Objective This study aimed to implement and evaluate machine learning based-models to predict COVID-19’ diagnosis and disease severity. Methods COVID-19 test samples (positive or negative results) from patients who attended a single hospital were evaluated. Patients diagnosed with COVID-19 were categorised according to the severity of the disease. Data were submitted to exploratory analysis (principal component analysis, PCA) to detect outlier samples, recognise patterns, and identify important variables. Based on patients’ laboratory tests results, machine learning models were implemented to predict disease positivity and severity. Artificial neural networks (ANN), decision trees (DT), partial least squares discriminant analysis (PLS-DA), and K nearest neighbour algorithm (KNN) models were used. The four models were validated based on the accuracy (area under the ROC curve). Results The first subset of data had 5,643 patient samples (5,086 negatives and 557 positives for COVID-19). The second subset included 557 COVID-19 positive patients. The ANN, DT, PLS-DA, and KNN models allowed the classification of negative and positive samples with >84% accuracy. It was also possible to classify patients with severe and non-severe disease with an accuracy >86%. The following were associated with the prediction of COVID-19 diagnosis and severity: hyperferritinaemia, hypocalcaemia, pulmonary hypoxia, hypoxemia, metabolic and respiratory acidosis, low urinary pH, and high levels of lactate dehydrogenase. Conclusion Our analysis shows that all the models could assist in the diagnosis and prediction of COVID-19 severity.

RESUMO:Foram analisadas 72 amostras de plantas medicinais, enviadas por produtores de sete regiões do Estado do Paraná, segundo metodologia preconizada pela Organização Mundial da Saúde (OMS). Os resultados das análises microbiológicas realizadas (contagem de microrganismos aeróbios viáveis, contagem de bolores e leveduras, pesquisa de enterobactérias, Escherichia coli, Salmonella sp., Staphylococcus aureus e Pseudomonas aeruginosa indicaram que a maioria das amostras (79%) não atendia os parâmetros estabelecidos pela OMS, tanto para utilização da planta medicinal na forma de chá ou para uso tópico quanto para uso interno. A maioria das amostras foi reprovada pelo fato de apresentar contagens de microrganismos aeróbios e de bolores e leveduras elevadas. Tal reprovação evidencia a necessidade de um programa de treinamento dos produtores, envolvendo as diversas etapas de produção e o posterior processamento.Unitermos: qualidade microbiológica; plantas medicinais.ABSTRACT: Microbiological quality of medicinal plants produced by the State of Paraná (Brazil). 72 samples of medicinal plants produced at seven different regions of Paraná State, Brazil, were analysed according to the World Health Organization methodology. The results of the microbiological analysis (total viable aerobic count, yeasts and moulds count, detection of Enterobacteriaceae and other Gram-negative bacteria: Escherichia coli, Salmonella sp., Pseudomonas aeruginosa and Staphylococcus aureus indicated that the majority of the samples (79%) is not in accord to World Health Organization parameters for medicinal plants to be used for herbal tea or topic and internal uses. The main reason of this poor microbiological quality was due to aerobic microrganisms and yeasts and moulds counting. It is an evidence that producers must be orientated by capable professionals in every stage of production in order to provide the necessary quality of the raw material to further medicinal usage.

The ability of plant extracts and preparations to reduce inflammation has been proven by different means in experimental models. Since inflammation enhances the release of specific mediators, inhibition of their production can be used to investigate the anti-inflammatory effect of plants widely used in folk medicine for this purpose. The study was performed for leaves and flowers of Malva sylvestris, and leaves of Sida cordifolia and Pelargonium graveolens. These are three plant species known in Brazil as Malva. The anti-inflammatory activity of extracts and fractions (hexane, chloroform, ethyl acetate, and residual) was evaluated by quantitation of prostaglandins (PG) PGE2, PGD2, PGF2α, and thromboxane B2 (the stable nonenzymatic product of TXA2) concentration in the supernatant of lipopolysaccharide (LPS)- induced RAW 264.7 cells. Inhibition of anti-inflammatory mediator release was observed for plants mainly in the crude extract, ethyl acetate fraction, and residual fraction. The results suggest superior activity of S. cordifolia, leading to significantly lower values of all mediators after treatment with its residual fraction, even at the lower concentration tested (10 μg/mL). M. sylvestris and P. graveolens showed similar results, such as the reduction of all mediators after treatment, with leaf crude extracts (50 μg/mL). These results suggest that the three species known as Malva have anti-inflammatory properties, S. cordifolia being the most potent.

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O efluente proveniente de abatedouro avícola tem grande capacidade produtora de biogás, devido a sua constituição rica em matéria orgânica. Os principais gases produzidos durante a degradação dos constituintes orgânicos são o metano, dióxido de carbono e o óxido nitroso. Um método amplamente empregado no tratamento de resíduos é a biodigestão anaeróbia para captação destes gases. Em indústrias do ramo avícola os efluentes gerados possuem a característica de elevadas cargas de óleos e graxas, sólidos suspensos, nitrogênio, fósforo, proteína e lipídios, que são os responsáveis pela alteração do pH, dos sólidos totais, da demanda bioquímica de oxigênio, da demanda química de oxigênio (DQO), entre outros parâmetros. A caracterização físico-química das águas, efluentes industriais e também dos resíduos industriais, consiste em serviços de determinação no campo e a utilização do controle analítico de laboratório relativo aos parâmetros físico-químicos. Objetivou-se a caracterização físico-química do efluente pré-tratado para se estimar o potencial de geração de biogás, a partir da instalação de um biodigestor anaeróbico na lagoa que o recebe, de um abatedouro de aves da região Oeste do Paraná. Para caracterização do efluente pré-tratado e avaliação da possibilidade de instalação de um biodigestor, foram analisados os parâmetros físico-químicos: demanda química de oxigênio, sólidos totais, sólidos voláteis, sólidos dissolvidos, sólidos fixos, sólidos suspensos, potencial hidrogeniônico, óleos e graxas, e nitrogênio amoniacal, seguindo as metodologias propostas pela APHA (1995). Os procedimentos foram realizados no Laboratório de química orgânica da Universidade Federal do Paraná – setor Palotina. As estimativas médias previstas pelo Centro para a Conservação de Energia, citadas por Brondani (2010) para o dimensionamento de propostas destinadas ao comércio de carbono mencionam uma produção de 0,35 m3 de metano para cada 1 kg de DQO de efluente avícola adicionada ao biodigestor. Se a DQO de entrada na lagoa é 2285 mg L-1, vazão média de 260 m3 h-1, é possível gerar 4990,44 m3 de metano/dia. Como parâmetro para cálculo do potencial de produção, adotou-se o valor de 70% de metano na composição do biogás, logo teremos o equivalente a 7129 m³ de biogás/dia. A biodigestão anaeróbia é um dos melhores processos para o tratamento de efluentes oriundos de abatedouros, como pontos positivos do processo, pode-se citar a produção de energia, redução da matéria orgânica, diminuição de odores desagradáveis e a eliminação de patógenos.

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The most recent rise in demand for bioethanol, due mainly to economic and environmental issues, has required highly productive and efficient processes. In this sense, mathematical models play an important role in the design, optimization, and control of bioreactors for ethanol production. Such bioreactors are generally modeled by a set of first-order ordinary differential equations, which are derived from mass and energy balances over bioreactors. Complementary equations have also been included to describe fermentation kinetics, based on Monod equation with additional terms accounting for inhibition effects linked to the substrate, products, and biomass. In this chapter, a reasonable number of unstructured kinetic models of 1-G ethanol fermentations have been compiled and reviewed. Segregated models, as regards the physiological state of the biomass (cell viability), have also been reviewed, and it was found that some of the analyzed kinetic models are also applied to the modeling of second-generation ethanol production processes.

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