6 research outputs found

    Physicochemical Evaluation And Fatty Acids Profile Of Broiler Chicken Fed Broiler Diets Containing Barley Brewer (hordeum Vulgare) [avaliação Físico-química E Perfil De ácidos Graxos Da Carne De Frangos De Corte Alimentados Com Rações Contendo Cevada Cervejeira (hordeum Vulgare)]

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    The aim of this study was to analyze the chemical composition and the fatty acids (FA) profile of broiler chicken (breast and thigh/on-thigh) fed diets containing 10 and 20% of barley and a control diet. The FAs were evaluated by gas chromatography. There was not a significant difference (p > 0.05) in the breast chemical composition. In the thigh/on-thigh cut, the ration containing 20% of barley reduced the ashes (p 0.05). As for the ω6/ω3 ratio, the highest one was observed in the 20% barley fed animals, in both cuts, being considered the worst quality. 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    Agrupamento de curvas de progresso de requeima, em tomateiro originado de cruzamento interespecífico

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    O objetivo deste trabalho foi estimar curvas de progresso de requeima, em genótipos de tomateiro, e identificar grupos de genótipos resistentes à doença. Foram avaliados 25 híbridos de tomateiro, originados de cruzamentos entre quatro variedades comerciais, um acesso do Banco de Germoplasma de Hortaliças (BGH), da Universidade Federal de Viçosa (UFV), e cinco linhagens F8 (Solanum lycopersicum x Solanum habrochaites), estas últimas selecionadas como fonte de resistência à requeima. As plantas foram inoculadas com uma mistura de esporângios de Phytophthora infestans e, em seguida, foram realizadas seis avaliações quanto à severidade de requeima, a intervalos de três dias. Ajustou-se o modelo exponencial aos dados de percentagem de severidade de requeima, e as estimativas obtidas quanto à incidência inicial da doença (y o) e taxa de progresso da doença (r) foram submetidas à análise de variância multivariada (Manova). As médias dessas estimativas, para cada genótipo, foram submetidas à análise de agrupamento. Observou-se um número ótimo de oito grupos distintos, o que possibilitou identificar genótipos resistentes e suscetíveis. Os híbridos experimentais Ikram x 73 A, Nemo-Netta x 133 A, Ikram x 163 A e Nemo-Netta x 163 A apresentaram a menor taxa de progresso de requeima e, portanto, maior resistência à doença

    30-day morbidity and mortality of sleeve gastrectomy, Roux-en-Y gastric bypass and one anastomosis gastric bypass: a propensity score-matched analysis of the GENEVA data

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    Background: There is a paucity of data comparing 30-day morbidity and mortality of sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and one anastomosis gastric bypass (OAGB). This study aimed to compare the 30-day safety of SG, RYGB, and OAGB in propensity score-matched cohorts. Materials and methods: This analysis utilised data collected from the GENEVA study which was a multicentre observational cohort study of bariatric and metabolic surgery (BMS) in 185 centres across 42 countries between 01/05/2022 and 31/10/2020 during the Coronavirus Disease-2019 (COVID-19) pandemic. 30-day complications were categorised according to the Clavien–Dindo classification. Patients receiving SG, RYGB, or OAGB were propensity-matched according to baseline characteristics and 30-day complications were compared between groups. Results: In total, 6770 patients (SG 3983; OAGB 702; RYGB 2085) were included in this analysis. Prior to matching, RYGB was associated with highest 30-day complication rate (SG 5.8%; OAGB 7.5%; RYGB 8.0% (p = 0.006)). On multivariate regression modelling, Insulin-dependent type 2 diabetes mellitus and hypercholesterolaemia were associated with increased 30-day complications. Being a non-smoker was associated with reduced complication rates. When compared to SG as a reference category, RYGB, but not OAGB, was associated with an increased rate of 30-day complications. A total of 702 pairs of SG and OAGB were propensity score-matched. The complication rate in the SG group was 7.3% (n = 51) as compared to 7.5% (n = 53) in the OAGB group (p = 0.68). Similarly, 2085 pairs of SG and RYGB were propensity score-matched. The complication rate in the SG group was 6.1% (n = 127) as compared to 7.9% (n = 166) in the RYGB group (p = 0.09). And, 702 pairs of OAGB and RYGB were matched. The complication rate in both groups was the same at 7.5 % (n = 53; p = 0.07). Conclusions: This global study found no significant difference in the 30-day morbidity and mortality of SG, RYGB, and OAGB in propensity score-matched cohorts. © 2021, The Author(s)

    At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

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    By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024
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