Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

Objective To observe the effects of Ditan Yizhi Decoction on mitochondrial dynamics-mediated glucose metabolism in vascular dementia(VaD)rats based on the ROS/Drp1 axis;To explore its mechanism in treating VaD.Methods Ten male SD rats were randomly selected from 70 as the sham-operation group,and VaD models were prepared using the modified bilateral common carotid artery permanent ligation method for the remaining rats.The successfully modeled rats were randomly divided into model group,positive drug group(donepezil hydrochloride),inhibitor group(Mdivi-1)and Ditan Yizhi Decoction low-,medium-and high-dosage groups(12.86,25.725,51.45 g/kg),and intervened with corresponding method for 4 consecutive weeks.Morris water maze experiment was used to assess the learning memory ability of rats,HE and Nissl staining were used to observe the morphology of hippocampal tissue,transmission electron microscopy was used to observe the mitochondrial ultrastructure of hippocampal neurons,DHE fluorescent probe was used to detect the content of ROS in hippocampal neurons,Western blot was used to detect the expressions of Drp1,p-Drp1,Mfn2,Opa1,HK1,PKM2,GLUT1 and LDHA,the contents of serum IL-1β,IL-6 and TNF-α were detected by ELISA.Results Compared with the sham-operation group,rats in the model group had a prolonged escape latency(P<0.01)and a reduced number of crossing platforms(P<0.01);neuronal gaps in the CA1 region of the hippocampus were enlarged,with irregular cell morphology and blurred borders,neuronal consolidation,lysis and fragmentation of Nissl bodies and reduced number of Nissl bodies,swelling and deformation of mitochondria,disorganization of the cristae,and disruption of the bilayer membrane structure;the content of ROS in CA1 region of the hippocampus was elevated,the protein expressions of Mfn2 and Opa1 significantly decreased(P<0.01),the expressions of p-Drp1,HK1,PKM2,GLUT1,LDHA proteins significantly increased(P<0.01),and serum contents of IL-1β,IL-6 and TNF-α significantly increased(P<0.01).Compared with the model group,the escape latency was significantly shortened in Ditan Yizhi Decoction groups,positive drug group and inhibitor group(P<0.01),and the number of crossing platforms increased(P<0.05,P<0.01);the number of neurons in the hippocampal CA1 region increased,with normal morphology,orderly arrangement,abundant Nissl bodies,recovered mitochondrial morphology,and decreased rupture;the ROS content in hippocampal CA1 region decreased(P<0.01),while the expressions of Mfn2 and Opa1 proteins increased(P<0.01),the expressions of p-Drp1,HK1,PKM2,GLUT1 and LDHA proteins decreased(P<0.01),and the serum contents of IL-1β,IL-6 and TNF-α decreased(P<0.05).Conclusion Ditan Yizhi Decoction can improve cognitive impairment and neuronal morphology in VaD rats,and the mechanism maybe related to regulation of mitochondrial dynamics through the ROS/Drp1 axis,attenuating glycometabolic disorders,and reducing inflammatory response.

Objective To observe the effects of Ditan Yizhi Decoction on mitochondrial dynamics-mediated glucose metabolism in vascular dementia(VaD)rats based on the ROS/Drp1 axis;To explore its mechanism in treating VaD.Methods Ten male SD rats were randomly selected from 70 as the sham-operation group,and VaD models were prepared using the modified bilateral common carotid artery permanent ligation method for the remaining rats.The successfully modeled rats were randomly divided into model group,positive drug group(donepezil hydrochloride),inhibitor group(Mdivi-1)and Ditan Yizhi Decoction low-,medium-and high-dosage groups(12.86,25.725,51.45 g/kg),and intervened with corresponding method for 4 consecutive weeks.Morris water maze experiment was used to assess the learning memory ability of rats,HE and Nissl staining were used to observe the morphology of hippocampal tissue,transmission electron microscopy was used to observe the mitochondrial ultrastructure of hippocampal neurons,DHE fluorescent probe was used to detect the content of ROS in hippocampal neurons,Western blot was used to detect the expressions of Drp1,p-Drp1,Mfn2,Opa1,HK1,PKM2,GLUT1 and LDHA,the contents of serum IL-1β,IL-6 and TNF-α were detected by ELISA.Results Compared with the sham-operation group,rats in the model group had a prolonged escape latency(P<0.01)and a reduced number of crossing platforms(P<0.01);neuronal gaps in the CA1 region of the hippocampus were enlarged,with irregular cell morphology and blurred borders,neuronal consolidation,lysis and fragmentation of Nissl bodies and reduced number of Nissl bodies,swelling and deformation of mitochondria,disorganization of the cristae,and disruption of the bilayer membrane structure;the content of ROS in CA1 region of the hippocampus was elevated,the protein expressions of Mfn2 and Opa1 significantly decreased(P<0.01),the expressions of p-Drp1,HK1,PKM2,GLUT1,LDHA proteins significantly increased(P<0.01),and serum contents of IL-1β,IL-6 and TNF-α significantly increased(P<0.01).Compared with the model group,the escape latency was significantly shortened in Ditan Yizhi Decoction groups,positive drug group and inhibitor group(P<0.01),and the number of crossing platforms increased(P<0.05,P<0.01);the number of neurons in the hippocampal CA1 region increased,with normal morphology,orderly arrangement,abundant Nissl bodies,recovered mitochondrial morphology,and decreased rupture;the ROS content in hippocampal CA1 region decreased(P<0.01),while the expressions of Mfn2 and Opa1 proteins increased(P<0.01),the expressions of p-Drp1,HK1,PKM2,GLUT1 and LDHA proteins decreased(P<0.01),and the serum contents of IL-1β,IL-6 and TNF-α decreased(P<0.05).Conclusion Ditan Yizhi Decoction can improve cognitive impairment and neuronal morphology in VaD rats,and the mechanism maybe related to regulation of mitochondrial dynamics through the ROS/Drp1 axis,attenuating glycometabolic disorders,and reducing inflammatory response.

Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

Objective To construct a nomogram prediction model for the risk of mild cognitive impair-ment (MCI) in elderly people aged ≥ 60-year-old.Methods A total of 502 elderly permanent residents in Guangxi were selected as the research subjects by the multi-stage stratified random sampling method,and the general situation questionnaire and the Beijing edition of MoCA-BJ scale were used to investigate the elderly people,and their anthropometric indicators were collected.The minimum absolute shrinkage rate and selection operator (LASSO) regression were used to screen the characteristic variables.The MCI risk nomogram pre-diction model was constructed.The receiver operating characteristic (ROC) curve and calibration curve were adopted to conduct the fitting effect test on the prediction model.Results Among the 502 elderly people,244 cases (46.04％) had the normal cognition and 258 cases (48.68％) had MCI.The logistic regression analysis showed that the age,education background,month income,children support,calf circumference,BMI and body fat index were the influencing factors of MCI in the elderly people,and the nomogram prediction model of the MCI risk in the elderly people was constructed by these seven variables.The area under the ROC curve (AUC) of the model was 0.790 (95％CI:0.750-0.829),the sensitivity was 0.64,the specificity was 0.62,the C-index index was 0.790,and the model fitting x2=8.111,P=0.454,the predictive value was basically consistent with the actual value.Conclusion The nomogram prediction model of MCI risk in the elderly peo-ple is successfully constructed with good predictive effect.

At present, coronavirus disease 2019 (COVID-19) caused by 2019 novel coronavirus (2019-nCoV) infection has spread rapidly in China and more than 70 countries around the world and thus become a public health event of international concern. In addition to fever and respiratory symptoms, varying degrees of liver injury is also observed after 2019-nCoV infection. This article reviews the clinical features, pathology, pathogenic mechanism, and therapeutic strategies of liver injury associated with COVID-19, hoping to provide a reference for clinical decision-making on the prevention and treatment of COVID-19.

Background:Small intestinal bleeding is difficult to diagnose and treat because of its complex etiology and limit to examination method. Aims:To analyze the etiology,diagnosis,treatment and prognosis of small intestinal bleeding. Methods:The clinical data of 118 consecutive patients with small intestinal bleeding admitted from Oct. 2006 to Oct. 2016 at Daping Hospital,the Third Military Medical University were retrospectively analyzed. Results:Melena was the most common manifestation of small intestinal bleeding (41. 5％),followed by dark bloody stool,positive fecal occult blood test,hematochezia,and anemia with unknown cause. The major causes of bleeding were benign or malignant tumors (43. 2％),vascular lesions (28. 0％)and inflammatory lesions (15. 3％). Diagnosis was made by means of capsule endoscopy,colonoscopy,digital subtraction angiography (DSA),barium meal examination,multi-slice CT (MSCT)and CT enterography (CTE). Forty-one patients were treated by surgical operation,7 by selective arterial embolization,2 by endoscopic therapy,56 by conservative therapy,and all these patients achieved hemostasis. One patient died of massive hemorrhage and 11 were discharged with giving up of treatment. Conclusions:The leading cause of small intestinal bleeding is tumor,followed by vascular and inflammatory lesions. Capsule endoscopy is able to make definite diagnosis with high accuracy,and MSCT is the most widely used diagnostic approach. In addition to conventional treatment,surgical operation,interventional and endoscopic therapies also play important roles in treating small intestinal bleeding.