Dataset Information

Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools.

ABSTRACT: Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software. The results of acquisition methods for quantitative analysis validated the remarkable analytical performance of the constructed workflow, proving it to be a sensitive and reproducible pipeline for biological complex fluids.

SUBMITTER: Shah SMZ

PROVIDER: S-EPMC9317224 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools.

Shah Syed Muhammad Zaki SMZ Ali Arslan A Khan Muhammad Noman MN Khadim Adeeba A Asmari Mufarreh M Uddin Jalal J Musharraf Syed Ghulam SG

Pharmaceuticals (Basel, Switzerland) 20220721 7

Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from ful ...[more]

PMID: 35890199

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Project description:Background: Open data on the locations and services provided by health facilities have, in some countries, allowed the development of software tools contributing to COVID-19 response. The UN and WHO encourage countries to make health facility location data open, to encourage use and improvement. We provide a summary of open access health facility location data in Africa using re-useable R code. We aim to support data analysts developing software tools to address COVID-19 response in individual countries. In Africa there are currently three main sources of such open data; 1) direct from national ministries of health, 2) a database for sub-Saharan Africa collated and published by a team from KEMRI-Wellcome Trust Research Programme and now hosted by WHO, and 3) The Global Healthsites Mapping Project in collaboration with OpenStreetMap. Methods: We searched for and documented official national facility location data that were openly available. We developed re-useable open-source R code to summarise and visualise facility location data by country from the three sources. This re-useable code is used to provide a web user interface allowing data exploration through maps and plots of facility type. Results: Out of 52 African countries, seven currently provide an official open facility list that can be downloaded and analysed reproducibly. Considering all three sources, there are over 185,000 health facility locations available for Africa. However, there are differences and overlaps between sources and a lack of data on capacities and service provision. Conclusions: These summaries and software tools can be used to encourage greater use of existing health facility location data, incentivise further improvements in the provision of those data by national suppliers, and encourage collaboration within wider data communities. The tools are a part of the afrimapr project, actively developing R building blocks to facilitate the use of health data in Africa.

Dataset Information

Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools.

Publications

Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools.

Similar Datasets

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