ABSTRACT OBJECTIVE Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS Patients’ mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers

ABSTRACT OBJECTIVE Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS Patients’ mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers

In order to study the influence of the insulator layer thickness in heterojunctions, (YBa2Cu3O7-δ[20nm]/ PrBa2Cu3Oy/La1/3Ca2/3MnO3[20nm])x20 superlattices were prepared by pulsed laser deposition using three PrBa2Cu3Oy layer thicknesses and two different sequences of deposition. Sample characterization showed primitive orthorhombic crystalline arrangement for YBCO and LCMO, however, a slightly disordered crystalline structure was observed for the sample having thicker PBCO layer. Microscopy analyses indicated influence of both parameters (PBCO thickness and sequence of deposition) on the texture of the upper layer. Electrostatic Force Microscopy analyses showed evident contrast on the phase images, what suggests that samples are conductive. Distinct surface aspect and highest contrast (highest shift in the electric mode phase image) were observed for the surface of the sample in which the sequence of deposition was inverted, with the YBCO ceramic as upper layer of the superlattice.

OBJETIVO: Esta pesquisa tem como objetivo estudar as propriedades do autolisado de levedura (Saccharomyces cerevisiae) proveniente de cachaça de alambique, investigando a composição centesimal, o perfil acídico e a análise microbiológica do material, bem como realizar análise sensorial do pão de queijo adicionado com o autolisado desidratado. MÉTODOS: O autolisado foi obtido pela lavagem e autólise da biomassa. A secagem foi realizada em secador de bandeja na temperatura de 70ºC. Realizaram-se as seguintes análises: caracterização físico-química (teor lipídico, proteico, fibras totais, fibras solúveis e insolúveis, e cinzas); composição de aminoácidos; análises microbiológicas do produto desidratado; e avaliação sensorial do pão de queijo contendo o autolisado desidratado, através de escala hedônica e teste de atitude. RESULTADOS: O autolisado desidratado apresentou: 1,2% de lipídeos; 24,7% de proteínas; 51,3% de fibras totais, sendo 2,4% de fibras solúveis e 48,9% de insolúveis; e 6,2% de cinzas. As análises microbiológicas mostraram-se dentro do limite recomendado pela Agência Nacional de Vigilância Sanitária. O perfil aminoacídico mostrou deficiência de histidina e metionina+cistina. A análise sensorial do pão de queijo mostrou boa aceitação, tendo apenas cor e aparência recebido notas baixas. A maioria dos participantes disse que "gosta disto e compraria de vez em quando". CONCLUSÃO: Os valores de proteína e aminoácidos encontrados na levedura de cachaça de alambique foram inferiores ao mostrado na literatura para levedura de cervejaria e destilaria de álcool etílico. A levedura desidratada estava microbiologicamente apta para consumo humano. A baixa aceitabilidade da aparência do pão de queijo provavelmente ocorreu pelo fato de os consumidores não estarem habituados à cor escura do produto. Trabalhos futuros poderão sugerir adição em outros produtos que apresentem aparência mais atrativa.

OBJECTIVE: This study aimed to study the properties of yeast (Saccharomyces cerevisiae) autolysate obtained from pot still rum, including centesimal composition, amino acid profile and microbiological analysis, and perform a sensory analysis of cheese buns (also known as Brazilian cheese bread or rolls) enriched with dried autolysate. METHODS: Autolysate was obtained by rinsing and autolyzing the biomass and dried on a tray dryer at 70º. Its composition was then determined (fat, protein, total fiber, soluble fiber, insoluble fiber, ash and amino acid contents) followed by microbiological analysis. Finally, cheese buns enriched with dried autolysate were submitted to sensory analysis using the hedonic scale and attitude test. RESULTS: Dried autolysate contained 1.2% fats; 24.7% proteins; 51.3% total fiber, where 2.4% was soluble and 48.9% was insoluble; and 6.2% ash. According to microbiological analysis, the product complied with the microbiological limits established by the Agência Nacional de Vigilância Sanitária. Amino acid profile showed that histidine and methionine-cystine levels were low. Sensory analysis indicated good acceptance of the cheese buns. Only their color and appearance received low scores. Most participants reported liking the cheese buns and willingness to buy them on occasion. CONCLUSION: The protein and amino acid levels found in pot still rum were lower than those reported in the literature for breweries and ethanol distilleries. Dried yeast was microbiologically suitable for human consumption. The low grades given to the appearance of the cheese buns were probably due to their unappealing dark color, as opposed to the usual pale yellow color. Future studies could suggest the addition of dried yeast to products whose visual appeal will not be as affected.