Colorectal cancer is a malignant condition that can arise from multiple causative factors. It ranks second, behind lung cancer, as a leading cause of cancer-related deaths worldwide. Extensive research has been conducted to unravel the genetic underpinnings and molecular mechanisms underlying the development of colorectal cancer (CRC). However, epigenetic modifications of histones at the DNA level have become significantly involved in several malignant diseases such as CRC. Hence, this research sought to assess, for the first time locally, the immunoexpression of HDAC-1 and 3 in a group of colorectal patients. Additionally, we explored potential correlations between the expression of HDAC-1, 3 and VEGF. This retrospective study encompassed the analysis of 95 paraffin-embedded tissue samples from CRC cases. Participants in the research varied in age from 22 to 79 years, consisting of 60 males and 35 females. The study findings revealed a noteworthy correlation between VEGF expression and the patients' sex (p = 0.005, rho = 0.289). Intriguingly, the analysed data demonstrated a significant correlation between VEGF expression and the cytoplasmic localization of HDAC3 in colorectal cancer tissues (p < 0.001, rho = 0.476). However, the expression of VEGF showed a negative and statistically significant correlation with both HDAC3 expression (p = 0.02, rho = -0.243) and the cytoplasmic localization of HDAC1 (p = 0.02, rho = -0.305). The demonstrated negative regulatory relationship between HDAC3 and VEGF suggests this correlation could potentially be leveraged in both disease prognosis and treatment. Targeting the negative regulatory interaction between HDAC3 and VEGF may provide promising opportunities in both prognostic assessment and therapeutic strategies. This highlights the potential for developing targeted strategies that capitalize on the interplay between angiogenesis and epigenetic regulation.
One 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 MoreBackground: The presence of cancer has a profound psychological impact on the quality of life of patients and their families, on family and social relationships, and on role functioning.
Aim of the study: Assess the impact of childhood cancer on patients and their families.
Subjects and methods: A Prospective questionnaire-based study, for 151 patients, had malignancy identified by tumor registry of Children Welfare Teaching Hospital. The information was taken from the parent(s) in the presence of the patient who sometimes answered some questions during the interview.
Result: There was an interview with 151 families of children with cancer in t
... 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 MoreLetrozole (LZL) is a non-steroidal competitive aromatase enzyme system inhibitor. The aim of this study is to improve the permeation of LZL through the skin by preparing as nanoemulsion using various numbers of oils, surfactants and co-surfactant with deionized water. Based on solubility studies, mixtures of oleic acid oil and tween 80/ transcutol p as surfactant/co-surfactant (Smix) in different percentages were used to prepare nanoemulsions (NS). Therefore, 9 formulae of (o/w) LZL NS were formulated, then pseudo-ternary phase diagram was used as a useful tool to evaluate the NS domain at Smix ratios: 1:1, 2:1 and 3:1.
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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Abstract
Due to the momentum of winning in the streets of the city of Baghdad as a result of the large number of checkpoints so felt researcher to conduct a field visit to find out the main reasons that led to this congestion and to find practical solutions to mitigate wastage winning the arrival time citizen to where you want the least possible time.
This research aims to overcome the difficulties experienced by citizens to reach their places of work and reduce waste at the time of service and waiting time as well as reduce the cost of waiting.
Has emerged study a set of conclusions, including the use of model queue (G / G / C) and the mome
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