مجال التميز | تميز دراسي وبحثي |
البحوث المنشورة |
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البحث (1): | |
عنوان البحث: |
A highly Sensitive Modified Glassy Carbon Electrode with Carboxylated Multi-walled Carbon Nanotubes/Nafion Nano composite for Efficient and Cheap Voltammetric Sensing of Dianabol Steroid in Biological Fluid |
رابط إلى البحث: |
https://www.jstage.jst.go.jp/article/analsci/advpub/0/advpub_21P167/_pdf/-char/en |
تاريخ النشر: |
06/08/2021 |
موجز عن البحث: |
The extraordinary prerequisite for analysis of anabolic steroid has inspired of high-performance sensing probe, cost effectiveness and ease to use. The exogenous anabolic steroid methandrostenolone namely dianabol (DB) is a key and broadly used trade chemical due to its great ability to stimulate protein synthesis in the humans. Therefore, the aim of the current strategy is to develop non-enzymatic electrochemical probe for direct detection of trace levels of dianabol (DB) using glassy carbon electrode (GCE) modified with functionalized multi-walled carbon nanotubes (c-MWCNTs) and Nafion. Differential pulse-cathodic stripping voltammetry (DP-CSV) at pH 7.0 recorded cathodic peak current at -1.35 V that varied linearly over wide range (9.0×10-9 (2.7 ng mL-1) – 9.0×10-6 (2704 ng mL-1) mol L-1) of DB concentration. The lower limits of detection and quantification were 8.6×10-9 mol L-1 (2.5 ng mL-1) and 2.7 × 10-8 mol L-1 (8.3 ng mL-1), respectively. The method was applied fruitfully for DB analysis in human urine and subsequently compared with standard HPLC method. Interference of common metabolites in biological fluids samples to DB sensing was insignificant. This method has distinctive advantages e.g. precise, short analytical time, sensitive, economical, reproducible and miniaturized sample preparation for DB analysis in biological samples of human origin. |
المؤتمرات العلمية |
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المؤتمر (1): | |
عنوان المؤتمر: |
17th annual conference of the metabolomics society |
تاريخ الإنعقاد: |
22/06/2021 |
مكان الإنعقاد: |
Virtual |
طبيعة المشاركة: |
Poster presentation |
عنوان المشاركة: |
The metabolism of amitriptyline and verapamil: a comprehensive study using a KNIME workflow |
ملخص المشاركة: |
The metabolism of amitriptyline and verapamil is elucidated through the application of a KNIME based workflow. KNIME is an open-source data-analytics platform which unites a range of open-source metabolomics tools that can generate standard data visualisations, prediction of candidate metabolites, ranking of these against experimental data and generating reports on the observed metabolism. The application of this workflow is demonstrated by studying the in vitro metabolism of the drugs amitriptyline and verapamil using human, rat and Guinea pig liver microsomes. UPLC-MS (Orbitrap) data is generated and interrogated using this workflow. We can automatically assign formulae and structures to the metabolites of these drugs. The primary metabolic routes for amitriptyline are hydroxylation, N-dealkylation, N-oxidation, and conjugation, whilst for verapamil they are N-dealkylation, N-demethylation, and O-demethylation. The metabolites identified are compared with literature confirming the power of the workflow technique and demonstrating the usefulness of tools like KNIME in facilitating the integration and interoperation of emerging novel software packages in the metabolomics field. |
الرابط: | |
المؤتمر (2): | |
عنوان المؤتمر: |
16th annual conference of the metabolomics society |
تاريخ الإنعقاد: |
27/10/2020 |
مكان الإنعقاد: |
Shanghai/ China – (online) |
طبيعة المشاركة: |
Poster presentation |
عنوان المشاركة: |
An end-to-end open-source LC-MS/MS data analysis pipeline for metabolomics |
ملخص المشاركة: |
An active and growing eco-system of open-source software has emerged in recent years to facilitate the analysis of metabolomics datasets. These powerful approaches however suffer from multiple different input and output file formats making their integration challenging. Our aim is to address the growing complexity of data in this field and improve the analysis processes. We present a unique computational method for data analysis of drug metabolism studies which leverages KNIME, an open-source data analytics platform for visual programming, modelling, data analysis and visualization. We envisaged an end-to-end metabolism platform which, beginning from a user imputable molecular structure, predicts putative metabolites using the metabolomics tool SyGMa, generates in silico predications of MSMS spectra with CFM-ID. With these predictions in hand LC-MS/MS data can be used to score and rank metabolite candidates and generate extracted ion chromatograms for quantification and data analysis. This solution is flexible, efficient, accurate and reproducible. We demonstrate this method by applying it to an ESI-LC-MS/MS study of the in vitro metabolism of two drugs, verapamil and amitriptyline. It was possible to automatically generate the Total Ion Chromatogram, Mass Defect, Extracted Ion Chromatography and MS/MS spectra for the samples tested and the data be visualised. Using this method has enabled the putative identification of new metabolites of the drugs. By harnessing KNIME’s ability to extend and connect disparate packages created by the scientific community. We believe it offers an important opportunity to unite and aid the metabolomics community. |
الرابط: | |
المؤتمر (3): | |
عنوان المؤتمر: |
The 40th British mass spectrometry society annual meeting |
تاريخ الإنعقاد: |
03/09/2019 |
مكان الإنعقاد: |
Manchester/ UK |
طبيعة المشاركة: |
Poster presentation |
عنوان المشاركة: |
Analysis of LC-MS/MS metabolic datasets using Knime |
ملخص المشاركة: |
A method for data analysis of drug metabolism studies has been developed by using Knime (Konstanz Information Miner). Knime is free and open-source data analytics platform and consists of nodes that blending different data sources, including pre-processing (Extraction, Transformation, Loading) for modelling, data analysis and visualization without or with only minimal programming. This computational solution is flexible, efficient, accurate and reproducible. Subsequent, application of this method to in vitro drug metabolism studies using liver microsomes are aimed to address the growing variety and complexity of data in this field and thus contribute to improving the analysis process in general.Knime platform has been used with two drugs that analyzed by ESI- LC-MS/MS to create, treat, share and store the raw LC-MS/MS data in a transparent and straightforward way. By using Knime platform, it was possible to automatically generate the Total Ion Chromatogram (TIC), Mass Defect (MD), Extracted Ion Chromatography (EIC) and MS/MS for the tested samples and visualize these data. Moreover, Knime platform has provided an accurate and efficient method to identify a putative metabolite for our drugs by its ability to extend with many computational applications such as SyGMa (Systematic Generation of potential Metabolites) and CFM-ID (Competitive Fragmentation Modeling for metabolite Identification).By using SyGM, it was possible to predicts the putative metabolites of our drugs after applying a set of reaction rules covering a broad range of phase 1 and phase 2 metabolism that have been derived from metabolic reactions demonstrated in the metabolite database which take a place in humans. Next, we used a Competitive Fragmentation Modeling which is used by CFM-ID to produces a probabilistic generative model for the MS/MS fragmentation process and machine learning techniques to adapt the model parameters from data. This generated model can be used to predict the spectra for chemical structure of a given drugs (CFM predict) and then computes the predicted spectrum for each candidate and compares it to the input spectrum to rank the candidates metabolites according to how closely they match (CFM-ID- precomputed).Knime is a computational approach that is widely applicable and which can overcome many of the difficulties of other methodologies that arise due to different computational complexity and levels of theory. |
الرابط: | |
المؤتمر (4): | |
عنوان المؤتمر: |
15th Annual Conference of the Metabolomics Society |
تاريخ الإنعقاد: |
23/06/2019 |
مكان الإنعقاد: |
The Hague/ Netherland |
طبيعة المشاركة: |
Poster presentation |
عنوان المشاركة: |
Using Knime for the analysis of LC-MS/MS metabolic datasets of amitriptyline and verapamil |
ملخص المشاركة: |
A method for studying metabolic datasets has been developed by using Knime (Konstanz Information Miner). Knime is free and open-source data analytics platform and consists of nodes that blend different data sources, including pre-processing for modelling, data analysis and visualization without or with only minimal programming. Our aim is to address the growing variety and complexity of data in this field and thus contribute to improving the analysis process in general. We demonstrate our approach using datasets derived from an ESI-LC-MS/MS analysis of in vitro metabolites of amitriptyline and verapamil using liver microsomes. Knime platform has been used to create, treat, share and store the raw ESI-LC-MS/MS data in a transparent and straightforward way. By using our approach, it was possible to automatically generate standard MS data visualisations. Moreover, we extend Knime by generating putative metabolites and MSMS spectra of our drugs using computational applications such as SyGMa (Systematic Generation of potential Metabolites) and CFM-ID (Competitive Fragmentation Modeling for metabolite Identification). SyGMa consists of a set of reaction rules covering a broad range of phase 1 and phase 2 metabolism that has been derived from metabolic reactions demonstrated in the metabolite database which take a place in humans. Next, we used a Competitive Fragmentation Modeling which is used by CFM-ID to produces a probabilistic generative model for the MS/MS fragmentation process and machine learning techniques to adapt the model parameters from data. This generated model can be used to predict the spectra for chemical structure of a given drugs (CFM predict) and then computes the predicted spectrum for each candidate and compares it to the input spectrum to rank the candidates metabolites according to how closely they match (CFM-ID- precomputed). Using this workflow, we are able to assign structures to candidate metabolites within our samples. |
الرابط: |
http://metabolomics2019.org/images/2019-Metabolomics_Abstract_9.pdf |
المؤتمر (5): | |
عنوان المؤتمر: |
Advances in Hyphenated Mass Spectrometry British mass spectroscopy society |
تاريخ الإنعقاد: |
09/04/2019 |
مكان الإنعقاد: |
London UK |
طبيعة المشاركة: |
Poster Presentation |
عنوان المشاركة: |
Analysis of LC-MS/MS Metabolic Datasets Using Knime. |
ملخص المشاركة: |
A method for data analysis of drug metabolism studies has been developed by using Knime (Konstanz Information Miner). Knime is free and open-source data analytics platform and consists of nodes that blending different data sources, including pre-processing (Extraction, Transformation, Loading) for modelling, data analysis and visualization without or with only minimal programming. (My general aim and main tool). This computational solution is flexible, efficient, accurate and reproducible. Subsequent, application of this method to in vitro drug metabolism studies are aimed to address the growing variety and complexity of data in this field and thus contribute to improving the analysis process in general. ( Its advantages, main application and why ). Knime platform has been used with two drugs that analyzed by ESI- LC/MS to create, treat, share and store the raw LC/MS data in a transparent and straightforward way. (The achievements in general. ). By using Knime platform, it was possible to automatically generate the Total Ion Chromatogram (TIC), Mass Defect (MD), Extracted Ion Chromatography (EIC) and MS/MS for the tested samples and visualize these data. ( 1st part of achivement ). Moreover, Knime platform has provided an accurate and efficient method to identify a putative metabolite for our drugs by its ability to extend with many computational applications such as SyGMa (Systematic Generation of potential Metabolites) and CFM-ID (Competitive Fragmentation Modeling for metabolite Identification). ( second part of achivement ). By using SyGM, it was possible to predicts the putative metabolites of our drugs after applying a set of reaction rules covering a broad range of phase 1 and phase 2 metabolism that have been derived from metabolic reactions demonstrated in the metabolite database which take a place in humans. Then, we moved to a Competitive Fragmentation Modeling which is used by CFM-ID to produces a probabilistic generative model for the MS/MS fragmentation process and machine learning techniques to adapt the model parameters from data. This generated model can be used to predict the spectra for chemical structure of a given drugs (CFM predict) and then computes the predicted spectrum for each candidate and compares it to the input spectrum to rank the candidates metabolites according to how closely they match (CFM-ID- precomputed). (Explanation of the main steps in the id of metabolites). |
نوف مرزوق ناجي العرفي
دكتوراه
العلوم والتقنية
University of Bristol