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.

Aim To investigate the effect of vanillin on the regulation of cyclic guanylate adenylate synthetase(cGAS)/stimulator of interferon gene(STING)signa-ling pathway on hepatic tissue injury in rats with intra-hepatic cholestasis(IC).Methods SD rats were randomly divided into normal group,IC group,vanillin group,cGAS overexpression group,and vanillin+cGAS overexpression group,with continuous adminis-tration for seven days.The body weight,liver weight and liver to body weight ratio of rats were measured.Liver function(ALT,AST,ALP,LDH),IC(TBIL,TBA)and liver fibrosis(HA,LN,PC Ⅲ)index were determined by ELISA.Liver pathology and fibrosis were observed using HE and Masson staining,and col-lagen volume fraction was calculated.The expression of cGAS/STING pathway related proteins in liver tissue was detected by Western blot.Results Vanillin could improve liver pathology and fibrosis,increase body weight,and decrease liver weight,ALT,AST,ALP,LDH,TBIL,TBA,HA,LN,PC Ⅲ,collagen volume fraction,cGAS,STING protein in IC rats(P＜0.05).Overexpression of cGAS could reverse the effects of vanillin on the above indicators in IC rats(P＜0.05).Conclusions Vanillin may improve liver function,IC,liver fibrosis,and liver tissue damage in IC rats by downregulating the cGAS/STING signaling pathway.

This study was aimed at understanding the trends in,and scope of,severe fever with thrombocytopenia syndrome(SFTS)in Nanjing,analyzing the spatial distribution pattern,detecting high incidence cluster areas and key popula-tions,and scientifically guiding prevention and control strategies and measures.We obtained data on SFTS cases from 2010 to 2023 in Nanjing from the China Disease Control and Prevention Information System,and described the time,popu-lation,and spatial distribution characteristics.We used joinpoint regression to calculate the annual percentage change(APC)in incidence,then used FleXScan spatial clustering scan analysis to explore spatial clustering areas at the street level.A total of 507 SFTS cases were reported from 2010 to 2023 in Nanjing.The APC was 31.8％(95％CI:22.5％-41.9％,P＜0.001),and the reported incidence in 2023 was 1.42/100 000(134 cases).The seasonal indices from May to August were 2.7,2.1,3.0,and 1.3,respectively,accounting for 76.1％of the total cases.The median age was 66(IQR:55,73)years,which gradually increased from 59 years in 2010-2011 to 68 in 2022-2023(P＜0.001);94.1％of cases were in individuals 45 years or older.Farmers,homemakers/unemployed individuals,and retirees accounted for 90.1％.The epidemic area increased from 11 streets in four districts in 2010-2011 to 58 streets in 11 dis-tricts in 2022-2023.Except for 2012-2013,global spatial autocorrelation analysis showed positive Moran's I values(0.224-0.526,P＜0.001),and FlexScan scan indicated that several streets in Lishui District and Jiangning District were the most likely clusters.Four streets in Pukou District were the secondary clusters from 2018 to 2023,and three streets in Luhe District in 2022-2023 were the secondary clusters(all P＜0.05).The reported incidence of SFTS in Nanjing showed a rapid upward trend,with spread of epidemic areas.The spatial distribution pattern was clustered.Strengthened training in diagnosis and treatment technology and detection ability of medical institutions,surveillance in high-incidence areas,tracing of case flow,and health education of tick and disease prevention knowledge are recommended.

Objective:To investigate two cases of rare pathogenic genes,initiation codon mutations in HBA2 gene,combined with Southeast Asian deletion and their family members to understand the relationship of HBA2:c.2T＞C and HBA2:c.2delT mutations with clinical phenotype.Methods:The peripheral blood of family members was obtained for blood cell analysis and capillary electrophoresis hemoglobin analysis.Gap-PCR and reverse dot blotting(RDB)were used to detect common types of mutations in α-thalassaemia gene.Sanger sequencing was used to analyze HBA1 and HBA2 gene sequence.Results:Two proband genotypes were identified as--SEA/αα with HBA2:c.2T＞C and--SEA/αα with HBA2:c.2delT.HBA2:c.2T＞C/WT and HBA2:c.2delT/WT was detected in family members.They all presented with microcytic hypochromic anemia.Conclusion:When HBA2:c.2T＞C and HBA2:c.2delT are heterozygous that can lead to static α-thalassemia phenotype,and when combined with mild α-thalassemia,they can lead to the clinical manifestations of hemoglobin H disease.This study provides a basis for genetic counseling.

Objective:To perform molecular diagnosis and pedigree analysis for one case with α-thalassemia who does not conform to the genetic laws,and explore the effects of a newly discovered rare mutation(HBA2:c.*12G＞A)on clinical phenotypes.Methods:Blood samples of the proband and her family members were collected for blood routine analysis,and the hemoglobin components were analyzed by capillary electrophoresis.The common α-and β-globin gene loci in Chinese population were detected by conventional techniques(Gap-PCR,RDB-PCR).The α-globin gene sequences(HBA1,HBA2)were analyzed by Sanger sequencing.Results:By analyzing the test results of proband and her family members,the genotype of the proband was-α3,7/HBA2:c.*12G＞A,her father was HBA2:c.*12G＞A heterozygous mutation carrier.Conclusion:This study identifies a rare α-globin gene mutation(HBA2:c.*12G＞A)that has not been reported before.It is found that heterozygous mutation carriers present with static α-thalassemia.

Objective To develop and validate a risk prediction model based on early platelet-related parameters for bronchopulmonary dysplasia(BPD)in neonates admitted to the neonatal intensive care unit(NICU),and to facilitate early identification and intervention in high-risk populations.Methods Clinical data of 291 preterm infants with a gestational age(GA)≤32 weeks or a birth weight(BW)＜1 500 g,admitted to the NICU,were retrospectively analyzed.Out of these,214 cases were selected as the modeling group.This group was further categorized into the BPD group(n=76)and the non-BPD group(n=138),based on whether they required oxygen therapy at 28 days post-birth.Perinatal data,platelet-related parameters and other indicators between the two groups.Univariate and multivariate Logistic regression analyses were conducted to identify BPD risk factors,followed by the construction of a nomogram.An additional cohort of 105 preterm infants with GA≤32 weeks or BW＜1 500 g,were used to validate the model.This cohort was divided into the BPD group(n=43)and the non-BPD(n=62)group.Receiver operating characteristic(ROC)curve and calibration curve were used to internally verify the efficiency of the prediction model.Results The Logistic regression analysis identified GA,BW,Apgar score at 5 minutes≤7,invasive ventilation,platelet count(PLT)and mean platelet volume(MPV)as significant factors in the model(P＜0.05).The constructed nomogram was formulated using R language,and the areas under the ROC curve(AUC)for the three models were 0.908,0.931 and 0.918,respectively(P＜0.05).The verification group was verified by Bootstrap.The calibration curve showed a good fit.The internal validation AUC values of the three models were 0.877,0.890 and 0.886,respectively.Conclusion GA,BW,invasive ventilation,Apgar score at 5 minutes≤7,MPV and PLT are key risk factors for BPD onset.The risk prediction model based on these indicators can effectively predict BPD,providing clinicians with a valuable tool for early detection and intervention in the development of BPD.