Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Najmuddin Saquib"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data w
Externí odkaz:
https://doaj.org/article/3bc40a511eaa49c1b26ac9dc093b709b
Autor:
Ankur Gupta, Ganga Sagar, Zaved Siddiqui, Kanury V. S. Rao, Sujata Nayak, Najmuddin Saquib, Rajat Anand
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
Abstract We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analysis using machine learning algorithms to accurately detect early stages of the women specific cancers of breast, endometrium, cervix, and ovar
Externí odkaz:
https://doaj.org/article/389bc73245fb4a3fa45830ffae8fc71f
Publikováno v:
Bio-Protocol, Vol 5, Iss 8 (2015)
Fluctuations in metabolite levels in mammalian cells are the most direct form of readout of the cellular metabolic state. The current protocol describes a method for pulse labeling and subsequent isolation of metabolites from adherent mammalian cells
Externí odkaz:
https://doaj.org/article/db38f93e69ec470ca8e26a27b4d1f9ce
Autor:
Parul Mehrotra, Shilpa V Jamwal, Najmuddin Saquib, Neeraj Sinha, Zaved Siddiqui, Venkatasamy Manivel, Samrat Chatterjee, Kanury V S Rao
Publikováno v:
PLoS Pathogens, Vol 10, Iss 7, p e1004265 (2014)
The success of Mycobacterium tuberculosis as a pathogen derives from its facile adaptation to the intracellular milieu of human macrophages. To explore this process, we asked whether adaptation also required interference with the metabolic machinery
Externí odkaz:
https://doaj.org/article/0af258e6759c41edb7411f061e5363b5
Autor:
Ramu Adela, Siva Swapna Kasarla, Najmuddin Saquib, Sonu Kumar Gupta, Sneh Bajpai, Yashwant Kumar, Sanjay K Banerjee
Publikováno v:
Molecular Omics. 19:321-329
Untargeted metabolomics-based markers may predict the complexity of coronary artery disease in diabetic patients.
Autor:
Ankur Gupta, Ganga Sagar, Zaved Siddiqui, Kanury V. S. Rao, Sujata Nayak, Najmuddin Saquib, Rajat Anand
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)
We integrated untargeted serum metabolomics using high-resolution mass spectrometry with data analysis using machine learning algorithms to accurately detect early stages of the women specific cancers of breast, endometrium, cervix, and ovary across
Additional file 1: Methods S1. This document provides detailed information on the materials used and protocols followed during the experiments.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0497fb8edb82e2e4797a97ac4be21884