Objective: To construct the recombinant adenoviral containing fructose 1, 6-biphosphatase 1 (FBP1), and to investigate whether FBP1 has effect on autophagy and proliferation in liver cancer cells (HepG2). Methods: FBP1 cDNA sequence was amplified by PCR and cloned in adenovirus vector pAdTrack-TO4, and then recombinant adenovirus plasmid pAdTrack-FBP1 was constructed. The recombinant adenovirus plasmid was transfected into HEK293 cells by Lipofectamine 3000. High-titer of recombinant adenovirus AdFBP1 was obtained by packaging and amplification. HepG2 cells were infected with recombinant adenovirus AdFBP1, and the Mock and AdGFP group were set at the same time. Western blot and confocal laser scanning microscopy were used to observe the effect of FBP1 on the level of autophagy in hepatocellular carcinoma cells, and the effect of FBP1on the proliferation was observed by MTS and colony formation assay. A t-test and one-way ANOVA were used to compare the mean between group. Results: A high-titer recombinant adenovirus FBP1 was successfully constructed. Western blot and confocal laser scanning microscopy showed that the level of autophagy in AdFBP1 group was significantly lower than that in AdGFP group. Western blot results showed that LC3-II protein expression level in AdGFP was 1.10 ± 0.10 and 0.30 ± 0.01 in AdFBP1 group, F = 90.36, P < 0.01. Confocal laser scanning microscopy analysis showed that the average number of autophages in AdGFP was 28.33 ± 1.53 and 12.33 ± 1.53 in AdFBP1group, F = 97.40, P < 0.01. In addition, the results of colony formation assay and MTS assay showed that the proliferation of liver cancer cells in the AdFBP1 group was significantly inhibited compared with the AdGFP group. The results of colony formation showed that the cell clones in the AdGFP group was 65.66 ± 2.57 and 34.00 ± 2.00 in AdFBP1 group, F = 141.50, P < 0.01. MTS results showed that the absorbance of AdGFP group at 96h was 39.13 ± 2.21 and 30.61 ± 3.33 in AdFBP1 group, F = 7.80, P < 0.05. Conclusion FBP1 inhibited the autophagy and proliferation in liver cancer cells (HepG2).

Objective To investigate the clinical features and surgical treatment of cerebral amyloid angiopathy hemorrhage (CAAH) caused by cerebral vascular amyloidosis.Methods The clinical data,radiological diagnoses,pathology results and treatment efficacies of 76 patients with CAAH,admitted to and performed surgery in our hospital from August 2010 to September 2015,were retrospectively analyzed.Results The first preoperative CT indicated that prompt hematomas were located in the cerebral hemisphere brain cortex,forming lobulated or irregular shape;2 or more lesions of recurrent hemorrhage were noted in 14 (18.4％);34 (44.7％) were with subarachnoid hemorrhage,16 (21.1％) were with intraventricular hemorrhage.Histopathological examination supported the diagnosis.Sixteen patients (21.1％) underwent second operation during hospitalization due to large amount of bleeding.During hospitalization,18 (23.7％) died,and 20 (26.3％) had long-term coma.During the follow-up period,23 patients (30.3％) had recurrent intracranial hemorrhage,of which,6 patients (7.9％) underwent surgical treatment,and 17 patients (22.4％) died.Conclusion The bleeding sites of CAAH mainly locate in the lobes of the brain surface,with lobulated or irregular shape,which is easily complicated with subarachnoid hemorrhage or intraventricular hemorrhage,with multiple features;diagnostic rate of CAAH can be improved by pathological examination of brain tissues and blood vessels;CAAH after surgery has high relapse rate and poor surgical results.

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.