Abstract: The tracking allows to detect the tumor affections of cervical cancer, it is particularly complex and consuming time, because it consists in seeking some abnormal cells among a cluster of normal cells. In this paper, we present our proposed computer system for helping the doctors in tracking the cervical cancer. Knowing that the diagnosis of the malignancy is based in the set of atypical morphological details of all cells, herein, we present an unsupervised genetic algorithm for the separation of cell components since the diagnosis is doing by analysis of the core and the cytoplasm. We give also the various algorithms used for computing the morphological characteristics of cells (Ratio core/cytoplasm, cellular deformity, ...) necessary for the recognition of illness.
Abstract: Semiconductor nanomaterials like TiO2 nanoparticles
(TiO2-NPs) approximately less than 100 nm in diameter have become
a new generation of advanced materials due to their novel and
interesting optical, dielectric, and photo-catalytic properties. With the
increasing use of NPs in commerce, to date few studies have
investigated the toxicological and environmental effects of NPs.
Motivated by the importance of TiO2-NPs that may contribute to the
cancer research field especially from the treatment prospective
together with the fractal analysis technique, we have investigated the
effect of TiO2-NPs on colony morphology in the dark condition
using fractal dimension as a key morphological characterization
parameter. The aim of this work is mainly to investigate the cytotoxic
effects of TiO2-NPs in the dark on the growth of human cervical
carcinoma (HeLa) cell colonies from morphological aspect. The in
vitro studies were carried out together with the image processing
technique and fractal analysis. It was found that, these colonies were
abnormal in shape and size. Moreover, the size of the control
colonies appeared to be larger than those of the treated group. The
mean Df +/- SEM of the colonies in untreated cultures was
1.085±0.019, N= 25, while that of the cultures treated with TiO2-NPs
was 1.287±0.045. It was found that the circularity of the control
group (0.401±0.071) is higher than that of the treated group
(0.103±0.042). The same tendency was found in the diameter
parameters which are 1161.30±219.56 μm and 852.28±206.50 μm
for the control and treated group respectively. Possible explanation of
the results was discussed, though more works need to be done in
terms of the for mechanism aspects. Finally, our results indicate that
fractal dimension can serve as a useful feature, by itself or in
conjunction with other shape features, in the classification of cancer
colonies.
Abstract: The inhibition effect of brazilin to human bladder
tumor cell line T24 in vitro and in vivo was studied. The results of the
in vitro experiments showed that brazilin has strong inhibition activity
on the target cells. The inhibition ratio of 100 μg/mL brazilin and 100
μg/mL mitomycin to the target cells was 90.90 % and 63.24 %
respectively, which showed that brazilin has higher inhibition activity
than mitomycin under the same concentration. Brazilin could induce
cell apoptosis in T24 cells. Significant antitumor activity of brazilin
was also showed in the animals experiments. The life extention rate of
200 mg/mL, 300 mg/kg, and 400 mg/kg brazilin intraperitoneally
injected into Balb/c-nu-nu nude mice that with human bladder cancer
were 51.50 %, 56.90 %, and 58.42 %（P
Abstract: The objective of this paper, is to apply support vector machine (SVM) approach for the classification of cancerous and normal regions of prostate images. Three kinds of textural features are extracted and used for the analysis: parameters of the Gauss- Markov random field (GMRF), correlation function and relative entropy. Prostate images are acquired by the system consisting of a microscope, video camera and a digitizing board. Cross-validated classification over a database of 46 images is implemented to evaluate the performance. In SVM classification, sensitivity and specificity of 96.2% and 97.0% are achieved for the 32x32 pixel block sized data, respectively, with an overall accuracy of 96.6%. Classification performance is compared with artificial neural network and k-nearest neighbor classifiers. Experimental results demonstrate that the SVM approach gives the best performance.
Abstract: Segmenting the lungs in medical images is a
challenging and important task for many applications. In particular,
automatic segmentation of lung cavities from multiple magnetic
resonance (MR) images is very useful for oncological applications
such as radiotherapy treatment planning. However, distinguishing of
the lung areas is not trivial due to largely changing lung shapes, low
contrast and poorly defined boundaries. In this paper, we address
lung segmentation problem from pulmonary magnetic resonance
images and propose an automated method based on a robust regionaided
geometric snake with a modified diffused region force into the
standard geometric model definition. The extra region force gives the
snake a global complementary view of the lung boundary
information within the image which along with the local gradient
flow, helps detect fuzzy boundaries. The proposed method has been
successful in segmenting the lungs in every slice of 30 magnetic
resonance images with 80 consecutive slices in each image. We
present results by comparing our automatic method to manually
segmented lung cavities provided by an expert radiologist and with
those of previous works, showing encouraging results and high
robustness of our approach.
Abstract: Clusters of microcalcifications in mammograms are an
important sign of breast cancer. This paper presents a complete
Computer Aided Detection (CAD) scheme for automatic detection of
clustered microcalcifications in digital mammograms. The proposed
system, MammoScan μCaD, consists of three main steps. Firstly
all potential microcalcifications are detected using a a method for
feature extraction, VarMet, and adaptive thresholding. This will also
give a number of false detections. The goal of the second step,
Classifier level 1, is to remove everything but microcalcifications.
The last step, Classifier level 2, uses learned dictionaries and sparse
representations as a texture classification technique to distinguish
single, benign microcalcifications from clustered microcalcifications,
in addition to remove some remaining false detections. The system
is trained and tested on true digital data from Stavanger University
Hospital, and the results are evaluated by radiologists. The overall
results are promising, with a sensitivity > 90 % and a low false
detection rate (approx 1 unwanted pr. image, or 0.3 false pr. image).
Abstract: Broccoli has been widely recognized as a wealthy
vegetable which contains multiple nutrients with potent anti-cancer
properties. Lamb’s lettuce has been used as food for many centuries
but only recently became commercially available and literature is
therefore exiguous concerning these vegetables. The aim of this work
was to evaluate the influence of the extraction conditions on the yield
of phenolic compounds and the corresponding antioxidant capacity of
broccoli and lamb’s lettuce. The results indicate that lamb’s lettuce,
compared to broccoli, contains simultaneously a large amount of total
polyphenols as well as high antioxidant activity. It is clearly
demonstrated that extraction solvent significantly influences the
antioxidant activity. Methanol is the solvent that can globally
maximize the antioxidant extraction yield. The results presented
herein prove lamb’s lettuce as a very interesting source of
polyphenols, and thus a potential health-promoting food.
Abstract: An effective method for the early detection of breast
cancer is the mammographic screening. One of the most important
signs of early breast cancer is the presence of microcalcifications. For
the detection of microcalcification in a mammography image, we
propose to conceive a multiagent system based on a dual irregular
pyramid.
An initial segmentation is obtained by an incremental approach;
the result represents level zero of the pyramid. The edge information
obtained by application of the Canny filter is taken into account to
affine the segmentation. The edge-agents and region-agents cooper
level by level of the pyramid by exploiting its various characteristics
to provide the segmentation process convergence.
Abstract: Aiming the application of localized hyperthermia, a
magnetic induction system with new approaches is proposed. The techniques in this system for improving the effectiveness of localized hyperthermia are that using magnetic circuit and the multiple-coil array instead of a giant coil for generating magnetic field. Specially, amorphous metal is adopted as the material of magnetic circuit. Detail
design parameters of hardware are well described. Simulation tool is
employed for this work and experiment result is reported as well.
Abstract: Recently, the health of retired National Football
League players, particularly lineman has been investigated. A
number of studies have reported increased cardiometabolic risk,
premature cardiovascular disease and incidence of type 2 diabetes.
Rugby union players have somatotypes very similar to National
Football League players which suggests that rugby players may have
similar health risks. The International Golden Oldies World Rugby
Festival (GORF) provided a unique opportunity to investigate the
demographics of veteran rugby players. METHODOLOGIES: A
cross-sectional, observational study was completed using an online
web-based questionnaire that consisted of medical history and
physiological measures. Data analysis was completed using a one
sample t-test (50yrs) and Chi-square test. RESULTS:
A total of 216 veteran rugby competitors (response rate = 6.8%)
representing 10 countries, aged 35-72 yrs (mean 51.2, S.D. ±8.0),
participated in the online survey. As a group, the incidence of current
smokers was low at 8.8% (avg 72.4 cigs/wk) whilst the percentage
consuming alcohol was high (93.1% (avg 11.2 drinks/wk).
Competitors reported the following top six chronic
diseases/disorders; hypertension (18.6%), arthritis (OA/RA, 11.5%),
asthma (9.3%), hyperlipidemia (8.2%), diabetes (all types, 7.5%) and
gout (6%), there were significant differences between groups with
regard to cancer (all types) and migraines. When compared to the
Australian general population (Australian Bureau of Statistics data,
n=18,000), GORF competitors had a significantly lower incidence of
anxiety (p
Abstract: Elastic light single-scattering spectroscopy system
with a single optical fiber probe was employed to differentiate cancerous prostate tissue from non-cancerous prostate tissue ex-vivo just after radical prostatectomy. First, ELSSS spectra were acquired
from cancerous prostate tissue to define its spectral features. Then,
spectra were acquired from normal prostate tissue to define difference in spectral features between the cancerous and normal
prostate tissues. Of the total 66 tissue samples were evaluated from
nine patients by ELSSS system. Comparing of histopathology results
and ELSSS measurements revealed that sign of the spectral slopes of
cancerous prostate tissue is negative and non-cancerous tissue is positive in the wavelength range from 450 to 750 nm. Based on the
correlation between histopathology results and sign of the spectral
slopes, ELSSS system differentiates cancerous prostate tissue from
non- cancerous with a sensitivity of 0.95 and a specificity of 0.94.
Abstract: An evolutionary method whose selection and recombination
operations are based on generalization error-bounds of
support vector machine (SVM) can select a subset of potentially
informative genes for SVM classifier very efficiently [7]. In this
paper, we will use the derivative of error-bound (first-order criteria)
to select and recombine gene features in the evolutionary process,
and compare the performance of the derivative of error-bound with
the error-bound itself (zero-order) in the evolutionary process. We
also investigate several error-bounds and their derivatives to compare
the performance, and find the best criteria for gene selection
and classification. We use 7 cancer-related human gene expression
datasets to evaluate the performance of the zero-order and first-order
criteria of error-bounds. Though both criteria have the same strategy
in theoretically, experimental results demonstrate the best criterion
for microarray gene expression data.
Abstract: The study of proteomics reached unexpected levels of
interest, as a direct consequence of its discovered influence over some
complex biological phenomena, such as problematic diseases like
cancer. This paper presents the latest authors- achievements regarding
the analysis of the networks of proteins (interactome networks), by
computing more efficiently the betweenness centrality measure. The
paper introduces the concept of betweenness centrality, and then
describes how betweenness computation can help the interactome net-
work analysis. Current sequential implementations for the between-
ness computation do not perform satisfactory in terms of execution
times. The paper-s main contribution is centered towards introducing
a speedup technique for the betweenness computation, based on
modified shortest path algorithms for sparse graphs. Three optimized
generic algorithms for betweenness computation are described and
implemented, and their performance tested against real biological
data, which is part of the IntAct dataset.
Abstract: Breast skin-line estimation and breast segmentation is an important pre-process in mammogram image processing and computer-aided diagnosis of breast cancer. Limiting the area to be processed into a specific target region in an image would increase the accuracy and efficiency of processing algorithms. In this paper we are presenting a new algorithm for estimating skin-line and breast segmentation using fast marching algorithm. Fast marching is a partial-differential equation based numerical technique to track evolution of interfaces. We have introduced some modifications to the traditional fast marching method, specifically to improve the accuracy of skin-line estimation and breast tissue segmentation. Proposed modifications ensure that the evolving front stops near the desired boundary. We have evaluated the performance of the algorithm by using 100 mammogram images taken from mini-MIAS database. The results obtained from the experimental evaluation indicate that this algorithm explains 98.6% of the ground truth breast region and accuracy of the segmentation is 99.1%. Also this algorithm is capable of partially-extracting nipple when it is available in the profile.
Abstract: Natural organic matter (NOM) is heterogeneous
mixture of organic compounds that enter the water media from
animal and plant remains, domestic and industrial wastes.
Researches showed that NOM is likely precursor material for
disinfection by products (DBPs). Chlorine very commenly used for
disinfection purposes and NOM and chlorine reacts then
Trihalomethane (THM) and Haloacetic acids (HAAs) which are
cancerogenics for human health are produced. The aim of the study is
to search NOM removal by enhanced coagulation from drinking
water source of Eskisehir which is supplied from Porsuk Dam.
Recently, Porsuk dam water is getting highly polluted and therefore
NOM concentration is increasing. Enhanced coagulation studies were
evaluated by measurement of Dissolved Organic Carbon (DOC), UV
absorbance at 254 nm (UV254), and different trihalomethane
formation potential (THMFP) tests. Results of jar test experiments
showed that NOM can be removed from water about 40-50 % of
efficiency by enhanced coagulation. Optimum coagulant type and
coagulant dosages were determined using FeCl3 and Alum.
Abstract: Case based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computeraided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing intelligent medical diagnoses systems. Successful applications in cancer and heart diseases developed by Medical Informatics Research Group at Ain Shams University are also discussed.
Abstract: Alpfa-fetoprotein and its fragments may be an important vehicle for targeted delivery of radionuclides to the tumor. We investigated the effect of conditions on the labeling of biologically active synthetic peptide based on the (F-afp) with technetium-99m. The influence of the nature of the buffer solution, pH, concentration of reductant, concentration of the peptide and the reaction temperature on the yield of labeling was examined. As a result, the following optimal conditions for labeling of (F-afp) are found: pH 8.5 (phosphate and bicarbonate buffers) and pH from 1.7 to 7.0 (citrate buffer). The reaction proceeds with sufficient yield at room temperature for 30 min at the concentration of SnCl2 and (Fafp) (F-afp) is to be less than 10 mkg/ml and 25 mkg/ml, respectively. Investigations of the test drug accumulation in the tumor cells of human breast cancer were carried out. Results can be assumed that the in vivo study of the (F-afp) in experimental tumor lesions will show concentrations sufficient for imaging these lesions by SPECT.
Abstract: The prospective analysis is presented as an important tool to identify the most relevant opportunities and needs in research and development from planned interventions in innovation systems. This study chose Phyllanthus niruri, known as "stone break" to describe the knowledge about the specie, by using biotechnological forecasting through the software Vantage Point. It can be seen a considerable increase in studies on Phyllanthus niruri in recent years and that there are patents about this plant since twenty-five years ago. India was the country that most carried out research on the specie, showing interest, mainly in studies of hepatoprotection, antioxidant and anti-cancer activities. Brazil is in the second place, with special interest for anti-tumor studies. Given the identification of the Brazilian groups that exploit the species it is possible to mediate partnerships and cooperation aiming to help on the implementing of the Program of Herbal medicines (phytotherapics) in Brazil.
Abstract: The study of proteomics reached unexpected levels of
interest, as a direct consequence of its discovered influence over
some complex biological phenomena, such as problematic diseases
like cancer. This paper presents a new technique that allows for an
accurate analysis of the human interactome network. It is basically
a two-step analysis process that involves, at first, the detection of
each protein-s absolute importance through the betweenness centrality
computation. Then, the second step determines the functionallyrelated
communities of proteins. For this purpose, we use a community
detection technique that is based on the edge betweenness
calculation. The new technique was thoroughly tested on real biological
data and the results prove some interesting properties of those proteins that are involved in the carcinogenesis process. Apart from its
experimental usefulness, the novel technique is also computationally
effective in terms of execution times. Based on the analysis- results, some topological features of cancer mutated proteins are presented
and a possible optimization solution for cancer drugs design is suggested.
Abstract: As the Computed Tomography(CT) requires normally
hundreds of projections to reconstruct the image, patients are exposed
to more X-ray energy, which may cause side effects such as cancer.
Even when the variability of the particles in the object is very less,
Computed Tomography requires many projections for good quality
reconstruction. In this paper, less variability of the particles in an
object has been exploited to obtain good quality reconstruction.
Though the reconstructed image and the original image have same
projections, in general, they need not be the same. In addition
to projections, if a priori information about the image is known,
it is possible to obtain good quality reconstructed image. In this
paper, it has been shown by experimental results why conventional
algorithms fail to reconstruct from a few projections, and an efficient
polynomial time algorithm has been given to reconstruct a bi-level
image from its projections along row and column, and a known sub
image of unknown image with smoothness constraints by reducing the
reconstruction problem to integral max flow problem. This paper also
discusses the necessary and sufficient conditions for uniqueness and
extension of 2D-bi-level image reconstruction to 3D-bi-level image
reconstruction.