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.

Animals ; Callithrix ; Sex Factors ; Vocalization, Animal ; Sex Characteristics

Objective To investigate the efficacy of Jianpiwenyang Gel(SSWYG)for treating chronic diarrhea and explore its therapeutic mechanism.Methods Eighty patients with chronic diarrhea of spleen and stomach weakness type were randomized into two groups for interventions with lifestyle adjustment and treatment with bifid triple viable capsules(control group,n=40)or naval application with SSWYG(treatment group,n=40)for one week,after which symptoms of chronic diarrhea were evaluated.The Chinese medicine system pharmacology analysis platform(TCMSP),GeneCards,NCBI,OMIM database and GEO database(GSE14841)were used to obtain the active ingredients and target proteins of SSWYG and chronic diarrhea-related targets.The key targets were obtained by topological analysis for Gene Ontology(GO)and KEGG analyses.The affinity and binding characteristics of SSWYG for specific targets were verified by molecular docking using AutoDock software.Results In both groups,gastrointestinal symptom rating scale(GSRS),Bristol Scale and TCM syndrome scores significantly improved after the treatments(P<0.05),and better effects were observed in the treatment group(P<0.05).Sixty-eight targets of SSWYG in treating chronic diarrhea were obtained,and 33 most probable ones were screened out by topological analysis.GO and KEGG analyses identified several chronic diarrhea-related pathways including the TNF and IL-17 pathways.Molecular docking study showed good affinity of the core components of SSWYG for the key targets CASP3,JNK,IL1B,IL6,and AKT1.JUN and CASP3 had the lowest binding energy and the highest stable binding energy with multiple major active ingredients of SSWYG.Conclusion SSWYG can significantly improve clinical symptoms of chronic diarrhea possibly by regulating the TNF and IL-17 as well as other pathways via CASP3 and JUN,suggesting a complex therapeutic mechanism of SSWYG involving multiple ingredients and targets and coordinated regulation of multiple pathways.

Objective To investigate the efficacy of Jianpiwenyang Gel(SSWYG)for treating chronic diarrhea and explore its therapeutic mechanism.Methods Eighty patients with chronic diarrhea of spleen and stomach weakness type were randomized into two groups for interventions with lifestyle adjustment and treatment with bifid triple viable capsules(control group,n=40)or naval application with SSWYG(treatment group,n=40)for one week,after which symptoms of chronic diarrhea were evaluated.The Chinese medicine system pharmacology analysis platform(TCMSP),GeneCards,NCBI,OMIM database and GEO database(GSE14841)were used to obtain the active ingredients and target proteins of SSWYG and chronic diarrhea-related targets.The key targets were obtained by topological analysis for Gene Ontology(GO)and KEGG analyses.The affinity and binding characteristics of SSWYG for specific targets were verified by molecular docking using AutoDock software.Results In both groups,gastrointestinal symptom rating scale(GSRS),Bristol Scale and TCM syndrome scores significantly improved after the treatments(P<0.05),and better effects were observed in the treatment group(P<0.05).Sixty-eight targets of SSWYG in treating chronic diarrhea were obtained,and 33 most probable ones were screened out by topological analysis.GO and KEGG analyses identified several chronic diarrhea-related pathways including the TNF and IL-17 pathways.Molecular docking study showed good affinity of the core components of SSWYG for the key targets CASP3,JNK,IL1B,IL6,and AKT1.JUN and CASP3 had the lowest binding energy and the highest stable binding energy with multiple major active ingredients of SSWYG.Conclusion SSWYG can significantly improve clinical symptoms of chronic diarrhea possibly by regulating the TNF and IL-17 as well as other pathways via CASP3 and JUN,suggesting a complex therapeutic mechanism of SSWYG involving multiple ingredients and targets and coordinated regulation of multiple pathways.

Objective To observe the value of anatomical characteristics of femoral intercondylar notch of knee joint for predicting non-contact anterior cruciate ligament tear(NC-ACLT).Methods MRI data of knee joint of 55 patients with NC-ACLT(NC-ACLT group)and 55 controls(control group)were retrospectively analyzed.The parameters of intercondylar notch,including depth,width,depth/width ratio,opening width,opening width index,area and width of the femoral condyle's outer edge at the same level were measured between groups,and the types of intercondylar notch(type A,U and W)were recorded.Univariate and multivariate logistic regression analysis were used to screen the independent impact factors of NC-ACLT.Receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the efficacy of each intercondylar notch parameter for predicting NC-ACLT.Results The depth and depth/width ratio of intercondylar notch in NC-ACLT group were both higher,while the opening width and opening width index of intercondylar notch in NC-ACLT group were both lower than those in control group(all P＜0.05).Multivariate logistic regression analysis revealed that the depth of intercondylar notch was an independent impact factors of NC-ACLT(P＜0.05).Taken 29.55 mm in depth of intercondylar notch,1.45 in depth/width ratio of intercondylar notch,21.15 mm in opening width of intercondylar notch and 0.29 in opening width index as the optimal cut-off value,respectively,the sensitivity of the above parameters for predicting NC-ACLT was 74.55％,58.18％,67.27％and 67.27％,the specificity was 69.09％,80.00％,61.82％and 78.18％,and the AUC was 0.720,0.713,0.652 and 0.710,respectively.Conclusion The anatomical characteristics of femoral intercondylar notch of knee joint could be used to predict NC-ACLT.The depth,depth/width ratio,opening width and opening width index of intercondylar notch could be used as predictive indicators.

Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.
Humans ; Brain-Computer Interfaces ; Evoked Potentials, Visual ; Algorithms ; Discriminant Analysis ; Electroencephalography