Objective To develop a competitive immunoassay for quantitative determination of total immunoglobin E (tIgE) in human serum based on light-initiated chemiluminescent assay (LICA).Methods The LICA-tIgE assay was performed by incubating serum samples or calibrator with anti-human IgE antibody-coated chemiluminescet beads,biotinylated human IgE and streptavidin-coated sensitizer beads.The working conditions of this assay were optimized,analytical performance was detected and the correlation of tIgE results between LICA and Beckman Coulter IMMAGE 800 was evaluated.Results The precision of intra-assay and inter-assay (coefficient of variation) ranged from 5.50％ to 7.73％ and 6.45％ to 9.90％,respectively.The functional sensitivity of this assay was 12.65 IU/mL.The recovery rates measured by adding IgE calibrators to human sera with different IgE concentrations were ranged from 104.15％ to 109.37％.The disturbing rates measured by adding total bilirubin,hemoglobin and triacylglycerol to human sera with different IgE concentrations were ranged from-4.49％ to 8.46％.Also,the tIgE results of 111 patients measured by LICA correlated well with those by Beckman Coulter IMMAGE 800 (r2 =0.959).Conclusion LICA developed in this study for detecting tIgE of human serum showed effective perfomance and could meet the basic requirements of clinical diagnostic reagents.

Objective:To construct a combined radiomics model based on preoperative enhanced computed tomography (CT) examination for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC), and provide biological explanations for the radiomics model.Methods:The retrospective cohort study was conducted. The messenger RNA (mRNA) of 424 HCC patients, the clinicopathological data of 39 HCC patients entered into the Cancer Genome Atlas database from its establishment until January 2023, and the clinicopathological data of 53 HCC patients who were admitted to the Gansu Provincial People′s Hospital from January 2020 to January 2023 were collected. The 92 HCC patients were randomly divided into a training dataset of 64 cases and a test dataset of 28 cases with a ratio of 7∶3 based on a random number table method. The CT images of patients in the arterial phase and portal venous phase as well as the corresponding clinical data were analyzed. The 3Dslicer software (version 5.0.3) was used to register the CT images in the arterial phase and portal venous phase and delineate the three-dimensional regions of interest. The original images were preprocessed and the corresponding features were extracted by the open-source software FAE (version 0.5.5). After selecting features using the Least Absolute Shrinkage and Selection Operator, the radiomics model was constructed and the radiomics score (R-score) was calculated. The nomogram was constructed by integrating clinical parameters, imaging features and R-score based on Logistic regression. The gene modules related to radiomics model were obtained and subjected to enrichment analysis by conducting weighted gene co-expression network analysis and correlation analysis. Observation indicators: (1) comparison of clinical characteristics of patients with different MVI properties; (2) establishment of MVI risk model; (3) evaluation of MVI risk model; (4) clustering of gene modules; (5) functional enrichment of feature-correlated gene modules. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent sample t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Comparison of count data was conducted using the chi-square test. The intra-/inter-class correlation coefficient (ICC) was used to assess the inter-observer consistency of radiomics feature extracted by different observers. ICC >0.75 indicated a good consistency in feature extraction. The Logistic regression model was used for univariate and multivariate analyses. The receiver operating characteristic curve was drawn, and the area under curve (AUC), the decision curve and the calibration curve were used to evaluate the diagnostic efficacy and clinical practicality of the model. Results:(1) Comparison of clinical characteristics of patients with different MVI properties. Of 92 HCC patients, there were 47 cases with MVI-positive and 45 cases with MVI-negative, and there were significant differences in hepatitis, tumor diameter, peritumoral enhancement, intratumoral arteries, pseudocapsule and smoothness of tumor margin between them ( χ2=5.308, 9.977, 47.370, 32.368, 21.105, 31.711, P<0.05). (2) Establishment of MVI risk model. A total of 1 781 features were extrac-ted from arterial and portal venous phases of the intratumoral and peritumoral regions. After feature dimension reduction, 8 radiomics features were selected from arterial and portal venous phases to construct the combined model. Results of multivariate analysis showed that peritumoral enhancement, intratumoral arteries, pseudocapsule, smoothness of tumor margins, and R-score were independent risk factors for MVI in patients with HCC [ hazard ratio=0.049, 0.017, 0.017, 0.021, 2.539, 95% confidence interval ( CI) as 0.005-0.446, 0.001-0.435, 0.001-0.518, 0.001-0.473, 1.220-5.283, P<0.05]. A nomogram model was constructed incorporating peritumoral enhancement, intratumoral arteries, pseudocapsule, smoothness of tumor margins, and R-score. (3) Evaluation of the MVI risk model. The AUC of radiomics model was 0.923 (95% CI as 0.887-0.944) and 0.918 (95% CI as 0.894-0.945) in the training dataset and test dataset, respectively. The AUC of nomogram model, incorpora-ting both the R-score and radiomics features, was 0.973 (95% CI as 0.954-0.988) and 0.962 (95% CI as 0.942-0.987) in the training dataset and test dataset, respectively. Results of decision curve showed that the nomogram had better clinical utility compared to the R-score. Results of calibration curve showed good consistency between the actual observed outcomes and the nomogram or the R-score. (4) Clustering of gene module. Results of weighted gene co-expression network analysis showed that 8 gene modules were obtained. (5) Functional enrichment of feature-related gene modules. Results of correlation analysis showed 4 gene modules were significantly associated with radiomics features. The radiomics features predicting of MVI may be related to pathways such as the cell cycle, neutrophil extracellular trap formation, and PPAR signaling pathway. Conclusions:The combined radiomics model based on preoperative enhanced CT imaging can predict the MVI status of HCC. By obtaining mRNA gene expression profiles associated with radiomics features, a biological interpretation of the radiomics model is provided.

Objective: To develop and evaluate a beads-based light-initiated chemiluminescent assay (LICA) for quantitation of cow milk component (Bos d 5) specific IgG 4 antibody in human serum. Methods: The sIgG 4 -LICA was performed by incubated serum samples with biotinylated allergens, emission beads coated with mouse anti-human IgG 4 antibody and streptavidin-coated sensitizer beads. The reaction conditions of sIgG 4 -LICA were optimized and the analytical performance was evaluated. Results: The precision of intra-assay, within-day and inter-assay (coefficient of variation) were 1.78% to 3.13%, 6.65% to 8.41% and 7.94% to 12.30%, respectively. The functional sensitivity of this assay was 4.71 ng/mL. For the linear range, the sIgG 4 -LICA had a good linear relationship within the range between 28.13 and 1 800 ng/mL, and the linear regression equation was Y=0.98X-1.31(r 2 =0.997). Maximum dilution limit was 1∶64. The disturbing rates measured by adding hemoglobin, triacylglycerol, total bilirubin, acid resistance and biotin to human sera with different concentrations of Bos d 5 sIgG 4 were from -6.38% to 8.60%. Conclusions The sIgG 4 -LICA introduced in this study was demonstrated to have effective performance for quantitation of allergen-specific IgG 4 and can meet the need of clinical requirement.