Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

ObjectiveTo analyze the distribution characteristics of traditional Chinese medicine （TCM） syndrome elements in different risk populations of heart failure complicated with type 2 diabetes. MethodsClinical data of 675 type 2 diabetes patients were retrospectively collected. Lasso-multivariate Logistic regression was used to construct a clinical prediction nomogram model. Based on this， 441 non-heart failure patients were divided into a low-risk group （325 cases） and a high-risk group （116 cases） according to the median risk score of heart failure complicated with type 2 diabetes. TCM diagnostic information （four diagnostic methods） was collected for both groups， and factor analysis was applied to summarize the distribution of TCM syndrome elements in different risk populations. ResultsLasso-multivariate Logistic regression analysis identified age， disease duration， coronary heart disease， old myocardial infarction， arrhythmia， absolute neutrophil count， activated partial thromboplastin time， and α-hydroxybutyrate dehydrogenase as independent risk factors for heart failure complicated with type 2 diabetes. These were used as final predictive factors to construct the nomogram model. Model validation results showed that the area under the curve （AUC） of the receiver operating characteristic （ROC） curve for the modeling group and validation group were 0.934 and 0.935， respectively. The Hosmer-Lemeshow test （modeling group P = 0.996， validation group P = 0.121） indicated good model discrimination. Decision curve analysis showed that the curves for All and None crossed in the upper right corner， indicating high clinical utility. The low-risk and high-risk groups each obtained 14 common factors. Preliminary analysis revealed that the main disease elements in the low-risk group were qi deficiency （175 cases， 53.85%）， dampness （118 cases， 36.31%）， and heat （118 cases， 36.31%）， with the primary locations in the spleen （125 cases， 38.46%） and lungs （99 cases， 30.46%）. In the high-risk group， the main disease elements were yang deficiency （73 cases， 62.93%）， blood stasis （68 cases， 58.62%）， and heat （49 cases， 42.24%）， with the primary locations in the kidney （84 cases， 72.41%） and heart （70 cases， 60.34%）. ConclusionThe overall disease characteristics in different risk populations of type 2 diabetes patients with heart failure are a combination of deficiency and excess， with deficiency being predominant. Deficiency and heat are present throughout. The low-risk population mainly shows qi deficiency with dampness and heat， related to the spleen and lungs. The high-risk population shows yang deficiency with blood stasis and heat， related to the kidneys and heart.

ObjectiveTo investigate the antitumor effect and mechanism of Guiqi Yiyuan ointment on Lewis lung cancer mice based on the extracellular regulatory protein kinase （ERK） signaling pathway. MethodsA Lewis lung cancer mouse model was established. Except for the blank group， the model mice were randomly divided into the model group， Guiqi Yiyuan ointment low， medium， and high dose groups， and the extracellular ERK1/2 inhibitor group， with 10 mice per group. The Guiqi Yiyuan ointment was administered by gavage at doses of 1.75， 3.5， 7.0 g·kg^-1·d^-1 for the low， medium， and high dose groups， respectively. The ERK1/2 inhibitor group was given the ERK1/2 inhibitor LY3214996 （100 mg·kg^-1·d^-1） by gavage. The treatment was administered for 14 consecutive days， after which samples were collected. Tumor histopathological changes were observed using hematoxylin-eosin （HE） staining. Transmission electron microscopy was used to observe ultrastructural changes in tumor cells. Immunofluorescence was performed to measure the phosphorylation of ERK1/2 （p-ERK1/2） and the expression of programmed cell death ligand-1 （PD-L1） in tumor tissues. Western blot and real-time quantitative polymerase chain reaction （Real-time PCR） were used to detect the expression of p-ERK1/2， PD-L1， the autophagy marker Beclin-1， the autophagic protein p62， and the microtubule-associated protein light chains LC3Ⅰ and LC3Ⅱ at both the protein and gene levels. ResultsCompared with the model group， the average tumor weight was significantly reduced in the low and medium dose groups of Guiqi Yiyuan ointment （P<0.05）， and markedly reduced in the high dose and inhibitor groups （P<0.01）. Tumor cells in all treatment groups became progressively irregular， with ruptured nuclei and expanded areas of cell disintegration and necrosis. The number of organellar ablations in tumor tissues increased， and the number of autophagic vesicles also increased in all groups. The mean fluorescence intensity of p-ERK1/2 and PD-L1 was reduced in the low and medium dose groups of Guiqi Yiyuan ointment （P<0.05）， and significantly reduced in the high dose and inhibitor groups （P<0.01）. The mRNA expression of ERK1/2， PD-L1， Beclin-1， and p62 was reduced in the medium dose group （P<0.05）， while LC3Ⅰ/Ⅱ mRNA expression was elevated （P<0.05）. In the high dose and inhibitor groups， mRNA expression of ERK1/2， PD-L1， Beclin-1， and p62 was significantly reduced （P<0.01）， while LC3Ⅰ/Ⅱ mRNA expression was significantly increased （P<0.01）. Protein expression of p-ERK1/2， PD-L1， Beclin-1， and p62 was reduced in the medium dose group （P<0.05）， and LC3Ⅰ/Ⅱ protein expression was elevated （P<0.05）. In the high dose and inhibitor groups， protein expression of p-ERK1/2， PD-L1， Beclin-1， and p62 was significantly reduced （P<0.01）， while LC3Ⅰ/Ⅱ protein expression was significantly elevated （P<0.01）. ConclusionGuiqi Yiyuan ointment may inhibit the activation of the ERK signaling pathway， downregulate the expression of p-ERK1/2， promote autophagic flux in tumor cells， and regulate the expression of PD-L1， thereby exerting an inhibitory effect on tumor growth in Lewis lung cancer mice.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective To investigate the effectiveness of the integrated schistosomiasis control programme in Sichuan Province during the stage moving from transmission interruption to elimination (2015—2023), so as to provide insights into formulation of the schistosomiasis control measures during the post-elimination stage. Methods Schistosomiasis control data were retrospectively collected from departments of health, agriculture and rural affairs, forestry and grassland, water resources, and natural resources in Sichuan Province from 2015 to 2023, and a database was created to document examinations and treatments of human and livestock schistosomiasis, and snail survey and control, conversion of paddy fields to dry fields, ditch hardening, rivers and lakes management and building of forests for snail control and schistosomiasis prevention. The completion of schistosomiasis control measures was investigated, and the effectiveness was evaluated. Results A total of 20 545 155 person-times received human schistosomiasis examinations in Sichuan Province during the period from 2015 to 2023, and 232 157 person-times were seropositive, with a reduction in the seroprevalence from 2.10% (44 299/2 107 003) in 2015 to 1.12% (9 361/837 896) in 2023 (χ² = 7.68, P < 0.001). The seroprevalence of human schistosomiasis appeared a tendency towards a decline in Sichuan Province over years from 2015 to 2023 (b = −8.375, t = −10.052, P < 0.001); however, no egg positive individuals were identified during the period from 2018 to 2023, with the prevalence of human Schistosoma japonicum infections maintained at 0. Expanded chemotherapy was administered to 2 754 515 person-times, and medical assistance of advanced schistosomiasis was given to 6 436 persontimes, with the treatment coverage increasing from 46.80% (827/1 767) in 2015 to 64.87% (868/1 338) in 2023. Parasitological tests for livestock schistosomiasis were performed in 35 113 herd-times, and expanded chemotherapy was administered to 513 043 herd-times, while the number of fenced livestock decreased from 121 631 in 2015 to 103 489 in 2023, with a reduction of 14.92%. Snail survey covered 433 621.80 hm² in Sichuan Province from 2015 to 2023, with 204 602.81 hm² treated by chemical control and 4 637.74 hm² by environmental modifications. The area of snail habitats decreased from the peak of 5 029.80 hm² in 2016 to 3 709.72 hm² in 2023, and the actual area of snail habitats decreased from the peak of 8 585.48 hm² in 2016 to 473.09 hm² in 2023. The mean density of living snails remained low across the study period except in 2017 (0.62 snails/0.1 m²). Schistosomiasis control efforts by departments of agriculture and rural affairs in Sichuan Province included conversion of paddy fields to dry fields covering 153 346.93 hm², hardening of 6 110.31 km ditches, building of 70 356 biogas digesters, replacement of cattle with 227 161 sets of machines, and captive breeding of 21 161 070 livestock from 2015 to 2023, and the control efforts by departments of water resources included rivers and lakes management measuring 5 676.92 km and renovation of 2 331 irrigation areas, while the control efforts by departments of forestry and grassland included building of forests for snail control and schistosomiasis prevention covering 23 913.33 hm², renovation of snail control forests covering 8 720 hm² and newly building of shelterbelts covering 764 686.67 hm². All 63 endemic counties (cities and districts) had achieved the criterion for schistosomiasis elimination criteria in Sichuan Province by the end of 2023. Conclusion Following the integrated control efforts from 2015 to 2023, remarkable achievements have been obtained in the schistosomiasis control programme in Sichuan Province, with all endemic counties successfully attaining the schistosomiasis elimination target at the county level.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective:To establish a prediction method of diopter based on sequence of ocular biological parameters.Methods:A stratified random cluster sampling method was used to extract the dataset. The dataset consisted of data collected from January 2022 to January 2023 by the Eye & ENT Hospital, Fudan University, from children aged 5 to 13 years in 2 key schools and 2 general schools of Yangpu District, Shanghai. Children’s ocular biological parameters, including sex, age, diopter, axial length, corneal curvature, and anterior chamber depth were collected. The slope of the optimally fitted straight line was calculated using the least squares method. The least square-back propagation (BP) neural network model was established by combining baseline data and the pre-processed rate of the change of ocular biological parameters. The dataset was divided into the training set and the validation set according to the ratio of 8:2 for five-fold cross-validation. The model performance was evaluated by using the mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), correlation coefficient R, and coefficient of determination R2. Results:The optimal performances of R2, R, RMSE, MAE, and MSE of the least square-BP neural network model were 0.96, 0.981 9, 0.214 2, 0.139 9 D, 0.045 9, respectively. The regression equation between the predicted value and the true value of the diopter was y=0.97 x+ 0.014 8, R2=0.97, with good correlation. In the internal verification, MAE values of the diopter at three, six, nine, and twelve months of follow-up were 0.110 1, 0.136 0, 0.153 7, and 0.184 8 D, respectively, which achieved clinically acceptable performance (less than 0.25 D). In the external validation, the errors were less than 0.25 D at all ages. Conclusions:A prediction method of diopter based on sequence of ocular biological parameters was successfully developed.

Aim To explore the mechanism of the effect of rosiglitazone(Rsg)on the pancreatic cancer in diabetic mice based on the impact of PPARγ on glu-cose transport and metabolism.Methods A high-fat and high sugar diet combined with STZ was used to construct T2DM model;T2DM mice and normal mice were subcutaneously injected with PANC02 cells to construct a transplanted tumor model.T2DM trans-planted tumor mice and normal transplanted tumor mice were divided into the following groups:Rsg,PPARy inhibitor(PIN-2),rosiglitazone+PPARγ in-hibitor(Rsg+PIN-2),and normal transplanted tumor mice(NDM)and T2DM transplanted tumor mice(DM)were used as control groups,respectively.Tis-sue samples were collected after intervention.Tissue pathological changes were observed by HE staining.The expressions of Ki67 and PCNA proteins were de-tected by immunohistochemistry.Cell apoptosis was detected by TUNEL assay.The expression of PPARγwas detected by immunofluorescence.The expressions of Glucokinase,GLUT2,Nkx6.1,PDX-1RT-PCR were determined by Western blot.Results Rsg could significantly reduce the tumor mass,pathological chan-ges,Ki67 and PCNA expression of transplanted tumors(P＜0.05),increase cell apoptosis and the expression of PPARγ,Glucokinase,GLUT2,Nkx6.1,PDX-1 proteins in NDM and DM mice(P＜0.05).PIN-2 could reverse the indicator changes caused by Rsg in NDM and DM mice.However,compared with NDM mice,the above related indicators of the DM group mice were more sensitive to Rsg and PIN-2.Conclu-sions Compared to non-diabetic pancreatic cancer,rosiglitazone can more sensitively inhibit the prolifera-tion of pancreatic cancer with T2DM,induce apopto-sis,and reprogram the metabolism of pancreatic cancer with T2DM by activating PPA Rγ and altering the ex-pression of glucose and lipid metabolism genes,there-by exerting an anti-cancer effect.

Mycotoxins are secondary metabolites produced by pathogenic fungi.They often contaminate various crops,and are detrimental to human and animal health.Mycotoxins have a variety of toxic effects,such as neurotoxicity,hepatotoxicity,immunotox-icity,teratogenicity,and carcinogenicity.However,the mechanism of immunotoxicity is still unclear.Dendritic cells(DCs),as the most potent antigen presenting cells,play a vital role in initiating innate and adaptive immune responses.Previous studies have found that mycotoxins can affect the endocytosis of DCs,the ability to stimulate T cell activation,the secretion of cytokines and chemokines.Thus,this review is aim to summarize the effects of mycotoxins on DCs-mediated immune responses,which may provide reference for researches to clarify the immunotoxicity mechanism of mycotoxins.