Articles

Gastroenteritis: A Comprehensive Review

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Gastroenteritis, commonly referred to as stomach flu, is an acute inflammation of the gastrointestinal tract, marked by symptoms including diarrhea, vomiting, abdominal cramps, and fever. This review article provides a comprehensive overview of gastroenteritis, addressing its etiology, epidemiology, pathophysiology, diagnosis, management, and prevention strategies. The condition is caused by a variety of infectious agents such as viruses (noroviruses, rotaviruses), bacteria (Campylobacter, Salmonella), and parasites (Giardia lamblia), with transmission typically occurring through contaminated food, water, or person-to-person contact. Globally, gastroenteritis remains a significant public health issue, with high morbidity and mortality rates, particularly in children under five in developing countries. Diagnosis often relies on clinical evaluation and laboratory tests, while management focuses on rehydration therapy and symptomatic relief. Preventive measures include personal hygiene, food safety practices, environmental sanitation, and vaccination, with rotavirus vaccines significantly reducing severe cases in children. Emerging trends in gastroenteritis research aim at developing rapid diagnostic tools, novel therapeutic approaches, and new vaccines, highlighting the importance of a multidisciplinary approach to mitigate the global impact of this disease.

A Review of AI-powered Diagnosis of Rare Diseases

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The diagnosis of rare diseases presents significant challenges due to their low prevalence, complex symptomatology, and the scarcity of specialized knowledge. However, advancements in Artificial Intelligence (AI) offer promising solutions to these challenges. This review explores the current state of AI-powered diagnostic tools for rare diseases, focusing on the methodologies, algorithms, and platforms utilized in this emerging field. We examine how AI technologies, such as machine learning, deep learning, and natural language processing, are being integrated into clinical practice to enhance diagnostic accuracy and speed. The research also provides the examples that highlight the successes and limitations of AI in this domain, providing insights into how AI can be harnessed to improve patient outcomes in rare disease diagnosis and management.

Potential Biomarkers for Diagnosis and Prognosis of Acute Myeloid Leukemia

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For many years, cancer has affected the global population from an economic, social and political point of view and, in most cases, it is a malignant tumor with serious consequences for patients. The objective of this study is to answer the potential biomarkers for the diagnosis and prognosis of acute myeloid leukemia. Therefore, this is an exploratory, descriptive bibliographic study with a qualitative approach. The data were collected from a bibliometric survey carried out during a study of scientific production on the proposed topic from 2013 to 2023. After searching for articles, 210 articles were found on the PubMed platform, and no results were found for the key suggested by word in other databases. Among the 210 articles, 28 articles were selected for review. In this way, we seek to analyze which biomarkers have been addressed in the last 10 years in the scientific literature, thus aiming to demonstrate possible targets for new research. We divide our research into genes that are promising biomarkers for diagnosis and/or prognosis and the role of miRNAs as biomarkers.

The Effectiveness of MRI Techniques in Evaluating Multiple Sclerosis Patients

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: Background and objective: Multiple sclerosis (MS) is the most common neurodegenerative disease characterized by multiple focal areas of demyelination called plaques or lesions. The main aim of the study is to evaluate patients with multiple sclerosis disease using MRI technology   and to emphasis the effectiveness of this technology in diagnosis of the disease.

Methods:  This study was conducted in order to better recognition and understanding of MS disorder using radiologic MRI techniques and the main problem is the increase prevalence among public population   . The database registry was limited to hospitalized  patients whom diagnosed with  multiple sclerosis  using MRI techniques  and the population of the study was 80 participants  their age elder than 16 years old  in period from April  to  November  2020 .Normal condition or other neurological disorders were excluded.

Results: The most important results obtained in this study is that it is possible to diagnose Multiple sclerosis patients in an accurate manner using magnetic resonance imaging technology.  The outcome of variable detected prevalence of 80 samples 52.5% of them were male, and 47.5% were female and the big distribution group was 53% aged between 30-39 years. The obtained data also showed that the most affected brain region was periventricular matter with 30%, followed by the frontal lobe with 27.5%. clinical etiology big distribution data were achieved for vision Problems with percentage of   22.5 % followed by Dizziness & vertigo sensation with percentage of 18.7% The MRI techniques showed two appearance of MS lesions and plaques, the most distribution achieved for foci appearance with percentage of 75% and patchy appearance with 25% present.

Conclusion: Although for many years there was awareness of the morbidity and mortality associated with Multiple sclerosis however real progress only comes with the ability to early diagnosis using MRI technology.