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Automated Breast Cancer Detection Using FISH Spectral Linear Unmixing
Issue:
Volume 3, Issue 2-3, April 2015
Pages:
1-7
Received:
7 December 2014
Accepted:
9 December 2014
Published:
7 August 2015
DOI:
10.11648/j.ajbls.s.2015030203.11
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Abstract: Fluorescence microscopy plays an important role in the classification of cancerous Tissue. The dramatic increase in multicolor fluorescence microscopy applications witnessed over the past decade is due, in part, to the significant advances in instrument and detector design. A number of advanced microscopy techniques have been applied using multi-color fluorescence labeling, including fluorescence recovery after photo bleaching (FRAP), fluorescence correlation spectroscopy (FCS), fluorescence resonance energy transfer (FRET), fluorescence in situ hybridization (FISH), and fluorescence lifetime imaging (FLIM). Many of these methods benefit significantly from the ability to use specifically targeted fluorescent proteins in live-cell imaging experiments. In addition, live-cell imaging has been revolutionized by the introduction of ever increasingly useful genetically encoded fluorescent proteins spanning the entire visible spectral region. However, the problem of fluorescence microscopy is the crosstalk between the channels caused by the overlap of the emission spectra of the different fluorophores, The crosstalk cannot be solved on the filter level, and not by specialized florophores. To eliminate the crosstalk the hyperspectral imaging using the spectra unmixing (algorithmically reduce the overlap of spectra) can be the possible way to reduce the errors in the classification of the tissue. Spectral imaging is the combination of commuter vision and spectroscopy. In addition, because every object of interest consists of more than one pixels, every pixel is dependent on its neighboring pixels. Thus, the spatial context of the image contains useful information for a classification and increase the sensitivity and specificity of a spectral classification.
Abstract: Fluorescence microscopy plays an important role in the classification of cancerous Tissue. The dramatic increase in multicolor fluorescence microscopy applications witnessed over the past decade is due, in part, to the significant advances in instrument and detector design. A number of advanced microscopy techniques have been applied using multi-co...
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Maximum Likelihood and Spectral Angle Mapper and K-means algorithms used to detection of Melanoma
Issue:
Volume 3, Issue 2-3, April 2015
Pages:
8-15
Received:
7 December 2014
Accepted:
9 December 2014
Published:
7 August 2015
DOI:
10.11648/j.ajbls.s.2015030203.12
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Abstract: Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which causes the cell to grow out of control, leading to a tumor, which is much more dangerous, if it cannot be found or detected early. Only biopsy can determine exact malformation diagnose, though it can rise metastasizing. When a melanoma is suspected, the usual standard procedure is to perform a biopsy and to subsequently analyze the suspicious tissue under the microscope. In this Paper, we provide a new approach using methods known as "Imaging Spectroscopy" or "Spectral Imaging" for early detection of melanoma. Spectral imaging can fill this gap of the classical imaging, which carries little spectral information while spectroscopy is severely limited in terms of measuring (potentially) inhomogeneous samples. Three different classifiers were applied, Maximum Likelihood ML and Spectral Angle Mapper SAM and K-Means. SAM rests on the spectral "angular distances" and the conventional classifier ML rests on the spectral distance concept. SAM and ML are two methods of the supported classification routines and K-Means is the known unsupported classification (clustering) algorithm.
Abstract: Melanoma is a leading fatal illness responsible for 80% of deaths from skin cancer. It originates in the pigment-producing melanocytes in the basal layer of the epidermis. Melanocytes produce the melanin, (the dark pigment), which is responsible for the color of skin. As all cancers, melanoma is caused by damage to the DNA of the cells, which cause...
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Molecular expression analysis of different inflammatory mediators and their role in breast cancer progression and metastasis
Aula Ammar,
Amani Halabi,
Zuheir Al-Shehabi
Issue:
Volume 3, Issue 2-3, April 2015
Pages:
16-20
Received:
7 December 2014
Accepted:
9 December 2014
Published:
7 August 2015
DOI:
10.11648/j.ajbls.s.2015030203.13
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Abstract: Introduction: Cytokines include different subfamilies such as interleukins (IL), chemokines, and growth factors. They play an important role in inflammatory conditions such as cancer progression and metastasis. There is an increasing interest in developing strategies to antagonize the function of some cytokine/chemokine to interfere with tumor progression and metastasis, the leading cause of death in most patients. The aim of the research project is to study the molecular characteristics of a sample of Syrian patients with breast cancer and assess the protein and gene expression of different inflammatory mediators and correlate that with the clinicopathological criteria of tumors. Materials and methods: Patient samples will be evaluated histologically (H&E stain) and stained immunohistochemically with antibodies against important molecular markers, cytokines, and different types of activated leukocytes. Immunohistochemistry of CD206, a marker of alternatively activated macrophages in tumors is shown here. PCR and immunohistochemical analysis of cytokines (e.g. IL-2, GM-CSF, IFNγ, M-CSG, IL-4, IL-10, CXCL8, CXCL12, CCL21, CCL19, CCR7) will be further implemented. The staining intensity, localization and distribution within the tumor will be examined and correlated with the gene expression and other clinnicopathological information. Expected results: We expect to get new information about the role of different cytokines in breast cancer progression in addition to get an insight into the possible inter-relationship between these cytokines.
Abstract: Introduction: Cytokines include different subfamilies such as interleukins (IL), chemokines, and growth factors. They play an important role in inflammatory conditions such as cancer progression and metastasis. There is an increasing interest in developing strategies to antagonize the function of some cytokine/chemokine to interfere with tumor prog...
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Protein solvent accessibility prediction systemss
Ritta Shaheen,
Hani Amasha,
Majd Aljamali
Issue:
Volume 3, Issue 2-3, April 2015
Pages:
21-24
Received:
7 December 2014
Accepted:
9 December 2014
Published:
7 August 2015
DOI:
10.11648/j.ajbls.s.2015030203.14
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Abstract: Background: Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the ASA based on information such as amino acid composition. Results: In this study we use physicochemical properties of amino acid such as hydrophobicity for ASA prediction by considering amino acid composition. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The hydrophobicity of amino acid are used to generate features. Finally, Adaptive neuro fuzzy inference system (anfis) is adopted to construct a ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction. Conclusion: Experimental results based on a widely used benchmark reveal that the proposed method performs good among several of existing packages for performing ASA prediction depending on amino acid sequence only .The program and data are available from the authors upon request.
Abstract: Background: Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or burie...
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The relationship between segmentation failure and spermatozoa motility characteristics
Muhammad Harfous,
Hassn Hasan
Issue:
Volume 3, Issue 2-3, April 2015
Pages:
25-28
Received:
16 January 2015
Accepted:
19 January 2015
Published:
7 August 2015
DOI:
10.11648/j.ajbls.s.2015030203.15
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Abstract: Dynamics of sperm motility (sperm velocity, percentage of motility and flagellar beat frequency) and monthly variations of semen characteristics (semen volume and osmolality and sperm concentration and motility) were studied. These criteria were used in the embryology laboratory and reproductive biology in the city of Lattakia, Syria, This study showed that sperm velocity, percentage of motility and beat frequency significantly and rapidly decreased after the activation of sperm motility. To study the dynamics of sperm motility parameters, twenty permeating males were randomly selected and electronically tagged to study the variations of semen characteristics. Specially we focused on the relationship between segmentation failure in cases treated with In Vitro Fertilization (IVF) to define the characteristics using the Computer Assisted Sperm Analysis (CASA). This study compares the reference group (normal) and a group of individuals treated with IVF, who have failed the treatment where segmentation did not occur. The role of the spermatozoon in fertilization expresses itself especially through the motility criterion. Studying the motility characteristics using CASA method seems interesting in clarifying the relationship between some of the motility characteristics and the ability to fertilize in order to determine the notification in cases of IVF. This study based on the acquired information of 85 normal voluntaries as references cases. And 45 patients with segmentation failure in the Embryology-lab of Al-Andalus University for medical Sciences between 2012- 2013. The results were clear, that significant relationship and correlation between segmentation failure and motility of the sperms.
Abstract: Dynamics of sperm motility (sperm velocity, percentage of motility and flagellar beat frequency) and monthly variations of semen characteristics (semen volume and osmolality and sperm concentration and motility) were studied. These criteria were used in the embryology laboratory and reproductive biology in the city of Lattakia, Syria, This study sh...
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Early detection of melanoma using multispectral imaging and artificial intelligence techniques
Moataz Aboras,
Hani Amasha,
Issa Ibraheem
Issue:
Volume 3, Issue 2-3, April 2015
Pages:
29-33
Received:
18 December 2014
Accepted:
19 December 2014
Published:
7 August 2015
DOI:
10.11648/j.ajbls.s.2015030203.16
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Abstract: Biomedical spectral imaging is a non-invasive, non-destructive method, and has an important role in melanoma detection and all skin lesions monitoring during their various stages. In addition to spatial information, it contains spectral information that describes structure such as melanin content, and melanoma thickness, which, very well improve the sensitivity and specificity of melanoma detection. This article aims to describe the design of a multispectral imaging system that utilizes Artificial Neural Networks and Genetic Algorithm (Artificial Intelligence) for spectral images classification, in order to reduce the processing time of spectral images, memory and cost of the system. All system (Hardware and Software) works as an automatic detection system for malignant melanoma, which identifies malignant melanoma and common (benign) nevi by using wavelength scanning method with; CCD camera, filters wheel, and only eight optical filters range from 430nm to 620nm. 47 study cases were imaged. Good results were obtained: the sensitivity 91.67% and the specificity 91.43%.
Abstract: Biomedical spectral imaging is a non-invasive, non-destructive method, and has an important role in melanoma detection and all skin lesions monitoring during their various stages. In addition to spatial information, it contains spectral information that describes structure such as melanin content, and melanoma thickness, which, very well improve th...
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