SLAS Technology最新文献

The COVID-19 pandemic erupted at the beginning of 2020 and proved fatal, causing many casualties worldwide. Immediate and precise screening of affected patients is critical for disease control. COVID-19 is often confused with various other respiratory disorders since the symptoms are similar. As of today, the reverse transcription-polymerase chain reaction (RT-PCR) test is utilized for diagnosing COVID-19. However, this approach is sometimes prone to producing erroneous and false negative results. Hence, finding a reliable diagnostic method that can validate the RT-PCR test results is crucial. Artificial intelligence (AI) and machine learning (ML) applications in COVID-19 diagnosis has proven to be beneficial. Hence, clinical markers have been utilized for COVID-19 diagnosis with the help of several classifiers in this study. Further, five different explainable artificial intelligence techniques have been utilized to interpret the predictions. Among all the algorithms, the k-nearest neighbor obtained the best performance with an accuracy, precision, recall and f1-score of 84%, 85%, 84% and 84%. According to this study, the combination of clinical markers such as eosinophils, lymphocytes, red blood cells and leukocytes was significant in differentiating COVID-19. The classifiers can be utilized synchronously with the standard RT-PCR procedure making diagnosis more reliable and efficient.

Biophysical affinity screening is increasingly being adopted as a high-throughput hit finding technique in drug discovery. Automation is highly beneficial to high-throughput screening (HTS) since a large number of compounds need to be reproducibly tested against a biological target. Herein, we describe how we have automated two biophysical affinity screening methods that rely on a thermal shift in protein melting temperature upon small molecule binding: differential scanning fluorimetry (DSF) and the cellular thermal shift assay (CETSA).

Programmable liquid handling devices for cell culture systems have dramatically enhanced scalability and reproducibility. We previously reported a protocol to produce cell aggregates demonstrating growth plate-like structures containing hypertrophic chondrocytes from human induced pluripotent stem cells (hiPSCs). To apply this protocol to large-scale drug screening for growth plate-related diseases, we adapted it to the automated cell culture system (ACCS) consisting of programmable liquid handling devices connected to CO₂ incubators, a refrigerator, and labware feeders, designed for up to 4 batches with several cell culture plates culturing for several months. We developed a new program preparing culture media with growth factors at final concentration immediately before dispensing them to each well and precisely positioning the tip for the medium change without damaging cell aggregates. Using these programs on the ACCS, we successfully cultured cell aggregates for 56 days, only needing to replenish the labware, medium, and growth factors twice a week. The size of cell aggregates in each well increased over time, with low well-to-well variability. Cell aggregates on day 56 showed histochemical, immunohistochemical, and gene expression properties of growth plate-like structures containing hypertrophic chondrocytes, indicating proper quality as materials for basic research and drug discovery of growth plate related diseases. The established program will be a suitable reference for making programs of experiments requiring long term and complex culture procedures using ACCS.

Cholera is a waterborne disease caused by Vibrio cholerae bacteria generally transmitted through contaminated food or water sources. Although it has been eradicated in most Western countries, cholera continues to be a highly transmitted and lethal disease in several African and Southeast Asian countries. Unfortunately, current diagnostic methods for cholera have challenges including high cost or delayed diagnoses that can lead to increased disease transmission during pandemics, while current treatments such as therapeutic drugs and vaccines have limited efficacy against drug-resistant serogroups of Vibrio cholerae. As such, new solutions that can treat cholera in an efficient manner that avoids Vibrio cholerae’s adaptive immunity are needed. Nanoparticles (NPs) are a suitable platform for enhancing current theranostic tools because of their biocompatibility and ability to improve drug circulation and targeting. Nanoparticle surfaces can also be modified with various protein receptors targeting cholera toxins produced by Vibrio cholerae. This review will address recent developments in diagnostics, therapeutics, and prevention against cholera particularly focusing on the use of metal-based nanoparticles and organic nanoparticles. We will then discuss future directions regarding nanoparticle research for cholera.

The consistent production of high-quality cells in cell therapy highlights the potential of automated manufacturing. Humanoid robots are a useful option for transferring technology to automate human cell cultures. This study evaluated a robotic cell-processing facility (R-CPF) for clinical research on retinal cell therapy, incorporating the versatile humanoid robot Maholo LabDroid and an All-in-One CP unit. The R-CPF platform consists of a robot area for handling cells and an operator area for the maintenance of the robot, designed with a clean airflow to ensure sterility. Monitoring the falling, floating, and adhering bacteria demonstrated that the required cleanliness and aseptic environment for cell manufacturing were satisfied. We then conducted cell manufacturing equivalent to the transplantation therapy of induced pluripotent stem cell (iPSC)-derived retinal pigment epithelial cells that met the clinical quality standards for transplantation. These results indicate that R-CPF is suitable for cell manufacturing purposes and suggest that utilizing the same robotic system in basic and clinical research can accelerate the translation of basic research findings into clinical applications.

Many devices in a laboratory only have proprietary interfaces. Integrating them into an automation platform such as a Robotic Liquid Handler (RLH) thus usually requires upfront technical configuration and often dedicated software development. In this paper, we present the Tecan SiLA2 SDK, a BSD-3 licensed, royalty-free open-source framework on the.NET platform that makes it very easy for developers to implement the open SiLA2 standard: A developer only has to specify a dedicated automation interface, annotate it with informational constraints such as units or MIME-constraints and then the code required to expose implementations of this interface through SiLA2 can be generated. To validate the applicability and the savings in terms of integration efforts, we conducted multiple case studies. First, we analyze the integration of the Tecan SPARK™ microplate reader into a lab execution system (LES), allowing us admittedly rough comparisons of device integrations using SiLA2 vs. the previous proprietary interface. Next, we analyze the integration of a RLH into the voice control software LabVoice, demonstrating the flexibility of integrations made possible through SiLA2. Lastly, we review the integration potential of liquid handling hardware into custom control softwares using a semi-automated exposure of the Tecan MAPlinx™ software platform through SiLA2.

Pharma 4.0 is a digital evolution of the pharmaceutical industry that automates scientists’ traditional workflows with the implementation of modern technologies like cloud pipelines, artificial intelligence, robotic platforms, and augmented reality. Lab data capture (LDC) is an essential strategy for initiating Pharma 4.0 that aggregates and harmonizes siloed lab data from analytical instruments, reporting systems, and operational platforms. This publication describes the execution of LDC within a quantitative PCR (qPCR) workflow using the Tetra Data Platform (TDP). We selected this workflow because the qPCR instrument, the ViiA7, generates discrete file-based data that documents execution of individual assays for quantifying residual DNA throughout biologics process development and product profiling. TDP executes LDC through the deployment of file scanning software agents, scanning and ingestion processes, and a cloud-based parsing pipeline that harmonizes source data. Web applications were developed to query, visualize, and interpret harmonized qPCR data for automated experiment data processing and process control charting from the TDP platform. Our implementation of LDC enables analytical researchers to harness FAIR (Findable, Accessible, Interoperable, Reproducible) data practices across the organization and establishes a “compliance-by-code” culture in development labs.

Infectivity assays are essential for the development of viral vaccines, antiviral therapies, and the manufacture of biologicals. Traditionally, these assays take 2–7 days and require several manual processing steps after infection. We describe an automated viral infectivity assay (AVIA^TM), using convolutional neural networks (CNNs) and high-throughput brightfield microscopy on 96-well plates that can quantify infection phenotypes within hours, before they are manually visible, and without sample preparation. CNN models were trained on HIV, influenza A virus, coronavirus 229E, vaccinia viruses, poliovirus, and adenoviruses, which together span the four major categories of virus (DNA, RNA, enveloped, and non-enveloped). A sigmoidal function, fit between virus dilution curves and CNN predictions, results in sensitivity ranges comparable to or better than conventional plaque or TCID₅₀ assays, and a precision of ∼10%, which is considerably better than conventional infectivity assays. Because this technology is based on sensitizing CNNs to specific phenotypes of infection, it has potential as a rapid, broad-spectrum tool for virus characterization, and potentially identification.

Due to their physiological relevance, cell-based assays using human-induced pluripotent stem cell (iPSC)-derived cells are a promising in vitro pharmacological evaluation system for drug candidates. However, cell-based assays involve complex processes such as long-term culture, real-time and continuous observation of living cells, and detection of many cellular events. Automating multi-sample processing through these assays will enhance reproducibility by limiting human error and reduce researchers’ valuable time spent conducting these experiments. Furthermore, this integration enables continuous tracking of morphological changes, which is not possible with the use of stand-alone devices.

This report describes a new laboratory automation system called the Screening Station, which uses novel automation control and scheduling software called Green Button Go to integrate various devices. To integrate the above-mentioned processes, we established three workflows in Green Button Go: 1) For long-term cell culture, culture plates and medium containers are transported from the automatic CO₂ incubator and cool incubator, respectively, and the cell culture medium in the microplates is exchanged daily using the Biomek i7 workstation; 2) For time-lapse live-cell imaging, culture plates are automatically transferred between the CQ1 confocal quantitative image cytometer and the SCALE48W automatic CO₂ incubator; 3) For immunofluorescence imaging assays, in addition to the above-mentioned devices, the 405LS microplate washer allows for formalin-fixation and immunostaining of cells. By scheduling various combinations of the three workflows, we successfully automated the culture and medium exchange processes for iPSCs derived from patients with facioscapulohumeral muscular dystrophy, confirmation of their differentiation status by live-cell imaging, and confirmation of the presence of differentiation markers by immunostaining. In addition, deep learning analysis enabled us to quantify the degree of iPSC differentiation from live-cell imaging data. Further, the results of the fully automated experiments could be accessed via the intranet, enabling experiments and analysis to be conducted remotely once the necessary reagents and labware were prepared. We expect that the ability to perform clinically and physiologically relevant cell-based assays from remote locations using the Screening Station will facilitate global research collaboration and accelerate the discovery of new drug candidates.

Efficient sample preparation and accurate disease diagnosis under field conditions are of great importance for the early intervention of diseases in humans, animals, and plants. However, in-field preparation of high-quality nucleic acids from various specimens for downstream analyses, such as amplification and sequencing, is challenging. Thus, developing and adapting sample lysis and nucleic acid extraction protocols suitable for portable formats have drawn significant attention. Similarly, various nucleic acid amplification techniques and detection methods have also been explored. Combining these functions in an integrated platform has resulted in emergent sample-to-answer sensing systems that allow effective disease detection and analyses outside a laboratory. Such devices have a vast potential to improve healthcare in resource-limited settings, low-cost and distributed surveillance of diseases in food and agriculture industries, environmental monitoring, and defense against biological warfare and terrorism. This paper reviews recent advances in portable sample preparation technologies and facile detection methods that have been / or could be adopted into novel sample-to-answer devices. In addition, recent developments and challenges of commercial kits and devices targeting on-site diagnosis of various plant diseases are discussed.