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.

Tumors continue to be a major challenge in human survival that we have yet to overcome. Despite the variety of treatment options available, we have not yet found an effective method. As more and more research is conducted, attention has been turned to a new field for tumor treatment—the tumor microenvironment (TME). This is a dynamic and complex environment consisting of various matrix cells surrounding cancer cells, including surrounding immune cells, blood vessels, extracellular matrix, fibroblasts, bone marrow-derived inflammatory cells, signaling molecules, and some specific cell types. Firstly, endothelial cells play a key role in tumor development and the immune system’s protection of tumor cells. Secondly, immune cells, such as macrophages, Treg cells, Th17 cells, are widely involved in various immune responses and activities in the human body, such as inflammation responses promoting survival carefully orchestrated by the tumor. Even though many studies have extensively researched the TME and found many research schemes, so far, no key effective method has been found to treat tumors by affecting the TME. The TME is a key interaction area between the host immune system and the tumor. Cells within the TME influence each other and interact with cancer cells to affect cancer cell invasion, tumor growth, and metastasis. This is a new direction for cancer treatment. In the complex environment of the TME, post-translational modifications (PTMs) of proteins have been proven to play an important role in the TME. PTMs are dynamic, strictly regulated changes to proteins that control their function by regulating their structure, spatial location, and interaction. Among PTMs, a reversible post-translational modification called SUMOylation is a common regulatory mechanism in cellular processes. It is a post-translational modification that targets lysine residues with a small ubiquitin-like modifier (SUMO) in a reversible post-translational modification manner. SUMOylation is widely involved in carcinogenesis, DNA damage response, cancer cell proliferation, metastasis, and apoptosis, playing a pivotal role in the TME, such as DNA damage repair, tumor metastasis, and also participates in immune cell differentiation, activation, and inhibition of immune cells. On the other hand, SUMO or sentrin-specific protease (SENP) inhibitors can interfere with the SUMOylation process, thereby affecting many biological processes, including immune response, carcinogenesis, cell cycle progression, and cell apoptosis, etc. In summary, this review aims to introduce the dynamic modification of protein SUMOylation on various immune cells and the application of various inhibitors, thereby exploring its role in the TME. This is a challenging but hopeful field, and we look forward to future research that can bring more breakthroughs. In conclusion, the TME is a complex and dynamic environment that plays a crucial role in the development and progression of tumors. Understanding the intricate interactions within the TME and the role of PTMs, particularly SUMOylation, could provide valuable insights into the mechanisms of tumor development and potentially lead to the development of novel therapeutic strategies. The study of SUMOylation and its effects on various immune cells in the TME is an exciting and promising area of research that could significantly advance our understanding of tumor biology and potentially lead to the development of more effective treatments for cancer. This is a challenging but hopeful field, and we look forward to future research that can bring more breakthroughs.

Objective To evaluate the bioequivalence and safety of ezetimibe tablets in healthy Chinese subjects.Methods The study was designed as a single-center,randomized,open-label,two-period,two-way crossover,single-dose trail.Subjects who met the enrollment criteria were randomized into fasting administration group and postprandial administration group and received a single oral dose of 10 mg of the subject presparation of ezetimibe tablets or the reference presparation per cycle.The blood concentrations of ezetimibe and ezetimibe-glucuronide conjugate were measured by high-performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS),and the bioequivalence of the 2 preparations was evaluated using the WinNonlin 7.0 software.Pharmacokinetic parameters were calculated to evaluate the bioequivalence of the 2 preparations.The occurrence of all adverse events was also recorded to evaluate the safety.Results The main pharmacokinetic parameters of total ezetimibe in the plasma of the test and the reference after a single fasted administration:Cmax were(118.79±35.30)and(180.79±51.78)nmol·mL-1;tmax were 1.40 and 1.04 h;t1/2 were(15.33±5.57)and(17.38±7.24)h;AUC0-t were(1 523.90±371.21)and(1 690.99±553.40)nmol·mL-1·h;AUC0-∞ were(1 608.70±441.28),(1 807.15±630.00)nmol·mL-1·h.The main pharmacokinetic parameters of total ezetimibe in plasma of test and reference after a single meal:Cmax were(269.18±82.94)and(273.93±87.78)nmol·mL-1;Tmax were 1.15 and 1.08 h;t1/2 were(22.53±16.33)and(16.02±5.84)h;AUC0_twere(1 463.37±366.03),(1 263.96±271.01)nmol·mL-1·h;AUC0-∞ were(1 639.01±466.53),(1 349.97±281.39)nmol·mL-1·h.The main pharmacokinetic parameters Cmax,AUC0-tand AUC0-∞ of the two preparations were analyzed by variance analysis after logarithmic transformation.In the fasting administration group,the 90％CI of the log-transformed geometric mean ratios were within the bioequivalent range for the remaining parameters in the fasting dosing group,except for the Cmax of ezetimibe and total ezetimibe,which were below the lower bioequivalent range.The Cmax of ezetimibe,ezetimibe-glucuronide,and total ezetimibe in the postprandial dosing group was within the equivalence range,and the 90％CI of the remaining parameters were not within the equivalence range for bioequivalence.Conclusion This test can not determine whether the test preparation and the reference preparation of ezetimibe tablets have bioequivalence,and further clinical trials are needed to verify it.

Background::Oral anti-coagulants (OAC) are the intervention for the prevention of stroke, which consistently improve clinical outcomes and survival among patients with atrial fibrillation (AF). The main purpose of this study is to identify problems in OAC utilization among hospitalized patients with AF in China.Methods::Using data from the Improving Care for Cardiovascular Disease in China-Atrial Fibrillation (CCC-AF) registry, guideline-recommended OAC use in eligible patients was assessed.Results::A total of 52,530 patients with non-valvular AF were enrolled from February 2015 to December 2019, of whom 38,203 were at a high risk of stroke, 9717 were at a moderate risk, and 4610 were at a low risk. On admission, only 20.0% (6075/30,420) of patients with a diagnosed AF and a high risk of stroke were taking OAC. The use of pre-hospital OAC on admission was associated with a lower risk of new-onset ischemic stroke/transient ischemic attack among the diagnosed AF population (adjusted odds ratio: 0.54, 95% confidence interval: 0.43–0.68; P <0.001). At discharge, the prescription rate of OAC was 45.2% (16,757/37,087) in eligible patients with high stroke risk and 60.7% (2778/4578) in eligible patients with low stroke risk. OAC utilization in patients with high stroke risk on admission or at discharge both increased largely over time (all P <0.001). Multivariate analysis showed that OAC utilization at discharge was positively associated with in-hospital rhythm control strategies, including catheter ablation (adjusted odds ratio [OR] 11.63, 95% confidence interval [CI] 10.04–13.47; P <0.001), electronic cardioversion (adjusted OR 2.41, 95% CI 1.65–3.51; P <0.001), and anti-arrhythmic drug use (adjusted OR 1.45, 95% CI 1.38–1.53; P <0.001). Conclusions::In hospitals participated in the CCC-AF project, >70% of AF patients were at a high risk of stroke. Although poor performance on guideline-recommended OAC use was found in this study, over time the CCC-AF project has made progress in stroke prevention in the Chinese AF population.Registration::ClinicalTrials.gov, NCT02309398.

Objective:The transjugular or transfemoral approach is used as a common method for hepatic venous pressure gradient (HVPG) measurement in current practice. This study aims to confirm the safety and effectiveness of measuring HVPG via the forearm venous approach.Methods:Prospective recruitment was conducted for patients with cirrhosis who underwent HVPG measurement via the forearm venous approach at six hospitals in China and Japan from September 2020 to December 2020. Patients' clinical baseline information and HVPG measurement data were collected. The right median cubital vein or basilic vein approach for all enrolled patients was selected. The HVPG standard process was used to measure pressure. Research data were analyzed using SPSS 22.0 statistical software. Quantitative data were used to represent medians (interquartile ranges), while qualitative data were used to represent frequency and rates. The correlation between two sets of data was analyzed using Pearson correlation analysis.Results:A total of 43 cases were enrolled in this study. Of these, 41 (95.3%) successfully underwent HVPG measurement via the forearm venous approach. None of the patients had any serious complications. The median operation time for HVPG detection via forearm vein was 18.0 minutes (12.3~38.8 minutes). This study confirmed that HVPG was positively closely related to Child-Pugh score ( r = 0.47, P = 0.002), albumin-bilirubin score ( r = 0.37, P = 0.001), Lok index ( r = 0.36, P = 0.02), liver stiffness ( r = 0.58, P = 0.01), and spleen stiffness ( r = 0.77, P = 0.01), while negatively correlated with albumin ( r = -0.42, P = 0.006). Conclusion:The results of this multi-centre retrospective study suggest that HVPG measurement via the forearm venous approach is safe and feasible.

Infrared spectroscopy technology has many advantages such as high efficiency and non-destructiveness,and has an important research and application value in the field of petroleum pollutant classification and detection.In this study,a petroleum pollutant classification method by combing the discrete wavelet transform(DWT)algorithm and a one-dimensional convolutional neural network based on the Inception module(Inception-1D-CNN)was proposed.Firstly,the DWT algorithm was used to denoise the original infrared spectral data to eliminate the interference information caused by experimental environment,instrument error and manual operation.Then,the inception-1D-CNN model was used to obtain multi-scale infrared spectroscopy feature information,and then classify the petroleum pollutants.Experimental results showed that compared with preprocessing methods such as standard normal variable(SNV),adaptive iteratively reweighted penalized least squares(AirPLS),and Savitzky-Golay smoothing(S-G),the prediction accuracy of the DWT algorithm combined with the 1D-CNN model with a convolutional kernel size of 3×1 was 86.6%,which was 6.6%,6.6%and 3.3%higher,respectively.The prediction accuracy of DWT algorithm combined with 1D-CNN model with a convolutional kernel size of 5×1 was 93.3%,which was 10.0%,7.0%and 3.3%higher,respectively.The prediction accuracy of the DWT algorithm combined with the 1D-CNN model with a convolutional kernel size of 7×1 was 90.0%,which was 6.7%,10.0%and 3.4%higher,respectively.The prediction accuracy of the DWT algorithm combined with the inception-1D-CNN model was 100.0%,which was 10.0%,10.0%and 3.4%higher,respectively.Therefore,the DWT algorithm combined with the inception-1D-CNN model could accurately classify and predict petroleum pollutants,and provided a certain basis for the subsequent treatment of oil spills on the sea surface.