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.

Pseudomonas aeruginosa-derived infections are considered a public health problem once that P. aeruginosa is stated as a human pathogen highly resistant to antibiotics. Recently, essential oils (EOs) have been reported as an alternative to antibiotics. Polymeric microcapsules can include antimicrobial agents at the core and be surrounded by a polymeric shell, usually made of polysaccharides like chitosan (CS), aiming to work as drug carriers and protecting the encapsulated biomolecule from the surrounding environment. Hydrogel-like films are commonly produced to incorporate microcapsules because of their high porosity, that enables a high permeability of oxygen, nutrients and metabolites. Sodium alginate (SA) and gelatin (GN) are polymers that are frequently applied in the production of films. In this study, a delivery platform was developed for the controlled release of cinnamon leaf oil (CLO) entrapped in CS microcapsules produced via ionotropic gelation. CS solution was prepared without pH adjustment (CS1) and with pH adjusted to 5.0 (CS4), which according to the literature improves the polymer stability for microencapsulation. The microcapsules were then incorporated in hydrogel-like films, composed of a combination of SA and GN. Results confirmed an effective incorporation of CS microcapsules, containing CLO, within SA/GN films, as well as a continuous release of the entrapped CLO during 24h. Time kill kinetics tests showed that during the first hour of interaction with the CLO-containing films bacteria continued to grow. However, as the CLO release from the films increased, its action against the bacteria also improved with a >99% elimination. CS1 microcapsules were deemed more effective, due to their enhanced CLO release profile and antimicrobial action. All qualitative and quantitative antimicrobial tests proved the potential of CLO loaded films for the inhibition of multi-drug resistant bacteria.

Tarek Elshourbagy, Alveena Batool Syed, James Robert Brasic
May 26, 2021
Introduction
Parkinson's disease (PD) is the second most common neurodegenerative disorder afflicting 10 million people worldwide and the fourteenth leading cause of death in the United States. PD is caused by the death of dopaminergic neurons that regulate movement in the substantia nigra pars compacta. Mechanisms contributing to the development of PD in vulnerable individuals include protein misfolding, protein aggregation, and mitochondrial dysfunction. We have developed algorithms for diagnosis and treatment based on review of available knowledge.
Methods
We reviewed the key literature on the pathogenesis of PD in order to propose guidelines for the development of diagnostic and therapeutic interventions for people with PD and related conditions.
We approximated qualitative ratings of the available tools in the form of categorical discrete measurements. We then utilized the discrete measurements to distinguish people with PD and other movement disorders, and healthy controls. We then correlated clinical ratings with output of instrumentation measuring the movements at the same time. We then utilized transforms of output signals and clinical measurements to apply machine learning to general proposed algorithms.
Results and Discussion
In about 25 percent of patients, clinicians incorrectly diagnose the PD. Causes of misdiagnosis include a lack of algorithm and inadequate use of diagnostic modalities. Four main mechanisms that may contribute to the development of PD (misfolding of alpha synuclein, mitochondrial dysfunction, dysfunctional ubiquitin proteosomal pathways, and abnormal autophagy) and specific diagnostic modalities (structured interview and examination, laboratory assessments, neuropathology, genetic testing, neuroimaging)) form the basis for our algorithm for the diagnosis and treatment of PD..
Clinicians, administrators, policy planners, advocates, and other concerned individuals will
benefit from the adoption of our guidelines for the diagnosis and treatment of Parkinson’s disease and related conditions.
Conclusion
Clinicians, administrators, policy planners, advocates, and other concerned individuals will benefit from the adoption of our guidelines for the diagnosis and treatment of Parkinson’s disease and related conditions.

The cause of hyperandrogenism in adolescent girls, typical for polycystic ovary syndrome (PCOS) is still not fully understood.

The aim of the study was to check whether there is a correlation between macronutrient intake and testosterone, androstenedione, dehydroepiandrosterone-sulfate ( DHEA) and the sex hormone binding globulin (SHBG) serum concentration in adolescent girls.

The study included 96 Caucasian girls aged 13-18 years: 61 girls with PCOS and 35 healthy girls. A fasting blood sample was obtained for measurement of serum DHEA-S, SHBG, total testosterone and androstenedione. Macronutrient intake was assessed using the three-day food record method.

Our research indicates that the hypothesis of a relationship existing between macronutrient intake in girls in the peripubertal period and serum androgen concentration is true for dietary fat, protein and fiber consumption. There was a significant positive correlation between total fat, monosaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA), and testosterone and androstendione, and between saturated fatty acids (SFA) and testosterone. Fiber showed a negative correlation with the concentration of androstendione. SHBH concentrations showed a positive correlation only with total dietary protein.

Metabolic syndrome is a disease that simultaneously causes hyperglycemia, hypertension, hyperlipidemia, and abdominal obesity, which are major risk factors for cardiovascular diseases. Statistics Korea reported the ranking of diseases resulting in death in South Korea over the past 10 years, and it indicated that endocrine and metabolic diseases were the 4th major cause of death after cancer, heart disease, and respiratory diseases. Therefore, it is a serious health problem. The occurrence of metabolic syndrome is increasing worldwide, and it has been reported that it is increasing remarkably in Asia including South Korea.

Metabolic syndrome has different characteristics depending on race, region, gender, age, and family history, and various study results have been reported accordingly. Moreover, it has been revealed that lifestyle factors affect the components of metabolic syndrome and that intervening lifestyle can delay or prevent the onset of metabolic syndrome. Therefore, it can be said that metabolic syndrome requires medical treatment as well as correction of wrong living habits.

The 'Alameda County' study is a large-scale epidemiological study conducted in California, USA, in 1965 and is a representative study that identified the relationship between an individual's lifestyle with health status and disease susceptibility. This study suggested seven health behaviors (known as Alameda 7 model) related to health level, which were smoking, drinking, obesity, exercise, breakfast and snacks, and sleep. It was found that these health behaviors were highly associated with disease and death through follow-up studies. Nevertheless, prior studies based on the Alameda7 model just focus on identifying individual risk factors for diseases, and only a few studies considered multiple risk factors. In particular, since patient-centered (medical institution-centered) studies have low external validity, there is a limitation in generalizing and applying the results to the general population.

Consequently, it is necessary to analyze epidemiological data that can represent adults living in the community in order to understand and prevent metabolic syndrome. This study analyzed the relationship between health risk behaviors and metabolic syndrome in the Alameda7 model using an epidemiological survey representing Koreans. Moreover, this study developed a nomogram that allows clinicians to easily predict the group posing a high risk for metabolic syndrome in the primary health care setting.

This study analyzed 12,871 adults (≥19 years) who participated in the 6th Korean National Health and Nutrition Examination Survey. Metabolic syndrome was defined based on the Third Report of the National Cholesterol Education Program (NCEP)Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults(Adult Treatment Panel III)published by the National Institutes of Health. According to the criteria of the Adult Treatment Panel III, metabolic syndrome is diagnosed when a subject satisfies three conditions out of the following five conditions: waist circumference (≥102 cm for men and ≥88cm for women), triglyceride (≥150 mg/dl), HDL-cholesterol (<40mg/dl for men and <50mg/dl for women), blood pressure (systolic blood pressure ≥130 mmHg or more, or diastolic blood pressure ≥85 mmHg), and fasting blood sugar (100 mg/dl or more). Health risk behavior was defined as non-compliance with the guidelines of the Alameda7 model (smoking, excessive drinking, physical inactivity, inadequate weight control, inadequate sleep, skipping breakfast, and snacking). This study built a model for predicting metabolic syndrome using logistic regression to determine the relationship (influence) of each of the seven health risk behaviors with metabolic syndrome. The forward selection method was used to select variables in the logistic regression model, and the results were presented in the form of an unadjusted model and an adjusted multivariate model in which all seven variables were adjusted. The prediction accuracy of the developed metabolic syndrome prediction model was presented by AUC, balanced accuracy, general accuracy, and F1 score using 10-fold cross-validation.

The results of the analysis showed that the prevalence of metabolic syndrome among adults was 21.5%. Even after adjusting all variables in the multivariate model, smoking, skipping breakfast, and physical inactivity were independent risk factors for metabolic syndrome. It is necessary to promote and strengthen smoking cessation, regular breakfast, and regular physical activity for high-risk groups to prevent metabolic syndrome in Korean adults based on the results of this study. Furthermore, it is also needed to identify metabolic syndrome high-risk groups early based on multiple risk factors and establish a differentiated, personalized health policy that continuously manages them.

At the Kobe University Hospital, we have been conducting weekly multidisciplinary prospective audit and feedback since March 2009 to optimize antimicrobial use. However, daily immediate interventions after the initiation of antimicrobial therapy have not been sufficiently implemented. Therefore, a full-time pharmacist specializing in antimicrobial therapy joined the newly launched antimicrobial stewardship team in May 2018, and started daily monitoring to optimize the use of broad-spectrum antimicrobials, such as antipseudomonal antibiotics and anti-MRSA agents. For the medical staff to better understand antimicrobial therapy, the educational lectures were conducted four times after intervention. This study aimed to evaluate the impact of a full-time pharmacist’s intervention on antimicrobial stewardship. The effects before the intervention period (May–December 2017) and after the intervention period (May–December 2018) on antibiotic therapy and clinical outcomes were compared. The rate of blood collection for culture before starting broad-spectrum antibiotics significantly increased after intervention (71% vs. 83%, p < 0.001), and initially prescribed broad-spectrum antibiotics were significantly de-escalated (55% vs. 80%, p = 0.002). A significant reduction in the monthly use of antipseudomonal antibiotics was observed (50.5 vs. 41.6 defined daily doses per 1,000 patient-days, p = 0.012). The incidence of hospital-acquired Clostridioides difficile infection (HA-CDI) significantly decreased after intervention (0.11 vs. 0.054 cases per 1,000 patient-days, p = 0.033). The 30-day mortality rate did not change between the two periods (19% vs. 17%, p = 0.4). Our intervention ensured appropriate antimicrobial therapy and reduced the incidence of HA-CDI without worsening the clinical outcomes.