Health Literacy Among University Students in the COVID-19 Pandemic: A Systematic Review.

BACKGROUND: The purpose of this systematic review was to assess different studies that worked on university students' health literacy during covid19 pandemic and to make an overview of this issue to recognize possible determinants associated with health literacy. METHODS: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). Four databases (Google Scholar, Web of Science, Pubmed, and Scopus) were used for searching cross-sectional works that assessed the health literacy of university students. We searched papers from December 1st, 2019 up to June 10th, 2022. English language articles were used. Studies were done in countries including; Iran, Pakistan, the USA, Vietnam, China, Colombia, Germany, and Indonesia. RESULTS: The systematic review contains 12 research studies involving 17773 students. There was a relationship between health literacy and some determinants. Positive determinants included age, female gender, Urban background, cognitive maturity, Higher educational qualification, information source (Health workers), number of semesters, and parental education. Some negative determinants were male gender, Rural background, smoking, drinking, being able to pay for medication, lower conspiracy beliefs, and higher fear of COVID-19. CONCLUSION: University students around the world should have courses about health literacy according to university disciplines. These courses should be available for students of different fields to enhance their effectiveness, and training should be associated with students' needs and their subgroup traits.

Machine and deep learning algorithms for classifying different types of dementia: A literature review.

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.