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The increasing number of multidrug resistance microorganisms is an alarming threat, and their rapid detection is essential to prevent nosocomial, foodborne, or waterborne infections. Many peptides derived from the venom of wasp Synoeca surinama have antimicrobial activity against Gram-positive and Gram-negative bacteria. Synoeca-MP, an antimicrobial peptide (AMP) from mastoparan family, seems to increase bacterial membrane permeability, promoting cytotoxicity and membrane disruption. Here Synoeca-MP was evaluated as biorecognition element tethered over chitosan-coated magnetic nanoparticles (Fe 3 O 4 -Chit). The transducing layer of the biosensor was developed from the self-assembling of 4-mercaptobenzoic acid (4-MBA) monolayer onto gold substrate. Atomic force microscopy (AFM) analyses confirmed the biointeraction between AMP and different pathogens membranes. The fabrication and performance of the biosensing assembly were characterized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Detection of Enterococcus faecalis (G+), Klebsiella pneumoniae (G-), Pseudomonas aeruginosa (G-), and Candida tropicalis was assessed in a recognition range from 10 1 to 10 5 CFU.mL -1 . An instrumental limit of detection of 10 CFU.mL -1 was obtained for each specimen. However, the device presented a preferential selectivity towards Gram-negative bacteria. The proposed biosensor is a sensitive, fast, and straightforward platform for microbial detection in aqueous samples, envisaged for environmental monitoring applications., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Since the start of the COVID-19 pandemic many studies investigated the correlation between climate variables such as air quality, humidity and temperature and the lethality of COVID-19 around the world. In this work we investigate the use of climate variables, as additional features to train a data-driven multivariate forecast model to predict the short-term expected number of COVID-19 deaths in Brazilian states and major cities. The main idea is that by adding these climate features as inputs to the training of data-driven models, the predictive performance improves when compared to equivalent single input models. We use a Stacked LSTM as the network architecture for both the multivariate and univariate model. We compare both approaches by training forecast models for the COVID-19 deaths time series of the city of São Paulo. In addition, we present a previous analysis based on grouping K-means on AQI curves. The results produced will allow achieving the application of transfer learning, once a locality is eventually added to the task, regressing out using a model based on the cluster of similarities in the AQI curve. The experiments show that the best multivariate model is more skilled than the best standard data-driven univariate model that we could find, using as evaluation metrics the average fitting error, average forecast error, and the profile of the accumulated deaths for the forecast. These results show that by adding more useful features as input to a multivariate approach could further improve the quality of the prediction models., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Biosensors are pre-prepared diagnostic devices composed of at least one biological probe. These devices are envisaged for the practical identification of specific targets of microbiological interest. In recent years, the use of narrow-specific probes such as lectins has been proven to distinguish bacteria and glycoproteins based on their superficial glycomic pattern. For instance, Concanavalin A is a carbohydrate-binding lectin indicated as a narrow-specific biological probe for Gram-negative bacteria. As a drawback, Gram-positive bacteria are frequently overlooked from lectin-based biosensing studies because their identification results in low resolution and overlapped signals. In this work, the authors explore the effect that platform nanostructuration has over the electrochemical response of ConA-based platforms constructed for bacterial detection; one is formed of chitosan-capped magnetic nanoparticles, and another is composed of gold nanoparticle-decorated magnetic nanoparticles. The biosensing platforms were characterized by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) as a function of bacterial concentration. Our results show that probe-target interaction causes variations in the electrical responses of nanostructured transducers. Moreover, the association of gold nanoparticles to magnetic nanoparticles resulted in an electrical enhancement capable of overcoming low resolution and overlapping Gram-positive identification. Both platforms attained a limit of detection of 10 ° CFU mL -1 , which is useful for water analyses and sanitation concerns, where low CFU mL -1 are always expected. Although both platforms were able to detect Gram-negative bacteria, Gram-positives were only correctly differentiated by the gold nanoparticle-decorated magnetic nanoparticles, thus demonstrating the positive influence of hierarchically nanostructured platforms., (Copyright © 2021 Elsevier GmbH. All rights reserved.)

Introduction: Coronavirus disease 2019 (COVID-19) is the disease caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the ongoing obesity pandemic, its coexistence with COVID-19 becomes worrying and has a less favorable outcome., Objective: This study aimed to describe the clinical and epidemiological profiles of confirmed cases of COVID-19 in individuals with obesity in the state of Alagoas., Methods: The observational cross-sectional study involving 138 confirmed cases of COVID-19 who had obesity as a comorbidity reported at the time of notification of the disease. The data were collected from the COVID-19 database in the state of Alagoas, and the variables analyzed were sex, age (and age group), race/color, outcome, clinical manifestations, and associated comorbidities. The Kolmogorov-Smirnov, Mann-Whitney U, χ², or Fisher's exact tests were performed as appropriate. The significance was set at 5 and 95% confidence intervals., Results: There was a predominance of females (55.1%; n=76), aged <60 years (70.3%; n=97) and brown race/color (n=76; 55.1%). The most prevalent symptoms were cough (n=84; 60.9%), fever (n=78; 56.5%), headache (n=36; 26.1%), and adynamia (n=28; 20.3%). The median age was 49 years, with no difference between genders (p=0.340). The lethality rate was 17.4% (n=24), being higher in the male population (22.6% in males and 13.2% in females). Of the 24 deaths, 13 (54.2%) were recorded in the elderly people. In addition to obesity, 54.3% (n=75) had systemic arterial hypertension and 30.4% (n=42) had diabetes mellitus. There was no difference in the prevalence of comorbidity between genders., Conclusions: The profile studied demonstrates that obesity represents a challenge for coping with COVID-19.

This work identifies aspects of comprehensiveness in the proposals for healthcare evaluation of the Agência Nacional de Saúde Suplementar (ANS) in Brazil, and of the National Commitee Quality Assurance (NCQA) in the U.S.A. The investigation was based on a documental analysis of the proposals found in the websites of both institutions in the second semester of 2006. The data were systemized according to pre-established attributes, building an analytical matrix for evaluating the following three dimensions: management practices and service organization; quality of knowledge and of practices of the health workers; and quality of the control practices of the society. In the Brazilian case there was an emphasis on the first and the third dimensions. The evaluation focalized the operators; the health plans were not evaluated. In the American case there was a balance between the three sets of attributes and the evaluation was focused on the health plans. Both proposals measured the satisfaction of the beneficiaries and made the results of the evaluations available in their websites. In conclusion we emphasize the importance of evaluative concepts and approaches for measuring the integrality of private health plan operators in the Brazilian supplementary health sector.