Abstract: To investigate the distribution of avian influenza virus in environmental samples from live poultry markets (LPM) in China, samples were collected and tested by nucleic acid during 2009-2013 season. Each sample was tested by real-time RT PCR using flu A specific primers. If any real-time PCR was positive, the sample was inoculated into specific-pathogen-free (SPF) embryonated chicken eggs for viral isolation. The results indicated that the positive rate of nucleic acid in enviromental samples exhibited seasonality. The positive rate of nucleic acid was significantly higher in Winter and Spring. The positive rate of nucleic acid in LPM located in the south of China was higher than in northern China. Samples of Sewage for cleaning poultry and chopping board showed that higher positive rate of nucleic acid than other samples. The Subtype identification showed that H5 and H9 were main subtypes in the enviromental samples. Viral isolation indicated H5 subtypes was more than H9 subtypes between 2009 and 2013 while H9 subtypes increased in 2013. Our findings suggested the significance of public health based on LPM surveillance and provided the basis of prevention and early warning for avian flu infection human.
Animals ; China ; Feces ; virology ; Fresh Water ; virology ; Influenza A virus ; classification ; genetics ; isolation & purification ; Influenza in Birds ; virology ; Poultry ; Poultry Diseases ; virology ; Public Health ; Seasons ; Sewage ; virology

The historical surveillance results showed, there were 10 schistosomiasis cases in Huangshan City from 1994 to 2006. The survey in 2007 showed, the positive rates of blood examination for schistosomiasis in migrant workers and immigrant workers were 0.49% and 0.47% , respectively, but no schistosome-infected patients were detected by using the stool examination. An area with snails of 3 000 m~2 was found in the residence of the immigrant workers, but no infected snails were found. It is indicated that the mobile population has some impact on the transmission of schistosomiasis in the transmission-interrupted area. The surveillance and health education for the mobile population should be strengthened, and the imported infectious source should be prevented.

Objective:To construct a chronic obstructive pulmonary disease (COPD) assessment test (CAT) score prediction model based on a deep network fused with air data, and to explore its significance.Methods:From February 2015 to December 2017, the outdoor environmental monitoring air data near the residential area of the patients with COPD from the Respiratory Outpatient Clinics of Peking University Third Hospital, Peking University People′s Hospital and Beijing Jishuitan Hospital were collected and the daily air pollution exposure of patients was calculated. The daily CAT scores were recorded continuously. The CAT score of the patients in the next week was predicted by fusing the time series algorithm and neural network to establish a model, and the prediction accuracy of the model was compared with that of the long short-term memory model (LSTM), the LSTM-attention model and the autoregressive integrated moving average model (ARIMA).Results:A total of 47 patients with COPD were enrolled and followed up for an average of 381.60 days. The LSTM-convolutional neural networks (CNN)-autoregression (AR) model was constructed by using the collected air data and CAT score, and the root mean square error of the model was 0.85, and the mean absolute error was 0.71. Compared with LSTM, LSTM-attention and ARIMA, the average prediction accuracy was improved by 21.69%.Conclusion:Based on the air data in the environment of COPD patients, the fusion deep network model can predict the CAT score of COPD patients more accurately.

BACKGROUNDThe global strategy for the diagnosis, management and prevention of chronic obstructive pulmonary disease (COPD) guidelines classify patients into four groups according to the number of symptoms and the level of future risk of acute exacerbation COPD (AECOPD). This study aimed to compare the results of different methods used in diagnosis of COPD and evaluate the accuracy of the assessment methods in guiding clinical practice.

METHODSA survey was conducted of 194 COPD outpatients between March and September 2012. Demographic characteristics, the number of exacerbations the patient has had within the previous 12 months, COPD assessment test (CAT), Modified British Medical Research Council (mMRC) scale, and results of the lung function tests were recorded.

RESULTSOf the 194 patients assessed, 21 had a CAT score ≥10 and an mMRC grade ≤1, 13 had a CAT score <10 and an mMRC grade ≥2. A predicted forced expiratory volume in one second (FEV1%) of <50% with less than two acute exacerbations was observed in 39 patients, while a predicted FEV1% of ≥50% was noted in 20 patients with two or more acute exacerbations. The sensitivity of a predicted FEV1% <50% in predicting the risk of AECOPD in the future was 80.9%, while that in the real number of AECOPD events recorded was 62.8%, the difference being statistically significant (P = 0.004). The sensitivity of CAT in predicting the severity of symptoms was 90%, while that of mMRC was 83.8%, and the difference was not statistically significant.

CONCLUSIONSThe COPD assessment method recommended by the global initiative for chronic obstructive pulmonary disease (GOLD) 2011 is complicated and should be simplified. CAT is more comprehensive and accurate than mMRC. The lung function classification is a better tool for predicting the risk of AECOPD in the future, and the number of AECOPD can be referred to when required.

Dyspnea ; diagnosis ; Female ; Humans ; Male ; Pulmonary Disease, Chronic Obstructive ; diagnosis ; Respiratory Function Tests ; Risk Assessment