High-throughput Screening Tool Research Articles

An in silico design method of a peptide bioreceptor for cortisol using molecular modelling techniques

Cortisol is established as a reliable biomarker for stress prompting intensified research in developing wearable sensors to detect it via eccrine sweat. Since cortisol is present in sweat in trace quantities, typically 8–140 ng/mL, developing such biosensors necessitates the design of bioreceptors with appropriate sensitivity and selectivity. In this work, we present a systematic biomimetic methodology and a semi-automated high-throughput screening tool which enables rapid selection of bioreceptors as compared to ab initio design of peptides via computational peptidology. Candidate proteins from databases are selected via molecular docking and ranked according to their binding affinities by conducting automated AutoDock Vina scoring simulations. These candidate proteins are then validated via full atomistic steered molecular dynamics computations including umbrella sampling to estimate the potential of mean force using GROMACS version 2022.6. These explicit molecular dynamic calculations are carried out in an eccrine sweat environment taking into consideration the protein dynamics and solvent effects. Subsequently, we present a candidate baseline peptide bioreceptor selected as a contiguous sequence of amino acids from the selected protein binding pocket favourably interacting with the target ligand (i.e., cortisol) from the active binding site of the proteins and maintaining its tertiary structure. A unique cysteine residue introduced at the N-terminus allows orientation-specific surface immobilization of the peptide onto the gold electrodes and to ensure exposure of the binding site. Comparative binding affinity simulations of this peptide with the target ligand along with commonly interfering species e.g., progesterone, testosterone and glucose are also presented to demonstrate the validity of this proposed peptide as a candidate baseline bioreceptor for future cortisol biosensor development.

Utilizing a high-throughput visualization screening technology to develop a genetically encoded biosensor for monitoring 5-aminolevulinic acid production in engineered Escherichia coli

5-Aminolevulinic acid (5-ALA) is a non-protein amino acid widely used in agriculture, animal husbandry and medicine. Currently, microbial cell factories are a promising production pathway, but the lack of high-throughput fermentation strain screening tools often hinders the exploration of engineering strategies to increase cell factory yields. Here, mutant AC103-3H was screened from libraries of saturating mutants after response-specific engineering of the transcription factor AsnC of L-asparagine (Asn). Based on mutant AC103-3H, a whole-cell biosensor EAC103-3H with a specific response to 5-ALA was constructed, which has a linear dynamic detection range of 1–12 mM and a detection limit of 0.094 mM, and can be used for in situ screening of potential high-producing 5-ALA strains. With its support, overexpression of the C5 pathway genes using promoter engineering assistance resulted in a 4.78-fold enhancement of 5-ALA production in the engineered E. coli. This study provides an efficient strain screening tool for exploring approaches to improve the 5-ALA productivity of engineered strains.

Structure to Property: Chemical Element Embeddings for Predicting Electronic Properties of Crystals.

We present a new general-purpose machine learning model that is able to predict a variety of crystal properties, including Fermi level energy and band gap, as well as spectral ones such as electronic densities of states. The model is based on atomic representations that enable it to effectively capture complex information about each atom and its surrounding environment in a crystal. The accuracy achieved for band gaps exceeds results previously published. By design, our model is not restricted to the electronic properties discussed here but can be extended to fit diverse chemical descriptors. Its advantages are (a) its low computational requirements, making it an efficient tool for high-throughput screening of materials; and (b) the simplicity and flexibility of its architecture, facilitating implementation and interpretation, especially for researchers in the field of computational chemistry.

Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos.

In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on days or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for aneuploidy prediction and consequent live-birth outcomes. These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709-0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. Our study underscores the AI model's ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

Predicting ADMET Properties from Molecule SMILE: A Bottom-Up Approach Using Attention-Based Graph Neural Networks.

Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for their success. One key aspect is the characterization of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, which require early assessment in the drug discovery and development process. This study aims to present an innovative approach for predicting ADMET properties using attention-based graph neural networks (GNNs). The model utilizes a graph-based representation of molecules directly derived from Simplified Molecular Input Line Entry System (SMILE) notation. Information is processed sequentially, from substructures to the whole molecule, employing a bottom-up approach. The developed GNN is tested and compared with existing approaches using six benchmark datasets and by encompassing regression (lipophilicity and aqueous solubility) and classification (CYP2C9, CYP2C19, CYP2D6 and CYP3A4 inhibition) tasks. Results show the effectiveness of our model, which bypasses the computationally expensive retrieval and selection of molecular descriptors. This approach provides a valuable tool for high-throughput screening, facilitating early assessment of ADMET properties and enhancing the likelihood of drug success in the development pipeline.

Optimizing enzyme properties to enhance dihydroxyacetone production via methylglyoxal biosensor development

BackgroundDihydroxyacetone (DHA) stands as a crucial chemical material extensively utilized in the cosmetics industry. DHA production through the dephosphorylation of dihydroxyacetone phosphate, an intermediate product of the glycolysis pathway in Escherichia coli, presents a prospective alternative for industrial production. However, insights into the pivotal enzyme, dihydroxyacetone phosphate dephosphorylase (HdpA), remain limited for informed engineering. Consequently, the development of an efficient tool for high-throughput screening of HdpA hypermutants becomes imperative.ResultsThis study introduces a methylglyoxal biosensor, based on the formaldehyde-responding regulator FrmR, for the selection of HdpA. Initial modifications involved the insertion of the FrmR binding site upstream of the −35 region and into the spacer region between the −10 and −35 regions of the constitutive promoter J23110. Although the hybrid promoter retained constitutive expression, expression of FrmR led to complete repression. The addition of 350 μM methylglyoxal promptly alleviated FrmR inhibition, enhancing promoter activity by more than 40-fold. The methylglyoxal biosensor system exhibited a gradual increase in fluorescence intensity with methylglyoxal concentrations ranging from 10 to 500 μM. Notably, the biosensor system responded to methylglyoxal spontaneously converted from added DHA, facilitating the separation of DHA producing and non-producing strains through flow cytometry sorting. Subsequently, the methylglyoxal biosensor was successfully applied to screen a library of HdpA mutants, identifying two strains harboring specific mutants 267G > T and D110G/G151C that showed improved DHA production by 68% and 114%, respectively. Expressing of these two HdpA mutants directly in a DHA-producing strain also increased DHA production from 1.45 to 1.92 and 2.29 g/L, respectively, demonstrating the enhanced enzyme properties of the HdpA mutants.ConclusionsThe methylglyoxal biosensor offers a novel strategy for constructing genetically encoded biosensors and serves as a robust platform for indirectly determining DHA levels by responding to methylglyoxal. This property enables efficiently screening of HdpA hypermutants to enhance DHA production.

Aluminum chloride and D-galactose induced a zebrafish model of Alzheimer's disease with cognitive deficits and aging

Alzheimer’s disease (AD) is an age-related neurodegenerative disorder. Transgenic and pharmacological AD models are extensively studied to understand AD mechanisms and drug discovery. However, they are time-consuming and relatively costly, which hinders the discovery of potential anti-AD therapeutics. Here, we established a new model of AD in larval zebrafish by co-treatment with aluminum chloride (AlCl3) and D-galactose (D-gal) for 72 h. In particular, exposure to 150 μM AlCl3 + 40 mg/mL D-gal, 200 μM AlCl3 + 30 mg/mL D-gal, or 200 μM AlCl3 + 40 mg/mL D-gal successfully induced AD-like symptoms and aging features. Co-treatment with AlCl3 and D-gal caused significant learning and memory deficits, as well as impaired response ability and locomotor capacity in the plus-maze and light/dark test. Moreover, increased acetylcholinesterase and β-galactosidase activities, β-amyloid 1–42 deposition, reduced telomerase activity, elevated interleukin 1 beta mRNA expression, and enhanced reactive oxygen species production were also observed. In conclusion, our zebrafish model is simple, rapid, effective and affordable, incorporating key features of AD and aging, thus may become a unique and powerful tool for high-throughput screening of anti-AD compounds in vivo.

Development of a Měnglà virus minigenome and comparison of its polymerase complexes with those of other filoviruses

Ebola virus (EBOV) and Marburg virus (MARV), members of the Filoviridae family, are highly pathogenic and can cause hemorrhagic fevers, significantly impacting human society. Bats are considered reservoirs of these viruses because related filoviruses have been discovered in bats. However, due to the requirement for maximum containment laboratories when studying infectious viruses, the characterization of bat filoviruses often relies on pseudoviruses and minigenome systems. In this study, we used RACE technology to sequence the 3′-leader and 5′-trailer of Měnglà virus (MLAV) and constructed a minigenome. Similar to MARV, the transcription activities of the MLAV minigenome are independent of VP30. We further assessed the effects of polymorphisms at the 5′ end on MLAV minigenome activity and identified certain mutations that decrease minigenome reporter efficiency, probably due to alterations in the RNA secondary structure. The reporter activity upon recombination of the 3′-leaders and 5′-trailers of MLAV, MARV, and EBOV with those of the homologous or heterologous minigenomes was compared and it was found that the polymerase complex and leader and trailer sequences exhibit intrinsic specificities. Additionally, we investigated whether the polymerase complex proteins from EBOV and MARV support MLAV minigenome RNA synthesis and found that the homologous system is more efficient than the heterologous system. Remdesivir efficiently inhibited MLAV as well as EBOV replication. In summary, this study provides new information on bat filoviruses and the minigenome will be a useful tool for high-throughput antiviral drug screening.

Intestinal Stem Cells (ISCs) Derived from Menopausal Mice and Implications for Screening Cardioprotective Probiotic Species

Cardiovascular disease (CVD) is the leading cause of death in both men and women; however, morbidity and mortality sharply rise after the onset of menopause in women implicating loss of estrogen as key driver of increased CVD risk. The mechanisms contributing to this shift in CVD risk following menopause are not well understood, therefore limiting our abilities to develop sex-specific therapeutics. Given menopausal women show shifts in their gut microbiomes and our previous work demonstrating strain-specific probiotic cardioprotection, we are investigating the impact of the gut microbiome on CVD susceptibility during menopause. Using our 4-vinylcyclohexene diepoxide model of ovarian failure that uniquely preserves the peri-menopause transition, we generated 3D-cultures of intestinal stem cells (ISCs) derived from pre-, peri-, and menopausal mice. Following protein isolation, samples were used for in-solution, global, unbiased proteomics. We found 2,086 proteins were identified with two or more unique peptides across all groups and treatments, but 398 proteins were found to be significant by ANOVA (p<0.05). Hierarchical clustering and principle component analysis indicated that ISCs cultured from pre- and peri-menopausal mice showed less variance between samples when compared to menopause. Additionally, pairwise comparisons (p<0.05 and |log2 fold change| ≥ 1.5) showed menopausal samples have an overall downregulation (37 and 73 proteins compared to pre- and peri-menopausal cultures, respectively) of proteins that were most enriched for genes associated to metabolic pathways including amino acid biosynthesis, central carbon metabolism, and glycolysis pathway. Interestingly, we found a decreased peptide abundance in menopausal samples for the polymeric immunoglobulin receptor (pIgR). pIgR plays an important role in transcytosis of IgA and IgM across mucosal barriers and found to be involved in tightly regulating populations of gut microbiota. Immunoblotting of freshly isolated ISCs from pre-menopausal and menopausal mice showed that pIgR is significantly decreased in menopausal samples compared to pre-menopause. Furthermore, the gut microbiome and microbial metabolites directly impact gene expression within the mucosal barrier by modulating histone deacetylase (HDAC) activity. Therefore, through immunoblotting we demonstrated that histone H3 acetylation at Lys27 was significantly decreased in ISCs isolated from menopausal mice. These findings validate the use of ISCs isolated from menopausal mice as a model system to elucidate mechanisms of host-microbe interactions in a controlled cell system with implications to derive high-throughput probiotic screening tools. Ralph and Shirley Morgan Cardiovascular Research Award '21-'22, Sarver Heart Center, University of Arizona. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Breaking Boundaries in Malaria Research: Design of a Genetic Tool for High-Throughput Gametocidal Drug Screening

Breaking Boundaries in Malaria Research: Design of a Genetic Tool for High-Throughput Gametocidal Drug Screening

Spatiotemporal Control of Protein Refolding through Flash-Change Reaction Conditions.

Recombinant protein production is an essential aspect of biopharmaceutical manufacturing, with Escherichia coli serving as a primary host organism. Protein refolding is vital for protein production; however, conventional refolding methods face challenges such as scale-up limitations and difficulties in controlling protein conformational changes on a millisecond scale. In this study, we demonstrate the novel application of flow microreactors (FMR) in controlling protein conformational changes on a millisecond scale, enabling efficient refolding processes and opening up new avenues in the science of FMR technology. FMR technology has been primarily employed for small-molecule synthesis, but our novel approach successfully expands its application to protein refolding, offering precise control of the buffer pH and solvent content. Using interleukin-6 as a model, the system yielded an impressive 96% pure refolded protein and allowed for gram-scale production. This FMR system allows flash changes in the reaction conditions, effectively circumventing protein aggregation during refolding. To the best of our knowledge, this is the first study to use FMR for protein refolding, which offers a more efficient and scalable method for protein production. The study results highlight the utility of the FMR as a high-throughput screening tool for streamlined scale-up and emphasize the importance of understanding and controlling intermediates in the refolding process. The FMR technique offers a promising approach for enhancing protein refolding efficiency and has demonstrated its potential in streamlining the process from laboratory-scale research to industrial-scale production, making it a game-changing technology in the field.

Abstract 6892: Murine ex vivo platform: A powerful tool for biomarker assessment and high throughput screening of translationally relevant in vivo tumor models for PD and efficacy studies

Abstract Ex vivo model systems using tumor biopsies from patients are often utilized to develop predictive biomarkers. However, these readouts are often proximal and do not inform on downstream mechanisms (distal pharmacodynamics) and their relationship to anti-tumor efficacy. In vivo murine models represent a powerful tool to bridge this gap. However, selection of an appropriate translationally relevant murine model can often be a time-consuming process given that the complexity of the tumor microenvironment (TME). Additionally, in vivo models are generally not amenable to high throughput screening. Here, we demonstrate that ex vivo mouse tumor platforms can be used for rapid screening of treatment responsive and translationally relevant tumor models. These models can be used to interrogate the impact of a therapeutic on anti-tumor efficacy in a long-term in vivo study, thus allowing for the establishment of biomarker/response relationships in a rational and rapid manner. To operationalize this platform, ex vivo cultures of dissociated tumor cells (DTC) or tumor tissue slices (histoculture) were first optimized for maximum viability and/or preservation of tissue architecture. To allow for high throughput screening, feasibility of freezing DTCs or tumor chunks prior to sectioning was also explored. Finally, both ex vivo tumor cultures and in vivo tumors were treated with a Innate agonist as a proof of concept to demonstrate functional equivalency. In conclusion, this work provides a novel way of screening responsive, translationally relevant murine tumor models in a high throughput and cost-effective manner. These efforts have the potential benefits of saving time and animal usage, while also maximizing the probability of successfully evaluating a therapeutic’s benefit. Citation Format: Sunitha Bachawal, Janet Leung, Remie Mandawe, Malia Garza, Kimberly ly, Ofir Stefanson, Jack Lohre, Diba Kamali, Ngan Doan, Benjamin King, Elliot Kim, Ganapathi Hegde, Xiaoyun Liao, Scot Liu, Jeong Kim, Amanda Mikels Vigdal, Anandaroop Mukhopadhyay. Murine ex vivo platform: A powerful tool for biomarker assessment and high throughput screening of translationally relevant in vivo tumor models for PD and efficacy studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6892.

Lipid Microfluidic Biomimetic Models for Drug Screening: A Comprehensive Review

AbstractDeveloping new drugs is a complex, time‐consuming, and expensive process, essential to identify potential pharmacokinetic issues in the early stages to avoid investment in nonpromising candidates. Nearly half of all drug targets and key drug‐metabolizing enzymes are found in intracellular environments, compartmentalized by semipermeable barriers, the cell membranes. Inspired by their lipidic bilayer structure, lipid‐based biomimetic platforms are being developed to understand the biochemical and biophysical processes at the cellular membrane level. Considering the pharmaceutical industry's demands for efficient in vitro high throughput screening tools, microfluidic technology emerges as a promising solution to address challenges in lipid biomimetic models for screening applications. This involves the transformation of commonly used lipid‐based biomimetic models, like supported lipid bilayers and lipid vesicles, to miniaturized approachs and their evolution into a new generation of bilayer models. These include pore‐suspended and free‐standing lipid bilayers, black lipid membranes, and droplet interface bilayers. This review provides an overview of both generations of lipid biomimetic models used in drug‐membrane screening applications. Moreover, it delves into the intricacies of their production through microfluidic approaches and examines their screening applications in drug‐membrane interaction. The review concludes with a critical analysis of potential future directions in this rapidly evolving field.

A64 HUMAN INTESINAL ORGANOIDS AS A MODEL FOR GASTROINTESTINAL FUNCTION AND TOXICITY SCREENING

Abstract Aims Drug-induced gastrointestinal (GI) toxicity is a common adverse event in clinical trials, with symptoms including diarrhea, ulceration, and inflammation resulting from impaired barrier function. Furthermore, the intestine is a key mediator of both drug absorption and metabolism. The human colon carcinoma cell line (Caco-2) is the most common model used in drug discovery, however, intestinal organoids provide a model that better recapitulates the self-renewal, cellular composition, and function of the intestinal epithelium.This study utilizes human intestinal organoids as a high-throughput model for evaluating intestinal function and toxicity in preclinical drug discovery. Methods To assess toxicity, intestinal organoids were treated with two GI-toxic drugs, gefitinib and colchicine, and post-treatment cell viability was compared to Caco-2 control cultures. To assess barrier function, organoid-derived monolayers cultured in Transwell® plates were treated with colchicine and sapitinib, and post-treatment permeability was evaluated using the fluorescent low-permeability markers, 4 kDa FITC-dextran and Lucifer yellow. Results Human intestinal organoid cultures exhibited 14-fold greater sensitivity to gefitinib (average organoid IC50 = 0.82 μM, n = 3; Caco-2 IC50 =11.4 μM, n = 2) and 2,238 times greater sensitivity to colchicine (average organoid IC50 = 0.028 μM, n = 3; Caco-2 IC50 = 62.89 μM, n = 6) compared to Caco-2 controls. This 96-well assay was reproducible, producing comparable IC50 concentrations in repeated experiments with cells from the same and different donors (n = 3 donors). Organoid-derived monolayers exhibited ampersand:003E 10-fold (n = 3) increase in permeability to both sapitinib and colchicine compared to Caco-2 cultures, which displayed minimal responses to either drug treatment. Finally, organoid-derived monolayer cultures in transwell® plates can be utilized to quantify drug transport and metabolism by the intestinal epithelium. Using ileal organoid-derived monolayers and Caco-2 cultures, we quantified the active transport of methotrexate by the efflux transporter BCRP, calculating an efflux ratio of 11.46 from the basal to apical chambers. We also quantified CYP3A4 activity of ileal monolayers relative to Caco-2 cultures. Ileal monolayers converted midazolam to hydroxymidazolam ampersand:003E 50-fold (n = 2) more than Caco-2 cultures, which displayed minimal CYP3A4 activity below the limit of detection. Together, these data demonstrate that human intestinal organoids are a valuable tool for high-throughput GI toxicity screening and for evaluating intestinal function in preclinical drug discovery. Conclusions Together, these data demonstrate that human intestinal organoids are a valuable tool for high-throughput GI toxicity screening and for evaluating intestinal function in preclinical drug discovery. Funding Agencies None

Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability.

Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess the impact of mutations on protein stability, we used a sequence-based Potts Hamiltonian model trained on a kinase family multiple-sequence alignment (MSA) to calculate the statistical energy (fitness) effects of mutations and compared these against relative folding free energies (ΔΔGs) calculated from all-atom molecular dynamics free energy perturbation (FEP) simulations in explicit solvent. The fitness effects of mutations in the Potts model (ΔEs) showed good agreement with experimental thermostability data (Pearson r = 0.68), similar to the correlation we observed with ΔΔGs predicted from structure-based relative FEP simulations. Recognizing the possible advantages of using Potts models to rapidly estimate protein stability effects of kinase mutations seen in cancer genomics data, we used the Potts statistical energy model to estimate the stability effects of 65 conservative and nonconservative mutations across three distinct kinases (Wee1, Abl1, and Cdc7) with somatic mutations reported in the Genomic Data Commons (GDC) database. The ΔEs of these mutations calculated from the Potts model are consistent with the corresponding ΔΔGs from FEP simulations (Pearson ratio of 0.72). The agreement between these methods suggests that the Potts model may be used as a sequence-based tool for high-throughput screening of mutational effects as part of a computational pipeline for predicting the stability effects of mutations. We also demonstrate how the scalability of the fitness-based Potts model calculations permits analyses that are not easily accessed using FEP simulations. To this end, we employed site-saturation mutagenesis in the Potts model in order to investigate the relative stability effects of mutations seen in different cancer evolutionary scenarios. We used this approach to analyze the effects of drug pressure in Abl kinase by contrasting the relative fitness penalties of somatic mutations seen in miscellaneous cancer types with those calculated for mutations associated with cancer drug resistance. We observed that, in contrast to somatic mutations of Abl seen in various tumors that appear to have evolved neutrally, cancer mutations that evolved under drug pressure in Abl-targeted therapies tend to preserve enzyme stability.

Screening Techniques for Drug Discovery in Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive and irreversible impairment of memory and other cognitive functions of the aging brain. Pathways such as amyloid beta neurotoxicity, tau pathogenesis and neuroinflammatory have been used to understand AD, despite not knowing the definite molecular mechanism which causes this progressive disease. This review attempts to summarize the small molecules that target these pathways using various techniques involving high-throughput screening, molecular modeling, custom bioassays, and spectroscopic detection tools. Novel and evolving screening methods developed to advance drug discovery initiatives in AD research are also highlighted.

Rapid Laboratory Diagnosis of Epizootic Hemorrhagic Disease Virus Using a Loop-Mediated Isothermal Amplification Assay.

Reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) is a molecular diagnostic assay that is particularly useful for the detection of viral diseases of livestock. A major advantage of RT-LAMP is that it can be used either as a rapid field test or as a high-throughput screening tool in veterinary laboratories, with sensitivity comparable to the real-time RT-PCR assay. Unlike conventional or qPCR, RT-LAMP uses a strand displacement polymerase and a set of four to six primers that bind to several regions of the target nucleic acid. Amplification occurs without thermal cycling, and coupled with the numerous primers, RT-LAMP offers a rapid and highly specific molecular assay. In this chapter, we describe the RT-LAMP protocol for the detection of epizootic hemorrhagic disease virus (EHDV) as a low-cost, specific, and sensitive screening tool in veterinary diagnostic laboratories. We also provide guidance on how to adapt the RT-LAMP assay for rapid field testing.

Heterologous antigen selection of chicken single-chain variable fragments against thiamethoxam.

Single-chain variable fragments (scFvs) are valuable in the development of immunoassays for pesticide detection. In this study, scFvs specific to thiamethoxam (Thi) were successfully isolated from a library generated by chicken immunization through heterologous coating selection. These scFvs were subsequently expressed with fusion with an Avi tag and alkaline phosphatase. After combination and optimization, a scFv-biotin based enzyme linked immunosorbent assay (ELISA) was developed for the detection of Thi, demonstrating an impressive half-maximum signal inhibition concentration (IC50) of 30 ng mL-1 and a limit of detection (LOD) of 1.8 ng mL-1. The immunoassay exhibited minimal cross-reactivity with other neonicotinoid insecticides, except for 7.5% for imidacloprid and 6.7% for imidaclothiz. The accuracy of the assay was confirmed by testing spiked samples of apple, pear, cabbage, and cucumber, which resulted in average recoveries ranging between 82% and 119%, closely aligning with the results obtained through high-performance liquid chromatography. Therefore, the chicken scFv-biotin based assay showed promise as a high-throughput screening tool for Thi in agricultural samples.

Correlation between plasma renin concentration and plasma renin activity assays on the Maglumi 800 chemiluminescence immunoassay analyzer

The ratio of plasma aldosterone concentration to plasma renin activity (PAC/PRA) is the most common screening test for primary hyperaldosteronism, but it is not standardized among laboratories. We evaluated the correlation between plasma renin concentration (PRC) and plasma renin activity (PRA), and then PAC/PRC and PAC/PRA ratios on Maglumi 800 immunoassay analyzer. Forty-five plasma samples from volunteers were collected. PRC, PRA and PAC were measured by Maglumi 800 - a fully-auto chemiluminescence immunoassay analyzer. Correlation between plasma renin concentration (PRC) and plasma renin activity (PRA), and then PAC/PRC and PAC/PRA ratios were determined by regression analysis. PRC and PRA showed a good correlation (r = 0.88). The PAC/PRC and PAC/PRA ratios have a similar relationship (r = 0.757). Because of the advantages of the procedure and the independence of PRC from endogenous angiotensinogen levels, PRC may be used preferentially over PRA as high throughput screening tool.

Automated Electrolyte Formulation and Coin Cell Assembly for High-Throughput Lithium-Ion Battery Research

Battery research involving performance evaluation by battery cycling is a time-consuming process with sometimes large cell-to-cell variations. To enhance the rate of testing and reproducibility, automation is one accelerator that can be used. In this study, we introduce ODACell1 (Figure 1), an automated system for high-throughput electrolyte screening and Li-ion cell assembly.ODACell enables the assembly of Li-ion battery cells in ambient atmosphere. We use LiFePO4||Li4Ti5O12-based full cells with a dimethyl sulfoxide-based model electrolyte for demonstrating the automated system. We investigate the reproducibility of the automated system on this system as well as the influence of water on the performance of the model electrolyte when exposed to ambient air. Our system is uniquely equipped for combinatorial experimentation, combining electrolyte formulation, batch coin cell assembly, and cycling into a single system. The integration of multiple setups into a single unit is made possible, overcoming the incompatibility issues with integrating multiple, different automated systems together.While there are several other robotic systems besides ODACell for battery material acceleration, ODACell’s unique combination of robots incorporated in the robotic setup allow a broader set of tasks to be automated. For example, Clio by Dave et al. 2 focus on electrolyte formulation and characterization, lacking assembly of battery cells; AutoBASS by Zhang et al. 3 only automates battery assembly; and Poseidon by Svensson et al. 4 can automate electrolyte formulation, characterization, and coin cell assembly, but lack high-throughput batch processing.ODACell ensures high reproducibility, which is needed to establish robust models and determine accurate electrochemical and physical properties. The system assembled 131 coin cells, with a conservative fail rate of only 5%. The relative standard deviation of the discharge capacity after 10 cycles was 2% for the studied system. The investigation into the influence of water on cycling performance show overlapping performance trends between coin cells with 2 vol% and 4 vol% of water in the electrolyte highlighting the nontrivial relationship between water and cycling performance.In conclusion, ODACell is a promising tool for high-throughput electrolyte screening and Li-ion cell assembly. It builds upon previously published robotic setups by automating more of the research workflow. Our study highlights the importance of reproducibility in battery research and the complex relationship between water contaminants in electrolytes and cycling performance. ODACell has the potential to be a useful tool for advancing battery research by enhancing the rate of testing and reducing the variation of results.