Burn accidents present a relevant frequency in Brazil and worldwide, maintaining high morbidity and mortality rates. Burn care requires long treatment, numerous consultations, dressing changes and long hospital stays, generating high costs for the health system. The objective of this study is to propagate the theme to increase the understanding of the recent biological dressing with the use of tilapia skin and its applicability in the treatment of burns. The innovative method created in Brazil, has as principle the use of tilapia skin for dressing on burns, with eminent potential in medicine for the treatment of these wounds, where the technique with “biological dressing” has been used since 2016. 19 contributed to a significant increase in burns due to the use of alcohol, both liquid and gel, in the home environment and many accidents with child burns. This study is of an applied nature, exploratory objective, qualitative approach and bibliographic procedure from reliable scientific bases, with a 10-year periodicity, where studies were found, reinforcing the effectiveness of the use of tilapia skin, as well as its benefits , histological features and tests in humans. The research was conducted at the Federal University of Mato Grosso do Sul (UFMS) – Faculty of Medicine (FAMED). The research demonstrated that the biological dressing with tilapia skin is an innovative method in the treatment of burns, being a high quality bioproduct, showing biological capacity to cover burns, collagen properties, tensiometric strength and a biomaterial potential in regenerative medicine.
Depression is a disease that affects approximately 60 million people in the world each year, affecting around 11.5 million Brazilians, which presents in the individual severe anxiety disorders, which may evolve into cases of suicide, ranking among the 20 major causes of death/ year. As a result of Covid-19, approximately 90% of cases of depression in Brazil in the last year increased and people with anxiety crisis and symptoms of acute stress doubled in 2020, and this increases concerns regarding the social isolation that extends. This study aimed to investigate the resources of assistive technologies applied to depressive cases with potential application as treatment mechanisms in the context of the COVID-19 pandemic. This is a study with an exploratory objective, a quantitative approach and a bibliographic procedure and descriptors databases. The study evaluated the presence of 306 articles, being used as a filter the interval between 5 and 10 years of publication. The tools used to depression acquired during the pandemic were also discussed, where the results contributed to broader reflections on the challenges imposed by the pathology. Some interesting results were obtained with the application of Tec. Assistive (AT) depressed patients by Covid-19, highlighting: remote care, electroconvulsive therapy, Deprexis, in addition to art therapy and music therapy, the latter being calls for integrative and complementary practices in health (PICs) with application encouraged by SUS showing great relevance and effectiveness in the treatment. The research carried out has shown the potential of ED tools to address the effects of depression in people with Covid-19. This study encourages the development of new research and the development of instruments that are more specific to this need in order to minimize the problems arising from this disease.
Since the emergence of the pandemic caused by the SARS-CoV-2 virus (coronavirus disease or COVID-19), the generalities since its emergence, from the clinical picture, as well as the findings observed in AI (Artificial Intelligence) diagnostic methods applied to medicine personalized. This article is a literature review regarding the use of personalized medicine combined with artificial intelligence to monitor people with covid-19. The continuous evolution of intelligent systems aims to provide better reasoning and more efficient use of collected data. This use is not restricted to retrospective interpretation, that is, to provide diagnostic conclusions. It can also be extended to prospective interpretation, providing an early prognosis. That said, physicians who could be assisted by these systems find themselves in the gap between the clinical case and in-depth technical analyses. What is missing is a clear starting point for approaching the world of machine learning in medicine.
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