SLAS Technology最新文献

This study presents an interactive visualization-based Model-Based Systems Engineering (MBSE) technique that is especially designed to handle requirements analysis and design issues in joint test settings. Standardized operational requirement description, test requirement analysis, test design, and first test system plan generation are the four primary phases that make up the technique. This method provides a more thorough and approachable depiction of both operational and test requirements by utilizing interactive visualization techniques. This makes it possible for test staff to more accurately and efficiently develop, visualize, and improve test plans. The methodology's efficacy in augmenting the capabilities of joint testing platforms is demonstrated through a thorough case study of a joint test. Higher levels of accuracy and dependability in test results are eventually a result of this approach's strong support for requirements analysis, test design, and the general execution and assessment of joint tests inside complex systems.

At GSK, we have implemented custom integrated robotics platforms housed in bespoke biosafety enclosures to augment our capabilities in advanced cellular screening. Here we present and discuss the impact of one such system, the Cellular Automated Screening Platform (CASPer). We evaluate the benefits of implementing specific processes on CASPer that include increasing the throughput of safety screening assays and improving data integrity when testing complex in vitro 3D primary human hepatocyte models. This article provides an overview of the platforms installed and offers insight into their utilisation by presenting example workflows and quality control solutions which have been implemented. We offer perspective on the advantages of such custom integrated systems and their limitations in cellular screening for early drug discovery.

Dentists often suggest dental implants to replace missing teeth; nevertheless, mechanical issues can develop with these implants, which could lead to prosthesis replacement or repairs. When investigating implant systems' mechanical characteristics and stress distribution, finite element analysis (FEA) is a popular computational tool. In biomechanical investigations, this strategy is widely used. However, traditional FEA methods can be tedious and require expert expertise for accurate simulation and translation of results. To automate and simplify the process of mending oral implant prostheses, the article suggests a new framework called AI-FEA. The three primary parts that make up the suggested AI-FEA framework are 1. An AI-powered model creation module that utilizes data from medical imaging to autonomously construct 3D finite element designs that are unique to each patient. Utilizing deep learning approaches, this module segments and reconstructs three-dimensional geometries from computed tomography (CT) or cone-beam CT data using material properties and boundary conditions. 2. A FEA solver that runs simulations to test the way the implant system handles different loads. This component uses state-of-the-art numerical methods to model the implant and bone interface and determine stress distributions. 3. An AI-based decision support system that takes all that data and recommends the best way to fix the prosthesis. Combining FEA findings with patient-specific variables, this decision support system uses machine learning algorithms educated on an extensive dataset of implant failure instances and repair results to provide the optimal repair strategy. For patients experiencing issues with oral implants, the suggested AI-FEA framework might mean huge time and skill savings in prosthesis repair planning, leading to better, more individualized care.

To facilitate the translation of immunotherapies from bench to bedside, predictive preclinical models are essential. We developed the in vivo immuno-oncology Hollow Fiber Assay (HFA) to bridge the gap between simpler cell-based in vitro assays and more complex mouse models for immuno-oncology drug evaluation. The assay involves co-culturing human cancer cell lines or primary patient-derived cancer cells with human immune cells inside semipermeable hollow fibers. Implanted intraperitoneally in mice, the fibers captured treatment-induced immune cell-mediated cancer cell killing following treatments with aCD3 and/or IL-2, demonstrating the feasibility of the assay. Traditional models require lengthy observation periods to monitor tumor growth and treatment response. The immuno-oncology HFA enables a rapid initial in vivo evaluation of immunological agents on cancer and immune cells of human origin, addressing two of the 3Rs - reduction and refinement - in animal research.

Pharmaceutical and biotechnology companies are increasingly being challenged to shorten the cycle time between design, make, test, and analyze (DMTA) compounds. Automation of multiplex assays to obtain multiparameter data on the same robotic run is instrumental in reducing cycle time. Consequently, an increasing need in automated systems to streamline laboratory workflows with the goal to expedite assay cycle time and enhance productivity has grown in industrial and academic institutions in the past decades. Herein, we present a customized robotic platform with operational modularity and flexibility, designed to automate entire assay workflows involving multistep reagent dispensing, mixing, lidding/de-lidding, incubation, centrifugation, and final readout steps by linking spinnaker robot with various peripheral instruments. Compared to manual workflows, the system can seamlessly execute processes with high efficiency, evaluated by standard assay validation protocols for robustness and reproducibility. Furthermore, the system can perform multiple, independent protocols in parallel, and has high-throughput capacity. In this publication, we demonstrate that the modular robotic platform can fully automate multiplex assay workflows through 'one-click-and-run' solution with tremendous benefits in liberating manual intervention, boosting productivity while producing high-quality data combined with reduced cycle time (>20 %), as well as expanding the capacity for higher throughput.

The purpose of this study was to examine the therapeutic potential of core traditional Chinese medicine (CTCM) in the treatment of diabetic peripheral neuropathy (DPN) through the use of a data-driven approach that combined network pharmacology and data mining. Important components of traditional Chinese medicine (TCM) and the targets that correspond with them were found through the examination of numerous databases and clinical prescriptions. The possible therapeutic pathways were investigated, with an emphasis on the AGE-RAGE pathway that was discovered via network pharmacology analysis. By evaluating histopathological alterations, inflammatory and apoptotic markers, microcirculation, and blood hypercoagulability in a rat model of DPN, the effectiveness of CTCM was confirmed.Through experimental validation in DPN rats, it was shown that CTCM improved histopathology, decreased inflammation and apoptosis, improved microcirculation, and corrected coagulation abnormalities in addition to alleviating neuropathic pain. These studies show the value of data-driven approaches in advancing traditional medicine research for drug development and offer a mechanistic basis for CTCM's therapeutic potential in DPN.

Nitroreductase (NTR) plays a critical role in the oxygen-deficient environment of anoxic tumor cells, and its identification is crucial for the diagnosis and treatment of cancer. This research introduces an innovative Surface Enhanced Raman Scattering (SERS) probe, created by attaching p-nitrothiophenol (p-NTP) to gold nanoparticles (Au NPs). This probe leverages the specific enzymatic reaction of NTR in hypoxic status, utilizing decreased NADH. The enzymatic activity of NTR transforms nitroaromatic compounds into aromatic amines, which is then reflected as a measurable shift in the SERS signal of the probe. This novel approach allows for the accurate quantification of NTR, with the sensitivity reaching a detection threshold of less than 0.02 μg/mL. The probe's non-toxic nature and superior biocompatibility facilitate its use for direct SERS investigations in A549 cells under reduced oxygen levels. We also applied this method to xenograft model. The results demonstrate a marked increase in NTR levels in tumor cells and tumor tissues in hypoxic conditions, highlighting the significance of this nanoprobe in enhancing cancer diagnostics, helping medical doctors making treatment decisions more swiftly and effectively.