RESUMEN
OBJECTIVE: Automated systems are needed for the rapid and accurate diagnosis of Pseudomonas-associated nosocomial infections among critically ill patients admitted to the intensive care unit. We assessed the performance of TDR-300B and VITEK®2 for the identification of P. aeruginosa using VITEK®-MS as the gold standard. METHODS: This analytical study employed a cross-sectional approach. First, 44 clinical isolates of P. aeruginosa were collected and refreshed. Next, a single colony of oxidase-positive, gram-negative rods (30 samples) was inoculated into a TDR-300B NF-64 card and VITEK®2 GN cassette for each isolate. Finally, bacterial identification was performed using VITEK®-MS for comparative analysis. RESULTS: Compared with the results for VITEK®-MS, the congruence rates for TDR-300B and VITEK®2 were 80.76% (21/26) and 92.30% (24/26), respectively. Further, high sensitivity was observed for TDR-300B and VITEK®2 (95.45% and 100%, respectively). In addition, TDR-300B had a lower positive predictive value and accuracy than VITEK®2, albeit without significance. CONCLUSIONS: Conclusively, there were no significant differences regarding the diagnostic efficiency of TDR-300B and VITEK®2 for P. aeruginosa.
Asunto(s)
Bacterias Gramnegativas , Pseudomonas aeruginosa , Estudios Transversales , Humanos , Pruebas de Sensibilidad Microbiana , Espectrometría de Masa por Láser de Matriz Asistida de Ionización DesorciónRESUMEN
The moisture content of forest surface soil is an important parameter in forest ecosystems. It is practically significant for forest ecosystem related research to use microwave remote sensing technology for rapid and accurate estimation of the moisture content of forest surface soil. With the aid of TDR-300 soil moisture content measuring instrument, the moisture contents of forest surface soils of 120 sample plots at Tahe Forestry Bureau of Daxing'anling region in Heilongjiang Province were measured. Taking the moisture content of forest surface soil as the dependent variable and the polarization decomposition parameters of C band Quad-pol SAR data as independent variables, two types of quantitative estimation models (multilinear regression model and BP-neural network model) for predicting moisture content of forest surface soils were developed. The spatial distribution of moisture content of forest surface soil on the regional scale was then derived with model inversion. Results showed that the model precision was 86.0% and 89.4% with RMSE of 3.0% and 2.7% for the multilinear regression model and the BP-neural network model, respectively. It indicated that the BP-neural network model had a better performance than the multilinear regression model in quantitative estimation of the moisture content of forest surface soil. The spatial distribution of forest surface soil moisture content in the study area was then obtained by using the BP neural network model simulation with the Quad-pol SAR data.