Separating fluorescence from Raman spectra using a CMOS SPAD TCSPC line sensor for biomedical applications

2019 ◽  
Author(s):  
Andrea Usai ◽  
Neil Finlayson ◽  
Christopher D. Gregory ◽  
Colin Campbell ◽  
Robert K. Henderson
Keyword(s):  
Raman Spectra ◽  
Biomedical Applications

scholarly journals Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

2021 ◽  
Author(s):  
Mohammadrahim Kazemzadeh ◽  
Colin Hisey ◽  
Kamran Zargar ◽  
Peter Xu ◽  
Neil Broderick
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Biomedical Applications ◽  
Data Augmentation ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Algorithms ◽  
Cancer Tissue ◽  
Deep Convolutional Neural Networks ◽  
Surface Enhanced Raman

<div>Machine learning has shown great potential for classifying diverse samples in biomedical applications based on their Raman spectra. However, the acquired spectra typically require several preprocessing steps before standard machine learning algorithms can accurately and reliably classify them. To simplify this workflow and enable future growth of this technology, we present a unified solution for classifying biological Raman spectra without any need of prepossessing, including denoising and baseline establishment. This method is developed based on a custom version of a convolutional neural network (CNN) elicited from ResNet architecture, combined with our proposed data augmentation technique. The superiority of this method compared to conventional classification techniques is shown by applying it to Raman spectra of different grades of bladder cancer tissue and surface enhanced Raman spectroscopy (SERS) spectra of various strains of E. Coli extracellular vesicles (EVs). These results show that our method is far more robust compared to its conventional counterparts when dealing with the various kinds of spectral baselines produced by different Raman spectrometers.</div>

scholarly journals Background Estimation of Biomedical Raman Spectra Using a Geometric Approach

2012 ◽  
Vol 27 ◽  
pp. 441-447 ◽  
Author(s):  
Nikolaos Kourkoumelis ◽  
Alexandros Polymeros ◽  
Margaret Tzaphlidou
Keyword(s):  
Raman Spectra ◽  
Biomedical Applications ◽  
Geometric Approach ◽  
Raman Signal ◽  
Background Signal ◽  
Background Estimation ◽  
Biological Interest ◽  
Definition Of ◽  
Geometry Method ◽  
User Intervention

Raman spectroscopy grows into an essential tool for biomedical applications. Nevertheless, the weak Raman signal associated mainly with biological samples is often obscured by a broad background signal due to the intrinsic fluorescence of the organic molecules present, making further analysis unfeasible. A computational geometry method based on the definition of convex hull is described to estimate the background from Raman spectra of samples with biological interest. The method is semiautomated requiring sample-dependent user intervention. It does not depend, however, on curve fitting, requires no information about background distribution or source, and keeps the original spectral data intact.

scholarly journals Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

2021 ◽  
Author(s):  
Mohammadrahim Kazemzadeh ◽  
Colin Hisey ◽  
Kamran Zargar ◽  
Peter Xu ◽  
Neil Broderick
Keyword(s):  
Machine Learning ◽  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Biomedical Applications ◽  
Data Augmentation ◽  
Surface Enhanced Raman Spectroscopy ◽  
Machine Learning Algorithms ◽  
Cancer Tissue ◽  
Deep Convolutional Neural Networks ◽  
Surface Enhanced Raman

<div>Machine learning has shown great potential for classifying diverse samples in biomedical applications based on their Raman spectra. However, the acquired spectra typically require several preprocessing steps before standard machine learning algorithms can accurately and reliably classify them. To simplify this workflow and enable future growth of this technology, we present a unified solution for classifying biological Raman spectra without any need of prepossessing, including denoising and baseline establishment. This method is developed based on a custom version of a convolutional neural network (CNN) elicited from ResNet architecture, combined with our proposed data augmentation technique. The superiority of this method compared to conventional classification techniques is shown by applying it to Raman spectra of different grades of bladder cancer tissue and surface enhanced Raman spectroscopy (SERS) spectra of various strains of E. Coli extracellular vesicles (EVs). These results show that our method is far more robust compared to its conventional counterparts when dealing with the various kinds of spectral baselines produced by different Raman spectrometers.</div>

Applications of Scanning Microscopy in Biomedical Research

1968 ◽  
Vol 26 ◽  
pp. 354-355
Author(s):  
T. L. Hayes
Keyword(s):  
Information Content ◽  
Biomedical Research ◽  
Biomedical Applications ◽  
Three Dimensional ◽  
Dental Enamel ◽  
Resolving Power ◽  
Whole Mount ◽  
Two Parameters ◽  
Content Information ◽  
Sectioned Tissue

Biomedical applications of the scanning electron microscope (SEM) have increased in number quite rapidly over the last several years. Studies have been made of cells, whole mount tissue, sectioned tissue, particles, human chromosomes, microorganisms, dental enamel and skeletal material. Many of the advantages of using this instrument for such investigations come from its ability to produce images that are high in information content. Information about the chemical make-up of the specimen, its electrical properties and its three dimensional architecture all may be represented in such images. Since the biological system is distinctive in its chemistry and often spatially scaled to the resolving power of the SEM, these images are particularly useful in biomedical research.In any form of microscopy there are two parameters that together determine the usefulness of the image. One parameter is the size of the volume being studied or resolving power of the instrument and the other is the amount of information about this volume that is displayed in the image. Both parameters are important in describing the performance of a microscope. The light microscope image, for example, is rich in information content (chemical, spatial, living specimen, etc.) but is very limited in resolving power.

How SIMS microscopy can be used in medicine

1992 ◽  
Vol 50 (2) ◽  
pp. 1602-1603
Author(s):  
Philippe Fragu
Keyword(s):  
Mass Spectrometry ◽  
Biomedical Applications ◽  
Secondary Ion Mass Spectrometry ◽  
Chemical Elements ◽  
Biological Applications ◽  
The Past ◽  
Potential Source ◽  
Secondary Ion ◽  
Chemical Integrity ◽  
Scientific Endeavour

The identification, localization and quantification of intracellular chemical elements is an area of scientific endeavour which has not ceased to develop over the past 30 years. Secondary Ion Mass Spectrometry (SIMS) microscopy is widely used for elemental localization problems in geochemistry, metallurgy and electronics. Although the first commercial instruments were available in 1968, biological applications have been gradual as investigators have systematically examined the potential source of artefacts inherent in the method and sought to develop strategies for the analysis of soft biological material with a lateral resolution equivalent to that of the light microscope. In 1992, the prospects offered by this technique are even more encouraging as prototypes of new ion probes appear capable of achieving the ultimate goal, namely the quantitative analysis of micron and submicron regions. The purpose of this review is to underline the requirements for biomedical applications of SIMS microscopy.Sample preparation methodology should preserve both the structural and the chemical integrity of the tissue.

Biomedical applications: Pitfalls in the practice

1994 ◽  
Vol 52 ◽  
pp. 378-379
Author(s):  
J. D. Shelburne ◽  
Peter Ingram ◽  
Victor L. Roggli ◽  
Ann LeFurgey
Keyword(s):  
Operating Room ◽  
Liquid Nitrogen ◽  
Lung Tissue ◽  
Biomedical Applications ◽  
Microprobe Analysis ◽  
Tissue Sample ◽  
Cellular Level ◽  
Tissue Samples ◽  
Asbestos Fibers

At present most medical microprobe analysis is conducted on insoluble particulates such as asbestos fibers in lung tissue. Cryotechniques are not necessary for this type of specimen. Insoluble particulates can be processed conventionally. Nevertheless, it is important to emphasize that conventional processing is unacceptable for specimens in which electrolyte distributions in tissues are sought. It is necessary to flash-freeze in order to preserve the integrity of electrolyte distributions at the subcellular and cellular level. Ideally, biopsies should be flash-frozen in the operating room rather than being frozen several minutes later in a histology laboratory. Electrolytes will move during such a long delay. While flammable cryogens such as propane obviously cannot be used in an operating room, liquid nitrogen-cooled slam-freezing devices or guns may be permitted, and are the best way to achieve an artifact-free, accurate tissue sample which truly reflects the in vivo state. Unfortunately, the importance of cryofixation is often not understood. Investigators bring tissue samples fixed in glutaraldehyde to a microprobe laboratory with a request for microprobe analysis for electrolytes.

Biological and biomedical applications of collagenous biomaterials

1990 ◽  
Vol 48 (3) ◽  
pp. 856-857
Author(s):  
Yasushi P. Kato ◽  
Michael G. Dunn ◽  
Frederick H. Silver ◽  
Arthur J. Wasserman
Keyword(s):  
Biomedical Applications ◽  
Soft Tissues ◽  
Collagen Fibers ◽  
Bone Collagen ◽  
Cover Slip ◽  
Fibroblast Migration ◽  
Cellular Expression ◽  
Defect Repair

Collagenous biomaterials have been used for growing cells in vitro as well as for augmentation and replacement of hard and soft tissues. The substratum used for culturing cells is implicated in the modulation of phenotypic cellular expression, cellular orientation and adhesion. Collagen may have a strong influence on these cellular parameters when used as a substrate in vitro. Clinically, collagen has many applications to wound healing including, skin and bone substitution, tendon, ligament, and nerve replacement. In this report we demonstrate two uses of collagen. First as a fiber to support fibroblast growth in vitro, and second as a demineralized bone/collagen sponge for radial bone defect repair in vivo.For the in vitro study, collagen fibers were prepared as described previously. Primary rat tendon fibroblasts (1° RTF) were isolated and cultured for 5 days on 1 X 15 mm sterile cover slips. Six to seven collagen fibers, were glued parallel to each other onto a circular cover slip (D=18mm) and the 1 X 15mm cover slip populated with 1° RTF was placed at the center perpendicular to the collagen fibers. Fibroblast migration from the 1 x 15mm cover slip onto and along the collagen fibers was measured daily using a phase contrast microscope (Olympus CK-2) with a calibrated eyepiece. Migratory rates for fibroblasts were determined from 36 fibers over 4 days.

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