MBRU Knowledge Repository
Knowledge Repository at Mohammed Bin Rashid University of Medicine and Health Sciences
Welcome to digital archive and research repository of Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU). MBRU Knowledge Repository is a digital service that collects, preserves, and distributes digital material. MBRU's scholarly communications including theses, faculty publications, student projects, and departmental records and publications are the key digital records available in this repository. Repositories are important tools for preserving an organization's legacy; they facilitate digital preservation and scholarly communication.
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- Theses and dissertations submitted by MBRU graduate and post-graduate students.
Recent Submissions
Retinal blood vessel diameter changes with 60-day head-down bedrest are unaffected by antioxidant nutritional cocktail.
(2024-11-15) Goswami, Nandu
Long-term human spaceflight can lead to cardiovascular deconditioning, but little is known about how weightlessness affects microcirculation. In this study, we examined how the retinal microvessels and cerebrovascular regulation of 19 healthy male participants responded to long-term head-down bedrest (HDBR), an earth-based analog for weightlessness. In addition, we examined whether an anti-inflammatory/antioxidant cocktail could prevent the vascular changes caused by HDBR. In all study participants, we found a decrease in retinal arteriolar diameter by HDBR day 8 and an increase in retinal venular diameter by HDBR day 16. Concurrently, blood pressure at the level of the middle cerebral artery and the cerebrovascular resistance index were higher during HDBR, while cerebral blood flow velocity was lower. None of these changes were reversed in participants receiving the anti-inflammatory/antioxidant cocktail, indicating that this cocktail was insufficient to restore the microvascular and cerebral blood flow changes induced by HDBR.
Maternal COVID-19 infection and risk of respiratory distress syndrome among newborns: a systematic review and meta-analysis.
(2024-11-19) Alfaresi, Mubarak
Background: The COVID-19 pandemic has significantly impacted public health, with emerging evidence suggesting substantial effects on maternal and neonatal health. This systematic review and meta-analysis aimed to quantify the prevalence and risk of respiratory distress syndrome (RDS) in newborns born to mothers infected with SARS-CoV-2, the virus responsible for COVID-19.
An Integrated Multimodal-Based CAD System for Breast Cancer Diagnosis.
(2024-11-05) Alkhnbashi, Omer S
Simple Summary:
Diagnosis of breast cancer goes through multiple processes. Recently, a variety of system-aided diagnosis (CAD) systems have been proposed as Primary systems for initial diagnosis based on mammogram screenings. This paper enhances the diagnosis accuracy by using mammograms of both sides of the patient’s breasts instead of the infected side only. In addition, the paper boosts CAD accuracy by adding patient information and medical history along with mammogram images’ features. The proposed multimodal approach will serve as the nucleus for future work at both data and system levels to diagnose breast cancer and other diseases caused by various factors.
Abstract:
Breast cancer has been one of the main causes of death among women recently, and it has been the focus of attention of many specialists and researchers in the health field. Because of its seriousness and spread speed, breast cancer-resisting methods, early diagnosis, diagnosis, and treatment have been the points of research discussion. Many computers-aided diagnosis (CAD) systems have been proposed to reduce the load on physicians and increase the accuracy of breast tumor diagnosis. To the best of our knowledge, combining patient information, including medical history, breast density, age, and other factors, with mammogram features from both breasts in craniocaudal (CC) and mediolateral oblique (MLO) views has not been previously investigated for breast tumor classification. In this paper, we investigated the effectiveness of using those inputs by comparing two combination approaches. The soft voting approach, produced from statistical information-based models (decision tree, random forest, K-nearest neighbor, Gaussian naive Bayes, gradient boosting, and MLP) and an image-based model (CNN), achieved 90% accuracy. Additionally, concatenating statistical and image-based features in a deep learning model achieved 93% accuracy. We found that it produced promising results that would enhance the CAD systems. As a result, this study finds that using both sides of mammograms outperformed the result of using only the infected side. In addition, integrating the mammogram features with statistical information enhanced the accuracy of the tumor classification. Our findings, based on a novel dataset, incorporate both patient information and four-view mammogram images, covering multiple classes: normal, benign, and malignant.
In-silico modelling of insulin secretion and pancreatic beta-cell function for clinical applications: is it worth the effort?
(2024) Rizzo, Manfredi
Introduction: Recently, there has been ongoing dialogue with clinical researchers about the practical benefits of in-silico mathematical modelling in studying glucose metabolism. In fact, several in-silico models have been developed in such field, as outlined by some review studies (1–4). Among the different metabolic processes addressed by such models, one relevant is insulin secretion and pancreatic beta-cell function. Indeed, although it is currently known that several factors affect glucose homeostasis (5), the impairment in insulin secretion/betacell function, in addition to that of insulin sensitivity, are typically the most important determinants of glycemic control deterioration and possible development of type 2 diabetes. In this opinion article, we will provide considerations about in-silico modelling of beta-cell function. Some models of beta-cell function describe aspects of such process at molecular or cellular level (6–13). These models are useful to get further insights in relevant molecular/ cellular mechanisms, and in addition they can stimulate new experimental research activity in an in-vitro context. Other models are instead oriented to describe insulin secretion/betacell function at whole body level, and these models are those typically having potential for clinical applications (14–17). In some of the following paragraphs, we focus on the main characteristics and findings of the model by Mari et al. (17). This model has been applied in the clinical context for the analysis of thousands of glucose tolerance tests, including those in wide multicenter projects (such as the IMI-DIRECT Project), focused on longitudinal study of participants with both type 2 diabetes (T2D) (18) and impaired glucose regulation, but also normal glucose tolerance (19). The model by Mari et al. (17) describes three main processes of beta-cell function: the glucose-insulin dose-response relation (“DR” component), the early insulin secretion (“E” component), and the insulin secretion potentiation (“P” component”). We succinctly describe those characteristics in the next section. For brevity, we refer to the model as the DR-EP model.
Regulatory Framework for Supporting the Integration and Use of Biosimilars in the Private Healthcare System of the United Arab Emirates (UAE).
(2024-11) Nathwani, Rahul; Reda, Ashraf; Lee, Martin
Introduction:
Biologics are substantial in the treatment of different diseases; however, they can burden the healthcare systems due to their high cost. Biosimilars can help healthcare systems keep their financial sustainability and patients access to biological therapies. The research objective is to formulate a framework for integrating biosimilars in the private healthcare sector of the United Arab Emirates (UAE). This framework was based on local stakeholders' recommendations to ensure alignment with the UAE's healthcare market dynamics and needs.