ABSTRACT
Objective:To explore the classification performance of combined model constructed from CT signs combined with radiomics for discriminating COVID-19 pneumonia and other viral pneumonia.Methods:The clinical and CT imaging data of 181 patients with viral pneumonia confirmed by reverse transcription-polymerase chain reaction in 15 hospitals of Yunnan Province from March 2015 to March 2020 were analyzed retrospectively. The 181 patients were divided into COVID-19 group (89 cases) and non-COVID-19 group (92 cases), which were further divided into training cohort (126 cases) and test cohort (55 cases) at a ratio of 7∶3 using random stratified sampling. The CT signs of pneumonia were determined and the radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models for predicting COVID-19 pneumonia. The diagnostic performance of the models were evaluated using receiver operating characteristic (ROC) analysis, continuous net reclassification index (NRI) calibration curve and decision curve analysis.Results:The combined models consisted of 3 significant CT signs and 14 selected radiomics features. For the radiomics model alone, the area under the ROC curve (AUC) were 0.904 (sensitivity was 85.5%, specificity was 84.4%, accuracy was 84.9%) in the training cohort and 0.866 (sensitivity was 77.8%, specificity was 78.6%, accuracy 78.2%) in the test cohort. After combining CT signs and radiomics features, AUC of the combined model for the training cohort was 0.956 (sensitivity was 91.9%, specificity was 85.9%, accuracy was 88.9%), while that for the test cohort was 0.943 (sensitivity was 88.9%, specificity was 85.7%, accuracy was 87.3%). The AUC values of the combined model and the radiomics model in the differentiation of COVID-19 group and the non-COVID-19 group were significantly different in the training cohort ( Z=-2.43, P=0.015), but difference had no statistical significance in the test cohort ( Z=-1.73, P=0.083), and further analysis using the NRI showed that the combined model in both the training cohort and the test cohort had a positive improvement ability compared with radiomics model alone (training cohort: continuous NRI 1.077, 95 %CI 0.783-1.370; test cohort: continuous NRI 1.421, 95 %CI 1.051-1.790). The calibration curve showed that the prediction probability of COVID-19 predicted by the combined model was in good agreement with the observed value in the training and test cohorts; the decision curve showed that a net benefit greater than 0.6 could be obtained when the threshold probability of the combined model was 0-0.75. Conclusion:The combination of CT signs and radiomics might be a potential method for distinguishing COVID-19 and other viral pneumonia with good performance.
ABSTRACT
Aim To investigate whether the pain modi-fication by group I metabotropic glutamate receptors (mGluRs)required the involvement of Src homology-2 domain-containing phosphatase-2 (SHP-2 ).Methods Co-immunoprecipitation was performed to examine the possible interaction between SHP-2 and group I mGluRs in spinal cord dorsal horn of mice.By measur-ing the paw withdrawal thresholds,the effects of SHP-2 inhibitor NSC-87877 or its catalytically inactive SHP-2 (C459S ) mutant on allodynia induced by group I mGluRs agonist DHPG (50 nmol)were observed.Re-sults Anti-mGluR5 antibody was able to co-immuno-precipitate SHP-2 from spinal dorsal horn of mice, while no SHP-2 was precipitated by anti-mGluR1 anti-body.Inactivation of SHP-2 by NSC-87877 (6 nmol) or SHP-2 (C459S ) effectively attenuated allodynia caused by DHPG.Conclusion SHP-2 can physically interact with mGluR5.The activation of SHP-2 may be necessary for group I mGluRs to process the nocicep-tive information.
ABSTRACT
Pd nanoparticles (Pd NPs) were electrodeposited on the glassy carbon electrode (GCE). Then, Pd NPs/GCE was further inserted into H2 SO4 solution to polarize for 5 min to absorb a certain amount of active hydrogen. Then, the electrode was quickly inserted to HAuCl4 solution for 15 min. AuNPs were deposited spontaneously on the surface of Pd NPs due to the reduction of active hydrogen. As a result, Pd-AuNPs were modified on the surface of GCE. Next, aptamer I of thrombin was immobilized on Pd-Au nanoparticles. In the presence of thrombin, it bound with the aptamer immobilized on Pd-Au nanoparticles and thus the formed complex obstructed the catalysis of Pd-Au nanoparticles to H2 O2 . Hence, the reduction current of H2 O2 decreased with the increase of thrombin concentration. The aptamer sensor had a good linear relationship with the concentration of thrombin in the range of 2. 98-297 nmol/L with a detection limit of 0. 98 nmol/L.
ABSTRACT
A label-free electrochemical immunosensor using hollow structure nanomaterials based on its ordered porous and big surface area was designed. Au nanocage, with good conductivity, catalysis, and biocompatibility, was prepared and modified on the surface of glassy carbon electrode with graphene to immobilize antibody of microcystin directly. In the absence of microcystin, biosensor can obtain high current response signal of electrochemical probe ( [ Fe( CN) 6 ] 3-/4-. When microcystin was combined with its antibody specifically, the charge density and mass transfer resistance on the surface of electrode increased, resulting in a decrease of the corresponding peak current of [ Fe ( CN ) 6 ] 3-/4-. This change was in proportion to the concentration of microcystin indirectly. Experiment conditions such as cultivation time of antigen and concentration of antibody were optimized. The results showed wide linear range of 0. 05 μg/L-1. 0 mg/L and the detection limit of 0. 017 ng/mL. This sensor has good stability and simple production procedure. This sensor provides a new and simple means for the ultrasensitive determination of microcystins in real water samples.