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AbstractObjectivesIn December 2019, a novel coronavirus (SARS-CoV-2)-infected pneumonia (COVID-19) occurred in Wuhan, China. Laboratory-based diagnostic tests utilized real-time reverse transcriptase polymerase chain reaction (RT-PCR) on throat samples. This study evaluated the diagnostic value to analyzing throat and sputum samples in order to improve accuracy and detection efficiency.MethodsPaired specimens of throat swabs and sputum were obtained from 54 cases, and RNA was extracted and tested for 2019-nCoV (equated with SARS-CoV-2) by the RT-PCR assay.ResultsThe positive rates of 2019-nCoV from sputum specimens and throat swabs were 76.9% and 44.2%, respectively. Sputum specimens showed a significantly higher positive rate than throat swabs in detecting viral nucleic acid using the RT-PCR assay (p = 0.001).ConclusionsThe detection rates of 2019-nCoV from sputum specimens were significantly higher than those from throat swabs. We suggest that sputum would benefit for the detection of 2019-nCoV in patients who produce sputum. The results can facilitate the selection of specimens and increase the accuracy of diagnosis.

Abstract:The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model.

Sunitinib, a novel oral multi-targeted tyrosine kinase inhibitor for patients with metastatic renal cell carcinoma (mRCC) and advanced gastrointestinal stromal tumor, has a good prospect for clinical application and is being investigated for the potential therapy of other tumors. We observed the phenomenon that drinking tea interfered with symptom control in an mRCC patient treated with sunitinib and speculated that green tea or its components might interact with sunitinib. This study was performed to investigate whether epigallocatechin-3-gallate (EGCG), the major constituent of green tea, interacted with sunitinib. The interaction between EGCG and sunitinib was examined in vitro and in vivo. (1)H nuclear magnetic resonance ((1)H-NMR) spectroscopy and mass spectrometry (MS) were used to analyze the interaction between these two molecules and whether a new compound was formed. Solutions of sunitinib and EGCG were intragastrically administered to rats to investigate whether the plasma concentrations of sunitinib were affected by EGCG. In this study, we noticed that a precipitate was formed when the solutions of sunitinib and EGCG were mixed under both neutral and acidic conditions. (1)H-NMR spectra indicated an interaction between EGCG and sunitinib, but no new compound was observed by MS. Sticky semisolid contents were found in the stomachs of sunitinib and EGCG co-administrated mice. The AUC(0-∞) and C (max) of plasma sunitinib were markedly reduced by co-administration of EGCG to rats. Our study firstly showed that EGCG interacted with sunitinib and reduced the bioavailability of sunitinib. This finding has significant practical implications for tea-drinking habit during sunitinib administration.

Background: In December 2019, a novel coronavirus (SARS-CoV-2) infected pneumonia (COVID-19) occurred in Wuhan, China. Diagnostic test based on real-time reverse transcription polymerase chain reaction assay (qRT-PCR) was the main means of confirmation, and sample collection was mostly throat swabs, which was easy to miss the diagnosis. It is necessary to seek specimen types with higher detection efficiency and accuracy. Methods: Paired specimens of throat swabs and sputum were obtained from 54 cases, and RNA was extracted and tested for 2019-nCoV (equated with SARS-CoV-2) by qRT-PCR assay. Results: The positive rates of 2019-nCoV from sputum specimens and throat swabs were 76.9% and 44.2%, respectively. Sputum specimens showed a significantly higher positive rate than throat swabs in detecting viral nucleic acid using qRT-PCR assay (P=0.001). Conclusions: The detection rates of 2019-nCoV from sputum specimens are significantly higher than throat swabs. We suggest that sputum would benefit for the detection of 2019-nCoV in patients who produce sputum. The results can facilitate the selection of specimens and increase the accuracy of diagnosis.