Development of a User-Friendly Self-Screening Tool for Assessing Metabolic Syndrome Risk in Youths from Economically Challenged Regions.

Background: Metabolic syndrome increases the risk of heart disease and diabetes. Early identification and management are crucial, especially in economically challenged regions with limited healthcare access.

Aims: To develop nomograms for individualized risk estimation for metabolic syndrome in young people from low-income regions.

Development of Potential Multi-Target Inhibitors for Human Cholinesterases and Beta-Secretase 1: A Computational Approach.

Alzheimer's disease causes chronic neurodegeneration and is the leading cause of dementia in the world. The causes of this disease are not fully understood but seem to involve two essential cerebral pathways: cholinergic and amyloid. The simultaneous inhibition of AChE, BuChE, and BACE-1, essential enzymes involved in those pathways, is a promising therapeutic approach to treat the symptoms and, hopefully, also halt the disease progression.

Identification of Potential Multitarget Compounds against Alzheimer's Disease through Pharmacophore-Based Virtual Screening.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive loss of cognitive functions, and it is the most prevalent type of dementia worldwide, accounting for 60 to 70% of cases. The pathogenesis of AD seems to involve three main factors: deficiency in cholinergic transmission, formation of extracellular deposits of β-amyloid peptide, and accumulation of deposits of a phosphorylated form of the TAU protein. The currently available drugs are prescribed for symptomatic treatment and present adverse effects such as hepatotoxicity, hypertension, and weight loss.

β-Glucosidase activity and antimicrobial properties of potentially probiotic autochthonous lactic cultures.

Background: The demand for lactic acid bacteria products, especially probiotics, has increased. Bacteria that increase polyphenol bioavailability and act as bio preservatives are sought after. This study aims to identify autochthonous lactic acid cultures from EMBRAPA that demonstrate β-glucosidase activity and inhibitory effect on microbial sanitary indicators.

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study.

Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms.

Prediction models for carbapenem-resistant Enterobacterales carriage at liver transplantation: A multicenter retrospective study.

Background: Carbapenem-resistant Enterobacterales (CRE) colonisation at liver transplantation (LT) increases the risk of CRE infection after LT, which impacts on recipients' survival. Colonization status usually becomes evident only near LT. Thus, predictive models can be useful to guide antibiotic prophylaxis in endemic centres.

Identification of potential human beta-secretase 1 inhibitors by hierarchical virtual screening and molecular dynamics.

Alzheimer's disease (AD) is a neurodegenerative pathology responsible for 70% of dementia cases worldwide. Despite its relevance, the few drugs available for the treatment of this disease offer only symptomatic relief, with limited efficacy and serious adverse effects. The most accepted hypothesis about the pathogenesis involves the aggregation and deposition of β-amyloid peptides, mainly in the cerebral cortex and hippocampus, through the catalytic action of beta-secretase 1 (BACE-1), making this enzyme a promising target for the development of new drugs.

Cox model and decision trees: an application to breast cancer data.

Objective: To evaluate, using semiparametric methodologies of survival analysis, the relationship between covariates and time to death of patients with breast cancer, as well as the determination discriminatory power in the conditional inference tree of patients who had cancer.

Methods: A retrospective cohort study was conducted using data collected from medical records of women who had breast cancer and underwent treatment between 2005 and 2015 at the Hospital da Fundação de Assistencial da Paraíba in Campina Grande, State of Paraiba, Brazil. Survival curves were estimated using the Kaplan-Meier method, Cox regression, and conditional decision tree.

Evaluation of Docking Machine Learning and Molecular Dynamics Methodologies for DNA-Ligand Systems.

DNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into Molecular Architect (MolAr), were evaluated for their ability to analyze those interactions, considering visual inspection, redocking, and ROC curve. Ligands were refined by Parametric Method 7 (PM7), and ligands and decoys were docked into the minor DNA groove (PDB code: 1VZK).

The role of renin angiotensin system in the pathophysiology of rheumatoid arthritis.

Background: In rheumatoid arthritis (RA) and osteoarthritis (OA), chronic inflammatory processes lead to progresive joint destruction. The renin-angiotensin system (RAS) is involved in the pathogenesis of RA and OA. The aim of this mini-review article is to summarize evidence on the role of RAS in RA and OA.

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence.

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs.

Comparison of dairy desserts produced with a potentially probiotic mixed culture and dispersions obtained from Gracilaria birdiae and Gracilaria domingensis seaweeds used as thickening agents.

Dairy desserts have emerged as interesting options for the incorporation of probiotics, bioactive ingredients and alternative sources of thickeners. This shows an opportunity to investigate the use of Gracilaria seaweeds in the formulation of potentially probiotic dairy desserts. This study aimed to compare the effects of dispersions obtained from Gracilaria domingensis and Gracilaria birdiae used as thickening agents on texture properties of dairy desserts fermented with SAB 440-A, composed of the starter Streptococcus thermophilus and the potential probiotics Bifidobacterium animalis and Lactobacillus acidophilus, and also to study their physicochemical characteristics, microbial viability and sensory acceptability.

Effect of Aqueous Extract of the Seaweed Gracilaria domingensis on the Physicochemical, Microbiological, and Textural Features of Fermented Milks.

The effects of the Gracilaria domingensis seaweed aqueous extract in comparison with gelatin on the physicochemical, microbial, and textural characteristics of fermented milks processed with the mixed culture SAB 440 A, composed of Streptococcus thermophilus, Lactobacillus acidophilus, and Bifidobacterium animalis ssp. lactis, were investigated. The addition of G.

Waterborne Risperidone Decreases Stress Response in Zebrafish.

The presence of drugs and their metabolites in surface waters and municipal effluents has been reported in several studies, but its impacts on aquatic organisms are not yet well understood. This study investigated the effects of acute exposure to the antipsychotic risperidone on the stress and behavioral responses in zebrafish. It became clear that intermediate concentration of risperidone inhibited the hypothalamic-pituitary-interrenal axis and displayed anxiolytic-like effects in zebrafish.

Resonant electronic coupling enabled by small molecules in nanocrystal solids.

The future exploitation of the exceptional properties of nanocrystal (NC) thin films deposited from liquid dispersions of nanoparticles relies upon our ability to produce films with improved electrical properties by simple and inexpensive means. Here, we demonstrate that the electronic conduction of solution-processed NC films can be strongly enhanced without the need of postdeposition treatments, via specific molecules adsorbed at the surfaces of adjacent NCs. This effect is demonstrated for Si NC films doped with the strong molecular oxidizing agent tetrafluoro-tetracyanoquinodimethane (F4-TCNQ).