Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Advanced Vibrational Spectroscopy Technology: Emerging Trends in Biomedical Applications
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Vibrational Spectroscopy Technology: Emerging Trends in Biomedical Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 10 January 2026 | Viewed by 2017

Special Issue Editor

Dr. Mario D’Acunto

E-Mail Website
Guest Editor
National Research Council of Italy, Institute of Biophysics, Italian National Research Council, CNR-IBF, Via Moruzzi 1, I- 56124 Pisa, Italy
Interests: Raman spectroscopy; scanning probe microscopy; scanning optical microscopy; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Vibrational spectroscopy is one of the key instrumentations that allows non-invasive investigation of the structural and chemical composition of both organic and inorganic materials. In recent years, relevant progress has been achieved within the biospectroscopy field, with applications mainly in clinical chemistry (disease pattern recognition, non-invasive assays, or the screening of neurodegenerative diseases), medical diagnostics (in particular, cancer vs. healthy), or the characterization of biomaterials (e.g., cartilage or bone composition). Instrumental developments with regard to components include fibre lasers, quantum cascade lasers as tuneable radiation sources, components for bed-side patient monitoring, and instruments for microscopy and imaging.

On the side of precision medicine, spectral histo- and cytopathology have advanced to deliver results that are ready for application in intraoperative decisions. Spectral data handling for tissue classification poses the challenge of an enormous amount of data, which requires efficient algorithms, chemometric methods, and high computer power with appropriate data storage. Machine learning and deep learning algorithms have been successfully applied to manage large datasets originated by the applications of such techniques.

This Special Issue, “Advanced Vibrational Spectroscopy Technology: Emerging Trends in Biomedical Applications”, aims to collect and publish recent advances in this interdisciplinary area. Research articles as well as reviews dealing with innovative measurement techniques, instrumentation development, and novel applications of all parts of vibrational spectroscopy as well as AI methods are invited. Manuscripts referring to the analysis of biofluid or tissue biopsy samples, cellular and subcellular investigations, or integral tissue characterization and related innovative methods of data analysis are welcome.

Dr. Mario D’Acunto
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. 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

  • vibrational spectroscopy
  • infrared spectroscopy
  • Raman spectroscopy
  • biomedical applications, machine learning, and deep learning
  • multivariate statistics methods

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

15 pages, 4973 KiB  
Article
Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues
by Gianmarco Lazzini and Mario D’Acunto
Appl. Sci. 2024, 14(22), 10555; https://doi.org/10.3390/app142210555 - 15 Nov 2024
Cited by 1 | Viewed by 921
Abstract
Raman spectroscopy (RS) is a promising tool for cancer diagnosis. In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. Recently, [...] Read more.
Raman spectroscopy (RS) is a promising tool for cancer diagnosis. In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. Recently, it has been demonstrated that RS can perform an accurate classification of chondrosarcoma tissues. Chondrosarcoma is a cancer of bones, that can occur in the soft tissues near the bones. It is normally characterized by three different malignant degrees and a benign counterpart, knows as enchondroma. In line with these findings, in this paper, we exploited ML algorithms to distinguish, as well as possible, between the three grades of chondrosarcoma and to distinguish between chondrosarcoma and enchondroma. We obtained a high level of accuracy of classification by analyzing a dataset composed of a relatively small number of Raman spectra, collected in a previous study by one of the authors of this paper. Such spectra were acquired from micrometric tissue sections with a confocal Raman microscope. We tested the classification performances of a support vector machine (SVM) and a random forest classifier (RFC), as representatives of ML algorithms, and two versions of the multi-layer perceptron (MLPC) as representatives of deep learning (DL). These models, especially RFC and MLPC, showed excellent classification performances, with accuracy reaching 99.7%. This outcome makes the aforementioned models a promising route for future improvements of diagnostic devices focused on detecting cancerous bone tissues. Alongside the diagnostic purpose, the aforementioned approach allowed us to identify characteristic molecules, i.e., amino acids, nucleic acids, and bioapatites, relevant for obtaining the final diagnostic response, through the use of a tool named by us Raman Band Identification (RBI). The method to evaluate RBI is the most important contribution of this paper, because RBI could represent a relevant parameter for the identification of biochemical processes on the basis of the tumor progression and associated malignant degree. In turn, the spectral bands highlighted by RBI could provide precious indicators in an attempt to restrict the spectral acquisition to specific Raman bands. This last objective could help to reduce the amount of experimental data needed to obtain an accurate final grading outcome, with a consequent reduction in the computational cost. Full article
(This article belongs to the Special Issue Advanced Vibrational Spectroscopy Technology: Emerging Trends in Biomedical Applications)
►▼ Show Figures

Figure 1

Review

Jump to: Research

28 pages, 5599 KiB  
Review
From a Spectrum to Diagnosis: The Integration of Raman Spectroscopy and Chemometrics into Hepatitis Diagnostics
by Muhammad Kashif and Hugh J. Byrne
Appl. Sci. 2025, 15(5), 2606; https://doi.org/10.3390/app15052606 - 28 Feb 2025
Cited by 1 | Viewed by 401
Abstract
Hepatitis, most importantly hepatitis B and hepatitis C, is a significant global health concern, requiring an accurate and early diagnosis to prevent severe liver damage and ensure effective treatment. The currently employed diagnostic methods, while effective, are often limited in their sensitivity, specificity, [...] Read more.
Hepatitis, most importantly hepatitis B and hepatitis C, is a significant global health concern, requiring an accurate and early diagnosis to prevent severe liver damage and ensure effective treatment. The currently employed diagnostic methods, while effective, are often limited in their sensitivity, specificity, and rapidity, and the quest for improved diagnostic tools is ongoing. This review explores the innovative application of Raman spectroscopy combined with a chemometric analysis as a powerful diagnostic tool for hepatitis. Raman spectroscopy offers a non-invasive, rapid, and detailed molecular fingerprint of biological samples, while chemometric techniques enhance the interpretation of complex spectral data, enabling precise differentiation between healthy and diseased states and moreover the severity/stage of disease. This review aims to provide a comprehensive overview of the current research, foster greater understanding, and stimulate further innovations in this burgeoning field. The Raman spectrum of blood plasma or serum provides fingerprints of biochemical changes in the blood profile and the occurrence of disease simultaneously, while Raman analyses of polymerase chain reaction/hybridization chain reaction (PCR/HCR)-amplified nucleic acids and extracted DNA/RNA as the test samples provide more accurate differentiation between healthy and diseased states. Chemometric tools enhance the diagnostic efficiency and allow for quantification of the viral loads, indicating the stage of disease. The incorporation of different methodologies like surface enhancement and centrifugal filtration using membranes provides the ability to target biochemical changes directly linked with the disease. Immunoassays and biosensors based on Raman spectroscopy offer accurate quantitative detection of viral antigens or the immune response in the body (antibodies). Microfluidic devices enhance the speed of detection through the continuous testing of flowing samples. Raman diagnostic studies with massive sample sizes of up to 1000 and multiple reports of achieving a greater than 90% differentiation accuracy, sensitivity, and specificity using advanced multivariate data analysis tools indicate that Raman spectroscopy is a promising tool for hepatitis detection. Its reproducibility and the identification of unique reference spectral features for each hepatic disease are still challenges in the translation of Raman spectroscopy as a clinical tool, however. The development of databases for automated comparison and the incorporation of automated chemometric processors into Raman diagnostic tools could pave the way for their clinical translation in the near future. Full article
(This article belongs to the Special Issue Advanced Vibrational Spectroscopy Technology: Emerging Trends in Biomedical Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop