The study aimed at investigating the perception on the impact of homecare accreditation on patients safety and quality of care which relate to infection rate, risk of fall, and medical documentation. The study will be a quantitative research method using a cross-sectional descriptive analysis and time series regression analysis which will test the theory that healthcare accreditation has a positive influence on patients safety and quality of care. The study is to be conducted in homecare facilities in the United Arab Emirates through sampling method for a group of 200 respondents to evaluate the impact of homecare accreditation. Questionnaires will be administered, and the response rate will be assessed using a Likert scale for measurement. The study results should assist in encouraging private and public homecare facilities to be accredited by international or national groups to achieve an optimum level of patients care at homecare setting.
Honey and its potential benefits are deeply engrained in our culture and religion. Manuka honey (MH), produced in New Zealand by bees that pollinate the Manuka bush, is one of the best studied honeys in the world. The medicinal properties of MH have been extensively studied, particularly in terms of its wound healing and antimicrobial activities. However, until recently, the scientific basis of Honey's effect on health and disease was largely unknown. Over the past eight years, we demonstrated the potential of using MH to prevent and treat cancer. So far, we have highlighted the beneficial effect of MH on breast, colon and skin cancers. We recently identified the precise molecular target of MH on human breast cancer cells. At present, we are continuing our work to characterize the precise mechanism(s) by which MH acts against different functional properties of cancer cells and map the potential active component responsible for each of its actions.
The stem cells are becoming a powerful tool in different fields of biomedical research such as disease modeling, drug testing and tissue engineering for regenerative medicine. Because little is known about the characteristic features and functional properties of stem cells, isolating stem cells from organs and maintaining them to differentiate are challenging tasks. In this study, we have used two protocols of three-dimensional (3D) culture to reveal some properties of gastric stem cells. In the first model, an immortalized epithelial cell line with molecular and morphological features representative of the mouse gastric stem (mGS) cells has been used to develop 3D culture models known as spheroids. In the second 3D spheroids model, stomach glands isolated from neonatal mice were used. Spheroids are characterized by using RT-qPCR, electron microscopy and immunohistochemistry to detect cellular phenotypes and any evidences of differentiation. These spheroids will remain as a valuable tool to develop models for various diseases such as Helicobacter Pylori and viral infections. In conclusion, this study will provide some data for the better understanding of the biological features of gastric stem cells in health and disease.
Cervical cancer is a global health problem that affect many women. Little is known about the cellular origin and the epithelial lining of the uterine cervix. The aim of this study is to characterize the cells lining the uterine cervix in mice using lectins histochemistry, immunohistochemistry and electron microscopy methods. To identify dividing cells and define their dynamics, the bromodeoxyuridine (BrdU) labeling technique is used. We noted that Griffonia simplicifolia agglutinin-1(GS-1) lectin was specific for the stratified epithelium in ectocervix region. Single injection studies of BrdU positive cells were scattered along the simple columnar glandular epithelium of endocervix and in the basal layer of the stratified squamous epithelium of ectocervix.
The gastric stem cell proliferates and migrate bidirectionally to maintain the glandular homeostasis. Abnormality in this dynamic process leads to gastric diseases. Vitamin A influences the cell proliferation and differentiation of various epithelial tissues. This study aims to analyse the role of vitamin A in the development of gastric glands. Vitamin A free diet was given to establish vitamin A deficient (VAD) model. A reduction in cell proliferation and an increase in the census of surface mucus cells was observed in the vitamin A deficient gastric glands. The findings from this study suggested that the vitamin A is an important factor that controls cell proliferation and differentiation in the mouse stomach.
Src homology region 2 domain-containing protein tyrosine phosphatase2 (SHP2) is ubiquitously overexpressed in lung, breast, leukemia and cervical tumors. The phosphorylation of SHP2 activates RAS-MAPK, mTOR, and PI3K-AKT signaling pathways which involved in cell proliferation, invasion and metastasis. Therefore, targeting SHP2 pathway becomes a novel approach in therapeutic intervention to inhibit tumor progression and metastasis. In the present study, we have investigated the SHP2 inhibitory effects of two newly synthesized 5-aminosalicylate-4- thiazolinone derivatives (HH3 & HH13) in HeLa, MCF-7 and MDA-MB-231 cells. In-silico molecular docking studies showed preferential affinity of HH compounds towards the catalytic site of SHP2 enzyme. An enzymatic SHP2 allosteric inhibitory assay showed HH compound's potential to suppress SHP2 activity. A confirmatory western-blot result further demonstrate the inhibitory effects for SHP2 expression in HeLa cells as similar to a positive control (NSC 87877). Furthermore, our data showed that these compounds suppresses RAS/MAPK pathway and regulate STAT3 and JNK expression in HeLa cells and thereby inhibit tumor cell proliferation and migration. Overall, our novel HH compounds showed remarkable potential to inhibit protein tyrosine phosphatase SHP2 and can lead to the development of a successful anti-SHP2 drugs.
Frequency modulated continuous wave radar is used to measure the target's distance and velocity. This paper elaborates the analysis of stationary target by using the FMCW radar. The radar was tested to detect a stationary target at considerable distances. Experimental results are compared with theoretical values to demonstrate the capability of the radar to detect targets.
In this paper, we propose a transfer learning approach with Convolutional Neural Networks (CNN) for radar Automatic Target Recognition (ATR). Radar echo signals of moving targets introduce micro-Doppler signatures that can be analyzed using spectrograms. Spectrograms can show distinctive micro-Doppler signatures of different targets, and they are used in our approach as inputs. We are utilizing a pre-trained CNN as a feature extractor in which feature maps can be extracted from any of the layers to train a classifier. AlexNet was used as the pre-trained CNN and softmax was used as the classifier. Our approach was tested on RadEch database of 8 ground moving target classes. Our approach outperformed the state of the art methods, using the same database, and reached an accuracy of 99.9%.
In this paper, a deep learning approach for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is proposed. The novelty of the proposed framework stems from the fact that it is based on a transfer learning scheme, where a pre-trained Convolutional Neural Network (CNN) is employed to extract learned features in combination with a classical Support Vector Machine (SVM) for classification. The efficiency of the presented approach is validated on the MSTAR dataset, where ten target classes are used. A classification accuracy of 99.27% is achieved
This paper presents a system that uses a thermal image as an input in order to detect and distinguish man-made objects, humans, animals, military targets or any other objects of prominent heat profile. The object's classification should be implemented based on the shape analysis of the object, and how this shape is changing over the time. Many techniques were provided in the literature for detection, description and identification of shapes. Therefore, it is very important to verify those techniques and select the applicable one for thermal shape processing. Moreover, there is a need to study pre-processing of thermal images to identify the most useful algorithm for shape detection. After completion of this project, the future work can be done on some specific applications, where the image that includes, for example, animals will be captured and the remaining images will be ignored (such as in visual surveillance of wild life at night and recognition of different types of animals).
