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Diagnostics
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Advances of Raman Spectroscopy in Medical Applications
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Advances of Raman Spectroscopy in Medical Applications

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Biomedical Optics".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 10546

Special Issue Editor

Dr. Armida Sodo

E-Mail Website
Guest Editor
Dipartimento di Scienze, Universitá degli Studi Roma Tre, Rome, Italy
Interests: Raman spectroscopy; medical application; tailored Raman Instrumentation; cultural Heritage applicazione; geophysical application

Special Issue Information

Dear Colleagues,

Raman spectroscopy (RS) is a non-invasive and non-destructive optical technique based on molecular vibrational analyses. Thanks to its high specificity, sensitivity, and spatial resolution, RS can spread over different areas of science and technology and has been successfully applied to fields such as Cultural Heritage, forensic applications and quality control. Moreover, since RS allows fast measurements without labeling and sample preparation, it can make important contributions in both pharmaceutical research and medical diagnosis.

In particular, over the last few decades, RS has been widely applied to investigate and characterize biomolecules, cells, tissues, and human organs as a tool to improve disease diagnosis and support surgery. RS, in fact, is currently used as a screening and diagnostic method for cancers, viral and bacterial infections, and neurodegenerative and autoimmune disorders. The most recent developments of RS include cluster analysis and machine learning approach for the Raman spectra data analyses. This Special Issue aims to provide a complete and upgraded view of advances of Raman spectroscopy in medical applications, including both in vitro and in vivo studies, with a particular attention to the development of tailored instrumentation and new procedures for user-friendly data analyses.

Dr. Armida Sodo
Guest Editor

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. Diagnostics 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 2600 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

  • Raman Spectroscopy
  • Medical application
  • Biomolecules
  • Cells
  • Tissues
  • Organs
  • Disease screening and diagnosis
  • Cluster analyses
  • Machine learning analyses
  • Tailored instrumentation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

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11 pages, 2988 KiB  
Article
Raman Spectroscopy for Assessment of Hard Dental Tissues in Periodontitis Treatment
by Elena V. Timchenko, Irina V. Bazhutova, Oleg O. Frolov, Larisa T. Volova and Pavel E. Timchenko
Diagnostics 2021, 11(9), 1595; https://doi.org/10.3390/diagnostics11091595 - 1 Sep 2021
Cited by 3 | Viewed by 2060
Abstract
The objective of this work was to use Raman spectroscopy to assess hard dental tissues after professional oral hygiene treatment and curettage. Spectral changes were identified, and the discriminant model of the specific changes of intensity of the Raman lines (i.e., of dentin, [...] Read more.
The objective of this work was to use Raman spectroscopy to assess hard dental tissues after professional oral hygiene treatment and curettage. Spectral changes were identified, and the discriminant model of the specific changes of intensity of the Raman lines (i.e., of dentin, cementum, and enamel), before and after the dental procedures, was developed. This model showed that 6 weeks after the procedures, the hard dental tissues did not have differences and, thus, provided similar conditions for bio-film and dental plaque formation, tissue repair, and new attachment to the surface of the root. Full article
(This article belongs to the Special Issue Advances of Raman Spectroscopy in Medical Applications)
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12 pages, 1675 KiB  
Article
Raman Spectroscopy Technique: A Non-Invasive Tool in Celiac Disease Diagnosis
by Giuseppe Acri, Claudio Romano, Stefano Costa, Salvatore Pellegrino and Barbara Testagrossa
Diagnostics 2021, 11(7), 1277; https://doi.org/10.3390/diagnostics11071277 - 16 Jul 2021
Cited by 9 | Viewed by 2941
Abstract
Celiac disease (CD) is diagnosed by a combination of specific serology and typical duodenal lesions. The histological confirmation of CD, mandatory in the majority of patients with suspected CD, is based on invasive and poorly tolerated procedures, such as upper gastrointestinal endoscopy. In [...] Read more.
Celiac disease (CD) is diagnosed by a combination of specific serology and typical duodenal lesions. The histological confirmation of CD, mandatory in the majority of patients with suspected CD, is based on invasive and poorly tolerated procedures, such as upper gastrointestinal endoscopy. In this study we propose an alternative and non-invasive methodology able to confirm the diagnosis of CD based on the analysis of serum samples using the Raman spectroscopy technique. Three different bands centered at 1650, 1450 and 1003 cm−1 have been considered and the A1450/A1003 and A1650/A1003 ratios have been computed to discriminate between CD and non-CD subjects. The reliability of the methodology was validated by statistical analysis using receiver operating characteristic (ROC) curves. The Youden index was also determined to obtain optimal cut-off points. The obtained results highlighted that the proposed methodology was able to distinguish between CD and non-CD subjects with 98% accuracy. The optimal cut-off points revealed, for both the A1450/A1003 and A1650/A1003 ratios, high values of sensitivity and specificity (>95.0% and >92.0% respectively), confirming that Raman spectroscopy may be considered a valid alternative to duodenal biopsy and demonstrates spectral changes in the secondary structures of the protein network. Full article
(This article belongs to the Special Issue Advances of Raman Spectroscopy in Medical Applications)
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Review

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21 pages, 3238 KiB  
Review
Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature
by Nathan Blake, Riana Gaifulina, Lewis D. Griffin, Ian M. Bell and Geraint M. H. Thomas
Diagnostics 2022, 12(6), 1491; https://doi.org/10.3390/diagnostics12061491 - 17 Jun 2022
Cited by 24 | Viewed by 4361
Abstract
Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this [...] Read more.
Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models. Full article
(This article belongs to the Special Issue Advances of Raman Spectroscopy in Medical Applications)
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