Objective:To explore the abnormal patterns of brain functional network connectivity in depression adolescents and their diagnostic value in adolescent depression.Methods:A total of 94 depression adolescents (adolescent depression group) admitted to Outpatient Department of Psychiatric Imaging, West China Hospital, Sichuan University from January 2020 to December 2022 were selected. In addition, 78 age- and gender-matched healthy adolescents were recruited from local community advertisements at the same time-period as healthy control group. Resting-state functional magnetic resonance imaging was performed; after image preprocessing, group-level spatial independent component analysis was performed to identify the intrinsic network connectivity, and differences in network connectivity between the two groups were compared. Functional connectivity edges were employed as classification features, and feature ranking and screening were then performed. A support vector machine (SVM) with linear kernel function was used to construct a classification model, and receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of this classification model in adolescent depression.Results:No significant difference was noted in age, gender, years of education, and body mass index between the two groups ( P>0.05). Compared with the healthy control group, the adolescent depression group had significantly decreased functional connectivity intensity within and between the networks of sensorimotor network (SMN), visual network (VN), auditory network (AN), default mode network (DMN), and cognitive control network (CCN), and significantly increased functional connectivity intensity within CCN ( P<0.05). When using the 75 top-ranked functional connectivity features, this classification model had the best performance (accuracy rate: 70.35%, sensitivity: 70.21%, specificity: 71.80%, P<0.001). ROC curve showed that area under the curve of this classification model in diagnosing adolescent depression was 0.724 (95% CI: 0.648-0.800, P<0.001). A total of 51 consistent functional connectivities were identified and they were mainly located within or between the networks of SMN, VN, AN, DMN, and CCN. Conclusion:The abnormal resting-state brain functional connectivity in depression adolescents can provide imaging basis for their clinical diagnosis.

Objective:To explore the abnormal patterns of brain functional network connectivity in depression adolescents and their diagnostic value in adolescent depression.Methods:A total of 94 depression adolescents (adolescent depression group) admitted to Outpatient Department of Psychiatric Imaging, West China Hospital, Sichuan University from January 2020 to December 2022 were selected. In addition, 78 age- and gender-matched healthy adolescents were recruited from local community advertisements at the same time-period as healthy control group. Resting-state functional magnetic resonance imaging was performed; after image preprocessing, group-level spatial independent component analysis was performed to identify the intrinsic network connectivity, and differences in network connectivity between the two groups were compared. Functional connectivity edges were employed as classification features, and feature ranking and screening were then performed. A support vector machine (SVM) with linear kernel function was used to construct a classification model, and receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of this classification model in adolescent depression.Results:No significant difference was noted in age, gender, years of education, and body mass index between the two groups ( P>0.05). Compared with the healthy control group, the adolescent depression group had significantly decreased functional connectivity intensity within and between the networks of sensorimotor network (SMN), visual network (VN), auditory network (AN), default mode network (DMN), and cognitive control network (CCN), and significantly increased functional connectivity intensity within CCN ( P<0.05). When using the 75 top-ranked functional connectivity features, this classification model had the best performance (accuracy rate: 70.35%, sensitivity: 70.21%, specificity: 71.80%, P<0.001). ROC curve showed that area under the curve of this classification model in diagnosing adolescent depression was 0.724 (95% CI: 0.648-0.800, P<0.001). A total of 51 consistent functional connectivities were identified and they were mainly located within or between the networks of SMN, VN, AN, DMN, and CCN. Conclusion:The abnormal resting-state brain functional connectivity in depression adolescents can provide imaging basis for their clinical diagnosis.

Objective To explore the feasibility of second-generation dual-layer detector spectral CT ab-dominal virtual non-contrast(VNC) for diagnosing fatty liver.Methods The imaging data of 128 patients with second-generation dual-layer detector spectral CT abdominal enhanced CT in West China Hospital of Si-chuan University from June 2022 to December 2022 were retrospectively analyzed.The CT values of the left lobe,right anterior lobe,right posterior lobe and spleen of all patients were measured on TNC,arterial stage VNC (A-VNC) and venous stage VNC (V-VNC) images.The difference value (L-S) and ratio value (L/S) between the CT value of liver and CT value of spleen were calculated.According to the threshold value of TNC image diagnosis of fatty liver in previous studies,the included cases were divided into two groups:fatty liver group and non-fatty liver group.The Mann-Whitney U test was used to compare the difference of quanti-tative parameters between the fatty liver group and non-fatty liver group.The efficiency of VNC in the diagno-sis of fatty liver was evaluated by the receiver operating characteristic (ROC) curve,and the difference be-tween ROC curves was compared with DeLong's test.The intra-group and inter-group correlation coefficient (ICC) values were used to evaluate the consistency of data measurement.Results The ICC values of intra-group and inter-group consistency of data ranged (0.835-0.986) and (0.810-0.978),respectively (P<0.05).The mean value of left lobe,right anterior lobe,right posterior lobe and triple lobe of liver and CT val-ue of spleen in A-VNC and V-VNC images were lower than those of TNC images,and the differences were statistically significant (P<0.05).The CT values of TNC,A-VNC,V-VNC,(L-S) values and (L/S) values of the non-fatty liver group were higher than those of the fatty liver group,and the differences were statistical-ly significant (P<0.05).The CT value,(L-S) value and (L/S) value of TNC,A-VNC and V-VNC images in the non-fatty liver group were higher than those in the fatty liver group,and the differences were statistically significant (P<0.05).The area under the curve (AUC) of CT values of A-VNC and V-VNC for the diagnosis of fatty liver all were 0.997,and their efficiencies for diagnosing fatty liver had no statistical difference (Z=0.407,P=0.684).AUC of A-VNC (L-S) and V-VNC (L-S) in the diagnosis of fatty liver was 1.000 and 0.981,respectively,and their efficiencies for diagnosing fatty liver had no statistical difference (Z=1.790,P=0.074).AUC of A-VNC (L/S) and V-VNC (L/S) in the diagnosis of fatty liver was 0.992 and 0.987,respec-tively,and their efficiencies for diagnosing fatty liver also had no statistical difference (Z=0.665,P=0.506). Conclusion The CT values of liver and spleen in VNC images reconstructed by second-generation dual-layer detector spectral CT are lower than TNC,but it is still feasible to diagnose fatty liver based on VNC images.

Objective To appraise the analytical capability of flow cytometric bead array for lung cancer markers through the tests of limit of detection,relative standard deviation,specificity,methods comparation and linearity rang.Methods The limit of detection,relative standard deviation,specificity and linearity rang in detection of Carcinoembryonic antigen (CEA),cytokeratin 19 (Cyfra21-1) and neuron specific enolase (NSE) in serum were evaluated by flow cytometer.Western blotting method was ultilized to validate the specificity of antibody-antigen recognization.The interference of hemoglobin,three acyl glycerol and bilirubin on the detection of CEA,Cyfra21-1 and NSE was tested.Compared to electrochemiluminescence immunoassay,the relative error for flow cytometric bead array was assessed.Results Flow cytometric bead array demonstrated that the limit of detection was 1.71 pg/mL for CEA,3.97 pg/mL for cyfra21-1,and 2.27 pg/mL for NSE.The relative standard deviation for intra-assay and inter-assay were below 10％ and 15％,respectively.The pair of antibodies can defferentially recognize antigens.The measurement for CEACAM6,CK18,NSE appeared that there was no significant cross-talking reaction.Three acyl glycerol and bilirubin did not significantly interfere with the detection for serum samples.Hemoglobin of 500 ng/mL can significantly interfere with the detection of Cyfra21-1 (P ＜ 0.05) and NSE (P ＜ 0.05).The correlation coefficient between flow cytometric array and electrochemiluminescence immunoassay was 0.984 2 for serum CEA,0.962 2 for serum cyfra 21-1 and 0.982 0 for serum NSE.The linearity ranged from 355.76 pg/mL to 367.74 ng/mL for CEA,from 87.89 pg/mL to 107.8 ng/mL for cyfra21-1,and from 90.12 pg/mL to 86.07 ng/mL for NSE.Conclusion Flow cytometric array for lung cancer markers may be of use in clinical detection.