Objective Build a predictive model integrating radiomics features with clinical characteristics for the prognosis prediction of acute anterior circulation large vessel occlusion(LVO)stroke patients after mechanical thrombectomy(MT),and explore its predictive value.Methods Patients with acute ischemic stroke who underwent endovascular treatment for LVO of the anterior circulation were enrolled consecutively from the endovascular treatment registry database for acute anterior circulation ischemic stroke(ACTUAL)and the Nanjing stroke registry system from January 2014 to January 2025 retrospectively.Baseline,clinical and imaging data were collected from enrolled patients,including gender,age,medical history(atrial fibrillation,hypertension,diabetes),smoke history,admission blood pressure,blood glucose,National Institutes of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),occluded blood vessels(internal carotid artery,middle cerebral artery),trial of Org 10172 in acute stroke treatment(TOAST)classification(atherosclerotic,cardiogenic embolism,others),collateral status(American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology[ASITN/SIR]classification),the onset-to-door time,the time from onset to puncture,the operation time,the time from onset to recanalization,recanalization status(modified thrombolysis in cerebral infarction[mTICI]score),symptomatic intracerebral hemorrhage(sICH)within 72 hours after MT and functional outcome at 90 days post-MT(modified Rankin scale[mRS]score).Divide all patients into a training set and a validation set in a ratio of 7∶3.The training set is used to build the predictive model,and the validation set is used to verify the predictive model.In the training set,patients were divided into a good prognosis group(mRS score 0-2)and a poor prognosis group(mRS score 3-6),the variables with P＜0.05 from the univariate Logistic regression analysis were enrolled into the multivariate Logistic regression analysis to screen the clinical risk factors affecting prognosis.The preoperative head MR axial diffusion weighted imaging sequence images of patients in the training set were selected.The Pyradiomics toolkit of the Python 3.6 platform was used to implement radiomics feature extraction.After conducting consistency analysis on the extracted features,standardization processing was performed.In the training set,feature dimension reduction is carried out on the radiomics feature values obtained after extraction and processing.The least absolute shrinkage and selection operator(LASSO)model was used to screen the features.The support vector machine(SVM),k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm are used to respectively construct models based on the screened radiomics features,use grid search with cross validation(GridSearchCV)to gain specific parameters in each model.The receiver operating characteristic(ROC)curve was used to analyze and compare the area under the curve(AUC)of each radiomics model,screen the most suitable radiomics model,and verify it in the validation set.The predicted probability value of prognosis calculated by this model is taken as the radiomics score.In the training set,the radiomics scores and the screened clinical risk factors were taken as independent variables,and a multivariate Logistic regression analysis was conducted.A nomogram was used to construct a comprehensive prediction model of radiomics plus clinical factors for predicting the prognosis of MT in acute stroke patients of LVO.The AUC of the clinical factor prediction model,the radiomics prediction model,and the radiomics plus clinical factor comprehensive prediction model were compared in the training set and the validation set,respectively.Results A total of 107 acute anterior LVO patients who underwent MT were included,comprising 72 males and 35 females,aged 27 to 87 years,with a median age of 64(56,71)years.There were 74 cases in the training set,among which 48 cases had a good prognosis and 26 cases had a poor prognosis.There were 33 cases in the validation set,among which 24 cases had a good prognosis and 9 cases had a poor prognosis.The NIHSS score of patients in the training set was lower than that of patients in the validation set(12[8,19]points vs.15[11,21]points,P=0.03),while there were no statistically significant differences in the remaining baseline,clinical and imaging data compared with the validation set(all P＞0.05).(1)Included the variables with P＜0.05 from the univariate Logistic regression analysis into the multivariate Logistic regression analysis.The results showed that age(OR,1.066,95％CI 1.003-1.133,P=0.039)and admission NIHSS score(OR,1.126,95％CI 1.028-1.233,P=0.011)were independent risk factors for poor prognosis of MT in patients with acute anterior circulation LVO stroke.(2)A total of 725 radiomics features were extracted.The results of intra-observer consistency analysis showed that the median intraclass correlation coefficient(ICC)of radiomics features was 0.75(0.56,0.87),and there were 424 features with ICC＞0.7 and 127 features with ICC＞0.9.The results of the inter-observer consistency analysis showed that the median ICC of radiomics features was 0.73(0.53,0.86).After dimensionality reduction using the LASSO,12 most relevant features were selected and incorporated into the radiomics-based prognostic model.The AUCs of the radiomics prediction models constructed by applying SVM,k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm were 0.803,0.890,0.969,1.000 and 1.000,respectively.The AUCs in the validation set were 0.769,0.743,0.817,0.792 and 0.799,respectively.SVM was selected as the final algorithm for the construction of the radiomics model.The radiomics data were input into SVM to obtain the radiomics score of each patient.(3)A comprehensive predictive nomogram model combining radiomics and clinical factors was constructed based on radiomics score,age,and the NIHSS score at admission.In the validation group,the integrated model demonstrated a significantly higher AUC-ROC(0.918,95％CI 0.831-0.969)compared to the radiomics model(AUC 0.803,95％CI0.694-0.886,P=0.026)and the clinical-feature model(AUC 0.784,95％CI0.674-0.872,P=0.009).In the validation set,there were no statistically significant difference among the integrated model(AUC 0.935,95％CI 0.792-0.991),radiomics model(AUC 0.769,95％CI 0.589-0.897,P=0.111)and the clinical-feature model(AUC 0.894,95％CI 0.737-0.974,P=0.602).The integrated model exhibited good calibration in both the training set and the validation set(Hosmer-Lemeshow test,P values were respectively 0.350,0.580).Conclusion The integrated radiomics-clinical model can provide effective prediction of MT on outcomes in acute anterior circulation LVO stroke patients,and it may offer an objective basis for clinical decision-making.

For patients with symptomatic non-acute intracranial artery total occlusion(NIATO),successful endovascular recanalization can improve the clinical prognosis of some patients.High-resolution magnetic resonance vessel wall imaging(HR-VWI)can qualitatively and quantitatively describe the characteristics of intracranial arterial lesions,which is helpful for preoperative evaluation,selecting suitable patients,guiding intraoperative treatment and regular follow-up.This article systematically reviewed the application progress of HR-VWI in the endovascular recanalization of NIATO,analyzed the correlation between HR-VWI characteristics and technical success rates for recanalization as well as perioperative complications,and discussed the limitations and future development directions of current research.

Objective Build a predictive model integrating radiomics features with clinical characteristics for the prognosis prediction of acute anterior circulation large vessel occlusion(LVO)stroke patients after mechanical thrombectomy(MT),and explore its predictive value.Methods Patients with acute ischemic stroke who underwent endovascular treatment for LVO of the anterior circulation were enrolled consecutively from the endovascular treatment registry database for acute anterior circulation ischemic stroke(ACTUAL)and the Nanjing stroke registry system from January 2014 to January 2025 retrospectively.Baseline,clinical and imaging data were collected from enrolled patients,including gender,age,medical history(atrial fibrillation,hypertension,diabetes),smoke history,admission blood pressure,blood glucose,National Institutes of Health stroke scale(NIHSS)score,Alberta stroke program early CT score(ASPECTS),occluded blood vessels(internal carotid artery,middle cerebral artery),trial of Org 10172 in acute stroke treatment(TOAST)classification(atherosclerotic,cardiogenic embolism,others),collateral status(American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology[ASITN/SIR]classification),the onset-to-door time,the time from onset to puncture,the operation time,the time from onset to recanalization,recanalization status(modified thrombolysis in cerebral infarction[mTICI]score),symptomatic intracerebral hemorrhage(sICH)within 72 hours after MT and functional outcome at 90 days post-MT(modified Rankin scale[mRS]score).Divide all patients into a training set and a validation set in a ratio of 7∶3.The training set is used to build the predictive model,and the validation set is used to verify the predictive model.In the training set,patients were divided into a good prognosis group(mRS score 0-2)and a poor prognosis group(mRS score 3-6),the variables with P＜0.05 from the univariate Logistic regression analysis were enrolled into the multivariate Logistic regression analysis to screen the clinical risk factors affecting prognosis.The preoperative head MR axial diffusion weighted imaging sequence images of patients in the training set were selected.The Pyradiomics toolkit of the Python 3.6 platform was used to implement radiomics feature extraction.After conducting consistency analysis on the extracted features,standardization processing was performed.In the training set,feature dimension reduction is carried out on the radiomics feature values obtained after extraction and processing.The least absolute shrinkage and selection operator(LASSO)model was used to screen the features.The support vector machine(SVM),k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm are used to respectively construct models based on the screened radiomics features,use grid search with cross validation(GridSearchCV)to gain specific parameters in each model.The receiver operating characteristic(ROC)curve was used to analyze and compare the area under the curve(AUC)of each radiomics model,screen the most suitable radiomics model,and verify it in the validation set.The predicted probability value of prognosis calculated by this model is taken as the radiomics score.In the training set,the radiomics scores and the screened clinical risk factors were taken as independent variables,and a multivariate Logistic regression analysis was conducted.A nomogram was used to construct a comprehensive prediction model of radiomics plus clinical factors for predicting the prognosis of MT in acute stroke patients of LVO.The AUC of the clinical factor prediction model,the radiomics prediction model,and the radiomics plus clinical factor comprehensive prediction model were compared in the training set and the validation set,respectively.Results A total of 107 acute anterior LVO patients who underwent MT were included,comprising 72 males and 35 females,aged 27 to 87 years,with a median age of 64(56,71)years.There were 74 cases in the training set,among which 48 cases had a good prognosis and 26 cases had a poor prognosis.There were 33 cases in the validation set,among which 24 cases had a good prognosis and 9 cases had a poor prognosis.The NIHSS score of patients in the training set was lower than that of patients in the validation set(12[8,19]points vs.15[11,21]points,P=0.03),while there were no statistically significant differences in the remaining baseline,clinical and imaging data compared with the validation set(all P＞0.05).(1)Included the variables with P＜0.05 from the univariate Logistic regression analysis into the multivariate Logistic regression analysis.The results showed that age(OR,1.066,95％CI 1.003-1.133,P=0.039)and admission NIHSS score(OR,1.126,95％CI 1.028-1.233,P=0.011)were independent risk factors for poor prognosis of MT in patients with acute anterior circulation LVO stroke.(2)A total of 725 radiomics features were extracted.The results of intra-observer consistency analysis showed that the median intraclass correlation coefficient(ICC)of radiomics features was 0.75(0.56,0.87),and there were 424 features with ICC＞0.7 and 127 features with ICC＞0.9.The results of the inter-observer consistency analysis showed that the median ICC of radiomics features was 0.73(0.53,0.86).After dimensionality reduction using the LASSO,12 most relevant features were selected and incorporated into the radiomics-based prognostic model.The AUCs of the radiomics prediction models constructed by applying SVM,k-nearest neighbor,lightweight gradient boosting algorithm,random forest method and extreme gradient boosting algorithm were 0.803,0.890,0.969,1.000 and 1.000,respectively.The AUCs in the validation set were 0.769,0.743,0.817,0.792 and 0.799,respectively.SVM was selected as the final algorithm for the construction of the radiomics model.The radiomics data were input into SVM to obtain the radiomics score of each patient.(3)A comprehensive predictive nomogram model combining radiomics and clinical factors was constructed based on radiomics score,age,and the NIHSS score at admission.In the validation group,the integrated model demonstrated a significantly higher AUC-ROC(0.918,95％CI 0.831-0.969)compared to the radiomics model(AUC 0.803,95％CI0.694-0.886,P=0.026)and the clinical-feature model(AUC 0.784,95％CI0.674-0.872,P=0.009).In the validation set,there were no statistically significant difference among the integrated model(AUC 0.935,95％CI 0.792-0.991),radiomics model(AUC 0.769,95％CI 0.589-0.897,P=0.111)and the clinical-feature model(AUC 0.894,95％CI 0.737-0.974,P=0.602).The integrated model exhibited good calibration in both the training set and the validation set(Hosmer-Lemeshow test,P values were respectively 0.350,0.580).Conclusion The integrated radiomics-clinical model can provide effective prediction of MT on outcomes in acute anterior circulation LVO stroke patients,and it may offer an objective basis for clinical decision-making.

For patients with symptomatic non-acute intracranial artery total occlusion(NIATO),successful endovascular recanalization can improve the clinical prognosis of some patients.High-resolution magnetic resonance vessel wall imaging(HR-VWI)can qualitatively and quantitatively describe the characteristics of intracranial arterial lesions,which is helpful for preoperative evaluation,selecting suitable patients,guiding intraoperative treatment and regular follow-up.This article systematically reviewed the application progress of HR-VWI in the endovascular recanalization of NIATO,analyzed the correlation between HR-VWI characteristics and technical success rates for recanalization as well as perioperative complications,and discussed the limitations and future development directions of current research.

This article summarizes Professor Xu Zhiyin's experience in treating non-puerperal mastitis from the perspective of mid-dle jiao qi movement and believes that the pathological viscera of non-puerperal mastitis are located in the liver,spleen,and stomach,and closely related to the middle jiao qi movement.According to clinical manifestations,the disease can be divided into lump stage,abscess stage,and fistula stage.The main pathogenesis of the disease is liver depression-spleen deficiency and phlegm obstructing the breast collaterals;long-term heat stagnation and excessive heat corroding the flesh;qi deficiency-evil stagnation and lingering restrai-ning.In terms of treatment,the pathogenesis should be clearly identified according to the different stages of the disease,and the medi-cine should be changed according to the pathogenesis.The key of treatment is to regulate the middle jiao qi movement and adjust the treatment according to the stage.In the lump stage,use pungency to disperse and bitterness to descend,and suppress wood and sup-port soil;in the abscess stage,lower the turbidity and raise the clearness,and discharge the fire and calm the wood;in the fistula stage,mobilize the central prefecture,and cultivate the soil and nourish the wood,so as to obtain satisfactory curative effect.

Objective:To explore the prognostic prediction value of quantitative digital subtraction angiography (DSA) parameters in patients with acute anterior circulation ischemic stroke undergoing mechanical thrombectomy, and whether the clinical values vary by stroke etiology.Methods:This study was a post hoc analysis of the Multicenter Prospective Captor Trial. Patients with acute anterior circulation large-vessel occlusion and successful recanalization from April 2018 to July 2019 were screened. Post-processing analysis was performed on the DSA imaging sequence after recanalization, and 4 regions of interest (ROI) were selected in the target vessel: ROI1 (the proximal of the internal carotid artery-C2 segment), ROI2 (the starting point of the internal carotid artery-C7 segment), ROI3 (the end of the middle cerebral artery-M1 segment), and ROI4 (the end of the middle cerebral artery-M2 segment). Time to peak (TTP) was defined as the time at contrast concentration of selected ROI reached its maximum. Relative TTP (rTTP) was calculated by subtracting the TTP of ROI1 from the TTP of distalis ROIs. Successful recanalization was defined as modified Thrombolysis In Cerebral Infarction (mTICI) grade≥2b. Favorable outcomes at 3 months were defined as the modified Rankin Scale score≤2. According to the modified Rankin Scale score, the patients were divided into good prognosis group and poor prognosis group. The differences in clinical characteristics, postoperative hemodynamic parameters, and other data were compared between patients with good and poor prognoses. Univariate and multivariate Logistic regression was used to analyze factors related to a good prognosis. Finally, the prognostic prediction value of hemodynamic parameters was analyzed in patients with different Trial of Org10172 in Acute Stroke Treatment etiological classifications.Results:A total of 245 patients were collected, of which 161 patients [age 69 (60, 76) years, 92 (57.1%) male] were finally included in the analysis, including 36 cases of large artery atherosclerosis (LAA) stroke, 76 cases of cardiogenic embolism (CE), and 49 cases of other causes of stroke. Seventy-one (44.1%) patients had favorable outcomes at 3 months. The post-operative hemodynamic analysis indicated that patients with favorable outcomes ( n=71) had a higher proportion of mTICI grade 3 [54/71 (76.1%) vs 41/90 (45.6%),χ 2=15.26, P<0.001] and lower rTTP 31 [means TTP ROI3-TTP ROI1;0.33 (0.23, 0.54) s vs 0.47 (0.31, 0.65) s, Z=-2.71, P=0.007] than patients with unfavorable outcomes ( n=90). The mTICI score and rTTP 31 were respectively included in multivariate Logistic regression models. It was shown that mTICI grade 3 (adjusted OR=5.97, 95% CI 2.49-14.27, P<0.001) and rTTP 31 (adjusted OR=0.24, 95% CI 0.06-0.99, P=0.048) were significantly associated with favorable outcomes, and the area under the receiver operating characteristic curve of the models had no statistically significant difference ( P=0.170). Subgroup analysis showed that rTTP 31 was significantly associated with the prognosis of patients with LAA stroke ( OR=0, 95% CI 0-0.25, P=0.014), while mTICI grade was associated with the prognosis of patients with CE ( OR=3.91, 95% CI 1.40-10.91, P=0.009) and other etiologies ( OR=7.35, 95% CI 1.92-28.14, P=0.004). Conclusions:In patients with acute anterior circulation ischemic stroke and successful recanalization, both mTICI score and rTTP 31 had significant predictive value for favorable outcomes at 3 months. Moreover, rTTP 31 was significantly associated with the prognosis of patients with LAA stroke, while mTICI score was significantly related to the prognosis of patients with CE and other causes of stroke.

Objective: To analyze the curative effects of traditional Chinese herbal decoction on breast carcinoma patients in doxorubicin-induced cardiotoxicity by meta-analysis to provide evidences for doctors in treating breast carcinoma patients who were in doxorubicin-induced cardiotoxicity by traditional Chinese herbal decoction.Method: Published papers about the curative effects of traditional Chinese herbal decoction on doxorubicin-induced cardiotoxicity in breast carcinoma patients in clinical random control experiment were collected. To analyze the prescription rules and do metaanalysis in the eligible ones by the software RevMan5.2. Evaluate clinical effects of traditional Chinese medicine in treating breast carcinoma patients who were in doxorubicin-induced cardiotoxicity.Results: Ten references and 648 cases in total meeting eligibility criteria were included. Compared with pure anthracycline-based chemotherapy drugs, the breast carcinoma patients who received Anthracycline-based chemo therapy drugs with traditional Chinese medicine, their ECG changes less, their difference was statistically significant (OR=0.23, 95％ CI (0.16, 0.16), P ＜0.00001) . They had better cardiac function. the difference was statistically significant (OR=0.18, 95％ CI (0.09, 0.09), P ＜0.00001) . Their left ventricular systolic function improved. Their difference was statistically significant (MD = 3.82, 95％CI (0.29, 0.29), P=0.03 ＜ 0.05) . The myocardial enzyme spectrum, cardiac troponin I and myocardial troponin T were improved. Conclusion: To some extent, decoction of Chinese herbal medicine has effects on breast carcinoma patients with myocardial anthracycline-based drugs damage.

This article was aimed to explore the effect ofSan-Huang (SH) decoction on improving chemosensitivity of MCF-7 cells to epirubicin and inhibition of Aurora kinase A, in order to discuss its underlying mechanism. The inhibition of MCF-7 cells proliferation on breast cancer by SH decoction was determined by CCK-8 assay. RT-PCR and western blot were used to detect the Aurora A, p53 mRNA and protein expression level of MCF-7 cells by SH decoction. The siRNA silenced Aurora A of MCF-7 cells. CCK-8 assay was used to detect the inhibition of MCF-7 cells proliferation. CCK-8 assay and AnnexinV-FITC/PI staining were used to detect the inhibition rate and apoptosis rate of MCF-7 cells treated by the combination of SH decoction and epirubicin. Western blot analysis was used to detect the expression of apoptosis-related proteins. The results showed that SH decoction inhibited the proliferation of MCF-7 cells in a dose-dependent manner (P< 0.05). The effect of 48 h medication was better than 24 h (P < 0.05). There was no statistical difference with medication for 72 h (P > 0.05). SH decoction can regulate the Aurora A, p53 protein and mRNA expression of MCF-7 cells. siRNA silenced Aurora A, which downregulated the inhibition rate of MCF-7 cells by SH decoction for 50.0% (from 49.2% to 24.8%). The combination of SH decoction and epirubicin enhanced the effect of epirubicin on inhibiting the proliferation rate and apoptosis rate of MCF-7 cell, regulated the expression levels of apoptosis-related protein such as c-PARP, c-Caspase 3, Bcl-2, Bax, as well as the protein level of Aurora A. It was concluded that SH decoction can increase the chemosensitivity of MCF-7 cells to epirubicin, which may be related to the inhibition of Aurora Kinase A by SH decoction.

ABSTRACTOBJECTIVE To explore the effect of San Huang decoction on the apoptosis of breast cancer cells and the effect on the mRNA and protein expression and function of Aurora kinase A and discuss the underlying mechanism of San Huang in-duced apoptosis.METHODS The inhibition of breast cancer cells proliferation was determined by CCK?8 assay.The apoptosis of breast cancer cells was detected by AnnexinV?FITC/PI Staining.The expression of mRNA of Aurora A was examined by q ?PCR analysis.The expression of apoptosis?related proteins and Aurora A were determined by Western Blot analysis.RE-SULTS San Huang decoction inhibited the proliferation of breast cancer cells in a does?dependent mannerP <0.05.The effect of inhibition caused by San Huang decoction 48 hours after delivering to breast cancer cells was better than 24 hoursP <0.05 although similar as 72 hoursP >0.05.San Huang decoction was also found to induce apoptosis in both MCF?7 and MDA?MB?231 cell lines in a dose?dependent manner.Consistent with cellular resultsSan Huang decoction treatment signifi-cantly increased the apoptosis?related protein level of cleaved?PARPc?PARPcleaved?Caspase 3c?Caspase 3and Baxdown ?regulated Bcl?2 in a does?dependent manner.MeanwhileSan Huang decoction decreased the mRNA and protein level of Auro-ra A and increased those of p53 in a does?dependent manner.CONCLUSION San Huang decoction at the first time was able to promote the apoptosis of breast cancer cells via inducing the suppression of Aurora A.

Objective To evaluate the effects of Chuanglingye decoction on angiogenesis and wound healing. Methods With a series of dosages of Chuanglingye decoction, their optimal effects of angiogenesis were searched for through the chicken embryo allantois membrane model(CAM). The vascular endothelial cell proliferation experiment (MTT) and the migration assay were used for the detection of effects. The gauze loading with Chuanglingye decoction of 0.2 ml as the experimental group and with saline of 0.2ml as the control group were applied on the total skin mechanical round wound of 1.5cm diameter and changed every other day. The sizes of area were detected on the day of 0,3,7,14 and 28 as well as the scores of inflammatory response, contains of TNF-αand Il-6 were detected on the day of 3 and 7. Results The CAM experiments showed that the angiogenic effects of 0.2 ml and 0.3 ml dosage of the Chuanglingye group were significantly lower than those of the control group(P<0.05). The 0.2 ml dosage of Chuanglingye decoction was chosen for the further experiment. The HUVEC proliferation rate of the experimental group decreased 21%, as compared with the results of control group. The cell migration movement of 12 hours, 24 hours in the experimental group were significantly lower than those in the control group. For theanimal experiments, the area sizes of the wound were similar in the experimental and control group without any significant differences. The scores of inflammatory response and contains of TNF-α(768±107)ng/L,(380±47)ng/L and Il-6(664±133)ng/L,(363±43)ng/L in the experimental group were significant decreased than those of the control group on the day of 3(958± 140)ng/L,(2215±314)ng/Land 7(512±62)ng/L,(1562±174)ng/L. Conclusion It showed that Chuanglingye decoction had negative effects on vascular endothelial cell migration and proliferation and thus inhibiting angiogenesis. These effects did not infer the process of the wound healing due to its ameliorating the inflammatory response which may be a help to wound healing.