Dataset Information

Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples.

ABSTRACT: Quantitative nuclear magnetic resonance (NMR) spectroscopy of blood plasma is widely used to investigate perturbed metabolic processes in human diseases. The reliability of biochemical data derived from these measurements is dependent on the quality of the sample collection and exact preparation and analysis protocols. Here, we describe systematically, the impact of variations in sample collection and preparation on information recovery from quantitative proton (1H) NMR spectroscopy of human blood plasma and serum. The effects of variation of blood collection tube sizes and preservatives, successive freeze-thaw cycles, sample storage at -80 °C, and short-term storage at 4 and 20 °C on the quantitative lipoprotein and metabolite patterns were investigated. Storage of plasma samples at 4 °C for up to 48 h, freezing at -80 °C and blood sample collection tube choice have few and minor effects on quantitative lipoprotein profiles, and even storage at 4 °C for up to 168 h caused little information loss. In contrast, the impact of heat-treatment (56 °C for 30 min), which has been used for inactivation of SARS-CoV-2 and other viruses, that may be required prior to analytical measurements in low level biosecurity facilities induced marked changes in both lipoprotein and low molecular weight metabolite profiles. It was conclusively demonstrated that this heat inactivation procedure degrades lipoproteins and changes metabolic information in complex ways. Plasma from control individuals and SARS-CoV-2 infected patients are differentially altered resulting in the creation of artifactual pseudo-biomarkers and destruction of real biomarkers to the extent that data from heat-treated samples are largely uninterpretable. We also present several simple blood sample handling recommendations for optimal NMR-based biomarker discovery investigations in SARS CoV-2 studies and general clinical biomarker research.

SUBMITTER: Loo RL

PROVIDER: S-EPMC7640974 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples.

Loo Ruey Leng RL Lodge Samantha S Kimhofer Torben T Bong Sze-How SH Begum Sofina S Whiley Luke L Gray Nicola N Lindon John C JC Nitschke Philipp P Lawler Nathan G NG Schäfer Hartmut H Spraul Manfred M Richards Toby T Nicholson Jeremy K JK Holmes Elaine E

Journal of proteome research 20201016 11

Quantitative nuclear magnetic resonance (NMR) spectroscopy of blood plasma is widely used to investigate perturbed metabolic processes in human diseases. The reliability of biochemical data derived from these measurements is dependent on the quality of the sample collection and exact preparation and analysis protocols. Here, we describe systematically, the impact of variations in sample collection and preparation on information recovery from quantitative proton (<sup>1</sup>H) NMR spectroscopy o ...[more]

PMID: 32852212

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Project description:Metal artifact reduction has long been an important topic in x-ray CT image reconstruction. In this work, the authors propose an iterative method that sequentially minimizes a reweighted total variation (TV) of the image and produces substantially artifact-reduced reconstructions.A sequentially reweighted TV minimization algorithm is proposed to fully exploit the sparseness of image gradients (IG). The authors first formulate a constrained optimization model that minimizes a weighted TV of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available projection measurements, with image non-negativity enforced. The authors then solve a sequence of weighted TV minimization problems where weights used for the next iteration are computed from the current solution. Using the complete projection data, the algorithm first reconstructs an image from which a binary metal image can be extracted. Forward projection of the binary image identifies metal traces in the projection space. The metal-free background image is then reconstructed from the metal-trace-excluded projection data by employing a different set of weights. Each minimization problem is solved using a gradient method that alternates projection-onto-convex-sets and steepest descent. A series of simulation and experimental studies are performed to evaluate the proposed approach.Our study shows that the sequentially reweighted scheme, by altering a single parameter in the weighting function, flexibly controls the sparsity of the IG and reconstructs artifacts-free images in a two-stage process. It successfully produces images with significantly reduced streak artifacts, suppressed noise and well-preserved contrast and edge properties.The sequentially reweighed TV minimization provides a systematic approach for suppressing CT metal artifacts. The technique can also be generalized to other "missing data" problems in CT image reconstruction.

Dataset Information

Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples.

Publications

Quantitative In-Vitro Diagnostic NMR Spectroscopy for Lipoprotein and Metabolite Measurements in Plasma and Serum: Recommendations for Analytical Artifact Minimization with Special Reference to COVID-19/SARS-CoV-2 Samples.

Similar Datasets

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