This study aims to comprehensively analyze the material basis of toad visceral oil(hereafter referred to as toad oil), and explore the pharmacological effect of toad oil on atopic dermatitis(AD). Ultra-high performance liquid chromatography-linear ion trap/orbitrap high-resolution mass spectrometry(UHPLC-LTQ-Orbitrap-MS) and gas chromatography-mass spectrometry(GC-MS) were employed to comprehensively identify the chemical components in toad oil. The animal model of AD was prepared by the hapten stimulation method. The modeled animals were respectively administrated with positive drug(0.1% hydrocortisone butyrate cream) and low-and high-doses(1%, 10%) of toad oil by gavage. The effect of toad oil on AD was evaluated with the AD score, ear swelling rate, spleen index, and pathological section results as indicators. A total of 99 components were identified by UHPLC-LTQ-Orbitrap-MS, including 14 bufadienolides, 7 fatty acids, 6 alkaloids, 10 ketones, 18 amides, and other compounds. After methylation of toad oil samples, a total of 20 compounds were identified by GC-MS. Compared with the model group, the low-and high-dose toad oil groups showed declined AD score, ear swelling rate, and spleen index, alleviated skin lesions, and reduced infiltrating mast cells. This study comprehensively analyzes the chemical composition and clarifies the material basis of toad oil. Meanwhile, this study proves that toad oil has a good therapeutic effect on AD and is a reserve resource of traditional Chinese medicine for external use in the treatment of AD.
Animals ; Dermatitis, Atopic/immunology* ; Disease Models, Animal ; Mice ; Male ; Gas Chromatography-Mass Spectrometry ; Humans ; Bufonidae ; Oils/administration & dosage* ; Chromatography, High Pressure Liquid ; Female ; Mice, Inbred BALB C

Aconitum kusnezoffii is a perennial herbaceous medicinal plant of the family Ranunculaceae, with unique medicinal value. Damping off is one of the most important seedling diseases affecting A. kusnezoffii, occurring widely and often causing large-scale seedling death in the field. To clarify the species of the pathogen causing damping off in A. kusnezoffii and to formulate an effective control strategy, this study conducted pathogen identification, research on biological characteristics, and evaluation of fungicide inhibitory activity. Through morphological characteristics, cultural traits, and phylogenetic tree analysis, the pathogen causing damping off in A. kusnezoffii was identified as Rhizoctonia solani, belonging to the AG5 anastomosis group. The optimal temperature for mycelial growth of the pathogen was 25-30 ℃, with OA medium as the most suitable medium, pH 8 as the optimal pH, and sucrose and yeast as the best carbon and nitrogen sources, respectively. The effect of light on mycelial growth was not significant. In evaluating the inhibitory activity of 45 chemical fungicides, including 30% hymexazol, and 4 biogenic fungicides, including 0.3% eugenol, it was found that 30% thifluzamide and 50% fludioxonil had significantly better inhibitory effects on R. solani than other tested agents, with EC_(50) values of 0.129 6,0.220 6 μg·mL~(-1), respectively. Among the biogenic fungicides, 0.3% eugenol also showed an ideal inhibitory effect on the pathogen, with an EC_(50) of 1.668 9 μg·mL~(-1). To prevent the development of resistance in the pathogen and to reduce the use of chemical fungicides, it is recommended that the three fungicides above be used in rotation during production. These findings provide a theoretical basis for the accurate diagnosis and effective control strategy for R. solani causing damping off in A. kusnezoffii.
Fungicides, Industrial/pharmacology* ; Plant Diseases/microbiology* ; Rhizoctonia/growth & development* ; Aconitum/microbiology* ; Phylogeny ; Mycelium/growth & development*

OBJECTIVE: To evaluate the anti-hepatocellular carcinoma (HCC) activity of total alkaloids from Gelsemium elegans Benth. (TAG) in vivo and in vitro and to elucidate their potential mechanisms of action through transcriptomic analysis. METHODS: TAG extraction was conducted, and the primary components were quantified using high-performance liquid chromatography (HPLC). The effects of TAG (100, 150, and 200 µg/mL) on various tumor cells, including SMMC-7721, HepG2, H22, CAL27, MCF7, HT29, and HCT116, were assessed. Effects of TAG on HCC proliferation and apoptosis were detected by colony formation assays and cell stainings. Caspase-3, Bcl-2, and Bax protein levels were detected by Western blotting. In vivo, a tumor xenograft model was developed using H22 cells. Totally 40 Kunming mice were randomly assigned to model, cyclophosphamide (20 mg/kg), TAG low-dose (TAG-L, 0.5 mg/kg), and TAG high-dose (TAG-H, 1 mg/kg) groups, with 10 mice in each group. Tumor volume, body weight, and tumor weight were recorded and compared during 14-day treatment. Immune organ index were calculated. Tissue changes were oberseved by hematoxylin and eosin staining and immunohistochemistry. Additionally, transcriptomic and metabolomic analyses, as well as quatitative real-time polymerase chain reaction (RT-qPCR), were performed to detect mRNA and metabolite expressions. RESULTS: HPLC successfully identified the components of TAG extraction. Live cell imaging and analysis, along with cell viability assays, demonstrated that TAG inhibited the proliferation of SMMC-7721, HepG2, H22, CAL27, MCF7, HT29, and HCT116 cells. Colony formation assays, Hoechst 33258 staining, Rhodamine 123 staining, and Western blotting revealed that TAG not only inhibited HCC proliferation but also promoted apoptosis (P<0.05). In vivo experiments showed that TAG inhibited the growth of solid tumors in HCC in mice (P<0.05). Transcriptomic analysis and RT-qPCR indicated that the inhibition of HCC by TAG was associated with the regulation of the key gene CXCL13. CONCLUSION TAG inhibits HCC both in vivo and in vitro, with its inhibitory effect linked to the regulation of the key gene CXCL13.
Animals ; Apoptosis/drug effects* ; Liver Neoplasms/genetics* ; Carcinoma, Hepatocellular/genetics* ; Humans ; Alkaloids/therapeutic use* ; Gelsemium/chemistry* ; Cell Line, Tumor ; Cell Proliferation/drug effects* ; Mice ; Xenograft Model Antitumor Assays

In this work,a rapid and label-free sensing platform was designed for visual detection of p53 gene.The rolling circle amplification(RCA)process of the assay platform was activated by p53 gene to produce long DNA-wires,which could bound with berberine hydrochloride(BBH)and further enhanced its fluorescence.This method showed high sensitivity with a low detection limit of 5.63 pmol/L,and high specificity toward p53 gene over other interference materials,even for single-base mutation gene.The method could realize the visual detection of targets under the illumination of a UV lamp.In addition,the designed fluorescence detection platform was successfully applied to p53 gene analysis in 10% fetal bovine serum samples,and the relative standard deviation and the recoveries were 0.1% -1.2% and 99.5% -104.7%,respectively.This approach had satisfactory characteristics,such as low cost,label-free,rapidity,high sensitivity,good selectivity and anti-interference ability,and reliable detection capability for complex practical samples,demonstrating a promising prospect in the diagnosis and treatment of diseases,especially for cancer.

AIM To study the chemical constituents from Euphorbia humifusa Willd.and their in vitro anti-hepatoma activity.METHODS Silica gel,D101 macroporous adsorption resin and semi-preparative RP-HPLC were used for isolated and purified,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The anti-hepatocellular carcinoma activity was determined by MTT mothod.RESULTS Eighteen compounds were isolated and identified as 22-O-angeloyl-R1-barrigenol(1),dimethyl 3,3'-[oxybis(4,1-phenylene)](2E,2'E)-diacrylate(2),N-(3-methoxy-1,3-dioxopropyl)-D-tryptophan methyl ester(3),N-acetyltryptophan methyl ester(4),N-(methoxycarbonyl)-tryptophan methyl ester(5),(3β,5α,17β)-4,4,8,14-tetramethyl-18-norandrostane-3,17-diol(6),3β,18,19β-trihydroxylupane(7),pregnenolone(8),3-hydroxy-5,6-epoxy-7-megastigmen-9-one(9),dehydrovomifoliol(10),loliolide(11),2,2'-oxybis(1,4-di-tert-butylbenzene)(12),dibutyl phthalate(13),4-methoxycinnamic acid(14),3,4-dimethoxycinnamic acid(15),methyl 4-hydroxybenzoate(16),kaempferol(17),quercetin(18).The IC50 values of compounds 1,7 and 8 on HepG2 cells were(17.27±0.92),(19.11±2.14)and(7.53±1.09)μmol/L,respectively.CONCLUSION Compounds 1-16 are first isolated from this plant.Compounds 1,7 and 8 have anti-hepatoma activity.

Objective To investigate the dosimetric differences in preoperative short-course volumetric modulated arc therapy(VMAT)for rectal cancer using different fluence smoothing(FS)levels in the Monaco Treatment Planning System(Monaco TPS).Methods Twenty rectal cancer patients who received preoperative neoadjuvant short-course VMAT at some hospital from September 2021 to December 2022 were retrospectively selected.Four groups of radiotherapy plans were formulated using the Monaco TPS for each case,which were classified into an off group,a low group,a medium group and a high group based on the FS levels.Then the four groups were compared in terms of the dosimetric parameters,monitor unit and number of the segments in the planning target volume(PTV)and organ at risk(OAR).Statistical analysis was performed using SPSS 27.0 software.Results All the four groups had the doses to the target volume meeting clinical requirements,which had no significant differences in the doses to 5％(D5％)and 95％(D95％)to the target volume and the maximum dose(Dmax),minimum dose(Dmin),mean dose(Dmean)and conformity index(all P＞0.05).Statistical differences were found between the homogeneity indexes of the four groups(P＜0.05),with the medium group behaving the best.The number of the segments rose while the mornitor units decreased siginificantly with the increase of FS levels,with the differences being statistically significant(P＜0.05).There were no significant differences between the V25,V20,V15 and V10 of the small intestine,the V25 and V20 of the bladder and the V15 and V10 of the left and right femur(all P＞0.05).Conclusion In preoperative short-course VMAT for rectal cancer,clinical requirements can be met with different FS levels in the Monaco TPS,and medium-level FS results in optimal overall dose distribution in terms of treatment planning.[Chinese Medical Equipment Journal,2025,46(5):48-53]

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective To investigate the dosimetric differences in preoperative short-course volumetric modulated arc therapy(VMAT)for rectal cancer using different fluence smoothing(FS)levels in the Monaco Treatment Planning System(Monaco TPS).Methods Twenty rectal cancer patients who received preoperative neoadjuvant short-course VMAT at some hospital from September 2021 to December 2022 were retrospectively selected.Four groups of radiotherapy plans were formulated using the Monaco TPS for each case,which were classified into an off group,a low group,a medium group and a high group based on the FS levels.Then the four groups were compared in terms of the dosimetric parameters,monitor unit and number of the segments in the planning target volume(PTV)and organ at risk(OAR).Statistical analysis was performed using SPSS 27.0 software.Results All the four groups had the doses to the target volume meeting clinical requirements,which had no significant differences in the doses to 5％(D5％)and 95％(D95％)to the target volume and the maximum dose(Dmax),minimum dose(Dmin),mean dose(Dmean)and conformity index(all P＞0.05).Statistical differences were found between the homogeneity indexes of the four groups(P＜0.05),with the medium group behaving the best.The number of the segments rose while the mornitor units decreased siginificantly with the increase of FS levels,with the differences being statistically significant(P＜0.05).There were no significant differences between the V25,V20,V15 and V10 of the small intestine,the V25 and V20 of the bladder and the V15 and V10 of the left and right femur(all P＞0.05).Conclusion In preoperative short-course VMAT for rectal cancer,clinical requirements can be met with different FS levels in the Monaco TPS,and medium-level FS results in optimal overall dose distribution in terms of treatment planning.[Chinese Medical Equipment Journal,2025,46(5):48-53]

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

AIM To study the chemical constituents from Euphorbia humifusa Willd.and their in vitro anti-hepatoma activity.METHODS Silica gel,D101 macroporous adsorption resin and semi-preparative RP-HPLC were used for isolated and purified,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The anti-hepatocellular carcinoma activity was determined by MTT mothod.RESULTS Eighteen compounds were isolated and identified as 22-O-angeloyl-R1-barrigenol(1),dimethyl 3,3'-[oxybis(4,1-phenylene)](2E,2'E)-diacrylate(2),N-(3-methoxy-1,3-dioxopropyl)-D-tryptophan methyl ester(3),N-acetyltryptophan methyl ester(4),N-(methoxycarbonyl)-tryptophan methyl ester(5),(3β,5α,17β)-4,4,8,14-tetramethyl-18-norandrostane-3,17-diol(6),3β,18,19β-trihydroxylupane(7),pregnenolone(8),3-hydroxy-5,6-epoxy-7-megastigmen-9-one(9),dehydrovomifoliol(10),loliolide(11),2,2'-oxybis(1,4-di-tert-butylbenzene)(12),dibutyl phthalate(13),4-methoxycinnamic acid(14),3,4-dimethoxycinnamic acid(15),methyl 4-hydroxybenzoate(16),kaempferol(17),quercetin(18).The IC50 values of compounds 1,7 and 8 on HepG2 cells were(17.27±0.92),(19.11±2.14)and(7.53±1.09)μmol/L,respectively.CONCLUSION Compounds 1-16 are first isolated from this plant.Compounds 1,7 and 8 have anti-hepatoma activity.