scholarly journals High-throughput UHPLC-MS to screen metabolites in feces for gut metabolic health

2021 ◽  
Author(s):  
Andressa de Zawadzki ◽  
Maja Thiele ◽  
Tommi Suvitaival ◽  
Asger Wretlind ◽  
Min Kim ◽  
...  
Keyword(s):  
High Throughput ◽  
Metabolic Diseases ◽  
Quality Data ◽  
High Quality Data ◽  
Fecal Samples ◽  
Heterogeneous Samples ◽  
Relative Standard ◽  
High Throughput Method ◽  
Related Liver Disease ◽  
Metabolite Detection

(1) Background: Feces are the product of our diets and have been linked to diseases of the gut, including Crohn's disease and metabolic diseases such as diabetes. For screening metabolites in heterogeneous samples such as feces, it is necessary to use fast and reproducible analytical methods that maximize metabolite detection. (2) Methods: As sample preparation is crucial to obtain high quality data in MS-based clinical metabolomics, we developed a novel, efficient and robust method for preparing fecal samples for analysis with a focus in reducing aliquoting and detecting both polar and non-polar metabolites. Fecal samples (n= 475) from patients with alcohol-related liver disease and healthy controls were prepared according to the proposed method and analyzed in an UHPLC-QQQ targeted platform in order to obtain a quantitative profile of compounds that impact liver-gut axis metabolism. (3) Results: MS analyses of the prepared fecal samples have shown reproducibility and coverage of n=28 metabolites, mostly comprising bile acids and amino acids. We report metabolite-wise relative standard deviation (RSD) in quality control samples, inter-day repeatability, LOD, LOQ and range of linearity. The average concentrations for 135 healthy participants are reported here for clinical applications. (4) Conclusions: our high-throughput method provides an efficient tool for investigating gut-liver axis metabolism in liver-related diseases using a noninvasive collected sample.

scholarly journals Rapid Optical Clearing for High-Throughput Analysis of Tumour Spheroids

2021 ◽  
Author(s):  
Gency Gunasingh ◽  
Alexander Browning ◽  
Nikolas Haass
Keyword(s):  
High Throughput ◽  
Three Dimensional ◽  
Quality Data ◽  
High Quality ◽  
Size Distortion ◽  
High Throughput Analysis ◽  
High Quality Data ◽  
Tumour Spheroids ◽  
High Throughput Method ◽  
Significant Size

Tumour spheroids are fast becoming commonplace in basic cancer research and drug development. Obtaining high-quality data relating to the inner structure of spheroids is important for analysis, yet existing techniques often use equipment that is not commonly available, are expensive, laborious, cause significant size distortion, or are limited to relatively small spheroids. We present a high-throughput method of mounting, clearing, and imaging tumour spheroids that causes minimal size distortion. Spheroids are mounted in an agarose gel to prevent movement, cleared using a solution prepared from commonly available materials, and imaged using confocal microscopy. We find that our method yields high quality two- and three-dimensional images that provide information about the inner structure of spheroids.

scholarly journals REdiii: a pipeline for automated structure solution

2015 ◽  
Vol 71 (5) ◽  
pp. 1059-1067 ◽  
Author(s):  
Markus-Frederik Bohn ◽  
Celia A. Schiffer
Keyword(s):  
High Throughput ◽  
Scientific Discovery ◽  
Workflow Engine ◽  
Quality Data ◽  
Crystallographic Structure ◽  
Molecular Replacement ◽  
High Quality Data ◽  
Adaptive Workflow ◽  
Structure Solution ◽  
Integrated Software

High-throughput crystallographic approaches require integrated software solutions to minimize the need for manual effort.REdiiiis a system that allows fully automated crystallographic structure solution by integrating existing crystallographic software into an adaptive and partly autonomous workflow engine. The program can be initiated after collecting the first frame of diffraction data and is able to perform processing, molecular-replacement phasing, chain tracing, ligand fitting and refinement without further user intervention. Preset values for each software component allow efficient progress with high-quality data and known parameters. The adaptive workflow engine can determine whether some parameters require modifications and choose alternative software strategies in case the preconfigured solution is inadequate. This integrated pipeline is targeted at providing a comprehensive and efficient approach to screening for ligand-bound co-crystal structures while minimizing repetitiveness and allowing a high-throughput scientific discovery process.

scholarly journals Iterative Experimental Design Based on Active Machine Learning Reduces the Experimental Burden Associated with Reaction Screening

2020 ◽  
Author(s):  
Natalie Eyke ◽  
William H. Green ◽  
Klavs F. Jensen
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Learning Algorithm ◽  
Quality Data ◽  
Experiment Planning ◽  
Planning Tool ◽  
High Quality Data ◽  
Test Set ◽  
Screening Experiments ◽  
Selection Of

High-throughput reaction screening has emerged as a useful means of rapidly identifying the influence of key reaction variables on reaction outcomes. We show that active machine learning can further this objective by eliminating dependence on complete screens through iterative selection of maximally informative experiments from the subset of all possible experiments in the domain. To demonstrate our approach, we conduct retrospective analyses of the preexisting results of high-throughput reaction screening experiments. We compare the test set errors of models trained on actively-selected reactions to models trained on reactions selected at random from the same domain. We find that the degree to which models trained on actively-selected data outperform models trained on randomly-selected data depends on the domain being modeled, with it being possible to achieve very low test set errors when the dataset is heavily skewed in favor of low- or zero-yielding reactions. Our results confirm that the active learning algorithm is a useful experiment planning tool that can change the reaction screening paradigm, by allowing discovery and process chemists to focus their reaction screening efforts on the generation of a small amount of high-quality data.

scholarly journals Low-dose fixed-target serial synchrotron crystallography

2017 ◽  
Vol 73 (4) ◽  
pp. 373-378 ◽  
Author(s):  
Robin L. Owen ◽  
Danny Axford ◽  
Darren A. Sherrell ◽  
Anling Kuo ◽  
Oliver P. Ernst ◽  
...  
Keyword(s):  
High Throughput ◽  
Low Dose ◽  
Quality Data ◽  
Translation System ◽  
Fixed Target ◽  
High Quality Data ◽  
X Ray ◽  
Serial Sampling ◽  
Serial Data ◽  
Serial Crystallography

The development of serial crystallography has been driven by the sample requirements imposed by X-ray free-electron lasers. Serial techniques are now being exploited at synchrotrons. Using a fixed-target approach to high-throughput serial sampling, it is demonstrated that high-quality data can be collected from myoglobin crystals, allowing room-temperature, low-dose structure determination. The combination of fixed-target arrays and a fast, accurate translation system allows high-throughput serial data collection at high hit rates and with low sample consumption.

scholarly journals Iterative Experimental Design Based on Active Machine Learning Reduces the Experimental Burden Associated with Reaction Screening

2020 ◽  
Author(s):  
Natalie Eyke ◽  
William H. Green ◽  
Klavs F. Jensen
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Learning Algorithm ◽  
Quality Data ◽  
Experiment Planning ◽  
Planning Tool ◽  
High Quality Data ◽  
Test Set ◽  
Screening Experiments ◽  
Selection Of

High-throughput reaction screening has emerged as a useful means of rapidly identifying the influence of key reaction variables on reaction outcomes. We show that active machine learning can further this objective by eliminating dependence on complete screens through iterative selection of maximally informative experiments from the subset of all possible experiments in the domain. To demonstrate our approach, we conduct retrospective analyses of the preexisting results of high-throughput reaction screening experiments. We compare the test set errors of models trained on actively-selected reactions to models trained on reactions selected at random from the same domain. We find that the degree to which models trained on actively-selected data outperform models trained on randomly-selected data depends on the domain being modeled, with it being possible to achieve very low test set errors when the dataset is heavily skewed in favor of low- or zero-yielding reactions. Our results confirm that the active learning algorithm is a useful experiment planning tool that can change the reaction screening paradigm, by allowing discovery and process chemists to focus their reaction screening efforts on the generation of a small amount of high-quality data.

A Fluorescent High Throughput Method To Identify Potential Skin Sensitizers

Planta Medica ◽  
2016 ◽  
Vol 82 (05) ◽  
Author(s):  
C Avonto ◽  
AG Chittiboyina ◽  
D Rua ◽  
IA Khan
Keyword(s):  
High Throughput ◽  
High Throughput Method

What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

DETECTION OF ALFACORONAVIRUSES, BETACORONAVIRUSES AND ASTROVIRUSES IN BAT FECAL SAMPLES FROM MOSCOW REGION

2020 ◽  
Author(s):  
E.V. Korneenko ◽  
◽  
А.E. Samoilov ◽  
I.V. Artyushin ◽  
M.V. Safonova ◽  
...  
Keyword(s):  
High Throughput ◽  
Viral Genome ◽  
High Throughput Sequencing ◽  
Pcr Amplification ◽  
Moscow Region ◽  
Fecal Samples ◽  
Genus Level ◽  
Zoonotic Viruses ◽  
Reservoir Hosts ◽  
Blastn Search

In our study we analyzed viral RNA in bat fecal samples from Moscow region (Zvenigorod district) collected in 2015. To detect various virus families and genera in bat fecal samples we used PCR amplification of viral genome fragments, followed by high-throughput sequencing. Blastn search of unassembled reads revealed the presence of viruses from families Astroviridae, Coronaviridae and Herpesviridae. Assembly using SPAdes 3.14 yields contigs of length 460–530 b.p. which correspond to genome fragments of Coronaviridae and Astroviridae. The taxonomy of coronaviruses has been determined to the genus level. We also showed that one bat can be a reservoir of several virus genuses. Thus, the bats in the Moscow region were confirmed as reservoir hosts for potentially zoonotic viruses.

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

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