Current formulation of objective performance criteria （OPC） in traditional Chinese medicine （TCM） faces methodological challenges， including poor reporting quality， insufficient standardization of methods， limited comparability of study populations， and inadequate consideration of the genetic and environment particularities of ethnic minority populations. In response to these issues， this paper proposes optimization strategies for setting OPC in TCM， with particular attention to ethnic minority medicine. These include improving methodological techniques and statistical descriptions， ensuring transparency in meta-analyses and data sources； standardizing the estimation of point estimates and confidence intervals， and incorporating multidisciplinary expert consensus for value determination； introducing local real-world data from the same period， especially cohort study evidence， to compensate for the lack of high-quality randomized controlled trial data. Through process transparency and dynamic updating mechanisms， the credibility of OPC method can be enhanced， thereby providing methodological support for strengthening evidence-based research capacity in this field.

Background: and Purpose Limited evidence exists on the effectiveness of endovascular treatment (EVT) for acute posterior circulation tandem lesion (PCTL). This study aimed to explore the role of extracranial vertebral artery (VA) stenting in patients with PCTL stroke undergoing EVT. Methods: Individual patient data were pooled from the BASILAR (EVT for Acute Basilar Artery Occlusion Study) and PERSIST (Posterior Circulation Ischemic Stroke) registries. Patients with PCTLs who underwent EVT were included in the present cohort and divided into the stenting and nonstenting groups based on the placement of extracranial VA stents. The primary efficacy outcome was the modified Rankin Scale (mRS) scores at 90 days and 1 year. Safety outcomes included 24-hour symptomatic intracranial hemorrhage (sICH) and all-cause mortality at 90 days and 1 year post-surgery. Results: A combined dataset of 1,320 patients with posterior circulation artery occlusion, including 263 (19.9%) with tandem lesions, of whom 217 (median age, 65 years; 82.9% male) met the inclusion criteria for the analysis. The stenting group had 84 (38.7%) patients, while the non-stenting group had 133 (61.3%). After adjustment for the potential confounders, extracranial VA stenting was associated with favorable shifts in mRS scores at both 90 days (adjusted common odds ratio [OR], 2.30; 95% confidence interval [CI], 1.23–4.28; P<0.01) and 1 year (adjusted OR [aOR], 2.04; 95% CI [1.05–3.97]; P=0.04), along with lower rate of mortality at both 90 days (aOR, 0.45; 95% CI [0.21–0.93]; P=0.01) and 1 year (aOR, 0.36; 95% CI [0.16–0.79]; P=0.01), with no significant difference in sICH incidence (aOR, 0.35; 95% CI [0.06–1.98]; P=0.24). Conclusion Extracranial VA stenting during EVT may improve functional outcomes and reduce mortality in patients with PCTL strokes.

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.

Objective:To establish a three-dimensional U-shaped residual coordinated attention network (URCA-Net) based on enhanced CT images for small bowel polyp detection and analyze its application effectiveness in intelligent detection of small bowel polyps.Methods:Abdominal CT data of patients with small bowel polyps were collected from the Air Force Medical Center between June 2019 and July 2023. All patients underwent bowel preparation followed by thin-slice spiral CT scanning to obtain enhanced CT arterial phase images. The data were randomly divided into training, validation and test sets in an 8∶1∶1 ratio. The URCA-Net deep learning model was used for small bowel polyp segmentation. The training set was used for model parameter training, the validation set for hyperparameter adjustment and monitoring of model generalization performance and the test set for final unbiased evaluation of the model. An early intelligent detection model for small bowel polyps was constructed, and its performance was evaluated. Evaluation metrics included pixel-level metrics for the segmentation task [Dice Similarity Coefficient (DSC)], as well as sensitivity and precision for polyp detection. A two-stage segmentation strategy was adopted: the first stage segmented the small bowel region to remove external interference, and the second stage performed polyp segmentation within the small bowel region.Results:A total of 78 subjects were included in the study, with an average age of (54±7) years. A total of 23 400 scan images were extracted, including 136 hyperplastic polyps, 298 hamartomatous polyps, 14 adenomatous polyps, and 4 cancerous polyps. On the test set, the average DSC for the first stage (small bowel segmentation) and the second stage (polyp segmentation) was 0.790 and 0.314, respectively. In the second stage task (polyp segmentation based on small bowel region), the polyp segmentation DSC increased to 0.701, with a precision of 0.836 (95% CI: 0.700-0.972) and a sensitivity of 0.759 (95% CI: 0.631-0.888) for polyp detection. Conclusion:The URCA-Net deep learning technique demonstrates good auxiliary diagnostic effectiveness in small bowel polyp detection and can provide a reference for screening and detection of small bowel polyps. The model is capable of generating high-quality segmentation results, which could facilitate evaluating polyp lesion morphology and provide support for downstream tasks such as preoperative navigation and risk prediction.

Since 2017,twelve chimeric antigen receptor gene-modified T lymphocyte(CAR-T cell)products have been approved for the treatment of hematological malignancies,including relapsed/refractory acute B lymphoblastic leukemia(B-ALL),specific subtypes of B cell lymphoma,and multiple myeloma.However,CAR-T cell therapy faces numerous challenges in its clinical application,such as resistance,lengthy production cycles,high individuation and costs in hematological tumors,and tumor heterogeneity/antigen escape,insufficient infiltration capability,immunosuppressive microenvironments,and poor therapeutic response in solid tumors.With the in-depth exploration of tumor immunology and the development of genetic engineering technology,various new strategies have been attempted to enhance the efficacy and generalizability of CAR-T cell therapy.This paper presents a commentary on CAR-T cell therapy,with a focus on key clinical issues and corresponding countermeasures,providing valuable insights for future basic research and clinical transformation of CAR-T cell therapy.

DSA is an essential technology for diagnosing and treating cerebrovascular diseases.Detailed vascular structures and hemodynamic information can be acquired through image post-processing technology from raw DSA images.Presently,DSA image analysis technology encompasses several methodologies,including automatic vascular segmentation and feature extraction,hemodynamic parameter derivation,and more intricate multimodal imaging fusion.This review elaborated on the development status of these techniques at the current stage and their probable application in clinical practice.

Endovascular treatment has become the first-line treatment for stroke caused by acute intracranial large vessel occlusion(LVO).Acute intracranial LVO caused by intracranial atherosclerosis(ICAS)is a common cause of thrombus removal in Chinese people,but it was difficult and complex for the surgical operation treatment.This consensus was based on the latest progress of domestic clinical research on ICAS-LVO and combined with the experience summary of clinical experts to summarize the identification,imaging features,surgical strategies,and perioperative management of ICAS-LVO.It aims to quickly identify ICAS-LVO,standardize its endovascular treatment strategies and techniques,reduce the disability and mortality rates of patients,and provide assistance for standardized clinical management.

Background: and Purpose Limited evidence exists on the effectiveness of endovascular treatment (EVT) for acute posterior circulation tandem lesion (PCTL). This study aimed to explore the role of extracranial vertebral artery (VA) stenting in patients with PCTL stroke undergoing EVT. Methods: Individual patient data were pooled from the BASILAR (EVT for Acute Basilar Artery Occlusion Study) and PERSIST (Posterior Circulation Ischemic Stroke) registries. Patients with PCTLs who underwent EVT were included in the present cohort and divided into the stenting and nonstenting groups based on the placement of extracranial VA stents. The primary efficacy outcome was the modified Rankin Scale (mRS) scores at 90 days and 1 year. Safety outcomes included 24-hour symptomatic intracranial hemorrhage (sICH) and all-cause mortality at 90 days and 1 year post-surgery. Results: A combined dataset of 1,320 patients with posterior circulation artery occlusion, including 263 (19.9%) with tandem lesions, of whom 217 (median age, 65 years; 82.9% male) met the inclusion criteria for the analysis. The stenting group had 84 (38.7%) patients, while the non-stenting group had 133 (61.3%). After adjustment for the potential confounders, extracranial VA stenting was associated with favorable shifts in mRS scores at both 90 days (adjusted common odds ratio [OR], 2.30; 95% confidence interval [CI], 1.23–4.28; P<0.01) and 1 year (adjusted OR [aOR], 2.04; 95% CI [1.05–3.97]; P=0.04), along with lower rate of mortality at both 90 days (aOR, 0.45; 95% CI [0.21–0.93]; P=0.01) and 1 year (aOR, 0.36; 95% CI [0.16–0.79]; P=0.01), with no significant difference in sICH incidence (aOR, 0.35; 95% CI [0.06–1.98]; P=0.24). Conclusion Extracranial VA stenting during EVT may improve functional outcomes and reduce mortality in patients with PCTL strokes.