scholarly journals Assessment of Skin Deep Layer Biochemical Profile Using Spatially Offset Raman Spectroscopy

2021 ◽  
Vol 11 (20) ◽  
pp. 9498
Author(s):  
Martha Z. Vardaki ◽  
Konstantinos Seretis ◽  
Georgios Gaitanis ◽  
Ioannis D. Bassukas ◽  
Nikolaos Kourkoumelis
Keyword(s):  
Raman Spectroscopy ◽  
Skin Cancer ◽  
Deep Layer ◽  
Signal To Noise Ratio ◽  
Skin Tissue ◽  
Raman Signal ◽  
Histopathological Analysis ◽  
Biochemical Profile ◽  
Skin Cancer Screening ◽  
Zero Offset

Skin cancer is currently the most common type of cancer with millions of cases diagnosed worldwide yearly. The current gold standard for clinical diagnosis of skin cancer is an invasive and relatively time-consuming procedure, consisting of visual examination followed by biopsy collection and histopathological analysis. Raman spectroscopy has been shown to efficiently aid the non-invasive diagnosis of skin cancer when probing the surface of the skin. In this study, we employ a recent development of Raman spectroscopy (Spatially Offset Raman Spectroscopy, SORS) which is able to look deeper in tissue and create a deep layer biochemical profile of the skin in areas where cancer lesions subtly evolve. After optimizing the measurement parameters on skin tissue phantoms, we then adopted SORS on human skin tissue from different anatomical areas to investigate the contribution of the different skin layers to the recorded Raman signal. Our results show that using a diffuse beam with zero offset to probe a sampling volume where the lesion is typically included (surface to epidermis-dermis junction), provides the optimum signal-to-noise ratio (SNR) and may be employed in future skin cancer screening applications.

Quantitative Raman Spectroscopy when the Signal-to-Noise is Below the Limit of Quantitation due to Fluorescence Interference: Advantages of a Moving Window Sequentially Shifted Excitation Approach

2016 ◽  
Vol 70 (9) ◽  
pp. 1489-1501 ◽  
Author(s):  
Sarah Marshall ◽  
John B. Cooper
Keyword(s):  
Raman Spectroscopy ◽  
Quantitative Analysis ◽  
Signal To Noise Ratio ◽  
Raman Signal ◽  
Moving Window ◽  
Linear Calibration ◽  
Signal To Noise ◽  
Raman Analysis ◽  
Binary Model ◽  
Limit Of Quantitation

Raman spectroscopy is a useful analytical tool. However, its application is often limited because shot noise from fluorescence obscures the Raman signal. In such cases, quantitative analysis is not possible when the signal-to-noise ratio (SNR) drops below two. A method is described for performing quantitative Raman spectroscopy that not only removes fluorescence backgrounds, but also results in a significant improvement in the SNR. The Raman data is extracted using a moving window sequentially shifted excitation algorithm. To demonstrate the capabilities of the method, a binary mixture of two analytes at varying concentrations is quantified in the presence of a highly fluorescent dye. Linear calibration plots were constructed and validated for the binary model using individual Raman peaks with SNR ranging from 0.073–12.6; r2 values are greater than 0.96 in all cases, with all but the weakest peaks yielding values greater than 0.997. The presented method demonstrates a universal and autonomous approach for the quantitative analysis of highly fluorescent samples via Raman spectroscopy. The lower limit on the SNR ratio for quantitative Raman analysis with the described method is 0.1. In order to assess the effectiveness of the presented method, the entire set of experiments was also processed using the more common shifted excitation Raman difference spectroscopy (SERDS) approach. The advantages of the proposed method over SERDS are demonstrated for both the detection limit and the SNR of the processed spectra.

scholarly journals Characterization of Ex Vivo Nonmelanoma Skin Tissue Using Raman Spectroscopy

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 282
Author(s):  
Hieu T. M. Nguyen ◽  
Yao Zhang ◽  
Austin J. Moy ◽  
Xu Feng ◽  
Katherine R. Sebastian ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Skin Cancer ◽  
Cell Carcinoma ◽  
Raman Spectra ◽  
Ex Vivo ◽  
Hair Follicles ◽  
Nonmelanoma Skin Cancer ◽  
Skin Tissue ◽  
Biophysical Model ◽  
Resection Margins

Raman spectroscopy has shown great potential in detecting nonmelanoma skin cancer accurately and quickly; however, little direct evidence exists on the sensitivity of measurements to the underlying anatomy. Here, we aimed to correlate Raman measurements directly to the underlying tissue anatomy. We acquired Raman spectra of ex vivo skin tissue from 25 patients undergoing Mohs surgery with a fiber probe. We utilized a previously developed biophysical model to extract key biomarkers in the skin from the Raman spectra. We then examined the correlations between the biomarkers and the major skin structures (including the dermis, sebaceous glands, hair follicles, fat, and two types of nonmelanoma skin cancer—basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)). SCC had a significantly different concentration of keratin, collagen, and nucleic acid than normal structures, while ceramide differentiated BCC from normal structures. Our findings identified the key proteins, lipids, and nucleic acids that discriminate nonmelanoma tumors and healthy skin using Raman spectroscopy. These markers may be promising surgical guidance tools for detecting tumors in resection margins.

scholarly journals Raman Signal Denoising Using Fully Convolutional Encoder Decoder Network

2022 ◽  
Author(s):  
Irem Loc ◽  
Ibrahim Kecoglu ◽  
Mehmet Burcin Unlu ◽  
Ugur Parlatan
Keyword(s):  
Raman Spectroscopy ◽  
Signal To Noise Ratio ◽  
Raman Signal ◽  
P Value ◽  
Signal To Noise ◽  
Tissue Samples ◽  
High Exposure ◽  
Noise Ratio ◽  
Living Tissues ◽  
Convolutional Encoder

Raman spectroscopy is a vibrational method that gives molecular information rapidly and non-invasively. Despite its advantages, the weak intensity of Raman spectroscopy leads to low-quality signals, particularly with tissue samples. The requirement of high exposure times makes Raman a time-consuming process and diminishes its non-invasive property while studying living tissues. Novel denoising techniques using convolutional neural networks (CNN) have achieved remarkable results in image processing. Here, we propose a similar approach for noise reduction for the Raman spectra acquired with 10x lower exposure times. In this work, we developed fully convolutional encoder-decoder architecture (FCED) and trained them with noisy Raman signals. The results demonstrate that our model is superior (p-value <0.0001) to the conventional denoising techniques such as the Savitzky-Golay filter and wavelet denoising. Improvement in the signal-to-noise ratio values ranges from 20% to 80%, depending on the initial signal-to-noise ratio. Thus, we proved that tissue analysis could be done in a shorter time without any need for instrumental enhancement.

Discrimination of non-melanoma skin cancer and keratosis from normal skin tissue in vivo and ex vivo by Raman spectroscopy

2019 ◽  
Vol 100 ◽  
pp. 131-141 ◽  
Author(s):  
Ana Mara Ferreira Lima ◽  
Camila Ribeiro Daniel ◽  
Ricardo Scarparo Navarro ◽  
Benito Bodanese ◽  
Carlos Augusto Pasqualucci ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Skin Cancer ◽  
Ex Vivo ◽  
Normal Skin ◽  
Skin Tissue ◽  
Non Melanoma Skin Cancer ◽  
Melanoma Skin

The use of targeted and non-targeted advertising to enrich skin cancer screening samples

1996 ◽  
Vol 135 (2) ◽  
pp. 268-274 ◽  
Author(s):  
P. KATRIS ◽  
R.J. DONOVAN ◽  
B.N. GRAY
Keyword(s):  
Cancer Screening ◽  
Skin Cancer ◽  
Skin Cancer Screening ◽  
Targeted Advertising

Skin Cancer Screening Prevalence Inching Higher

2009 ◽  
Vol 40 (4) ◽  
pp. 25
Author(s):  
BRUCE JANCIN
Keyword(s):  
Cancer Screening ◽  
Skin Cancer ◽  
Skin Cancer Screening

Skin Cancer Screening Prevalence Inches Up

2009 ◽  
Vol 42 (8) ◽  
pp. 20
Author(s):  
BRUCE JANCIN
Keyword(s):  
Cancer Screening ◽  
Skin Cancer ◽  
Skin Cancer Screening

scholarly journals Rapid uropathogen identification using surface enhanced Raman spectroscopy active filters

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon D. Dryden ◽  
Salzitsa Anastasova ◽  
Giovanni Satta ◽  
Alex J. Thompson ◽  
Daniel R. Leff ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Bacterial Infections ◽  
Surface Enhanced Raman Spectroscopy ◽  
Active Filters ◽  
Raman Signal ◽  
Rapid Diagnostics ◽  
Bacterial Classification ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

AbstractUrinary tract infection is one of the most common bacterial infections leading to increased morbidity, mortality and societal costs. Current diagnostics exacerbate this problem due to an inability to provide timely pathogen identification. Surface enhanced Raman spectroscopy (SERS) has the potential to overcome these issues by providing immediate bacterial classification. To date, achieving accurate classification has required technically complicated processes to capture pathogens, which has precluded the integration of SERS into rapid diagnostics. This work demonstrates that gold-coated membrane filters capture and aggregate bacteria, separating them from urine, while also providing Raman signal enhancement. An optimal gold coating thickness of 50 nm was demonstrated, and the diagnostic performance of the SERS-active filters was assessed using phantom urine infection samples at clinically relevant concentrations (105 CFU/ml). Infected and uninfected (control) samples were identified with an accuracy of 91.1%. Amongst infected samples only, classification of three bacteria (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae) was achieved at a rate of 91.6%.

Clinical practice gap: annual skin cancer screening examinations for visually impaired patients

2021 ◽  
Author(s):  
Nora S. Ali ◽  
Beija K. Villalpando ◽  
Gavin W. Roddy ◽  
Julio C. Sartori‐Valinotti
Keyword(s):  
Clinical Practice ◽  
Cancer Screening ◽  
Skin Cancer ◽  
Visually Impaired ◽  
Skin Cancer Screening ◽  
Practice Gap
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