Padronização das técnicas de processamento e extração de RNA viral de amostras de líquido sinovial.

O LS se mostrou uma amostra adequada para a detecção de RNA genômico do vírus da artrite-encefalite caprina. A centrifugação refrigerada por 30 minutos das amostras de LS se mostrou adequada para a sedimentação e isolamento de células caprinas e RNA livre do vírus, não acarretando em degradação significativa do último por Rnases

Detecção de RNA genômico do vírus da artrite-encefalite caprina (CAEV) por amplificação do gene estrutural gag em amostras sangüíneas e de líquido sinovial após injúria tecidual.

A artrite-encefalite caprina (CAE) é uma doença viral que acomete os caprinos provocando artrite, encefalite, mamite e pneumonia, acarretando perdas econômicas. A RT-nested PCR é uma técnica de diagnóstico molecular sensível e específica que foi aplicada em amostras sangüíneas e de líquido sinovial coletadas de animais infectados pelo CAEV e submetidos a uma injúria tecidual nas articulações carpo-metacárpicas uma semana antes da coleta. O RNA foi extraído das amostras, convertido a cDNA e então submetido a duas rodadas de amplificação com iniciadores externos e internos direcionados para o gene estrutural viral gag. Das 17 fêmeas analisadas, 12 apresentaram resultados positivos no sangue e 14 foram positivas (sendo 2 positivos fracos) no líquido sinovial. Estes resultados mostram que estas amostras podem ser utilizadas para o diagnóstico da CAE, não havendo diferenças estatísticas entre elas

Processamento de sangue e líquido sinovial para extração de RNA genomico do vírus da artrite-encefalite caprina e diagnóstico molecular por RT-nested PCR.

Este comunicado descreve os procedimentos de coleta e processamento de líquido sinovial e sangue seguido pela extração de RNA genômico e, finalmente, o diagnóstico molecular do vírus pela técnica de RT-nested PCR

Characteristics of the colorectal cancers diagnosed in the early 2000s in Italy. Figures from the IMPATTO study on colorectal cancer screening

The impact of organized screening programmes on colorectal cancer (CRC) can be observed at a population level only several years after the implementation of screening. We compared CRC characteristics by diagnostic modality (screen-detected, non-screen-detected) as an early outcome to monitor screening programme effectiveness. Data on CRCs diagnosed in Italy from 2000 to 2008 were collected by several cancer registries. Linkage with screening datasets made it possible to divide the cases by geographic area, implementation of screening, and modality of diagnosis (screen-detected, non-screen-detected).We compared the main characteristics of the different subgroups of CRCs through multivariate logistic regression models. The study included 23,668 CRCs diagnosed in subjects aged 50-69 years, of which 11.9%were screendetected (N=2,806), all from the North-Centre of Italy. Among screen-detected CRCs, we observed a higher proportion of males, of cases in the distal colon, and a higher mean age of the patients. Compared with pre-screening cases, screen-detected CRCs showed a better distribution by stage at diagnosis (OR for stage III or IV: 0.40, 95%CI: 0.36-0.44) and grading (OR for poorly differentiated CRCs was 0.86, 95%CI: 0.75-1.00). Screen-detected CRCs have more favourable prognostic characteristics than non-screen-detected cases. A renewed effort to implement screening programmes throughout the entire country is recommended

Mining Big Data Using Parsimonious Factor and Shrinkage Methods

A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using big data. In this paper, our over-arching question is whether such big data are useful for modelling low frequency macroeconomic variables such as unemployment, inflation and GDP. In particular, we analyze the predictive benefits associated with the use dimension reducing independent component analysis (ICA) and sparse principal component analysis (SPCA), coupled with a variety of other factor estimation as well as data shrinkage methods, including bagging, boosting, and the elastic net, among others. We do so by carrying out a forecasting horse-race, involving the estimation of 28 different baseline model types, each constructed using a variety of specification approaches, estimation approaches, and benchmark econometric models; and all used in the prediction of 11 key macroeconomic variables relevant for monetary policy assessment. In many instances, we find that various of our benchmark specifications, including autoregressive (AR) models, AR models with exogenous variables, and (Bayesian) model averaging, do not dominate more complicated nonlinear methods, and that using a combination of factor and other shrinkage methods often yields superior predictions. For example, simple averaging methods are mean square forecast error (MSFE) best in only 9 of 33 key cases considered. This is rather surprising new evidence that model averaging methods do not necessarily yield MSFE-best predictions. However, in order to beat model averaging methods, including arithmetic mean and Bayesian averaging approaches, we have introduced into our horse-race numerous complex new models involve combining complicated factor estimation methods with interesting new forms of shrinkage. For example, SPCA yields MSFE-best prediction models in many cases, particularly when coupled with shrinkage. This result provides strong new evidence of the usefulness of sophisticated factor based forecasting, and therefore, of the use of big data in macroeconometric forecasting