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Our method of systematic metabolite identification using an HSQC-based metabolite chemical shift database is practical.
Our concept consists of SI labeling of living plants and animals, and systematic metabolite identification using an HSQC-based metabolite chemical shift database combined with 2D and 3D NMR.
All the chosen platforms allow for metabolite identification using standard libraries and the resulting data can therefore be directly interpreted and evaluated in terms of the performance in detecting chemically diverse metabolites.
Seventy-six percent in negative and 60% in positive ion mode of the remaining features (791 and 1292 features) were fully annotated and given putative metabolite identification using a revised version of the MMD database (31648 entries).
Six replicates were prepared for each treatment class for the analysis of global changes in the metabolome (1D H NMR), while three replicates were prepared for metabolite identification using 2D H C HSQC experiment, where C-glucose in the medium was replaced with C-glucose (3.5 g/L).
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In this work, we focused in the in vivo phase I metabolites and in vivo phase II MST metabolites identification using LC MS/MS [16].
These platforms support metabolite identification and using a strategy from the field of drug discovery [29], we showed that the platforms are complementary.
For metabolite identification we used the Golm Metabolome Database (GMD) [ 27, 43] and our in-house mass spectral libraries.
Ten metabolites with significantly changed abundance were targeted for identification using MS/MS and comparison with chemical standards.
Identification using pigmentation alone is not sufficient.
Metabolite identification was accomplished using a predictive multiple reaction monitoring-information-dependent acquisition-enhanced product ion (pMRM-IDA-EPI) scan and precursor ion scan-information-dependent acquisition-enhanced product ion (PREC-IDA-EPI) scan in positive ion mode.
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