scholarly journals Omics Meeting Onics: Towards the Next Generation of Spectroscopic-Based Technologies in Personalized Medicine

2019 ◽  
Vol 9 (3) ◽  
pp. 39 ◽  
Author(s):  
Weng Peng ◽  
Daniele Paesani

This article aims to discuss the recent development of integrated point-of-care spectroscopic-based technologies that are paving the way for the next generation of diagnostic monitoring technologies in personalized medicine. Focusing on the nuclear magnetic resonance (NMR) technologies as the leading example, we discuss the emergence of -onics technologies (e.g., photonics and electronics) and how their coexistence with -omics technologies (e.g., genomics, proteomics, and metabolomics) can potentially change the future technological landscape of personalized medicine. The idea of an open-source (e.g., hardware and software) movement is discussed, and we argue that technology democratization will not only promote the dissemination of knowledge and inspire new applications, but it will also increase the speed of field implementation.

1982 ◽  
Vol 43 (C7) ◽  
pp. C7-305-C7-308
Author(s):  
H. Ackermann ◽  
B. Bader ◽  
P. Freiländer ◽  
P. Heitjans ◽  
G. Kiese ◽  
...  

2019 ◽  
Vol 5 (2) ◽  
pp. 20-28
Author(s):  
Isabelle Pianet ◽  
Anna Gutiérrez Garcia-M. ◽  
Marie-Claire Savin ◽  
Pilar Lapuente Mercadal ◽  
Marta Sánchez de la Torre ◽  
...  

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. F53-F64 ◽  
Author(s):  
Nico Skibbe ◽  
Raphael Rochlitz ◽  
Thomas Günther ◽  
Mike Müller-Petke

Nuclear-magnetic resonance (NMR) is a powerful tool for groundwater system imaging. Ongoing developments in surface NMR, for example, multichannel devices, allow for investigations of increasingly complex subsurface structures. However, with the growing complexity of field cases, the availability of appropriate software to accomplish the in-depth data analysis becomes limited. The open-source Python toolbox coupled magnetic resonance and electrical resistivity tomography (COMET) provides the community with a software for modeling and inversion of complex surface NMR data. COMET allows the NMR parameters’ water content and relaxation time to vary in one dimension or two dimensions and accounts for arbitrary electrical resistivity distributions. It offers a wide range of classes and functions to use the software via scripts without in-depth programming knowledge. We validated COMET to existing software for a simple 1D example. We discovered the potential of COMET by a complex 2D case, showing 2D inversions using different approximations for the resistivity, including a smooth distribution from electrical resistivity tomography (ERT). The use of ERT-based resistivity results in similar water content and relaxation time images compared with using the original synthetic block resistivity. We find that complex inversion may indicate incorrect resistivity by non-Gaussian data misfits, whereas amplitude inversion shows well-fitted data, but leading to erroneous NMR models.


2017 ◽  
Vol 212 (2) ◽  
pp. 1463-1467 ◽  
Author(s):  
Tingting Lin ◽  
Yujing Yang ◽  
Fei Teng ◽  
Mike Müller-Petke

Summary The technique of surface nuclear magnetic resonance (SNMR) has been widely used for hydrological investigations in recent years. Unfortunately, the detected SNMR signals are limited to tens of nanovolts and are thus susceptible to environmental noise. While pre-polarization pulses to enhance the detected signal amplitudes are common in laboratory applications, SNMR field testing has only utilized excitation pulses until now. In conducting measurements in China, we demonstrate that adding a pre-polarization field to the SNMR pulse sequence is feasible and allows for the reliable detection of SNMR signals in noisy scenarios that otherwise prohibit signal detection. We introduce a forward modelling for pre-polarization using SNMR and present a three-layer model obtained from inverse modelling that satisfies the observed data from the field experiment. We expect this development to open up new applications for SNMR technology, especially in high-noise level places, such as active mines.


2021 ◽  
Vol 8 ◽  
Author(s):  
Marine P. M. Letertre ◽  
Patrick Giraudeau ◽  
Pascal de Tullio

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.


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