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Novel Opportunities and Challenges for Metabolomics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 2629

Special Issue Editors


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Guest Editor
1. Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
2. Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
3. Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy
Interests: applications of NMR-based metabolomics in biomedical research, in particular in the framework of oncology, cardiovascular and respiratory diseases, coeliac disease, and Alzheimer’s disease; analysis of biospecimens via NMR spectroscopy; development of novel statistical tools for NMR data; network analysis of metabolomics data
Special Issues, Collections and Topics in MDPI journals
1. Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
2. Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
3. Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy
Interests: applications of nuclear magnetic resonance (NMR) for the characterization of food matrices in search of molecular factors capable of demonstrating geographic origin and quality; and for the characterization of biological samples in the framework of ageing; Parkinson’s disease; oncology; viral infections and periodontal disease
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. Magnetic Resonance Center (CERM), University of Florence, 50019 Sesto Fiorentino, Italy
2. Department of Chemistry “Ugo Schiff”, University of Florence, 50019 Sesto Fiorentino, Italy
3. Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy
Interests: applications of NMR-based metabolomics in biomedicine and in food science; NMR fingerprinting and profiling of biological samples; development of new analytical approaches for NMR metabolomics; development of new tools for NMR data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metabolomics entails the comprehensive characterization of the ensemble of endogenous and exogenous metabolites present in biological specimens. Metabolites simultaneously represent the downstream output of the genome and the upstream input from various external factors. Therefore, in recent years, metabolomic phenotyping has been applied to unravel the fundamental and molecular causes of several scientific questions, spanning from biomedicine to nutrition and food science. With respect to our previous Special Issue “Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry”, in this Issue we aim to publish high-quality research papers related to metabolomics performed with every analytical platform (mass spectrometry, nuclear magnetic resonance spectroscopy, Raman spectroscopy, etc.).

We welcome original articles from various fields of metabolomics applications ranging from clinical chemistry to biochemistry, pharmacology, data mining, nutrition, and food chemistry. Systematic reviews and meta-analyses that critically discuss crucial innovations or applications in the field, or which seek to investigate areas of controversy within the literature, will also be considered.

Dr. Alessia Vignoli
Dr. Gaia Meoni
Dr. Leonardo Tenori
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolomics
  • clinical chemistry
  • food chemistry
  • nutrition
  • biochemistry
  • data mining
  • metabolite quantification
  • pharmacometabolomics

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Published Papers (1 paper)

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Research

11 pages, 1808 KiB  
Article
NMR Spectroscopy Combined with Machine Learning Approaches for Age Prediction in Healthy and Parkinson’s Disease Cohorts through Metabolomic Fingerprints
by Giovanna Maria Dimitri, Gaia Meoni, Leonardo Tenori, Claudio Luchinat and Pietro Lió
Appl. Sci. 2022, 12(18), 8954; https://doi.org/10.3390/app12188954 - 6 Sep 2022
Cited by 1 | Viewed by 2111
Abstract
Biological aging can be affected by several factors such as drug treatments and pathological conditions. Metabolomics can help in the estimation of biological age by analyzing the differences between predicted and actual chronological age in different subjects. In this paper, we compared three [...] Read more.
Biological aging can be affected by several factors such as drug treatments and pathological conditions. Metabolomics can help in the estimation of biological age by analyzing the differences between predicted and actual chronological age in different subjects. In this paper, we compared three different and well-known machine learning approaches—SVM, ElasticNet, and PLS—to build a model based on the 1H-NMR metabolomic data of serum samples, able to predict chronological age in control individuals. Then, we tested these models in two pathological cohorts of de novo and advanced PD patients. The discrepancies observed between predicted and actual age in patients are interpreted as a sign of a (pathological) biological aging process. Full article
(This article belongs to the Special Issue Novel Opportunities and Challenges for Metabolomics)
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