Objective To develop a diagnostic model combining the CT angiography(CCTA)-derived myocardial radiomics signatures with the CT-derived fractional flow reserve(CT-FFR)based on coronary CCTA and investigate the diagnostic accuracy of the hybrid model for hemodynamically significant coronary artery disease(CAD).Methods The patients presenting stable angina pectoris,diagnosed with CAD,and clinically referred for CCTA examination and invasive coronary angiography were prospectively recruited.Radiomics features of the left ventricular myocardium were extracted from the three main perfusion territories demarcated according to the coronary blood supply.The extracted features were first selected by the minimum redundancy maximum relevance feature ranking method.A least absolute shrinkage and selection operator Logistic regression algorithm with leave-one-out cross-validation was then employed to construct a radiomics model.The CT-FFR value was generated for each blood vessel.The area under the receiver operating characteristics curve(AUC_ROC),sensitivity,and specificity were adopted to evaluate the performance of each model against the reference standard invasive coronary angiography/FFR.Results A total of 70 patients[42 men and 28 women;(61±10) years old] were included in this study and complemented CCTA examination,with 175 vessels and the corresponding myocardial territories undergoing invasive coronary angiography/FFR.A total of 1 656 specific radiomics parameters were extracted,from which 14 features were selected to establish the radiomics model.The AUC_ROC,sensitivity,and specificity were 0.797(95%CI=0.732-0.861),77.1%,and 73.7%for the radiomics model,0.892(95%CI=0.841-0.943),81.4%,and 88.8%for the CT-FFR model,and 0.928(95%CI=0.890-0.965),83.3%,and 88.4%for the hybrid model,respectively.The hybrid model outperformed the radiomics model and CT-FFR alone(P=0.040).Conclusions The radiomics signatures of the vessel-related myocardium from CCTA could provide incremental value to the diagnostic performance of CT-FFR and improve vessel-specific ischemia detection.The hybrid model combining CT-FFR with radiomics signatures is potentially feasible for improving the diagnostic accuracy for hemodynamically significant CAD.
Coronary Angiography/methods* ; Tomography, X-Ray Computed ; Humans ; Hemodynamics ; Coronary Artery Disease/diagnostic imaging* ; Male ; Female ; Middle Aged ; Aged ; Radiomics ; Angina Pectoris/diagnostic imaging* ; China ; Image Processing, Computer-Assisted ; Coronary Vessels/diagnostic imaging*

Psoriasis is an incurable chronic inflammatory disease that requires new interventions. Here, we found that fibroblasts exacerbate psoriasis progression by promoting macrophage recruitment via CCL2 secretion by single-cell multi-omics analysis. The natural small molecule celastrol was screened to interfere with the secretion of CCL2 by fibroblasts and improve the psoriasis-like symptoms in both murine and cynomolgus monkey models. Mechanistically, celastrol directly bound to the low-density lipoprotein receptor-related protein 1 (LRP1) β-chain and abolished its binding to the transcription factor c-Jun in the nucleus, which in turn inhibited CCL2 production by skin fibroblasts, blocked fibroblast-macrophage crosstalk, and ameliorated psoriasis progression. Notably, fibroblast-specific LRP1 knockout mice exhibited a significant reduction in psoriasis like inflammation. Taken together, from clinical samples and combined with various mouse models, we revealed the pathogenesis of psoriasis from the perspective of fibroblast-macrophage crosstalk, and provided a foundation for LRP1 as a novel potential target for psoriasis treatment.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective To explore the clinical effect of transcranial direct current stimulation(tDCS)treatment strategy based on bi-modal balance model on upper limb dysfunction after ischemic stroke.Methods From October,2023 to December,2024,60 patients with ischemic stroke in General Hospital of Ningxia Medi-cal University were randomly divided into control group(n=30)and experimental group(n=30).Both groups received basic rehabilitation,the control group received tDCS based on the theory of interhemispheric competi-tion model,and the experimental group received tDCS based on the theory of bimodal equilibrium model,for four weeks.Before and after intervention,the effect of both groups was evaluated using Fugl-Meyer Assessment-Upper Extremities(FMA-UE),modified Ashworth Scale(MAS),Action Research Arm Test(ARAT)and modi-fied Barthel Index(MBI).Neurophysiological parameters such as cortical latency(CL)and central motor conduc-tion time(CMCT)were detected and correlated analysis was performed.Results Two cases in the control group and one in the experimental group dropped down.After intervention,the scores of FMA-UE,ARAT and MBI increased in both groups(|t|>13.748,P<0.001),and the above scores were higher in the experimental group than in the control group(|t|>2.321,P<0.05);the MAS grade of the elbow flexor muscle group improved in the experimental group(|Z|=2.095,P<0.05).The CL and CMCT in both groups de-creased(|t|>2.752,P<0.001),and they were better in the experimental group than in the control group(|t|>2.082,P<0.05).There was a correlation between FMA-UE and CMCT(r=-0.433,P<0.05).Conclusion tDCS based on bimodal balance model can improve upper limb dysfunction more effectively in patients with ischemic stroke.

Objective:To explore the application value of problem-based learning (PBL) combined with mini-clinical evaluation exercise (Mini-CEX) in the development of post competency for interns in the Department of Neurology.Methods:A total of 56 interns rotating at the Department of Neurology of The First Affiliated Hospital of Chongqing Medical University from June 2023 to January 2024 were enrolled as the study subjects. They were randomly divided into a control group and an experiment group using the random number table method, with 28 interns in each group. The control group received traditional methods including small lectures and teaching rounds, while the experimental group received the PBL teaching method combined with Mini-CEX. The teaching effectiveness was evaluated through theoretical assessments, practical skill evaluations, teacher and student satisfaction surveys, and Mini-CEX scale assessments conducted at the beginning, middle, and end of the rotation for the experimental group. The data were analyzed using SPSS 23.0 software. For continuous data, the independent-samples t test or Mann-Whitney U test was used for comparison between groups. The chi-square test was used for categorical data and the Kruskal-Wallis H test for repeated-measurement data. Results:The theoretical scores [(45.36±2.67) vs. (42.00±4.29), P<0.01] and practical skill scores [(45.11±2.53) vs. (42.39±4.53), P<0.01] were significantly higher in the experimental group compared to the control group. The Mini-CEX score of the experimental group at the end of the rotation was notably higher than that at the beginning of rotation ( P<0.05), and their abilities improved continuously. The satisfaction rates of teachers and students in the experimental group were 71.43% (20/28) and 67.86% (19/28), respectively, which were significantly higher than those in the control group [39.29% (11/28) and 35.71% (10/28), P<0.05]. Conclusions:The teaching model integrating PBL and Mini-CEX can effectively enhance the post competency of interns in the Department of Neurology, thus offering a new perspective for clinical undergraduate teaching.

Prostate cancer is the most prevalent malignant tumor in the urinary and male reproductive systems, with its incidence on the rise. In China, most patients are first diagnosed at an advanced stage, missing the window for surgical intervention. Over the years, there has been a continuous evolution in the exploration of neoadjuvant therapy for prostate cancer. Currently, several neoadjuvant approaches have been established, such as neoadjuvant endocrine therapy based on androgen deprivation therapy, neoadjuvant chemotherapy, and neoadjuvant combination therapy. These therapeutic regimens have been shown to enhance pathological responses, including pathological downstaging rate, thereby offering new hope for patients with high-risk localized and locally advanced prostate cancer. However, it remains to be seen whether these therapies can demonstrate a clear advantage in extending overall survival and metastasis-free survival. Further research is necessary to delve into the efficacy differences and safety concerns of these therapeutic approaches.