This study focused on the expression and regulation of BRCA1 in breast cancer cell lines compared to normal breast. BRCA1 transcript levels were assessed by real time quantitative polymerase chain reaction (RT-qPCR) in the cancer cell lines. Our data show overexpression of BRCA1 mRNA level in all the studied breast cancer cell lines: MCF-7, T47D, MDA-MB-231 and MDA-MB-468 along with Jurkat, leukemia T-lymphocyte, the positive control, relative to normal breast tissue. To investigate whether a positive or negative correlation exists between BRCA1 and the transcription factor E2F6, three different si-RNA specific for E2F6 were used to transfect the normal and cancerous breast cell lines. Interestingly, strong negative relationship was found between BRCA1 and E2F6, in which depletion of E2F6 in MCF-10A, the normal breast cell line, resulted in more than four-fold increased expression of BRCA1 transcript level. On the other hand, our findings suggest that E2F6 might lost control on BRCA1 in breast cancer cells. E2F6 knockdown by either of two specific siRNA (i.e. si-E2F6#1 or si- E2F6#2) had no influence on the BRCA1 expression in MCF-7 cells. Although, transfecting these cells with si-E2F6#3 showed overexpression of BRCA1 compared to cells transfected with scrambled negative control.
Study of the development of an activated carbon nanotube catalyst for alkaline fuel cell technology. Through the prepared carbon nanotubes catalyst by an electrochemical deposition technique. Different analytical approaches such as X-ray diffraction (XRD) to determine the structural properties and Scanning Electron Microscope (SEM), were used to characterize, Mesh stainless steel catalyst substrate had an envelope structure and a large surface area. Voltages were also obtained at 1.83 V and current at 3.2 A of alkaline fuel cell. In addition, study the characterization of the electrochemical parameters.
In this paper had been studied the characterization of the nanocatalyst (NiO) Mesh electrodes. For fuel cell. The catalyst is prepared and also the electrodes The structural were studied through the analysis of X-ray diffraction of the prepared nanocatalyst for determining the yielding phase and atomic force microscope to identify the roughness of prepared catalyst surface, Use has been nanocatalyst led to optimization of cell voltage, current densities & power for a fuel cell.
Zerumbone (ZER), a natural compound has been extracted from Zingiber zerumbet with known pharmacological activities. The aim was to determine the anti-human Burkitt’s lymphoma (Raji) cell effect of ZER. The 3-(4,5-dimethylthiazol-2-yl)-2,5,-diphenyltetrazolium bromide (MTT) assay was used to determine cytotoxic effect while the Annexin-V-fluorescein isothiocyanate/propidium iodide-PI flow cytometric assays was used to determine apoptotic effect of ZER on the human Burkitt’s lymphoma (Raji) cell (ATCC CCL-86) cell line. The expressions of Bax, Bcl-2, and c-Myc genes were determined via real-time PCR. ZER suppressed the proliferation of Raji cells with a 48 h IC50 value of 5.1 μg/mL. Treated Raji cells also underwen
... Show MoreBackground: Urinary tract infections (UTIs) and their complications such as Bladder cancer (Bl. C.) are a health growing problem worldwide. Objective: To shed light on this subject, present study was done to investigate relationship between recurrent urinary tract infection (RUTI) due to Escherichia coli (E. coli) and Bl. C.Type of study: Cross-sectional study. Methods: This study included 130 patients with RUTI, 50 patients with Bl. C. and 50 control of both sexes (aged 7-85 years) attending Al-Zahra Teaching Hospital in Al-Kut/Wassit governorate and Al-Harery Teaching Hospital of specialized surgeries/Baghdad. The patients were divided into two groups: the first group (n=130) included those who were suffering from recurrent UTI without
... Show MoreOne of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p
... Show MoreThis study focused on extracting the outer membrane nanovesicles (OMVs) from Escherichia coli BE2 (EC- OMVs) by ultracentrifugation, and the yield was 2.3mg/ml. This was followed by purification with gel filtration chromatography using Sephadex G-150, which was 2mg/ml. The morphology and size of purified EC-OMVs were confirmed by transmission electron microscopy (TEM) at 40-200 nm. The nature of functional groups in the vesicle vesicle was determined by Fourier transforms infrared spectroscopy (FT-IR) analysis. The antitumor activity of EC-OMVs was conducted in vitro by MTT assay in human ovarian (OV33) cancer cell line at 24,48 and 96hrs. The cytotoxicity test showed high susceptibility to the vesicles in ovarian compared to normal
... Show MoreThis paper proposes a new method Object Detection in Skin Cancer Image, the minimum
spanning tree Detection descriptor (MST). This ObjectDetection descriptor builds on the
structure of the minimum spanning tree constructed on the targettraining set of Skin Cancer
Images only. The Skin Cancer Image Detection of test objects relies on their distances to the
closest edge of thattree. Our experimentsshow that the Minimum Spanning Tree (MST) performs
especially well in case of Fogginessimage problems and in highNoisespaces for Skin Cancer
Image.
The proposed method of Object Detection Skin Cancer Image wasimplemented and tested on
different Skin Cancer Images. We obtained very good results . The experiment showed that
Chemotherapy is one of the most efficient methods for treating cancer patients. Chemotherapy aims to eliminate cancer cells as thoroughly as possible. Delivering medications to patients’ bodies through various methods, either oral or intravenous is part of the chemotherapy process. Different cell-kill hypotheses take into account the interactions of the expansion of the tumor volume, external drugs, and the rate of their eradication. For the control of drug usage and tumor volume, a model based smooth super-twisting control (MBSSTC) is proposed in this paper. Firstly, three nonlinear cell-kill mathematical models are considered in this work, including the log-kill, Norton-Simon, and hypotheses subject to parametric uncertainties and exo
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