Pediatric Subacute Sclerosing Panencephalitis: A Narrative Review on Measles and the Future of Vaccination.

Subacute sclerosing panencephalitis is a rare complication due to persistent measles infection, characterized by cognitive and motor deterioration. Because subacute sclerosing panencephalitis is considered a potentially fatal complication of measles and usually presents in young populations, particularly those with measles infection under the age of 2 years, new approaches to implement vaccination programs must be devised to help avoid the worsening of patient outcome. Until the disease is eradicated globally, children in all regions of the world remain at risk of measles infection and its respective complications, and therefore, the vaccine is considered the optimal preventative measure. The legacy of measles virus goes beyond the immediate complications. Our study, therefore, aims to provide a comprehensive review on the updated insights into subacute sclerosing panencephalitis as a complication, as well as the extent and future considerations pertaining to vaccination programs in the pediatric population.

Machine Learning Techniques for Differential Diagnosis of Vertigo and Dizziness: A Review.

Vertigo is a sensation of movement that results from disorders of the inner ear balance organs and their central connections, with aetiologies that are often benign and sometimes serious. An individual who develops vertigo can be effectively treated only after a correct diagnosis of the underlying vestibular disorder is reached. Recent advances in artificial intelligence promise novel strategies for the diagnosis and treatment of patients with this common symptom. Human analysts may experience difficulties manually extracting patterns from large clinical datasets. Machine learning techniques can be used to visualize, understand, and classify clinical data to create a computerized, faster, and more accurate evaluation of vertiginous disorders. Practitioners can also use them as a teaching tool to gain knowledge and valuable insights from medical data. This paper provides a review of the literatures from 1999 to 2021 using various feature extraction and machine learning techniques to diagnose vertigo disorders. This paper aims to provide a better understanding of the work done thus far and to provide future directions for research into the use of machine learning in vertigo diagnosis.

Internet Addiction In Medical Students.

BACKGROUND: Internet is a technology that was designed to facilitate research and official communication. According to Internet World Stats, there are 3.36 billion internet users in the world. The internet usage has increased by 832.5% in the world since 2005. In Pakistan there are 25 million active users using internet. It is a multi-dimensional behavioural disorder that manifest in various physical, psychological and social disorders and causes a number of functional and structural changes in brain with related various comorbidities. There is paucity of local researches on this topic but the access to internet and its use is enormous. This study was conducted to find the magnitude of internet addiction in medical students. METHODS: It was a descriptive crosssectional study carried out in Ayub Medical College, Abbottabad. One hundred & forty-eight students were selected in the survey using stratified random sampling. The data was collected using academic and school competence scale and internet addiction diagnostic criteria. RESULTS: In this study, 11 (7.86%) fulfilled the criteria for internet addiction. Most of the students 93 (66.3%) used internet to visit social media applications. Majority of the students 10 (90.9%), showed tolerance as major non-essential symptom of internet addiction. Internet addicts showed significant p=0.01 below average academic performance when compared to non-addicts. Internet addiction showed a significant p=0.03 gender association with internet addiction more prevalent in females than males (12.5% Vs 2.9%). CONCLUSION: This study shows that excessive internet use leads to its addiction and is an entity of concern among medical students.

Conceptual data sampling for breast cancer histology image classification.

Data analytics have become increasingly complicated as the amount of data has increased. One technique that is used to enable data analytics in large datasets is data sampling, in which a portion of the data is selected to preserve the data characteristics for use in data analytics. In this paper, we introduce a novel data sampling technique that is rooted in formal concept analysis theory. This technique is used to create samples reliant on the data distribution across a set of binary patterns. The proposed sampling technique is applied in classifying the regions of breast cancer histology images as malignant or benign. The performance of our method is compared to other classical sampling methods. The results indicate that our method is efficient and generates an illustrative sample of small size. It is also competing with other sampling methods in terms of sample size and sample quality represented in classification accuracy and F1 measure.