Bacterial resistance to antibiotics represents a serious and worrying public health problem worldwide. Considering this scenario, the reuse of drugs already known is presented as a quick and less expensive opportunity, being considered an attractive alternative for the development of new drugs and treatments, this study proposes the contemplation of an ecumenical scenario on the antimicrobial potential of statins, in this context, its objective is to carry out a narrative review of the scientific literature, providing a basis for future research and new possibilities in the face of this public health problem. Narrative review was used as a method to conceive the state of the art and support a pertinent appreciation of the theme. To initiate the investigation, a guiding question was prepared to conduct the entire research process, using the PICo strategy (Population / Interest / Context). The scientific literature presents studies that reveal the antimicrobial potential of statins against different bacterial strains. The antibacterial activity appears to be a specific statin / specific bacterium, with an emphasis on simvastatin and atorvastatin, and Gram-positive bacteria, particularly S. aureus, as well as rifampicin adjuvant activity in the fight against mycobacteria.

In the context of the pandemic, negative implications for mental health have increased considerably, as well as the existing associations of psychotropics in this period to combat the new coronavirus. In view of this, the present integrative review aimed to verify the health impact of covid-19 infection in patients with mental disorders who use psychotropics. As a result, the co-administration of these drugs represented a great risk at the systemic level, and can be lethal in certain cases. Thus, careful evaluations must be taken in order to implement an effective intervention that meets the needs of patients, always considering the risks of these drug associations.

COVID-19 is characterized by pulmonary involvement, which has generated a large number of hospitalizations and studies worldwide, motivating researchers in search of a possible treatment and development of vaccines for the disease. However, other symptoms related to SARS-CoV-2 were less relevant in relation to studies published to date. Thus, there is a need to establish a relationship between patients with inflammatory bowel diseases and the symptoms of the gastrointestinal tract caused by COVID-19, since the involvement of the gastrointestinal tract affects up to 53% of patients who contract SARS-CoV-2 . In this perspective, the present study is an integrative review carried out at the Virtual Health Library and PubMed based on the health descriptors: gastrointestinal diseases and COVID-19, applying the Boolean operator “AND” between them. The selection criteria used were the eligibility criteria: articles in Portuguese, English and Spanish, published between December 2019 and July 2020. Individuals with inflammatory bowel diseases, even with greater expression of ACE2, are not at increased risk of symptoms or worsening. Thus, based on the relationship between pre-existing symptoms and the symptoms of the new COVID-19, health professionals, based on their clinical experience, will be able to compose prophylactic measures and manage patients with COVID-19 and gastrointestinal symptoms more effectively.

The balance between sodium and potassium represents a key point for the regulation of hypertension and in recent years, scientific research has contributed to its understanding from molecular to epidemiological aspects. This review aims to show evidence of regulation of the ENaC channel by the intake of potassium for the control of primary hypertension. The epithelial sodium channel -ENaC- regulated by the renin-angiotensin-aldosterone system at the renal level, is essential for sodium homeostasis and the maintenance of arterial hypertension, and several medical investigations have generated drugs that inhibit activation pathways for sodium action on blood pressure; also, observational, interventional and experimental studies in humans and animals demonstrate the consequences of increased sodium intake. In this context, potassium intake is suggested as a more comprehensive treatment for the control of arterial hypertension, acting at the molecular level with decreased activation of the ENaC channel by normalizing sodium and potassium concentrations, reducing sensitivity to the development of cardiovascular diseases; Moreover, nutritional evidence shows that populations with natural foods rich in fruits and vegetables that exceed potassium intake (150 mEq per day) and minimize sodium intake (20-40 mEq per day) suffer arterial hypertension at a rate of less than 3%, without negative effects on lipid balance, catecholamines, and ion concentrations in the kidney.

The primary symptom of PD is dyskinesia, but as Parkinson's disease (PD) progresses, non-motor symptoms such as depression are more likely to occur. According to previous studies, even though patients with PD frequently experience depression, only 1% of them recognized depression by themselves. These results suggested that it would be needed to diagnose Parkinson's disease depression as soon as possible. Previous studies that evaluated the factors related to Parkinson's disease depression in South Korea reported that neuropsychological level, health factors, socioeconomic status, education level, age, spouse, and social activities affected Parkinson's disease depression. Since regression analysis was mainly used as a modeling method to predict depression, they were efficient in identifying individual risk factors. However, they were limited in identifying compound-risk factors such as sociodemographic variables and living habits. Moreover, since regression analysis assumes independence, normality, and homoscedasticity, there is a possibility of producing biased results when the model is developed using data in violation of normality. As a way to overcome the limitation of the regression model, Classification is a machine learning technique has been widely used in Clinical decision supporting system and medical artificial intelligence. Machine learning can analyze data accurately even if the data somewhat violate the assumption of normality such as nonlinear data in the estimation process. In particular, Classifier Ensemble has a better accuracy than a single classifier, so active research has recently been conducted. The objectives of this study were to develop a model for predicting Parkinson’s disease depression based on stacking-based ensemble machine learning. This study was conducted with resources of'Parkinson's Disease Epidemiology Data' from National Biobank of Korea, the Centers for Disease Control and Prevention, Republic of Korea. This study analyzed 280 subjects who were 60 years or older with Parkinson's disease. Depression was measured using 30 items of Geriatric Depression Scale (GDS). This study was combined with base learners by stacking, in which a meta-learner (meta-classifier) was served to combine the predictions of base learners. A random forest was determined to play the role of meta-learner. This study compared the prediction performance of each model and determined that a model with the highest accuracy with 0.6 or higher sensitivity and specificity as the best model. If models have the same accuracy, the model with the high sensitivity value was selected as the best prediction model. This study suggests that stacking-based ensemble machine learning may be more effective in predicting Parkinson's disease depression than a single classifier.