واقع سياسة التعليم الجامع في ضوء بعض معايير الجودة في جامعة القدس من وجهة نظر الأكاديميين The reality of the policy of inclusive education in light of some quality standards at Al-Quds University from the point of view of academics

Abstract The study aimed to identify the reality of the policy of inclusive education in the light of some quality standards at Al-Quds University from the point of view of academics. The descriptive analytical method was used. A questionnaire was prepared and applied to a sample of (207) academics at Al-Quds University. The study found that the academics’ assessment of the reality of the inclusive education policy in light of some quality standards at Al-Quds University came to a medium degree, where the arithmetic mean of the total degree was (2.55), and the field of study planning and the inclusive curriculum got the highest arithmetic average of (2.84), followed by the field of curricula and inclusive education with a mean of (2.72), then the field of assessment in order to facilitate learning with an average of (2.52), followed by the field of classroom management and implementation skills to facilitate inclusive education with a mean of (2.47) and it came with a medium degree, followed by the field of the university environment with a mean of (2.17) came to a low degree . The results related to the gender hypothesis also indicated that there were differences in favor of males in the academics’ assessment of the reality of the inclusive education policy in the light of some quality standards at Al-Quds University, and according to the work of academics in the scientific and humanities faculties in favor of those working in the College of Science and Technology. The study recommended to work to enhance the level of inclusive education at Al-Quds University, given that education is a general right for all students, and the need to continuously train academics on all modern programs in dealing with students with disabilities so that they have the ability to plan sound and modern academic planning, and to include university curricula with everything that is modern in regard to students with disabilities and encourage them to think, create and innovate, and that academic attention should be paid to developing a comprehensive plan for evaluating all students through an evaluation plan for inclusive education, and that work is done to strengthen the university environment to be fair and secure in terms of equipment for students with different disabilities

Inducible clindamycin resistance and nasal carriage rates of Staphylococcus aureus among healthcare workers and community members

Background: Nasal carriage of Staphylococcus aureus is becoming an increasing problem among  healthcare workers and community individualsObjectives: To determine the prevalence of methicillin-resistant S. aureus (MRSA) nasal colonization and inducible clindamycin resistance (ICR) of S. aureus among healthcare workers at Soba University Hospital and community members in Khartoum State, Sudan.Methods: Five hundred nasal swabs samples were collected during March 2009 to April 2010. Isolates were identified using conventional laboratory assays and MRSA determined by the disk diffusion method. The D-test was performed for detection of ICR isolates with Clinical Laboratory Standard Institute guidelines.Results: Of the 114 S. aureus isolated, 20.2% represented MRSA. The occurrence of MRSA was  significantly higher among healthcare worker than community individuals [32.7% (18/55) vs. 6.9% (5/59)] (p=0.001). Overall the 114 S. aureus isolates tested for ICR by D-test, 29 (25.4%) yielded inducible resistance. Significantly higher (p=0.026) ICR was detected among MRSA (43.5%) than methicillin-susceptible S. aureus (MSSA) (20.9%).Conclusion: MRSA nasal carriage among healthcare workers needs infection control practice in hospitals to prevent transmission of MRSA. The occurrence of ICR in S. aureus is of a great concern, D- test should be carried out routinely in our hospitals to avoid therapeutic failure.Keywords: S. aureus nasal carriage, healthcare workers, community members, inducible clindamycin resistanc

Inducible clindamycin resistance and nasal carriage rates of Staphylococcus aureus among healthcare workers and community members.

Background: Nasal carriage of Staphylococcus aureus is becoming an increasing problem among healthcare workers and community individuals Objectives: To determine the prevalence of methicillin-resistant S. aureus (MRSA) nasal colonization and inducible clindamycin resistance (ICR) of S. aureus among healthcare workers at Soba University Hospital and community members in Khartoum State, Sudan. Methods: Five hundred nasal swabs samples were collected during March 2009 to April 2010. Isolates were identified using conventional laboratory assays and MRSA determined by the disk diffusion method. The D-test was performed for detection of ICR isolates with Clinical Laboratory Standard Institute guidelines. Results: Of the 114 S. aureus isolated, 20.2% represented MRSA. The occurrence of MRSA was significantly higher among healthcare worker than community individuals [32.7% (18/55) vs. 6.9% (5/59)] (p=0.001). Overall the 114 S. aureus isolates tested for ICR by D-test, 29 (25.4%) yielded inducible resistance. Significantly higher (p=0.026) ICR was detected among MRSA (43.5%) than methicillin-susceptible S. aureus (MSSA) (20.9%). Conclusion: MRSA nasal carriage among healthcare workers needs infection control practice in hospitals to prevent transmission of MRSA. The occurrence of ICR in S. aureus is of a great concern, D- test should be carried out routinely in our hospitals to avoid therapeutic failure

Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically-driven quantitative biomarkers

Existing Quantitative Imaging Biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials

A global research priority agenda to advance public health responses to fatty liver disease

Background & aims An estimated 38% of adults worldwide have non-alcoholic fatty liver disease (NAFLD). From individual impacts to widespread public health and economic consequences, the implications of this disease are profound. This study aimed to develop an aligned, prioritised fatty liver disease research agenda for the global health community. Methods Nine co-chairs drafted initial research priorities, subsequently reviewed by 40 core authors and debated during a three-day in-person meeting. Following a Delphi methodology, over two rounds, a large panel (R1 n = 344, R2 n = 288) reviewed the priorities, via Qualtrics XM, indicating agreement using a four-point Likert-scale and providing written feedback. The core group revised the draft priorities between rounds. In R2, panellists also ranked the priorities within six domains: epidemiology, models of care, treatment and care, education and awareness, patient and community perspectives, and leadership and public health policy. Results The consensus-built fatty liver disease research agenda encompasses 28 priorities. The mean percentage of ‘agree’ responses increased from 78.3 in R1 to 81.1 in R2. Five priorities received unanimous combined agreement (‘agree’ + ‘somewhat agree’); the remaining 23 priorities had >90% combined agreement. While all but one of the priorities exhibited at least a super-majority of agreement (>66.7% ‘agree’), 13 priorities had 90% combined agreement. Conclusions Adopting this multidisciplinary consensus-built research priorities agenda can deliver a step-change in addressing fatty liver disease, mitigating against its individual and societal harms and proactively altering its natural history through prevention, identification, treatment, and care. This agenda should catalyse the global health community’s efforts to advance and accelerate responses to this widespread and fast-growing public health threat. Impact and implications An estimated 38% of adults and 13% of children and adolescents worldwide have fatty liver disease, making it the most prevalent liver disease in history. Despite substantial scientific progress in the past three decades, the burden continues to grow, with an urgent need to advance understanding of how to prevent, manage, and treat the disease. Through a global consensus process, a multidisciplinary group agreed on 28 research priorities covering a broad range of themes, from disease burden, treatment, and health system responses to awareness and policy. The findings have relevance for clinical and non-clinical researchers as well as funders working on fatty liver disease and non-communicable diseases more broadly, setting out a prioritised, ranked research agenda for turning the tide on this fast-growing public health threat

Clinical Significance of HSPD1/MMP14/ITGB1/miR-6881-5P/Lnc-SPARCL1-1:2 RNA Panel in NAFLD/NASH Diagnosis: Egyptian Pilot Study

Background: Non-alcoholic steatohepatitis ((NASH) is the progressive form of (non-alcoholic fatty liver disease) (NAFLD), which can progress to liver cirrhosis and hepatocellular carcinoma. There is no available reliable non-invasive diagnostic tool to diagnose NASH, and still the liver biopsy is the gold standard in diagnosis. In this pilot study, we aimed to evaluate the Nod-like receptor (NLR) signaling pathway related RNA panel in the diagnosis of NASH. Methods: Bioinformatics analysis was done, with retrieval of the HSPD1/MMP14/ITGB1/miR-6881-5P/Lnc-SPARCL1-1:2 RNA panel based on the relation to the NLR-signaling pathway. Hepatitis serum markers, lipid profile, NAFLD score and fibrosis score were assessed in the patients’ sera. Reverse transcriptase real time polymerase chain reaction (RT-PCR) was done to assess the relative expression of the RNA panel among patients who had NAFLD without steatosis, NAFLD with simple steatosis, NASH and healthy controls. Results: We observed up-regulation of Lnc-SPARCL1-1:2 lncRNA that led to upregulation of miR-6881-5P with a subsequent increase in levels of HSPD1, MMP14, and ITGB1 mRNAs. In addition, ROC curve analysis was done, with discriminative cutoff values that aided discrimination between NASH cases and control, and also between NAFLD, simple steatosis and NASH. Conclusion: This pilot study concluded that HSPD1/MMP14/ITGB1/miR-6881-5P/Lnc-SPARCL1-1:2 panel expression has potential in the diagnosis of NASH, and also differentiation between NAFLD, simple steatosis and NASH cases

Artificial intelligence for diabetes: Enhancing prevention, diagnosis, and effective management

Introduction: Diabetes, a major cause of premature mortality and complications, affects millions globally, with its prevalence increasing due to lifestyle factors and aging populations. This systematic review explores the role of Artificial Intelligence (AI) in enhancing the prevention, diagnosis, and management of diabetes, highlighting the potential for personalised and proactive healthcare. Methods: A structured four-step method was used, including extensive literature searches, specific inclusion and exclusion criteria, data extraction from selected studies focusing on AI's role in diabetes, and thorough analysis to identify specific domains and functions where AI contributes significantly. Results: Through examining 43 experimental studies, AI has been identified as a transformative force across eight key domains in diabetes care: 1) Diabetes Management and Treatment, 2) Diagnostic and Imaging Technologies, 3) Health Monitoring Systems, 4) Developing Predictive Models, 5) Public Health Interventions, 6) Lifestyle and Dietary Management, 7) Enhancing Clinical Decision-Making, and 8) Patient Engagement and Self-Management. Each domain showcases AI's potential to revolutionize care, from personalizing treatment plans and improving diagnostic accuracy to enhancing patient engagement and predictive healthcare. Discussion: AI's integration into diabetes care offers personalised, efficient, and proactive solutions. It enhances care accuracy, empowers patients, and provides better understanding of diabetes management. However, the successful implementation of AI requires continued research, data security, interdisciplinary collaboration, and a focus on patient-centered solutions. Education for healthcare professionals and regulatory frameworks are also crucial to address challenges like algorithmic bias and ethics. Conclusion and Recommendations: AI in diabetes care promises improved health outcomes and quality of life through personalised and proactive healthcare. Future efforts should focus on continued investment, ensuring data security, fostering interdisciplinary collaboration, and prioritizing patient-centered solutions. Regular monitoring and evaluation are essential to adjust strategies and understand long-term impacts, ensuring AI's ethical and effective integration into healthcare