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A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: 10 June 2024 | Viewed by 8460

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Special Issue Editor

Prof. Dr. Francis L. Martin

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Department of Cellular Pathology, Blackpool Teaching Hospitals NHS Foundation Trust, Whinney Heys Road, Blackpool FY3 8NR, UK
Interests: cancer aetiology; endocrine disrupters; CYP1B1, biospectroscopy; genetic toxicology

Special Issue Information

Dear Colleagues,

Biospectroscopy involves an emerging inter-disciplinary approach with the potential to revolutionise healthcare diagnosis and screening. Its application requires a knowledge of the biological or clinical problem, the constraints in the physics underlying the spectrochemical-based technology, and the appropriate use of the computational chemometric algorithms required to explore complex spectral datasets. The potential for application in differing clinical arenas ranging from point-of-care primary or triage settings to the implementaiton of an intra-operative tool to a more complex imaging modality is vast. The range of clinical applications includes, but is not limited to, cancer diagnosis or imaging, characterisation of neurodegenerative disease, bone integrity analysis, antibiotic resistance in bacteria, and regenerative medicine. In various settings and for various requirements, spectrochemical methods may be employed that range from Fourier-transform infrared (FTIR) to Raman spectroscopy, with or without surface-enhanced effects. This then leads to the necessity for the appropriate handling of spectral-derived datasets, be it for exploratory analysis, classification, feature extraction for prediction or imaging (including hyperspectral imaging methods). Such complex inter-disciplinarity requires cross-talk between experts in different fields of expertise. In this Special Issue, the current knowledge as well as future perspectives in the contributing fields (i.e., defining the biological problem, the spectrochemical technique, computatonal analysis) of biospectroscopy approaches in screening, diagnosis, and imaging in a range of clinical settings will be presented.

Prof. Dr. Francis L. Martin
Guest Editor

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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. Journal of Personalized Medicine is an international peer-reviewed open access monthly 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

chemometric
Fourier-transform infrared
healthcare
imaging
Raman
surface-enhanced

Published Papers (7 papers)

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Editorial

Jump to: Research

6 pages, 234 KiB

Open AccessEditorial

Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics

by Francis L. Martin

J. Pers. Med. 2023, 13(10), 1511; https://doi.org/10.3390/jpm13101511 - 19 Oct 2023

Cited by 1 | Viewed by 986

Abstract

As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [...] Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

Research

Jump to: Editorial

12 pages, 3173 KiB

Open AccessFeature PaperArticle

Rapid and Label-Free Histopathology of Oral Lesions Using Deep Learning Applied to Optical and Infrared Spectroscopic Imaging Data

by Matthew P. Confer, Kianoush Falahkheirkhah, Subin Surendran, Sumsum P. Sunny, Kevin Yeh, Yen-Ting Liu, Ishaan Sharma, Andres C. Orr, Isabella Lebovic, William J. Magner, Sandra Lynn Sigurdson, Alfredo Aguirre, Michael R. Markiewicz, Amritha Suresh, Wesley L. Hicks, Jr., Praveen Birur, Moni Abraham Kuriakose and Rohit Bhargava

J. Pers. Med. 2024, 14(3), 304; https://doi.org/10.3390/jpm14030304 - 13 Mar 2024

Viewed by 1094

Abstract

Oral potentially malignant disorders (OPMDs) are precursors to over 80% of oral cancers. Hematoxylin and eosin (H&E) staining, followed by pathologist interpretation of tissue and cellular morphology, is the current gold standard for diagnosis. However, this method is qualitative, can result in errors during the multi-step diagnostic process, and results may have significant inter-observer variability. Chemical imaging (CI) offers a promising alternative, wherein label-free imaging is used to record both the morphology and the composition of tissue and artificial intelligence (AI) is used to objectively assign histologic information. Here, we employ quantum cascade laser (QCL)-based discrete frequency infrared (DFIR) chemical imaging to record data from oral tissues. In this proof-of-concept study, we focused on achieving tissue segmentation into three classes (connective tissue, dysplastic epithelium, and normal epithelium) using a convolutional neural network (CNN) applied to three bands of label-free DFIR data with paired darkfield visible imaging. Using pathologist-annotated H&E images as the ground truth, we demonstrate results that are 94.5% accurate with the ground truth using combined information from IR and darkfield microscopy in a deep learning framework. This chemical-imaging-based workflow for OPMD classification has the potential to enhance the efficiency and accuracy of clinical oral precancer diagnosis. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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13 pages, 2083 KiB

Open AccessArticle

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia

by José Joaquim Raposo-Neto, Eduardo Kowalski-Neto, Wilson Barros Luiz, Estherlita Almeida Fonseca, Anna Karla Costa Logrado Cedro, Maneesh N. Singh, Francis L. Martin, Paula Frizera Vassallo, Luciene Cristina Gastalho Campos and Valerio Garrone Barauna

J. Pers. Med. 2024, 14(1), 9; https://doi.org/10.3390/jpm14010009 - 21 Dec 2023

Viewed by 855

Abstract

The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use of NIR spectroscopy in the fingertip to detect neutropenia in solid-tumor oncologic patients. A total of 75 patients were enrolled in the study. Fingertip NIR spectra and complete blood counts were collected from each patient. The NIR spectra were pre-processed using Savitzky–Golay smoothing and outlier detection. The pre-processed data were split into training/validation and test sets using the Kennard–Stone method. A toolbox of supervised machine learning classification algorithms was applied to the training/validation set using a stratified 5-fold cross-validation regimen. The algorithms included linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and support vector machines (SVMs). The SVM model performed best in the validation step, with 85% sensitivity, 89% negative predictive value (NPV), and 64% accuracy. The SVM model showed 67% sensitivity, 82% NPV, and 57% accuracy on the test set. These results suggest that NIR spectroscopy in the fingertip, combined with machine learning methods, can be used to detect neutropenia in solid-tumor oncology patients in a non-invasive and timely manner. This approach could help reduce exposure to invasive tests and prevent neutropenic patients from inadvertently undergoing chemotherapy. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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12 pages, 2256 KiB

Open AccessArticle

A Linear Predictor Based on FTIR Spectral Biomarkers Improves Disease Diagnosis Classification: An Application to Multiple Sclerosis

by Francesca Condino, Maria Caterina Crocco, Domenico Pirritano, Alfredo Petrone, Francesco Del Giudice and Rita Guzzi

J. Pers. Med. 2023, 13(11), 1596; https://doi.org/10.3390/jpm13111596 - 11 Nov 2023

Viewed by 880

Abstract

Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that can lead to long-term disability. The diagnosis of MS is not simple and requires many instrumental and clinical tests. Sampling easily collected biofluids using spectroscopic approaches is becoming of increasing interest in the medical field to integrate and improve diagnostic procedures. Here we present a statistical approach where we combine a number of spectral biomarkers derived from the ATR-FTIR spectra of blood plasma samples of healthy control subjects and MS patients, to obtain a linear predictor useful for discriminating between the two groups of individuals. This predictor provides a simple tool in which the contribution of different molecular components is summarized and, as a result, the sensitivity (80%) and specificity (93%) of the identification are significantly improved compared to those obtained with typical classification algorithms. The strategy proposed can be very helpful when applied to the diagnosis of diseases whose presence is reflected in a minimal way in the analyzed biofluids (blood and its derivatives), as it is for MS as well as for other neurological disorders. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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12 pages, 991 KiB

Open AccessArticle

Attenuated Total Reflection Fourier-Transform Infrared Spectral Discrimination in Human Tissue of Oesophageal Transformation to Adenocarcinoma

by Ishaan Maitra, Camilo L. M. Morais, Kássio M. G. Lima, Katherine M. Ashton, Danielle Bury, Ravindra S. Date and Francis L. Martin

J. Pers. Med. 2023, 13(8), 1277; https://doi.org/10.3390/jpm13081277 - 20 Aug 2023

Cited by 2 | Viewed by 1134

Abstract

This study presents ATR-FTIR (attenuated total reflectance Fourier-transform infrared) spectral analysis of ex vivo oesophageal tissue including all classifications to oesophageal adenocarcinoma (OAC). The article adds further validation to previous human tissue studies identifying the potential for ATR-FTIR spectroscopy in differentiating among all classes of oesophageal transformation to OAC. Tissue spectral analysis used principal component analysis quadratic discriminant analysis (PCA-QDA), successive projection algorithm quadratic discriminant analysis (SPA-QDA), and genetic algorithm quadratic discriminant analysis (GA-QDA) algorithms for variable selection and classification. The variables selected by SPA-QDA and GA-QDA discriminated tissue samples from Barrett’s oesophagus (BO) to OAC with 100% accuracy on the basis of unique spectral “fingerprints” of their biochemical composition. Accuracy test results including sensitivity and specificity were determined. The best results were obtained with PCA-QDA, where tissues ranging from normal to OAC were correctly classified with 90.9% overall accuracy (71.4–100% sensitivity and 89.5–100% specificity), including the discrimination between normal and inflammatory tissue, which failed in SPA-QDA and GA-QDA. All the models revealed excellent results for distinguishing among BO, low-grade dysplasia (LGD), high-grade dysplasia (HGD), and OAC tissues (100% sensitivities and specificities). This study highlights the need for further work identifying potential biochemical markers using ATR-FTIR in tissue that could be utilised as an adjunct to histopathological diagnosis for early detection of neoplastic changes in susceptible epithelium. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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16 pages, 3510 KiB

Open AccessEditor’s ChoiceArticle

ATR-FTIR Spectroscopy with Chemometrics for Analysis of Saliva Samples Obtained in a Lung-Cancer-Screening Programme: Application of Swabs as a Paradigm for High Throughput in a Clinical Setting

by Francis L. Martin, Andrew W. Dickinson, Tarek Saba, Thomas Bongers, Maneesh N. Singh and Danielle Bury

J. Pers. Med. 2023, 13(7), 1039; https://doi.org/10.3390/jpm13071039 - 25 Jun 2023

Cited by 3 | Viewed by 1464

Abstract

There is an increasing need for inexpensive and rapid screening tests in point-of-care clinical oncology settings. Herein, we develop a swab “dip” test in saliva obtained from consenting patients participating in a lung-cancer-screening programme being undertaken in North West England. In a pilot study, a total of 211 saliva samples (n = 170 benign, 41 designated cancer-positive) were randomly taken during the course of this prospective lung-cancer-screening programme. The samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. An exploratory analysis using principal component analysis (PCA,) with or without linear discriminant analysis (LDA), was then undertaken. Three pairwise comparisons were undertaken including: (1) benign vs. cancer following swab analysis; (2) benign vs. cancer following swab analysis with the subtraction of dry swab spectra; and (3) benign vs. cancer following swab analysis with the subtraction of wet swab spectra. Consistent and remarkably similar patterns of clustering for the benign control vs. cancer categories, irrespective of whether the swab plus saliva sample was analysed or whether there was a subtraction of wet or dry swab spectra, was observed. In each case, MANOVA demonstrated that this segregation of categories is highly significant. A k-NN (using three nearest neighbours) machine-learning algorithm also showed that the specificity (90%) and sensitivity (75%) are consistent for each pairwise comparison. In detailed analyses, the swab as a substrate did not alter the level of spectral discrimination between benign control vs. cancer saliva samples. These results demonstrate a novel swab “dip” test using saliva as a biofluid that is highly applicable to be rolled out into a larger lung-cancer-screening programme. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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12 pages, 2020 KiB

Open AccessFeature PaperArticle

Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data

by Rubina Shaikh, Valeria Tafintseva, Ervin Nippolainen, Vesa Virtanen, Johanne Solheim, Boris Zimmermann, Simo Saarakkala, Juha Töyräs, Achim Kohler and Isaac O. Afara

J. Pers. Med. 2023, 13(7), 1036; https://doi.org/10.3390/jpm13071036 - 24 Jun 2023

Cited by 1 | Viewed by 1077

Abstract

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage. Full article

(This article belongs to the Special Issue Clinical Applications of Biospectroscopy and Imaging)

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