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Michaud A, Bertrand S, Akoka S, Farjon J, Martineau E, Ruiz N, Robiou du Pont T, Grovel O, Giraudeau P. Exploring the complementarity of fast multipulse and multidimensional NMR methods for metabolomics: a chemical ecology case study. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024. [PMID: 39028155 DOI: 10.1039/d4ay01225a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
This study investigates the potential and complementarity of high-throughput multipulse and multidimensional NMR methods for metabolomics. Through a chemical ecology case study, three methods are investigated, offering a continuum of methods with complementary features in terms of resolution, sensitivity and experiment time. Ultrafast 2D COSY, adiabatic INEPT and SYMAPS HSQC are shown to provide a very good classification ability, comparable to the reference 1D 1H NMR method. Moreover, a detailed analysis of discriminant buckets upon supervised statistical analysis shows that all methods are highly complementary, since they are able to highlight discriminant signals that could not be detected by 1D 1H NMR. In particular, fast 2D methods appear very efficient to discriminate signals located in highly crowded regions of the 1H spectrum. Overall, the combination of these recent methods within a single NMR metabolomics workflow allows to maximize the accessible metabolic information, and also raises exciting challenges in terms of NMR data analysis for chemical ecology.
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Affiliation(s)
- Aurore Michaud
- Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France.
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR 2160, F-44000 Nantes, France.
| | - Samuel Bertrand
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR 2160, F-44000 Nantes, France.
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Serge Akoka
- Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France.
| | - Jonathan Farjon
- Nantes Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France.
| | | | - Nicolas Ruiz
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR 2160, F-44000 Nantes, France.
| | - Thibaut Robiou du Pont
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR 2160, F-44000 Nantes, France.
| | - Olivier Grovel
- Nantes Université, Institut des Substances et Organismes de la Mer, ISOMer, UR 2160, F-44000 Nantes, France.
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Zhang C, Xu L, Huang Q, Wang Y, Tang H. Detecting Submicromolar Analytes in Mixtures with a 5 min Acquisition on 600 MHz NMR Spectrometers. J Am Chem Soc 2023; 145:25513-25517. [PMID: 37955622 DOI: 10.1021/jacs.3c07861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
Amino compounds are widely present in complex mixtures in chemistry, biology, medicine, food, and environmental sciences involving drug impurities and metabolisms of proteins, biogenic amines, neurotransmitters, and pyrimidine in biological systems. Nuclear magnetic resonance (NMR) spectroscopy is an excellent tool for simultaneously identifying and quantifying these in-mixture compounds but has a limit-of-detection (LOD) over several micromolarities (>5 μM). To break such a sensitivity barrier, we developed a sensitive and rapid method by combining the probe-induced sensitivity enhancement and nonuniform-sampling-based 1H-13C HSQC 2D-NMR (PRISE-NUS-HSQC). We introduced two 13CH3 tags for each analyte to respectively increase the 1H and 13C abundances for up to 6 and 200 fold. This enabled high-resolution detection of 0.4-0.8 μM analytes in mixtures in 5 mm tubes with a 5 min acquisition on 600 MHz spectrometers. The method is much more sensitive and faster than traditional 1H-13C HSQC methods (∼50 μM, >10 h). Using sulfanilic acid as a single reference, furthermore, we established a database covering chemical shifts and relative-response factors for >100 compounds, enabling reliable identification and quantification. The method showed good quantitation linearity, accuracy, precision, and applicability in multiple biological matrices, offering a rapid and sensitive approach for quantitative analysis of large cohorts of chemical, medicinal, metabolomic, food, and other mixtures.
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Affiliation(s)
- Congcong Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai 200438, China
| | - Li Xu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai 200438, China
| | - Qingxia Huang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai 200438, China
| | - Yulan Wang
- Singapore Phenome Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, 639798 Singapore
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Zhongshan Hospital, Fudan University, Shanghai 200438, China
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Feng LL, Huang Z, Nong YY, Guo BJ, Wang QY, Qin JH, He Y, Zhu D, Guo HW, Qin YL, Zhong XY, Guo Y, Cheng B, Ou SF, Su ZH. Evaluation of aristolochic acid Ι nephrotoxicity in mice via 1H NMR quantitative metabolomics and network pharmacology approaches. Toxicol Res (Camb) 2023; 12:282-295. [PMID: 37125334 PMCID: PMC10141773 DOI: 10.1093/toxres/tfad020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 03/06/2023] [Accepted: 03/12/2023] [Indexed: 03/29/2023] Open
Abstract
Background Although many studies have shown that herbs containing aristolochic acids can treat various human diseases, AAΙ in particular has been implicated as a nephrotoxic agent. Methods and results Here, we detail the nephrotoxic effect of AAΙ via an approach that integrated 1H NMR-based metabonomics and network pharmacology. Our findings revealed renal injury in mice after the administration of AAΙ. Metabolomic data confirmed significant differences among the renal metabolic profiles of control and model groups, with significant reductions in 12 differential metabolites relevant to 23 metabolic pathways. Among them, there were seven important metabolic pathways: arginine and proline metabolism; glycine, serine, and threonine metabolism; taurine and hypotaurine metabolism; ascorbate and aldehyde glycolate metabolism; pentose and glucosinolate interconversion; alanine, aspartate, and glutamate metabolism; and glyoxylate and dicarboxylic acid metabolism. Relevant genes, namely, nitric oxide synthase 1 (NOS1), pyrroline-5-carboxylate reductase 1 (PYCR1), nitric oxide synthase 3 (NOS3) and glutamic oxaloacetic transaminase 2 (GOT2), were highlighted via network pharmacology and molecular docking techniques. Quantitative real-time PCR findings revealed that AAI administration significantly downregulated GOT2 and NOS3 and significantly upregulated NOS1 and PYCR1 expression and thus influenced the metabolism of arginine and proline. Conclusion This work provides a meaningful insight for the mechanism of AAΙ renal injury.
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Affiliation(s)
- Lin-Lin Feng
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Zheng Huang
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Yun-Yuan Nong
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Bing-Jian Guo
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Qian-Yi Wang
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Jing-Hua Qin
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Ying He
- First Clinical Medical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Dan Zhu
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Hong-Wei Guo
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Yue-Lian Qin
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Xin-Yu Zhong
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Yue Guo
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Quality Standards, Guangxi Institute of Traditional Medical and Pharmaceutical Sciences, No. 20-1 Dongge Road, Qingxiu District, Nanning 530022, China
| | - Bang Cheng
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Song-Feng Ou
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
| | - Zhi-Heng Su
- Pharmaceutical College, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
- Guangxi Key Laboratory of Bioactive Molecules Research and Evaluation, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
- Guangxi Beibu Gulf Marine Biomedicine Precision Development, High-value Utilization Engineering Research Center, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
- Guangxi Health Commission Key Laboratory of Basic Research on Antigeriatric Drugs, Guangxi Medical University, No. 22 Shuangyong Road, Qingxiu District, Nanning 530021, China
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Wishart DS, Rout M, Lee BL, Berjanskii M, LeVatte M, Lipfert M. Practical Aspects of NMR-Based Metabolomics. Handb Exp Pharmacol 2023; 277:1-41. [PMID: 36271165 DOI: 10.1007/164_2022_613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.
| | - Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Marcia LeVatte
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
- Reference Standard Management & NMR QC, Lonza Group AG, Visp, Switzerland
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Updates and Original Case Studies Focused on the NMR-Linked Metabolomics Analysis of Human Oral Fluids Part I: Emerging Platforms and Perspectives. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031235] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
1H NMR-based metabolomics analysis of human saliva, other oral fluids, and/or tissue biopsies serves as a valuable technique for the exploration of metabolic processes, and when associated with ’state-of-the-art’ multivariate (MV) statistical analysis strategies, provides a powerful means of examining the identification of characteristic metabolite patterns, which may serve to differentiate between patients with oral health conditions (e.g., periodontitis, dental caries, and oral cancers) and age-matched heathy controls. This approach may also be employed to explore such discriminatory signatures in the salivary 1H NMR profiles of patients with systemic diseases, and to date, these have included diabetes, Sjörgen’s syndrome, cancers, neurological conditions such as Alzheimer’s disease, and viral infections. However, such investigations are complicated in view of quite a large number of serious inconsistencies between the different studies performed by independent research groups globally; these include differing protocols and routes for saliva sample collection (e.g., stimulated versus unstimulated samples), their timings (particularly the oral activity abstention period involved, which may range from one to 12 h or more), and methods for sample transport, storage, and preparation for NMR analysis, not to mention a very wide variety of demographic variables that may influence salivary metabolite concentrations, notably the age, gender, ethnic origin, salivary flow-rate, lifestyles, diets, and smoking status of participant donors, together with their exposure to any other possible convoluting environmental factors. In view of the explosive increase in reported salivary metabolomics investigations, in this update, we critically review a wide range of critical considerations for the successful performance of such experiments. These include the nature, composite sources, and biomolecular status of human saliva samples; the merits of these samples as media for the screening of disease biomarkers, notably their facile, unsupervised collection; and the different classes of such metabolomics investigations possible. Also encompassed is an account of the history of NMR-based salivary metabolomics; our recommended regimens for the collection, transport, and storage of saliva samples, along with their preparation for NMR analysis; frequently employed pulse sequences for the NMR analysis of these samples; the supreme resonance assignment benefits offered by homo- and heteronuclear two-dimensional NMR techniques; deliberations regarding salivary biomolecule quantification approaches employed for such studies, including the preprocessing and bucketing of multianalyte salivary NMR spectra, and the normalization, transformation, and scaling of datasets therefrom; salivary phenotype analysis, featuring the segregation of a range of different metabolites into ‘pools’ grouped according to their potential physiological sources; and lastly, future prospects afforded by the applications of LF benchtop NMR spectrometers for direct evaluations of the oral or systemic health status of patients at clinical ‘point-of-contact’ sites, e.g., dental surgeries. This commentary is then concluded with appropriate recommendations for the conduct of future salivary metabolomics studies. Also included are two original case studies featuring investigations of (1) the 1H NMR resonance line-widths of selected biomolecules and their possible dependence on biomacromolecular binding equilibria, and (2) the combined univariate (UV) and MV analysis of saliva specimens collected from a large group of healthy control participants in order to potentially delineate the possible origins of biomolecules therein, particularly host- versus oral microbiome-derived sources. In a follow-up publication, Part II of this series, we conduct censorious reviews of reported observations acquired from a diversity of salivary metabolomics investigations performed to evaluate both localized oral and non-oral diseases. Perplexing problems encountered with these again include those arising from sample collection and preparation protocols, along with 1H NMR spectral misassignments.
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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Leenders J, Grootveld M, Percival B, Gibson M, Casanova F, Wilson PB. Benchtop Low-Frequency 60 MHz NMR Analysis of Urine: A Comparative Metabolomics Investigation. Metabolites 2020; 10:metabo10040155. [PMID: 32316363 PMCID: PMC7240954 DOI: 10.3390/metabo10040155] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 04/08/2020] [Accepted: 04/09/2020] [Indexed: 01/21/2023] Open
Abstract
Metabolomics techniques are now applied in numerous fields, with the ability to provide information concerning a large number of metabolites from a single sample in a short timeframe. Although high-frequency (HF) nuclear magnetic resonance (NMR) analysis represents a common method of choice to perform such studies, few investigations employing low-frequency (LF) NMR spectrometers have yet been published. Herein, we apply and contrast LF and HF 1H-NMR metabolomics approaches to the study of urine samples collected from type 2 diabetic patients (T2D), and apply a comparative investigation with healthy controls. Additionally, we explore the capabilities of LF 1H-1H 2D correlation spectroscopy (COSY) experiments regarding the determination of metabolites, their resolution and associated analyses in human urine samples. T2D samples were readily distinguishable from controls, with several metabolites, particularly glucose, being associated with this distinction. Comparable results were obtained with HF and LF spectrometers. Linear correlation analyses were performed to derive relationships between the intensities of 1D and 2D resonances of several metabolites, and R2 values obtained were able to confirm these, an observation attesting to the validity of employing 2D LF experiments for future applications in metabolomics studies. Our data suggest that LF spectrometers may prove to be easy-to-use, compact and inexpensive tools to perform routine metabolomics analyses in laboratories and ‘point-of-care’ sites. Furthermore, the quality of 2D spectra obtained from these instruments in half an hour would broaden the horizon of their potential applications.
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Affiliation(s)
- Justine Leenders
- Leicester School of Pharmacy, De Montfort University, The Gateway, Leicester LE8 9BH, UK; (J.L.); (M.G.); (B.P.); (M.G.)
| | - Martin Grootveld
- Leicester School of Pharmacy, De Montfort University, The Gateway, Leicester LE8 9BH, UK; (J.L.); (M.G.); (B.P.); (M.G.)
| | - Benita Percival
- Leicester School of Pharmacy, De Montfort University, The Gateway, Leicester LE8 9BH, UK; (J.L.); (M.G.); (B.P.); (M.G.)
| | - Miles Gibson
- Leicester School of Pharmacy, De Montfort University, The Gateway, Leicester LE8 9BH, UK; (J.L.); (M.G.); (B.P.); (M.G.)
| | | | - Philippe B. Wilson
- Leicester School of Pharmacy, De Montfort University, The Gateway, Leicester LE8 9BH, UK; (J.L.); (M.G.); (B.P.); (M.G.)
- Correspondence:
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