Alanah Grant-St James

Lipidomic profiles and their relationship to incident diabetic retinopathy in type 2 diabetes: The Fremantle Diabetes Study Phase II

Timothy M. E. Davis, Jocelyn J. Drinkwater, Alanah Grant-St James, Elaine Holmes et autres

Aims To determine whether plasma lipid profiles predict diabetic retinopathy (DR). Methods DR status (none, mild non-proliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR/worse) was categorised in 762 adults with type 2 diabetes (mean age 64.5 years, 54.3% males) at baseline and …

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Regional Body Composition Outperforms BMI in Explaining Sex-Specific Lipidomic Signatures of Cardiometabolic Risk

Alanah Grant-St James, Luke Whiley, Nicola Gray, See PDF

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Analytical quality control in targeted lipidomics: Evaluating the performance of commercial plasma as a surrogate for pooled study samples

Alanah Grant-St James, Alex J. Lee, Julien Wist, Ferdous Ahmed Sohel et autres

BACKGROUND: Pooled quality control (PQC) samples are the gold standard for data quality monitoring in metabolic phenotyping studies. Typically composed of equal parts from all study samples, PQCs can be challenging to generate in large cohorts or when sample volumes are low. …

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Comprehensive Lipidomic Workflow for Multicohort Population Phenotyping Using Stable Isotope Dilution Targeted Liquid Chromatography-Mass Spectrometry

Monique Ryan, Alanah Grant-St James, Nathan G. Lawler, Mark William Fear et autres

Dysregulated lipid metabolism underpins many chronic diseases including cardiometabolic diseases. Mass spectrometry-based lipidomics is an important tool for understanding mechanisms of lipid dysfunction and is widely applied in epidemiology and clinical studies. With ever-increasing sample numbers, single batch acquisition is often unfeasible, …

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Exploring Machine Learning in Chemistry through the Classification of Spectra: An Undergraduate Project

Alanah Grant-St James, Luke Hand, THOMAS M. MILLS, Liwen Song et autres

High Resolution Image Download MS PowerPoint Slide Applications of machine learning in chemistry are many and varied, from prediction of structure–property relationships, to modeling of potential energy surfaces for large scale atomistic simulations. We describe a generalized approach for the application of …

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