Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Most patients with differentiated thyroid cancer have a good prognosis after radioiodine-131 therapy,but a small number of patients are insensitive to radioiodine-131 therapy and even continue to develop disease.At present,some targeted drugs can improve progression-free survival in patients with radioactive iodine-refractory differentiated thyroid cancer(RAIR-DTC),such as sorafenib and levatinib,have been approved for the treatment of RAIR-DTC.However,due to the presence of primary and acquired drug resistance,drug efficacy in these patients is unsatisfactory.This review introduces the acquired drug resistance mechanism of sorafenib in the regulation of mitogen-activated protein kinase(MAPK)and phosphatidylinositol-3-kinase(PI3K)pathways and proposes related treatment strategies,in order to provide a reference for similar drug resistance mechanism of sorafenib and effective treatment of RAIR-DTC.

Objective The relationship between parasitic infections and cancer has become a research hotpot.Although reports of single intestinal protozoan infection in gastrointestinal cancer patients,co-infections are rare.To investigate co-infections of intestinal protozoa in gastrointestinal cancer patients.Methods The DNA of 195 fecal specimens was amplified using nested PCR and sequenced for the presence of Pentatrichomonas hominis,Giardia duodenalis,Cryptosporidium parvum,Blastocystis hominis,Dientamoeba fragilis,and Enterocytozoon bieneusi.Results An overall infection rate of 48.72％(95/195),with 23 cases(24.21％)co-infected with two parasites,three cases(3.16％)co-infected with three parasites.Additionally,67 cases(70.52％)were infected with one protozoa,including 56 cases with Pentatrichomonas hominis,one with Blastocystis hominis,nine with Cryptosporidium parvum,and one case with Dientamoeba fragilis.No infection with Enterocytozoon bieneusi was detected.Conclusion The results indicated a high rate of intestinal protozoan co-infection among gastrointestinal cancer patients.Through one-way ANOVA analysis,it was observed that cases of individual infection with P.hominis were significantly higher compared to those of co-infection with two or three types of protozoa containing P.hominis(P=0.0022)and cases of co-infection with three types of protozoa(P=0.0019).However,no significant difference in the infection rates was observed between two and three types of protozoa(P=0.2775),suggesting that cases of single infection with P.hominis were higher than cases of co-infection with two or more types of protozoa in gastrointestinal cancer patients.BLAST and single nucleotide polymorphism analysis revealed that gene sequences of different infected protozoa,except for a few with 100％homology to the GenBank reference sequence,exhibited varying degrees of base mutations,insertions,or loss at different loci.This study offers crucial insights for understanding the etiology,diagnosis,and prevention of gastrointestinal cancer.

Oxalicine B ( 1) is an α-pyrone meroterpenoid with a unique bispirocyclic ring system derived from Penicillium oxalicum. The biosynthetic pathway of 15-deoxyoxalicine B ( 4) was preliminarily reported in Penicillium canescens, however, the genetic base and biochemical characterization of tailoring reactions for oxalicine B ( 1) has remained enigmatic. In this study, we characterized three oxygenases from the metabolic pathway of oxalicine B ( 1), including a cytochrome P450 hydroxylase OxaL, a hydroxylating Fe(II)/α-KG-dependent dioxygenase OxaK, and a multifunctional cytochrome P450 OxaB. Intriguingly, OxaK can catalyze various multicyclic intermediates or shunt products of oxalicines with impressive substrate promiscuity. OxaB was further proven via biochemical assays to have the ability to convert 15-hydroxdecaturin A ( 3) to 1 with a spiro-lactone core skeleton through oxidative rearrangement. We also solved the mystery of OxaL that controls C-15 hydroxylation. Chemical investigation of the wild-type strain and deletants enabled us to identify 10 metabolites including three new compounds, and the isolated compounds displayed potent anti-influenza A virus bioactivities exhibiting IC₅₀ values in the range of 4.0-19.9 μmol/L. Our studies have allowed us to propose a late-stage biosynthetic pathway for oxalicine B ( 1) and create downstream derivatizations of oxalicines by employing enzymatic strategies.

Schisandra chinensis, a traditional Chinese medicinal herb, is rich in chemical constituents, including lignans, triterpenes, polysaccharides, and volatile oils. Clinically, it is commonly used to treat cardiovascular, cerebrovascular, liver, gastrointestinal, and respiratory diseases. Modern pharmacological studies have shown that S. chinensis extract and monomers have multiple pharmacological activities in lowering liver fat, alleviating insulin resistance, and resisting oxidative stress, and have good application prospects in alleviating nonalcoholic fatty liver disease(NAFLD). Therefore, this study reviewed the research progress on chemical constituents of S. chinensis and its effect on NAFLD in recent years to provide references for the research on S. chinensis in the treatment of NAFLD.
Non-alcoholic Fatty Liver Disease ; Schisandra ; Insulin Resistance ; Lignans

In recent years, enterovirus infection has become a frequent epidemic and developed into an important public health problem. For example, hand-foot-mouth disease has become a common infection among children in China. Hand-foot-mouth disease (HFMD) has been spreading globally since 1997, especially in the Asia-Pacific region. Enterovirus 71 (EV71) is one of the main pathogens causing HFMD. And now there is no drug available to treat EV71 infection. This review summarizes the research progress of anti-enterovirus-71 drugs from the perspective of medicinal chemistry.

To scientifically evaluate the intervention effect of Chinese medicine preventive administration(combined use of Huo-xiang Zhengqi Oral Liquid and Jinhao Jiere Granules) on community population in the case of coronavirus disease 2019(COVID-19), a large cohort, prospective, randomized, and parallel-controlled clinical study was conducted. Total 22 065 subjects were included and randomly divided into 2 groups. The non-intervention group was given health guidance only, while the traditional Chinese medicine(TCM) intervention group was given two coordinated TCM in addition to health guidance. The medical instructions were as follows. Huoxiang Zhengqi Oral Liquid: oral before meals, 10 mL/time, 2 times/day, a course of 5 days. Jinhao Jiere Granules: dissolve in boiling water and take after meals, 8 g/time, 2 times/day, a course of 5 days, followed up for 14 days, respectively. The study found that with the intake of medication, the incidence rate of TCM intervention group was basically maintained at a low and continuous stable level(0.01%-0.02%), while the non-intervention group showed an overall trend of continuous growth(0.02%-0.18%) from 3 to 14 days. No suspected or confirmed COVID-19 case occurred in either group. There were 2 cases of colds in the TCM intervention group and 26 cases in the non-intervention group. The incidence of colds in the TCM intervention group was significantly lower(P<0.05) than that in the non-intervention group. In the population of 16-60 years old, the incidence rate of non-intervention and intervention groups were 0.01% and 0.25%, respectively. The difference of colds incidence between the two groups was statistically significant(P<0.05). In the population older than 60 years old, they were 0.04% and 0.21%, respectively. The incidence of colds in the non-intervention group was higher than that in the intervention group, but not reaching statistical difference. The protection rate of TCM for the whole population was 91.8%, especially for the population of age 16-60(95.0%). It was suggested that TCM intervention(combined use of Huoxiang Zhengqi Oral Liquid and Jinhao Jiere Granules) could effectively protect community residents against respiratory diseases, such as colds, which was worthy of promotion in the community. In addition, in terms of safety, the incidence of adverse events and adverse reactions in the TCM intervention group was relatively low, which was basically consistent with the drug instructions.
Adolescent ; Adult ; Betacoronavirus ; Coronavirus Infections ; drug therapy ; Drugs, Chinese Herbal ; Humans ; Medicine, Chinese Traditional ; Middle Aged ; Pandemics ; Pneumonia, Viral ; drug therapy ; Prospective Studies ; Young Adult