Abstract: Breast cancer is the most common malignancy in the
world among women. Many therapies have been designed to treat
this disease. Mamectomy, chemotherapy and radiotherapy are still
the main therapies of breast cancer. However, the results were
unsatisfactory and still far from the ideal treatment.
PM 701is a natural product, has anticancer activity. The bioactive
fraction PMF and subfraction PMFK had been isolated from PM701.
PM 701 and its fractions were proved to have a cytotoxic properties
against different cancer cell lines. This article is directed for the
further examination of lyophilized PM701 and its active fractions on
the growth of breast cancer cells (MCF-7). PM 701, PMF or PMFK
were adding to the cultural medium, where MCF-7 is incubated.
PM 701, PMF or PMFK were able to inhibit significantly the
proliferation of MCF-7 cells, Moreover these new agents were
proved to induce apoptosis of the breast cancer cells; through its
direct effect on the nuclei.
Abstract: Female breast cancer is the second in frequency after cervical cancer. Surgery is the most common treatment for breast cancer, followed by chemotherapy as a treatment of choice. Although effective, it causes serious side effects. Controlled-release drug delivery is an alternative method to improve the efficacy and safety of the treatment. It can release the dosage of drug between the minimum effect concentration (MEC) and minimum toxic concentration (MTC) within tumor tissue and reduce the damage of normal tissue and the side effect. Because an in vivo experiment of this system can be time-consuming and labor-intensive, a mathematical model is desired to study the effects of important parameters before the experiments are performed. Here, we describe a 3D mathematical model to predict the release of doxorubicin from pluronic gel to treat human breast cancer. This model can, ultimately, be used to effectively design the in vivo experiments.
Abstract: Chronic hepatitis B can evolve to cirrhosis and liver
cancer. Interferon is the only effective treatment, for carefully selected
patients, but it is very expensive. Some of the selection criteria are
based on liver biopsy, an invasive, costly and painful medical procedure.
Therefore, developing efficient non-invasive selection systems,
could be in the patients benefit and also save money. We investigated
the possibility to create intelligent systems to assist the Interferon
therapeutical decision, mainly by predicting with acceptable accuracy
the results of the biopsy. We used a knowledge discovery in integrated
medical data - imaging, clinical, and laboratory data. The resulted
intelligent systems, tested on 500 patients with chronic hepatitis
B, based on C5.0 decision trees and boosting, predict with 100%
accuracy the results of the liver biopsy. Also, by integrating the other
patients selection criteria, they offer a non-invasive support for the
correct Interferon therapeutic decision. To our best knowledge, these
decision systems outperformed all similar systems published in the
literature, and offer a realistic opportunity to replace liver biopsy in
this medical context.
Abstract: Topical photodynamic therapy (PDT) with
5-aminolevulinic acid (ALA) is an alternative therapy for treating
superficial cancer, especially for skin or oral cancer. ALA, a precursor
of the photosensitizer protoporphyrin IX (PpIX), is present as
zwitterions and hydrophilic property which make the low permeability
through the cell membrane. Collagen is a traditional carrier; its
molecular composed various amino acids which bear positive charge
and negative charge. In order to utilize the ion-pairs with ALA and
collagen, the study employed various pH values adjusting the net
charge. The aim of this study was to compare a series collagen form,
including solution, gel and sponge to investigate the topical delivery
behavior of ALA. The in vivo confocal laser scanning microscopy
(CLSM) study demonstrated that PpIX generation ability was different
pattern after apply for 6 h. Gel type could generate high PpIX, and
archived more deep of skin depth.
Abstract: Hepatitis C is an infectious disease transmitted by
blood and due to hepatitis C virus (HCV), which attacks the liver.
The infection is characterized by liver inflammation (hepatitis) that is
often asymptomatic but can progress to chronic hepatitis and later
cirrhosis and liver cancer. Our problem tends to highlight on the one
hand the prevalence of infectious disease in the population of the
region of Batna and on other hand the biological characteristics of
this disease by a screening and a specific diagnosis based on
serological tests, liver checkup (measurement of haematological and
biochemical parameters).
The results showed:
The serology of hepatitis C establishes the diagnosis of infection
with hepatitis C. In this study and with the serological test, 24 cases
of the disease of hepatitis C were found in 1000 suspected cases (7
cases with normal transaminases and 17 cases with elevated
transaminases). The prevalence of this disease in this study
population was 2.4%.
The presence of hepatitis C disrupts liver function including the
onset of cytolysis, cholestasis, jaundice, thrombocytopenia, and
coagulation disorders.
Abstract: In cancer progress, the optical properties of tissues
like absorption and scattering coefficient change, so by these
changes, we can trace the progress of cancer, even it can be applied
for pre-detection of cancer. In this paper, we investigate the effects of
changes of optical properties on light penetrated into tissues. The
diffusion equation is widely used to simulate light propagation into
biological tissues. In this study, the boundary integral method (BIM)
is used to solve the diffusion equation. We illustrate that the changes
of optical properties can modified the reflectance or penetrating light.
Abstract: Mammography is the most effective procedure for an
early diagnosis of the breast cancer. Nowadays, people are trying to
find a way or method to support as much as possible to the
radiologists in diagnosis process. The most popular way is now being
developed is using Computer-Aided Detection (CAD) system to
process the digital mammograms and prompt the suspicious region to
radiologist. In this paper, an automated CAD system for detection
and classification of massive lesions in mammographic images is
presented. The system consists of three processing steps: Regions-Of-
Interest detection, feature extraction and classification. Our CAD
system was evaluated on Mini-MIAS database consisting 322
digitalized mammograms. The CAD system-s performance is
evaluated using Receiver Operating Characteristics (ROC) and Freeresponse
ROC (FROC) curves. The archived results are 3.47 false
positives per image (FPpI) and sensitivity of 85%.
Abstract: The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to tumor progression. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.
Abstract: Trihalomethanes (THMs) were among the first
disinfection byproducts to be discovered in chlorinated water. The
substances form during a reaction between chlorine and organic
matter in the water. Trihalomethanes are suspected to have negative
effects on birth such as, low birth weight, intrauterine growth
retardation in term births, as well as gestational age and preterm
delivery. There are also some evidences showing these by-products to
be mutagenic and carcinogenic, the greatest amount of evidence being
related to the bladder cancer. However, there exist inconsistencies
regarding such effects of THMs as different studies have provided
different results in this regard. The aim of the present study is to
provide a review of the related researches about the above mentioned
health effects of THMs.
Abstract: Tumour suppressors are key participants in the
prevention of cancer. Regulation of their expression through
miRNAs is important for comprehensive translation inhibition of
tumour suppressors and elucidation of carcinogenesis mechanisms.
We studies the possibility of 1521 miRNAs to bind with 873 mRNAs
of human tumour suppressors using RNAHybrid 2.1 and ERNAhybrid
programmes. Only 978 miRNAs were found to be
translational regulators of 812 mRNAs, and 61 mRNAs did not have
any miRNA binding sites. Additionally, 45.9% of all miRNA binding
sites were located in coding sequences (CDSs), 33.8% were located
in 3' untranslated region (UTR), and 20.3% were located in the
5'UTR. MiRNAs binding with more than 50 target mRNAs and
mRNAs binding with several miRNAs were selected. Hsa-miR-5096
had 15 perfectly complementary binding sites with mRNAs of 14
tumour suppressors. These newly indentified miRNA binding sites
can be used in the development of medicines (anti-sense therapies)
for cancer treatment.
Abstract: MicroRNAs (miRNAs) are a class of non-coding
RNAs that hybridize to mRNAs and induce either translation
repression or mRNA cleavage. Recently, it has been reported that
miRNAs could possibly play an important role in human diseases. By
integrating miRNA target genes, cancer genes, miRNA and mRNA
expression profiles information, a database is developed to link
miRNAs to cancer target genes. The database provides experimentally
verified human miRNA target genes information, including oncogenes
and tumor suppressor genes. In addition, fragile sites information for
miRNAs, and the strength of the correlation of miRNA and its target
mRNA expression level for nine tissue types are computed, which
serve as an indicator for suggesting miRNAs could play a role in
human cancer. The database is freely accessible at
http://ppi.bioinfo.asia.edu.tw/mirna_target/index.html.
Abstract: In this paper, a clustering algorithm named KHarmonic
means (KHM) was employed in the training of Radial
Basis Function Networks (RBFNs). KHM organized the data in
clusters and determined the centres of the basis function. The popular
clustering algorithms, namely K-means (KM) and Fuzzy c-means
(FCM), are highly dependent on the initial identification of elements
that represent the cluster well. In KHM, the problem can be avoided.
This leads to improvement in the classification performance when
compared to other clustering algorithms. A comparison of the
classification accuracy was performed between KM, FCM and KHM.
The classification performance is based on the benchmark data sets:
Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM
algorithm shows better accuracy in classification problem.
Abstract: Accurate assessment of the primary tumor response to
treatment is important in the management of breast cancer. This
paper introduces a new set of treatment evaluation indicators for
breast cancer cases based on the computational process of three
known metrics, the Euclidian, Hamming and Levenshtein distances.
The distance principals are applied to pairs of mammograms and/or
echograms, recorded before and after treatment, determining a
reference point in judging the evolution amount of the studied
carcinoma. The obtained numerical results are indeed very
transparent and indicate not only the evolution or the involution of
the tumor under treatment, but also a quantitative measurement of the
benefit in using the selected method of treatment.
Abstract: Lung cancer accounts for the most cancer related deaths for men as well as for women. The identification of cancer associated genes and the related pathways are essential to provide an important possibility in the prevention of many types of cancer. In this work two filter approaches, namely the information gain and the biomarker identifier (BMI) are used for the identification of different types of small-cell and non-small-cell lung cancer. A new method to determine the BMI thresholds is proposed to prioritize genes (i.e., primary, secondary and tertiary) using a k-means clustering approach. Sets of key genes were identified that can be found in several pathways. It turned out that the modified BMI is well suited for microarray data and therefore BMI is proposed as a powerful tool for the search for new and so far undiscovered genes related to cancer.
Abstract: The objective of the present research manuscript is to
perform parametric, nonparametric, and decision tree analysis to
evaluate two treatments that are being used for breast cancer patients.
Our study is based on utilizing real data which was initially used in
“Tamoxifen with or without breast irradiation in women of 50 years
of age or older with early breast cancer" [1], and the data is supplied
to us by N.A. Ibrahim “Decision tree for competing risks survival
probability in breast cancer study" [2]. We agree upon certain aspects
of our findings with the published results. However, in this
manuscript, we focus on relapse time of breast cancer patients instead
of survival time and parametric analysis instead of semi-parametric
decision tree analysis is applied to provide more precise
recommendations of effectiveness of the two treatments with respect
to reoccurrence of breast cancer.
Abstract: In this study, we present an advanced detection
technique for mass type breast cancer based on texture information
of organs. The proposed method detects the cancer areas in three
stages. In the first stage, the midpoints of mass area are determined
based on AHE (Adaptive Histogram Equalization). In the second
stage, we set the threshold coefficient of homogeneity by using
MLE (Maximum Likelihood Estimation) to compute the uniformity
of texture. Finally, mass type cancer tissues are extracted from the
original image. As a result, it was observed that the proposed
method shows an improved detection performance on dense breast
tissues of Korean women compared with the existing methods. It is
expected that the proposed method may provide additional
diagnostic information for detection of mass-type breast cancer.
Abstract: The incidence of oral cancer in Taiwan increased year
by year. It replaced the nasopharyngeal as the top incurrence among
head and neck cancers since 1994. Early examination and earlier
identification for earlier treatment is the most effective medical
treatment for these cancers. Although the government fully subsidized
the expenses with tremendous promotion program for oral cancer
screening, the citizen-s participation remained low. Purpose of this
study is to understand the factors affecting the citizens- behavior
intensions of taking an oral cancer screening. Based on the Theory of
Planned Behavior, this study adopted four distinctive variables in
explaining the captioned behavior intentions.700 questionnaires were
dispatched with 500 valid responses or 71.4% returned by the citizens
with an age 30 or above from the eastern counties of Taiwan. Test
results has shown that attitude toward, subjective norms of, and
perceived behavioral control over the oral cancer screening varied
from some demographic factors to another. The study proofed that
attitude toward, subjective norms of, and perceived behavioral control
over the oral cancer screening had positive impacts on the
corresponding behavior intention. The test concluded that the theory
of planned behavior was appropriate as a theoretical framework in
explaining the influencing factors of intentions of taking oral cancer
screening. This study suggested the healthcare professional should
provide high accessibility of screening services other than just
delivering knowledge on oral cancer to promote the citizens-
intentions of taking the captioned screening. This research also
provided a practical implication to the healthcare professionals when
formulating and implementing promotion instruments for lifting the
screening rate of oral cancer.
Abstract: Fourier transform infrared (FT-IR) spectroscopic imaging
is an emerging technique that provides both chemically and
spatially resolved information. The rich chemical content of data
may be utilized for computer-aided determinations of structure and
pathologic state (cancer diagnosis) in histological tissue sections for
prostate cancer. FT-IR spectroscopic imaging of prostate tissue has
shown that tissue type (histological) classification can be performed to
a high degree of accuracy [1] and cancer diagnosis can be performed
with an accuracy of about 80% [2] on a microscopic (≈ 6μm)
length scale. In performing these analyses, it has been observed
that there is large variability (more than 60%) between spectra from
different points on tissue that is expected to consist of the same
essential chemical constituents. Spectra at the edges of tissues are
characteristically and consistently different from chemically similar
tissue in the middle of the same sample. Here, we explain these
differences using a rigorous electromagnetic model for light-sample
interaction. Spectra from FT-IR spectroscopic imaging of chemically
heterogeneous samples are different from bulk spectra of individual
chemical constituents of the sample. This is because spectra not
only depend on chemistry, but also on the shape of the sample.
Using coupled wave analysis, we characterize and quantify the nature
of spectral distortions at the edges of tissues. Furthermore, we
present a method of performing histological classification of tissue
samples. Since the mid-infrared spectrum is typically assumed to
be a quantitative measure of chemical composition, classification
results can vary widely due to spectral distortions. However, we
demonstrate that the selection of localized metrics based on chemical
information can make our data robust to the spectral distortions
caused by scattering at the tissue boundary.
Abstract: In this paper we present a method for gene ranking
from DNA microarray data. More precisely, we calculate the correlation
networks, which are unweighted and undirected graphs, from
microarray data of cervical cancer whereas each network represents
a tissue of a certain tumor stage and each node in the network
represents a gene. From these networks we extract one tree for
each gene by a local decomposition of the correlation network. The
interpretation of a tree is that it represents the n-nearest neighbor
genes on the n-th level of a tree, measured by the Dijkstra distance,
and, hence, gives the local embedding of a gene within the correlation
network. For the obtained trees we measure the pairwise similarity
between trees rooted by the same gene from normal to cancerous
tissues. This evaluates the modification of the tree topology due to
progression of the tumor. Finally, we rank the obtained similarity
values from all tissue comparisons and select the top ranked genes.
For these genes the local neighborhood in the correlation networks
changes most between normal and cancerous tissues. As a result
we find that the top ranked genes are candidates suspected to be
involved in tumor growth and, hence, indicates that our method
captures essential information from the underlying DNA microarray
data of cervical cancer.