Regulation of splicing integrin ɑ6 during development and differentiation

PhD ThesisAlternative splicing is an important mechanism for creating protein diversity. Integrins are significant in many aspects of cell biology, including cell signalling and interaction with the cell matrix. ITGA6 has two different cytoplasmic C-termini (a6A and a6B) that shift 100% between stem cells and fibroblasts. The primary aim in this thesis was to monitor splicing patterns during development and differentiation integrin subunit alpha 6 (ITGA6) to see which alternative splicing events are similarly regulated in fish and humans using early zebrafish development. The a6A and a6B integrins had been differentially implicated in the expression in the function of breast cancer and cancer stem cells. Therefore, the second aim was to monitor splicing patterns for ITGA6 in different cancer cell lines and to compare them with stem cell patterns, fibroblast, and zebrafish, determining which splicing regulator protein regulates the ITGA6 alternative exon. It was confirmed that the ITGA6 alternative exon 25 was activated by MBNL1, RBFOX2 and ESRP in cancer cell lines, and PTBP was discovered as a novel regulator for ITGA6 splicing that inhibited the exon of ITGA6 in cancer cell line. The third aim for this project was to identify the mechanism of splicing of this ITGA6 alternative exon, including identifying the PTB binding site that regulates ITGA6. A minigene system was established to study the regulation of the ITGA6 alternative exon. The ITGA6 1.3 minigene positively responded to siRNA mediated depletion of splicing factors in the same way as the endogenous gene, indicating this minigene was a good model. The alternative exon of ITGA6 was activated by MBNL1 and was inhibited by PTBP, leading to more production of ITGA6B. Using this minigene plasmid it was confirmed that PTBP inhibited alternative splicing of ITGA6. The last aim of this chapter was to discover the PTB binding sites. Through a series of in silico analyses, a binding site for PTB was identified downstream of the regulated exon. Surprisingly, loss of this PTB binding site actually repressed this splicing event. These data suggest that PTB both activates this alternative splicing event through direct RNA-protein interactions, but also more strongly represses this exon, possibly through protein interactions with other regulatory factors.Saudi Arabian culture bureau in London and the Najran Universit

Regulation of splicing integrin ɑ6 during development and differentiation

PhD ThesisAlternative splicing is an important mechanism for creating protein diversity. Integrins are significant in many aspects of cell biology, including cell signalling and interaction with the cell matrix. ITGA6 has two different cytoplasmic C-termini (a6A and a6B) that shift 100% between stem cells and fibroblasts. The primary aim in this thesis was to monitor splicing patterns during development and differentiation integrin subunit alpha 6 (ITGA6) to see which alternative splicing events are similarly regulated in fish and humans using early zebrafish development. The a6A and a6B integrins had been differentially implicated in the expression in the function of breast cancer and cancer stem cells. Therefore, the second aim was to monitor splicing patterns for ITGA6 in different cancer cell lines and to compare them with stem cell patterns, fibroblast, and zebrafish, determining which splicing regulator protein regulates the ITGA6 alternative exon. It was confirmed that the ITGA6 alternative exon 25 was activated by MBNL1, RBFOX2 and ESRP in cancer cell lines, and PTBP was discovered as a novel regulator for ITGA6 splicing that inhibited the exon of ITGA6 in cancer cell line. The third aim for this project was to identify the mechanism of splicing of this ITGA6 alternative exon, including identifying the PTB binding site that regulates ITGA6. A minigene system was established to study the regulation of the ITGA6 alternative exon. The ITGA6 1.3 minigene positively responded to siRNA mediated depletion of splicing factors in the same way as the endogenous gene, indicating this minigene was a good model. The alternative exon of ITGA6 was activated by MBNL1 and was inhibited by PTBP, leading to more production of ITGA6B. Using this minigene plasmid it was confirmed that PTBP inhibited alternative splicing of ITGA6. The last aim of this chapter was to discover the PTB binding sites. Through a series of in silico analyses, a binding site for PTB was identified downstream of the regulated exon. Surprisingly, loss of this PTB binding site actually repressed this splicing event. These data suggest that PTB both activates this alternative splicing event through direct RNA-protein interactions, but also more strongly represses this exon, possibly through protein interactions with other regulatory factors.Saudi Arabian culture bureau in London and the Najran Universit

The Compatibility of Developed Mathematics Textbooks' Content in Saudi Arabia (Grades 6-8) with NCTM Standards

This study aimed to investigate the compatibility of developed mathematics textbooks' content (grades 6-8) in Saudi Arabia with NCTM standards in the areas of: number and operations, algebra, geometry, measurement, data analysis and probability.  To achieve that goal, a list of (NCTM) standards for grades (6-8) were translated to Arabic language, and a content analysis card was developed in the light of standards list for mathematics textbooks for the academic year 1434-1435 AH / 2013-2014 AD.  The study results revealed that the content of developed mathematics textbooks for grades (6-8) is compatible with 96.3% with NCTM standards, since the content anticipate 52 expectations from the standards list, while 3.7% from the NCTM standards expectations list were not achieved in the mentioned five areas. Keywords: compatibility, NCTM Standards, developed curriculum, mathematics textbook

Mutated N-ras does not induce p19arf in CO25 cell line

The mouse cell line (CO25) used in this study was transfected with a glucocorticoid inducible mutated human N-ras oncogene under transcriptional control of the steroid-sensitive promoter of the mouse mammary tumors virus long terminal repeat MMTV-LTR. This study was aimed to investigate the expression of p19arf and MDM2 genes under the effect of N-ras oncogene induction and to invent the role of p19arf, MDM2 in N-ras pathway during various periods (12, 24, 48, 72, 96 h) using western blotting method. The levels of â-actin proteins in the same periods were our control group. The observations showed no increase of p19arf protein expression in normal, cancer and differentiated CO25 cells. MDM2 was accumulated until 72 h and after 96 h, it showed a dramatical decrease while β-actin levels were increased correlated to the volume of protein loaded to the gel. Because of the role of p19arf as tumor suppressor and p53-MDM2 linker, it is highly recommended to  investigate the relationship between N-ras and p53 and MDM2 in the same system to recognize the molecule that may play a linker molecule between p53 and MDM2 in p19arf lack system.Key words: Oncogene, N-ras, p19arf, myoblast, CO25 cells, differentiation, MDM2

Semi-metric spaces and fixed points of α - φ -contractive maps

A negative answer to an open problem is provided. Fixed point results for α -φ -contractive mappings in semi-metric spaces are proved. To show the generality of this results, examples are given. Finally, an application of this result to probabilistic spaces is derived

Sufficiency and Efficiency of Field Training for Radiology Students During Internship Experience in Najran University, Saudi Arabia

Purpose: The study was design to evaluate the effectiveness and adequacy of the internship period employing quantitative study descriptive survey approach.   Theoretical framework: Internship is requirement of every student of radiology program of Radiological Sciences patch for the award of bachelor's degree at Najran University, Saudi Arabia. The competency level would demonstrate influence the sufficiency and efficiency of clinical training during internship period which represent six months after completing nine levels of radiology program.   Design\Methodology\Approach: The survey was distributed to the tow levels of the last year of radiological sciences which composed of 81 male and female students which gathered seventy-seven (77) participants. Data collected through a questionnaire and summarized as percentages, frequencies, means and standard deviations using SPSS version 20.0.   Findings: The study revealed un adequacy of the internship period and showed low efficiency due to its short duration.   Research, Practical, Social Implication:The research construct and variables are identified the effectiveness and adequacy of the internship period.this  study will be the modele of internship with a new qualitative change related to a period of time acceptable to students, similar to other universities.   Originality/Value: The originality and value in this study are the framework conceptance and questionnaire that prepared and proved for evaluating the effectiveness and adequacy of the internship period for student of radiology program.   Conclusion: In general internship period must be efficient and adequate to enhance sufficiency and efficiency experience by intern trainees

A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

© 2023 Tech Science Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification. The proposed DTomatoDNet model is trained from scratch to determine the classification success rate. 10,000 tomato leaf images (1000 images per class) from the publicly accessible dataset, covering one healthy category and nine disease categories, are utilized in training the proposed DTomatoDNet approach. More specifically, we classified tomato leaf images into Target Spot (TS), Early Blight (EB), Late Blight (LB), Bacterial Spot (BS), Leaf Mold (LM), Tomato Yellow Leaf Curl Virus (YLCV), Septoria Leaf Spot (SLS), Spider Mites (SM), Tomato Mosaic Virus (MV), and Tomato Healthy (H). The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%, demonstrating excellent accuracy in differentiating between tomato diseases. The model could be used on mobile platforms because it is lightweight and designed with fewer layers. Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.Peer reviewe

Prevalence and correlates of diastolic dysfunction in patients with hypertension: a cross-sectional study from in The Kingdom of Saudi Arabia

Introduction: diastolic dysfunction refers to impaired ventricular relaxation or filling regardless of ejection fraction and symptoms. It accounts for 8% and 25% in the hospitalized and general population, respectively. The present study was conducted to determine the prevalence and correlates of diastolic dysfunction in hypertensive patients living in Saudi Arabia. Methods: a multicentric, cross-sectional study was conducted from February 2019 to February 2020 at King Khalid Hospital and Prince Sultan Center for Health Services, Prince Sattam Bin Abdulaziz University hospital in Al Kharj, and Al Kharj Military Industries Corporation hospital, KSA. All patients with hypertension who underwent an echocardiography were included in the study. Logistic regression analysis was performed to determine factors associated with left ventricular diastolic dysfunction (LVDD). Results: the study included a total of 104 participants, where 51.9% were females andthe mean age of the patients was 48.01±12.81 years.Most patients had an abnormal echocardiography finding (64.4%, n = 67). The most common abnormalities were left ventricular (LV) hypertrophy (44.2%, n = 46), and diastolic dysfunction, (35.6%, n = 37). The study revealed that age (aOR: 6.1, 95% CI 1.17-31.3; p = 0.032) and dyslipidemia (aOR: 3.45, 95% CI 1.16-10.24; p = 0.026) have significant association with LVDD in the patients with hypertension. Conclusion: in conclusion, diastolic dysfunction is prevalent among older hypertensive patients and those with dyslipidaemia. Age and dyslipidaemia were non-modifiable and modifiable factors associated with LVDD in hypertensive patients, respectively

Synergistic Effects Of Multidisciplinary Healthcare Approaches On Patient Care Quality In Hospital: A Meta-Analysis

The research conducts a meta-analysis to explore the effects of multidisciplinary healthcare approaches on patient care quality in hospitals. It includes a comprehensive review of literature on public health initiatives, nursing care, medical devices, and more, emphasizing their impact on patient outcomes. The methodology involves analyzing peer-reviewed articles to assess the impact of these approaches. Results show significant improvements in patient care quality, emphasizing the benefits of team-based, coordinated, comprehensive, and patient-centered care. The discussion highlights the synergy between different care components and the need for strategic planning to address resource allocation and information overload. Recommendations are provided for healthcare practitioners, policymakers, and future research, focusing on enhancing collaboration, continuous education, supportive policies, and resource allocation. The conclusion underscores the importance of multidisciplinary strategies in improving patient care quality and outcomes, advocating for a shift towards integrated team-based care

Sustainable Collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT)

Forests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained