parallelized, and automated approach to 13C-isotopic labeling experiments by establishing and validating a hot isopropanol quenching method on a robotic platform coupled with a microbioreactor cultivation system. This allows for the first time to conduct automated labeling experiments at a microtiter plate scale in up to 48 parallel batches. A further innovation enabled by the automated quenching method is the analysis of free amino acids instead of proteinogenic ones on said microliter scale. Capitalizing on the latter point and as a proof of concept, we present an isotopically instationary labeling experiment in Corynebacterium glutamicum ATCC 13032, generating dynamic labeling data of free amino acids in the process. Conclusions Our results show that a robotic liquid handler is sufficiently fast to generate informative isotopically transient labeling data. Furthermore, the amount of biomass obtained from a sub-milliliter cultivation in a microbioreactor is adequate for the detection of labeling patterns of free amino acids. Combining the innovations presented in this study, isotopically stationary and instationary automated labeling experiments can be conducted, thus fulfilling the prerequisites for 13C-metabolic flux analyses in high-throughput.

In this work, we address the problem of detecting anomalies in a certain laboratory automation setting. At first, we collect video images of liquid transfer in automated laboratory experiments. We mimic the real-world challenges of developing an anomaly detection model by considering two points. First, the size of the collected dataset is set to be relatively small compared to large-scale video datasets. Second, the dataset has a class imbalance problem where the majority of the collected videos are from abnormal events. Consequently, the existing learning-based video anomaly detection methods do not perform well. To this end, we develop a practical human-engineered feature extraction method to detect anomalies from the liquid transfer video images. Our simple yet effective method outperforms state-of-the-art anomaly detection methods with a notable margin. In particular, the proposed method provides 19% and 76% average improvement in AUC and Equal Error Rate, respectively. Our method also quantifies the anomalies and provides significant benefits for deployment in the real-world experimental setting.

Background: Advance technology and diagnostic approach to disease has increased workload burden on laboratory and timely reporting as well as reliable results provision to physicians is another challenge of laboratory. Objective: To compare pre-analytical errors, peak volume, flow rate and turn around time before and after total lab automation in clinical laboratory. Study type, settings & duration: A cross-sectional study was carried out at Shifa International Hospital, Islamabad from January 2018 to December 2020. Methodology: Present study assesses the key performance indicators based on the Total Lab Automation (TLA) work flow. Frequency and percentage was used to calculate increase in test volume before and after TLA implementation while one way ANOVA was used to test the difference in Turnaround Time (TAT). Results: Prior TLA average turnaround time (TAT) for routine chemistry was 4 hours while immunoassay TAT average reported was 6-7 hours. Post TLA average TAT for routine chemistry is 1-2 hour while for immunoassay it 3-4 hours. There is 5-10% increase in test volume after the TLA implementation. TAT shows significant mean difference before and after TLA installation (p value=0.00). Conclusion: In current study, findings prove that overall efficiency, turnaround time overall workload and optimize workflow occurred after implementation of TLA. Monitoring of key metrics help in continuous process improvement. Advance technology in laboratory makes work easier which was previously laborious. [ABSTRACT FROM AUTHOR]

Nanoparticles in contact with proteins form a "corona" of proteins adsorbed on the nanoparticle surface. Subsequent biological responses are then mediated by the adsorbed proteins rather than the bare nanoparticles. The use of nanoparticles as nanomedicines and biosensors would be greatly improved if researchers were able to predict which specific proteins will adsorb on a nanoparticle surface. We use a recently developed automated workflow with a liquid handling robot and low-cost proteomics to determine the concentration and composition of the protein corona formed on carboxylate-modified iron oxide nanoparticles (200 nm) as a function of incubation time and serum concentration. We measure the concentration of the resulting protein corona with a colorimetric assay and the composition of the corona with proteomics, reporting both abundance and enrichment relative to the fetal bovine serum (FBS) proteins used to form the corona. Incubation time was found to be an important parameter for corona concentration and composition at high (100% FBS) incubation concentrations, with only a slight effect at low (10%) FBS concentrations. In addition to these findings, we describe two methodological advances to help reduce the cost associated with protein corona experiments. We have automated the digest step necessary for proteomics and measured the variability between triplicate samples at each stage of the proteomics experiments. Overall, these results demonstrate the importance of understanding the multiple parameters that influence corona formation, provide new tools for corona characterization, and advance bioanalytical research in nanomaterials. [ABSTRACT FROM AUTHOR]

Abstract Background Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. Results In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies—an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale. Conclusions Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the ‘test’ phase of the design-build-test-learn cycle of metabolic engineering.

Background: Currently, the generation of genetic diversity for microbial cell factories outpaces the screening of strain variants with omics-based phenotyping methods. Especially isotopic labeling experiments, which constitute techniques aimed at elucidating cellular phenotypes and supporting rational strain design by growing microorganisms on substrates enriched with heavy isotopes, suffer from comparably low throughput and the high cost of labeled substrates. Results: We present a miniaturized, parallelized, and automated approach to 13C-isotopic labeling experiments by establishing and validating a hot isopropanol quenching method on a robotic platform coupled with a microbioreactor cultivation system. This allows for the first time to conduct automated labeling experiments at a microtiter plate scale in up to 48 parallel batches. A further innovation enabled by the automated quenching method is the analysis of free amino acids instead of proteinogenic ones on said microliter scale. Capitalizing on the latter point and as a proof of concept, we present an isotopically instationary labeling experiment in Corynebacterium glutamicum ATCC 13032, generating dynamic labeling data of free amino acids in the process. Conclusions: Our results show that a robotic liquid handler is sufficiently fast to generate informative isotopically transient labeling data. Furthermore, the amount of biomass obtained from a sub-milliliter cultivation in a microbioreactor is adequate for the detection of labeling patterns of free amino acids. Combining the innovations presented in this study, isotopically stationary and instationary automated labeling experiments can be conducted, thus fulfilling the prerequisites for 13C-metabolic flux analyses in high-throughput. [ABSTRACT FROM AUTHOR]

The combination of lab automation and design of experiments for the execution of screening experiments increases productivity and reduces error‐prone manual work. A self‐developed software tool allows for creating fractional‐factorial experimental design (FFED). Application of FFED on the screening of a Suzuki‐Miyaura cross‐coupling leads to a 93 % reduced design compared to full‐factorial design. The resulting regression model qualitatively shows the positive effect of educt concentrations, time, and temperature and reveals the decrease in conversion at high base concentrations. [ABSTRACT FROM AUTHOR]

The number of samples in biological experiments is continuously increasing, but complex protocols and human error in many cases lead to suboptimal data quality and hence difficulties in reproducing scientific findings. Laboratory automation can alleviate many of these problems by precisely reproducing machine‐readable protocols. These instruments generally require high up‐front investments, and due to the lack of open application programming interfaces (APIs), they are notoriously difficult for scientists to customize and control outside of the vendor‐supplied software. Here, automated, high‐throughput experiments are demonstrated for interdisciplinary research in life science that can be replicated on a modest budget, using open tools to ensure reproducibility by combining the tools OpenFlexure, Opentrons, ImJoy, and UC2. This automated sample preparation and imaging pipeline can easily be replicated and established in many laboratories as well as in educational contexts through easy‐to‐understand algorithms and easy‐to‐build microscopes. Additionally, the creation of feedback loops, with later pipetting or imaging steps depending on the analysis of previously acquired images, enables the realization of fully autonomous "smart" microscopy experiments. All documents and source files are publicly available to prove the concept of smart lab automation using inexpensive, open tools. It is believed this democratizes access to the power and repeatability of automated experiments. [ABSTRACT FROM AUTHOR]

Automated molecular analyzers have accelerated diagnosis, allowing earlier intervention and better patient follow-up. A recently developed completely automated molecular analyzer, Alinity™ m (Abbott), offers consolidated, continuous, and random-access testing that may improve molecular laboratory workflow.

Abstract In research and clinical genomics laboratories today, sample preparation is the bottleneck of experiments, particularly when it comes to high-throughput next generation sequencing (NGS). More genomics laboratories are now considering liquid-handling automation to make the sequencing workflow more efficient and cost effective. The question remains as to its suitability and return on investment. A number of points need to be carefully considered before introducing robots into biological laboratories. Here, we describe the state-of-the-art technology of both sophisticated and do-it-yourself (DIY) robotic liquid-handlers and provide a practical review of the motivation, implications and requirements of laboratory automation for genome sequencing experiments.

A major advantage of microfluidic devices is the ability to manipulate small sample volumes, thus reducing reagent waste and preserving precious sample. However, to achieve robust sample manipulation it is necessary to address device integration with the macroscale environment. To realize repeatable, sensitive particle separation with microfluidic devices, this protocol presents a complete automated and integrated microfluidic platform that enables precise processing of 0.15-1.5 ml samples using microfluidic devices. Important aspects of this system include modular device layout and robust fixtures resulting in reliable and flexible world to chip connections, and fully-automated fluid handling which accomplishes closed-loop sample collection, system cleaning and priming steps to ensure repeatable operation. Different microfluidic devices can be used interchangeably with this architecture. Here we incorporate an acoustofluidic device, detail its characterization, performance optimization, and demonstrate its use for size-separation of biological samples. By using real-time feedback during separation experiments, sample collection is optimized to conserve and concentrate sample. Although requiring the integration of multiple pieces of equipment, advantages of this architecture include the ability to process unknown samples with no additional system optimization, ease of device replacement, and precise, robust sample processing.

Here we describe a completely integrated and customizable microfluidic control and sensing architecture that can be readily implemented for laboratory or portable chemical or biological control and sensing applications. The compact platform enables control of 32 solenoid valves, a multitude of pumps and motors, a thermo-electric controller, a pressure controller, and a high voltage power supply. It also features two temperature probe interfaces, one relative humidity and ambient temperature sensor, two pressure sensors, and interfaces to an electrical conductivity sensor, flow sensor, and a bubble detector. The platform can be controlled via an onboard microcontroller and requires no proprietary software.

Background: Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. Results: In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies—an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale. Conclusions: Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering. [ABSTRACT FROM AUTHOR]

Next-generation multi-specific antibody therapeutics (MSATs) are engineered to combine several functional activities into one molecule to provide higher efficacy compared to conventional, mono-specific antibody therapeutics. However, highly engineered MSATs frequently display poor yields and less favorable drug-like properties (DLPs), which can adversely affect their development. Systematic screening of a large panel of MSAT variants in very high throughput (HT) is thus critical to identify potent molecule candidates with good yield and DLPs early in the discovery process. Here we report on the establishment of a novel, format-agnostic platform process for the fast generation and multiparametric screening of tens of thousands of MSAT variants. To this end, we have introduced full automation across the entire value chain for MSAT engineering. Specifically, we have automated the in-silico design of very large MSAT panels such that it reflects precisely the wet-lab processes for MSAT DNA library generation. This includes mass saturation mutagenesis or bulk modular cloning technologies while, concomitantly, enabling library deconvolution approaches using HT Sanger DNA sequencing. These DNA workflows are tightly linked to fully automated downstream processes for compartmentalized mammalian cell transfection expression, and screening of multiple parameters. All sub-processes are seamlessly integrated with tailored workflow supporting bioinformatics. As described here, we used this platform to perform multifactor optimization of a next-generation bispecific, cross-over dual variable domain-Ig (CODV-Ig). Screening of more than 25,000 individual protein variants in mono- and bispecific format led to the identification of CODV-Ig variants with over 1,000-fold increased potency and significantly optimized production titers, demonstrating the power and versatility of the platform.

Abstract A Materials Acceleration Operation System (MAOS) is designed, with unique language and compiler architecture. MAOS integrates with virtual reality (VR), collaborative robots, and a reinforcement learning (RL) scheme for autonomous materials synthesis, properties investigations, and self‐optimized quality assurance. After training through VR, MAOS can work independently for labor and intensively reduces the time cost. Under the RL framework, MAOS also inspires the improved nucleation theory, and feedback for the optimal strategy, which can satisfy the demand on both of the CdSe quantum dots (QDs) emission wavelength and size distribution quality. Moreover, it can work well for extensive coverages of inorganic nanomaterials. MAOS frees the experimental researchers out of the tedious labor as well as the extensive exploration of optimal reaction conditions. This work provides a walking example for the “On‐Demand” materials synthesis system, and demonstrates how artificial intelligence technology can reshape traditional materials science research in the future.

Nowadays, cell-based assays are an elementary tool for diagnostics, animal-free substance testing and basic research. Depending on the application, the spectrum ranges from simple static cell cultures in microtiter plates to dynamic co-cultures in complex micro physiological systems (organ-on-a-chip). Depending on the complexity of the assay, numerous working steps have to be performed and the data from different analysis systems have to be processed, combined and documented. A universal platform has been developed for the automated handling of cell-based assays, which combines a laboratory information management system (LIMS) with a laboratory execution system (LES), a universal laboratory automation platform and established laboratory equipment. The LIMS handles the administration of all laboratory-relevant information, the planning, control and monitoring of laboratory processes, as well as the direct and qualified processing of raw data. Using a kidney-on-achip system as an example, the realization of complex cellbased assays for the animal-free characterization of the toxicity of different antibiotics will be demonstrated. In the kidney-on-a-chip system the artificial proximal tubular barrier was formed by seeding human immortalized proximal tubule cells (RPTEC) and human blood outgrowth endothelial cells (BOEC) on ThinCert™ membranes. Transepithelial electrical resistance (TEER) was measured daily to evaluate the barrier function of the cellular layers. Fluid handling and TEER measurements were performed using a laboratory automation platform that communicates directly with the LIMS. The LES supports laboratory assistants in executing the manual handling steps of the experiments.

A programmable and fully automated platform is created to take over tedious manual operations in a typical catalyst development process. Synthesis and evaluation of the catalyst are coupled as an operation unit to enable continuous and unattended execution of combinatorial experiments. This platform is demonstrated in the identification of high‐performance bifunctional catalysts for water splitting from a trimetallic NiFeCo hydroxide system. The platform collects data for evaluating the performance of 66 different catalysts in oxygen evolution and hydrogen evolution reactions within a timeframe of four days. Timely feedback from the close‐coupled evaluation enables a guided focused‐area scanning of the parameter matrix, achieving both full coverage and a fine‐tuned hit with a relatively smaller number of experiments. The optimal compositions of the catalysts identified by the platform are well consistent with previous reports. Therefore, the platform is capable of generating useful data for catalyst screening, allowing a researcher to focus more on creative tasks. [ABSTRACT FROM AUTHOR]

In research and clinical genomics laboratories today, sample preparation is the bottleneck of experiments, particularly when it comes to high-throughput next generation sequencing (NGS). More genomics laboratories are now considering liquid-handling automation to make the sequencing workflow more efficient and cost effective. The question remains as to its suitability and return on investment. A number of points need to be carefully considered before introducing robots into biological laboratories. Here, we describe the state-of-the-art technology of both sophisticated and do-it-yourself (DIY) robotic liquid-handlers and provide a practical review of the motivation, implications and requirements of laboratory automation for genome sequencing experiments. [ABSTRACT FROM AUTHOR]

Thermocline‐dwelling foraminifera calcify over a depth range of several hundred meters; analysis of individual shells therefore allows insight to the hydrography of the upper water column. We analyzed δ18O, δ13C, and Mg/Ca of individual tests of the planktonic foraminifera Globorotalia inflata from a sediment core (GeoB7926‐2) obtained from 20°N in the eastern tropical Atlantic. To facilitate sample throughput, tests were cleaned before Mg/Ca analysis using a pipette robot. The eight samples came from five time periods with contrasting climate states. Median reconstructed temperatures were lowest during the warmth of the Bølling Allerød BA) (11.8°C), while highest temperatures (>14°C) were recorded during the cold periods of the Last Glacial Maximum (LGM), late Heinrich Stadial 1 (HS1), and the Younger Dryas (YD). Southward shift in the subtropical gyre during Northern Hemisphere cold periods and modulation by upwelling could explain the temperature change but not all of the salinity change. δ18Oseawater‐IVC indicated that salinity was higher than the global average during the LGM, with very high salinity excursions in HS1 and a smaller excursion in the YD. The upwelling signature was most strongly imprinted on range in δ13C. The large changes in salinity and δ13C between time slices cannot be explained by upwelling intensity but indicate the presence of a very saline water mass, with low δ13C, in the eastern North Atlantic subsurface during Northern Hemisphere cold periods. Key Points: Mg/Ca and stable isotope data from single tests of Globorotalia inflata indicate warm and saline central waters off NW Africa during the Last Glacial Maximum, Heinrich Stadial 1, and the Younger DryasHigh salinity and low δ13C indicate a change in water mass properties over the last 22 kaReduced standard deviation of individual δ13C values within a sample is indicative of increased upwelling [ABSTRACT FROM AUTHOR]

A Materials Acceleration Operation System (MAOS) is designed, with unique language and compiler architecture. MAOS integrates with virtual reality (VR), collaborative robots, and a reinforcement learning (RL) scheme for autonomous materials synthesis, properties investigations, and self‐optimized quality assurance. After training through VR, MAOS can work independently for labor and intensively reduces the time cost. Under the RL framework, MAOS also inspires the improved nucleation theory, and feedback for the optimal strategy, which can satisfy the demand on both of the CdSe quantum dots (QDs) emission wavelength and size distribution quality. Moreover, it can work well for extensive coverages of inorganic nanomaterials. MAOS frees the experimental researchers out of the tedious labor as well as the extensive exploration of optimal reaction conditions. This work provides a walking example for the "On‐Demand" materials synthesis system, and demonstrates how artificial intelligence technology can reshape traditional materials science research in the future. [ABSTRACT FROM AUTHOR]

We present our approach to rapidly establishing a standardized, multi-site, nation-wide COVID-19 screening program in Belgium. Under auspices of a federal government Task Force responsible for upscaling the country’s testing capacity, we were able to set up a national testing initiative with readily available resources, putting in place a robust, validated, high-throughput, and decentralized qPCR molecular testing platform with embedded proficiency testing. We demonstrate how during an acute scarcity of equipment, kits, reagents, personnel, protective equipment, and sterile plastic supplies, we introduced an approach to rapidly build a reliable, validated, high-volume, high-confidence workflow based on heterogeneous instrumentation and diverse assays, assay components, and protocols. The workflow was set up with continuous quality control monitoring, tied together through a clinical-grade information management platform for automated data analysis, real-time result reporting across different participating sites, qc monitoring, and making result data available to the requesting physician and the patient. In this overview, we address challenges in optimizing high-throughput cross-laboratory workflows with minimal manual intervention through software, instrument and assay validation and standardization, and a process for harmonized result reporting and nation-level infection statistics monitoring across the disparate testing methodologies and workflows, necessitated by a rapid scale-up as a response to the pandemic.

Distinguishing bacterial from viral infections is often challenging, leading to antibiotic misuse, and detrimental ramifications for the patient, the healthcare system and society. A novel ELISA-based assay that integrates the circulating levels of three host-response proteins (TRAIL, IP-10 and CRP) was developed to assist in differentiation between bacterial and viral etiologies. We developed a new protocol for measuring the host-based assay biomarkers using an automated ELISA workstation. The automated protocol was validated and was able to reduce technician hands-on time by 76%, while maintaining high analytical performance. Following automation, the assay has been incorporated into the routine workflow at a pediatric department, and is performed daily on admitted and emergency department patients. The automation protocol reduces the overall burden on the hospital laboratory performing the assay. This benefit has potential to promote adoption of the host-based assay, facilitating timely triage of febrile patients and prudent use of antibiotics.

The virulence of methicillin-resistant Staphylococcus aureus (MRSA) and its potentially fatal outcome necessitate rapid and accurate detection of patients colonized with MRSA in healthcare settings. Using the BD Kiestra Total Lab Automation (TLA) System in conjunction with the MRSA Application (MRSA App), an imaging application that uses artificial intelligence to interpret colorimetric information (mauve-colored colonies) indicative of MRSA pathogen presence on CHROMagar chromogenic media, anterior nares specimens from three sites were evaluated for the presence of mauve-colored colonies. Results obtained with the MRSA App were compared to manual reading of agar plate images by proficient laboratory technologists. Of 1,593 specimens evaluated, 1,545 (96.98%) were concordant between MRSA App and laboratory technologist reading for the detection of MRSA growth [sensitivity 98.15% (95% CI, 96.03, 99.32) and specificity 96.69% (95% CI, 95.55, 97.60)]. This multi-site study is the first evaluation of the MRSA App in conjunction with the BD Kiestra TLA System. Using the MRSA App, our results showed 98.15% sensitivity and 96.69% specificity for the detection of MRSA from anterior nares specimens. The MRSA App, used in conjunction with laboratory automation, provides an opportunity to improve laboratory efficiency by reducing laboratory technologists' labor associated with the review and interpretation of cultures., Competing Interests: S.M., J.L., D.M., C.O., J.-M.V., and K.B. are employees of the study sponsor, Becton, Dickinson and Company, and own BD shares; these authors have no other potential conflict of interest to disclose. D.R. has received research support for this investigation. He has also performed sponsored research in collaboration with Abbott, Altona, BD, bioMerieux, Cepheid, Luminex, Hardy Diagnostics, HelixBind, Hologic, Qiagen, Q-Linea, Roche, Specific Diagnostics, Cleveland Diagnostics, Thermo Fisher, & Vela Diagnostics; served as an advisor for Roche, Thermo Fisher, Luminex/DiaSorin, Seegene; received travel funds from Cepheid; and owns equity in Next Gen Diagnostics. M.D. has received research support for this investigation. P.P. and C.E.G. do not have conflicts of interest to disclose. E.M. has received research support for this investigation and is a sponsored speaker for BD.

• Utilization of preservative-containing urine tubes has expanded. • Preservatives may inhibit bacterial growth in pediatric low-volume urine. • We compared culture growth of 1ml to the recommended 3ml urine sample. • Minute volume in preservative-containing urine tubes yielded similar growth. Microbiological services consolidation has increased the usage of preservative-containing urine tubes, potentially inhibiting pathogens in low-volume pediatric urine samples, yielding false-negative results. Our study demonstrates comparable growth with 1 ml versus the recommended 3 ml urine, following different shipping intervals. We advocate for regulators to consider similar large-scale validations, ensuring results' consistency. [ABSTRACT FROM AUTHOR]

Dengue fever has remained a continuing global medical threat that impacts half of the world's population. Developing a highly effective dengue vaccine, with live-attenuated tetravalent vaccines as leading candidates, remains essential in preventing this disease. For the development of live virus vaccines (LVVs), potency measurements play a vital role in quantifying the active components of vaccine drug substance as well as drug product during various stages of research, development, and post-licensure evaluations. Traditional plaque-based assays are one of the most common potency test methods, but they generally take up to weeks to complete. Less labor and time-intensive potency assays are thus called for to aid in the acceleration of vaccine development, especially for multivalent LVVs. Here, we introduce a fully automated, 96-well format µPlaque assay that has been optimized as a high-throughput tool to evaluate process and formulation development of a live-attenuated tetravalent dengue vaccine. To the best of our knowledge, this is the first report of a miniaturized viral plaque method for dengue with full automation via an integrated robotic system. Compared to the traditional manual plaque assay, this newly developed method substantially reduces testing time by approximately half and allows for the evaluation of over ten times more samples per run. The fully automated workflow, from cell culture to plaque counting, significantly minimizes analyst hands-on time and improves assay repeatability. The study presents a pioneering solution for the rapid measurement of LVV viral titers, offering promising prospects for advancing vaccine development through high-throughput analytics., Competing Interests: All authors were employed by Merck & Co., Inc. The authors declare that this study received funding from Merck & Co., Inc. The funder had the following involvement in the study: study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication., (Copyright © 2024 Wang, Troutman, Hofmann, Gonzalez, Song, Levin, Pixley, Kearns, DePhillips and Loughney.)