Validation of Golden Gate Assemblies Using Highly Multiplexed Nanopore Amplicon Sequencing.

Golden Gate cloning has revolutionized synthetic biology. Its concept of modular, highly characterized libraries of parts that can be combined into higher order assemblies allows engineering principles to be applied to biological systems. The basic parts, typically stored in Level 0 plasmids, are sequence validated by the method of choice and can be combined into higher order assemblies on demand. Higher order assemblies are typically transcriptional units, and multiple transcriptional units can be assembled into multi-gene constructs. Higher order Golden Gate assembly based on defined and validated parts usually does not introduce sequence changes. Therefore, simple validation of the assemblies, e.g., by colony polymerase chain reaction (PCR) or restriction digest pattern analysis is sufficient. However, in many experimental setups, researchers do not use defined parts, but rather part libraries, resulting in assemblies of high combinatorial complexity where sequencing again becomes mandatory. Here, we present a detailed protocol for the use of a highly multiplexed dual barcode amplicon sequencing using the Nanopore sequencing platform for in-house sequence validation. The workflow, called DuBA.flow, is a start-to-finish procedure that provides all necessary steps from a single colony to the final easy-to-interpret sequencing report.

Biofoundry-Assisted Golden Gate Cloning with AssemblyTron.

Golden Gate assembly is a requisite method in synthetic biology that facilitates critical conventions such as genetic part abstraction and rapid prototyping. However, compared to robotic implementation, manual Golden Gate implementation is cumbersome, error-prone, and inconsistent for complex assembly designs. AssemblyTron is an open-source python package that provides an affordable automation solution using open-source OpenTrons OT-2 lab robots. Automating Golden Gate assembly with AssemblyTron can reduce failure-rate, resource consumption, and training requirements for building complex DNA constructs, as well as indexed and combinatorial libraries. Here, we dissect a panel of upgrades to AssemblyTron's Golden Gate assembly capabilities, which include Golden Gate assembly into modular cloning part vectors, error-prone polymerase chain reaction (PCR) combinatorial mutant library assembly, and modular cloning indexed plasmid library assembly. These upgrades enable a broad pool of users with varying levels of experience to readily implement advanced Golden Gate applications using low-cost, open-source lab robotics.

Mitigating calcar fracture risk with automated impaction during total hip arthroplasty.

Background: Automated broaching has recently been introduced for total hip arthroplasty (THA), with the goal of improving surgical efficiency and reducing surgeon workload. While studies have suggested that this technique may improve femoral sizing and alignment, little has been published regarding its safety, particularly with regard to calcar fractures. The purpose of our study was to evaluate the risk of calcar fracture during automated broaching, and to determine if this risk can be mitigated. Methods: We queried our prospective institutional database and identified 1596 unilateral THAs performed by the senior author using automated impaction between 2019 and 2023. We identified the incidence of calcar fracture with automated impaction, and whether the fracture occurred during broaching or stem insertion. We additionally determined calcar fracture incidence within two consecutive subgroups of patients using different stem insertion techniques; subgroup (1): automated broaching with automated stem insertion for all patients; versus subgroup (2): automated broaching with automated stem insertion ONLY if a cushion of cancellous bone separated the broach from the calcar, otherwise the stem was placed manually. Continuous and categorical variables were analyzed with Student's t-test and Fisher's exact test, respectively. Results: Seventeen calcar fractures occurred intraoperatively (1.1 %). Only two fractures occurred during automated broaching (0.1 %), while fifteen occurred during final stem impaction (0.9 %) (p = 0.007). Four calcar fractures (1.4 %) occurred in subgroup 1, compared to two in subgroup 2 (0.6 %) (p = 0.28). Conclusions: Our study found a calcar fracture incidence of 1.1 % using automated impaction, consistent with historically reported rates of 0.4-3.7 %. We found that calcar fractures are more likely to occur during stem insertion than during femoral broaching. We recommend that if any part of the final broach is in direct contact with the calcar, the final stem should be impacted manually to minimize fracture risk.

Contemporary Applications and Future Perspectives of Robots in Endodontics: A Scoping Review.

BACKGROUND: In the era of modern advanced endodontics, the reduction of reliance on human hands and shifting towards robotics could benefit the precision and accuracy of endodontic treatment. This scoping review aims to describe current and emerging trends in the applications of robots in endodontics and highlight their limitations and future perspectives. METHODS: This review followed the PRISMA Extension for Scoping Reviews (PRISMA-ScR) standards. A comprehensive search of internet databases was conducted until February 2024. Studies that specifically examined robots in the field of endodontics were included. RESULTS: The studies focused on root canal cleaning, shaping, surgical procedures, and other applications. Robotic systems demonstrated improved accuracy, precision, reduced errors, and time savings compared with manual techniques. CONCLUSION: Robotics in endodontics show promise, especially in surgical procedures, with AI integration enhancing accuracy and efficiency. Challenges such as cost, physical limitations, and absence of tactile feedback require further research and investment.

A Simulation-Based Approach for Evaluating the Effectiveness of Robotic Automation Systems in HMR Product Loading.

The food industry has tried to enhance production processes in response to the increasing demand for safe, high-quality Home Meal Replacement (HMR) products. While robotic automation systems are recognized for their potential to improve efficiency, their high costs and risks make them less accessible to small and medium-sized enterprises (SMEs). This study presents a simulation-based approach to evaluating the feasibility and impact of robotic automation on HMR production, focusing on two distinct production cases. By modeling large-scale and order-based production cases using simulation software, the study identified key bottlenecks, worker utilization, and throughput improvements. It demonstrated that robotic automation increased throughput by 31.2% in large-scale production (Case A) and 12.0% in order-based production (Case B). The actual implementation showed results that closely matched the simulation, validating the approach. Moreover, the study confirmed that a single worker could operate the robotic system effectively, highlighting the practicality of robotics for SMEs. This research provides critical insights into integrating robotics to enhance productivity, reduce labor dependency, and facilitate digital transformation in food manufacturing.

Mitigating Grand Challenges in Life Cycle Inventory Modeling through the Applications of Large Language Models.

The accuracy of life cycle assessment (LCA) studies is often questioned due to the two grand challenges of life cycle inventory (LCI) modeling: (1) missing foreground flow data and (2) inconsistency in background data matching. Traditional mechanistic methods (e.g., process simulation) and existing machine learning (ML) methods (e.g., similarity-based selection methods) are inadequate due to their limitations in scalability and generalizability. The large language models (LLMs) are well-positioned to address these challenges, given the massive and diverse knowledge learned through the pretraining step. Incorporating LLMs into LCI modeling can lead to the automation of inventory data curation from diverse data sources and to the implementation of a multimodal analytical capacity. In this article, we delineated the mechanisms and advantages of LLMs to addressing these two grand challenges. We also discussed the future research to enhance the use of LLMs for LCI modeling, which includes the key areas such as improving retrieval augmented generation (RAG), integration with knowledge graphs, developing prompt engineering strategies, and fine-tuning pretrained LLMs for LCI-specific tasks. The findings from our study serve as a foundation for future research on scalable and automated LCI modeling methods that can provide more appropriate data for LCA calculations.

Applications of ChatGPT in the diagnosis, management, education, and research of retinal diseases: a scoping review.

PURPOSE: This scoping review aims to explore the current applications of ChatGPT in the retina field, highlighting its potential, challenges, and limitations. METHODS: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, MEDLINE, and Embase, to identify relevant articles published from 2022 onwards. The inclusion criteria focused on studies evaluating the use of ChatGPT in retinal healthcare. Data were extracted and synthesized to map the scope of ChatGPT's applications in retinal care, categorizing articles into various practical application areas such as academic research, charting, coding, diagnosis, disease management, and patient counseling. RESULTS: A total of 68 articles were included in the review, distributed across several categories: 8 related to academics and research, 5 to charting, 1 to coding and billing, 44 to diagnosis, 49 to disease management, 2 to literature consulting, 23 to medical education, and 33 to patient counseling. Many articles were classified into multiple categories due to overlapping topics. The findings indicate that while ChatGPT shows significant promise in areas such as medical education and diagnostic support, concerns regarding accuracy, reliability, and the potential for misinformation remain prevalent. CONCLUSION: ChatGPT offers substantial potential in advancing retinal healthcare by supporting clinical decision-making, enhancing patient education, and automating administrative tasks. However, its current limitations, particularly in clinical accuracy and the risk of generating misinformation, necessitate cautious integration into practice, with continuous oversight from healthcare professionals. Future developments should focus on improving accuracy, incorporating up-to-date medical guidelines, and minimizing the risks associated with AI-driven healthcare tools.

repDilPCR: a tool for automated analysis of qPCR assays by the dilution-replicate method.

BACKGROUND: The dilution-replicate experimental design for qPCR assays is especially efficient. It is based on multiple linear regression of multiple 3-point standard curves that are derived from the experimental samples themselves and thus obviates the need for a separate standard curve produced by serial dilution of a standard. The method minimizes the total number of reactions and guarantees that Cq values are within the linear dynamic range of the dilution-replicate standard curves. However, the lack of specialized software has so far precluded the widespread use of the dilution-replicate approach. RESULTS: Here we present repDilPCR, the first tool that utilizes the dilution-replicate method and extends it by adding the possibility to use multiple reference genes. repDilPCR offers extensive statistical and graphical functions that can also be used with preprocessed data (relative expression values) obtained by usual assay designs and evaluation methods. repDilPCR has been designed with the philosophy to automate and speed up data analysis (typically less than a minute from Cq values to publication-ready plots), and features automatic selection and performance of appropriate statistical tests, at least in the case of one-factor experimental designs. Nevertheless, the program also allows users to export intermediate data and perform more sophisticated analyses with external statistical software, e.g. if two-way ANOVA is necessary. CONCLUSIONS: repDilPCR is a user-friendly tool that can contribute to more efficient planning of qPCR experiments and their robust analysis. A public web server is freely accessible at https://repdilpcr.eu without registration. The program can also be used as an R script or as a locally installed Shiny app, which can be downloaded from https://github.com/deyanyosifov/repDilPCR where also the source code is available.

Energy Aware Technology Mapping of Genetic Logic Circuits.

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Automation of biochemical assays using an open-sourced, inexpensive robotic liquid handler.

High Throughput Screening is crucial in pharmaceutical companies for efficient testing in drug discovery and development. Our Vaccines Analytical Research and Development (V-AR&D) department extensively uses robotic liquid handlers in their High Throughput Analytics (HTA) group for assay development and sample screening. However, these instruments are expensive and require extensive training. Opentrons' OT-2 liquid handler offers a more affordable option (<∼ $10,000) with Python programming language and open-source flexibility, reducing training requirements. OT-2 allows broadening of testing capabilities and method transfer without significant capital investments. Two biochemical assays were conducted to assess OT-2's performance, and it demonstrated accurate pipetting with low covariance compared to Tecan EVO liquid handlers. Though OT-2 has some limitations such as lack of a crash detection system and limited deck space, it is a cost-effective, medium-throughput, and accurate liquid handling tool suitable for early-stage development and method transfer.

Energy Aware Technology Mapping of Genetic Logic Circuits.

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers -as caused by the additional burden of artificial genetic circuits- shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state (NESS) model of gene expression, capturing characteristics like energy dissipation -which we link to the entropy production rate- and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energy efficiency of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

Evaluation of Microplate Handling Accuracy for Applying Robotic Arms in Laboratory Automation.

An inexpensive single-arm robot is widely utilized for recent laboratory automation solutions. The integration of a single-arm robot as a transfer system into a semi-automatic liquid dispenser without a transfer system can be realized as an inexpensive alternative to a fully automated liquid handling system. However, there has been no quantitative investigation of the positional accuracy of robot arms required to transfer microplates. In this study, we constructed a platform comprising aluminum frames and digital gauges to facilitate such measurements. We measured the position repeatability of a robot arm equipped with a custom-made finger by repeatedly transferring microplates. Further, the acceptable misalignment of plate transfer was evaluated by adding an artificial offset to the microplate position using this platform. The results of these experiments are expected to serve as benchmarks for the selection of robot arms for laboratory automation in biology. Furthermore, all information for replicating this device will be made publicly available, thereby allowing many researchers to collaborate and accumulate knowledge, hopefully contributing to advances in this field.

Transparency improves the accuracy of automation use, but automation confidence information does not.

Increased automation transparency can improve the accuracy of automation use but can lead to increased bias towards agreeing with advice. Information about the automation's confidence in its advice may also increase the predictability of automation errors. We examined the effects of providing automation transparency, automation confidence information, and their potential interacting effect on the accuracy of automation use and other outcomes. An uninhabited vehicle (UV) management task was completed where participants selected the optimal UV to complete missions. Low or high automation transparency was provided, and participants agreed/disagreed with automated advice on each mission. We manipulated between participants whether automated advice was accompanied by confidence information. This information indicated on each trial whether automation was "somewhat" or "highly" confident in its advice. Higher transparency improved the accuracy of automation use, led to faster decisions, lower perceived workload, and increased trust and perceived usability. Providing participant automation confidence information, as compared with not, did not have an overall impact on any outcome variable and did not interact with transparency. Despite no benefit, participants who were provided confidence information did use it. For trials where lower compared to higher confidence information was presented, hit rates decreased, correct rejection rates increased, decision times slowed, and perceived workload increased, all suggestive of decreased reliance on automated advice. Such trial-by-trial shifts in automation use bias and other outcomes were not moderated by transparency. These findings can potentially inform the design of automated decision-support systems that are more understandable by humans in order to optimise human-automation interaction.

Implementation of an Automated System Using Machine Learning Models to Accelerate the Process of In Silico Identification of Small Molecules As Drug Candidates.

Drugs are commonly utilized to diagnose, cure, or prevent the occurrence of diseases, as well as to restore, alter, or change organic functions. Drug discovery is a time-consuming, costly, difficult, and inefficient process that yields very few medicinal breakthroughs. Drug research and design involves the capturing of structural information for biological targets and small molecules as well as various in silico methods, such as molecular docking and molecular dynamic simulation. This article proposes the idea of expediting computational drug development through a collaboration of scientists and universities, similar to the Human Genome Project using machine learning (ML) strategies. We envision an automated system where readily available or novel small molecules (chemical or plant-derived), as well as their biological targets, are uploaded to an online database, which is constantly updated. For this system to function, machine learning strategies have to be implemented, and high-quality datasets and high quality assurance of the ML models will be required. ML can be applied to all computational drug discovery fields, including hit discovery, target validation, lead optimization, drug repurposing, and data mining of small compounds and biomolecule structures. Researchers from various disciplines, such as bioengineers, bioinformaticians, geneticists, chemists, computer and software engineers, and pharmacists, are expected to collaborate to establish a solid workflow and certain parameters as well as constraints for a successful outcome. This automated system may help speed up the drug discovery process while also lowering the number of unsuccessful drug candidates. Additionally, this system will decrease the workload, especially in computational studies, and expedite the process of drug design. As a result, a drug may be manufactured in a relatively short time.

Biological Reference Ranges for Healthy Indian Blood Donors: Experience of a Tertiary Care Center Vis-à-Vis International Literature.

INTRODUCTION: Complete blood count is the most common, basic test requisitioned in hematology. The normal reference ranges of hematological parameters are required owing to variable socioeconomic, environmental, and genetic factors in populations. The current study determines the reference ranges of the healthy Indian donor population of a high socioeconomic group. METHODS: The study was conducted in the Department of Transfusion Medicine at a tertiary care hospital in India and included 4098 individuals, aged 18-65 years coming for voluntary blood donation from July 2021 to October 2022. Blood samples were collected in K2EDTA, analyzed on the Sysmex XN-31 hematology analyzer, and using statistical tools, the normal reference ranges were calculated. RESULTS: The reference ranges for hemoglobin (HB) (137-185 g/L), WBC (5.1-1.7 × 109/L), platelet count (115.6-370.0 × 109/L) were noted. No statistically significant changes were observed in different age groups. There were gender-wise differences noted in nearly all parameters. The HB and hematocrit (HCT) range was slightly higher in other Indian and other Asian populations with comparable values with the Chinese, Korean populations, and Western populations; RBC parameters were overall comparable with minor differences; the WBC count was higher than the other Indian and Asian populations particularly the upper limit of lymphocyte and monocyte; and the range of platelet counts had a comparable upper limit with all populations and had the lowest lower value in males in our study, which was comparable to only the Chinese population. CONCLUSIONS: It is concluded that reference ranges of common parameters were calculated with minor changes noted in all hematological parameters on comparing with other Indian, Asian population, and Western data.

Revolutionizing biocatalysis: A review on innovative design and applications of enzyme-immobilized microfluidic devices.

Integrating microfluidic devices and enzymatic processes in biocatalysis is a rapidly advancing field with promising applications. This review explores various facets, including applications, scalability, techno-commercial implications, and environmental consequences. Enzyme-embedded microfluidic devices offer advantages such as compact dimensions, rapid heat transfer, and minimal reagent consumption, especially in pharmaceutical optically pure compound synthesis. Addressing scalability challenges involves strategies for uniform flow distribution and consistent residence time. Incorporation with downstream processing and biocatalytic reactions makes the overall process environmentally friendly. The review navigates challenges related to reaction kinetics, cofactor recycling, and techno-commercial aspects, highlighting cost-effectiveness, safety enhancements, and reduced energy consumption. The potential for automation and commercial-grade infrastructure is discussed, considering initial investments and long-term savings. The incorporation of machine learning in enzyme-embedded microfluidic devices advocates a blend of experimental and in-silico methods for optimization. This comprehensive review examines the advancements and challenges associated with these devices, focusing on their integration with enzyme immobilization techniques, the optimization of process parameters, and the techno-commercial considerations crucial for their widespread implementation. Furthermore, this review offers novel insights into strategies for overcoming limitations such as design complexities, laminar flow challenges, enzyme loading optimization, catalyst fouling, and multi-enzyme immobilization, highlighting the potential for sustainable and efficient enzymatic processes in various industries.

Towards Autonomous Retail Stocking and Picking: Methods Enabling Robust Vacuum-Based Robotic Manipulation in Densely Packed Environments.

With the advent of robotics and artificial intelligence, the potential for automating tasks within human-centric environments has increased significantly. This is particularly relevant in the retail sector where the demand for efficient operations and the shortage of labor drive the need for rapid advancements in robot-based technologies. Densely packed retail shelves pose unique challenges for robotic manipulation and detection due to limited space and diverse object shapes. Vacuum-based grasping technologies offer a promising solution but face challenges with object shape adaptability. The study proposes a framework for robotic grasping in retail environments, an adaptive vacuum-based grasping solution, and a new evaluation metric-termed grasp shear force resilience-for measuring the effectiveness and stability of the grasp during manipulation. The metric provides insights into how retail objects behave under different manipulation scenarios, allowing for better assessment and optimization of robotic grasping performance. The study's findings demonstrate the adaptive suction cups' ability to successfully handle a wide range of object shapes and sizes, which, in some cases, overcome commercially available solutions, particularly in adaptability. Additionally, the grasp shear force resilience metric highlights the effects of the manipulation process, such as in shear force and shake, on the manipulated object. This offers insights into its interaction with different vacuum cup grasping solutions in retail picking and restocking scenarios.

Automated CRISPR/Cas9-based genome editing of human pluripotent stem cells using the StemCellFactory.

CRISPR/Cas9 genome editing is a rapidly advancing technology that has the potential to accelerate research and development in a variety of fields. However, manual genome editing processes suffer from limitations in scalability, efficiency, and standardization. The implementation of automated systems for genome editing addresses these challenges, allowing researchers to cover the increasing need and perform large-scale studies for disease modeling, drug development, and personalized medicine. In this study, we developed an automated CRISPR/Cas9-based genome editing process on the StemCellFactory platform. We implemented a 4D-Nucleofector with a 96-well shuttle device into the StemCellFactory, optimized several parameters for single cell culturing and established an automated workflow for CRISPR/Cas9-based genome editing. When validated with a variety of genetic backgrounds and target genes, the automated workflow showed genome editing efficiencies similar to manual methods, with indel rates of up to 98%. Monoclonal colony growth was achieved and monitored using the StemCellFactory-integrated CellCelector, which allowed the exclusion of colonies derived from multiple cells or growing too close to neighbouring colonies. In summary, we demonstrate the successful establishment of an automated CRISPR/Cas9-based genome editing process on the StemCellFactory platform. The development of such a standardized and scalable automated CRISPR/Cas9 system represents an exciting new tool in genome editing, enhancing our ability to address a wide range of scientific questions in disease modeling, drug development and personalized medicine.

End-to-End Throughput Chemical Proteomics for Photoaffinity Labeling Target Engagement and Deconvolution.

Photoaffinity labeling (PAL) methodologies have proven to be instrumental for the unbiased deconvolution of protein-ligand binding events in physiologically relevant systems. However, like other chemical proteomic workflows, they are limited in many ways by time-intensive sample manipulations and data acquisition techniques. Here, we describe an approach to address this challenge through the innovation of a carboxylate bead-based protein cleanup procedure to remove excess small-molecule contaminants and couple it to plate-based, proteomic sample processing as a semiautomated solution. The analysis of samples via label-free, data-independent acquisition (DIA) techniques led to significant improvements on a workflow time per sample basis over current standard practices. Experiments utilizing three established PAL ligands with known targets, (+)-JQ-1, lenalidomide, and dasatinib, demonstrated the utility of having the flexibility to design experiments with a myriad of variables. Data revealed that this workflow can enable the confident identification and rank ordering of known and putative targets with outstanding protein signal-to-background enrichment sensitivity. This unified end-to-end throughput strategy for processing and analyzing these complex samples could greatly facilitate efficient drug discovery efforts and open up new opportunities in the chemical proteomics field.