Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

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 investigate the characteristics of m6A methylation modification patterns in mRNA of patients with major depressive disorder (MDD) and its effect in the pathogenesis of the disease.Methods:From March 2022 to May 2023, five untreated MDD patients were assigned to the MDD group, and five healthy individuals were enrolled as the healthy control group at the First Psychiatric Hospital of Harbin.Microarray analysis was performed to determine the m6A modification profiles and gene expression patterns of mRNA in MDD. Gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were conducted to elucidate the effect of m6A methylation in the development of depression. Finally, methylated RNA immunoprecipitation combined with quantitative PCR (MeRIP-qPCR) was used to validate the m6A methylation levels of key mRNAs (GRM4, CAMKK2). Data were analyzed using R software (version 4.2.0) with t-test and Fisher's exact test. Results:Significant differences in m6A-modified mRNAs were observed between MDD patients and healthy controls. A total of 513 mRNAs (180 hypermethylated and 333 hypomethylated) exhibited differential m6A modifications in MDD patients. GO and KEGG analysis revealed that hypermethylated mRNAs were primarily enriched in neuroactive ligand-receptor interactions, while hypomethylated mRNAs were associated with the AMP-activated protein kinase (AMPK) signaling pathway. Additionally, a total of 350 differentially expressed mRNAs were identified (171 upregulated and 179 downregulated), enriched in the cyclic adenosine monophosphate (cAMP) and tumor necrosis factor (TNF) signaling pathways. MeRIP-qPCR results demonstrated that the m6A methylation level of GRM4 in MDD patients (25.40±2.38) was significantly higher than that in healthy controls (9.40±1.00) ( t=13.88, P<0.05), whereas the methylation level of CAMKK2 (19.63±6.60) was significantly lower than that in healthy controls (30.51±7.20) ( t=2.48, P<0.05). Conclusion:The m6A modification expression profile is abnormal in patients with major depressive disorder, which may be involved in the pathogenesis and development of MDD, and the identification of key pathways may provide new clues and evidence for the development of more effective therapeutic targets for MDD.

Objective:To investigate the characteristics of m6A methylation modification patterns in mRNA of patients with major depressive disorder (MDD) and its effect in the pathogenesis of the disease.Methods:From March 2022 to May 2023, five untreated MDD patients were assigned to the MDD group, and five healthy individuals were enrolled as the healthy control group at the First Psychiatric Hospital of Harbin.Microarray analysis was performed to determine the m6A modification profiles and gene expression patterns of mRNA in MDD. Gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were conducted to elucidate the effect of m6A methylation in the development of depression. Finally, methylated RNA immunoprecipitation combined with quantitative PCR (MeRIP-qPCR) was used to validate the m6A methylation levels of key mRNAs (GRM4, CAMKK2). Data were analyzed using R software (version 4.2.0) with t-test and Fisher's exact test. Results:Significant differences in m6A-modified mRNAs were observed between MDD patients and healthy controls. A total of 513 mRNAs (180 hypermethylated and 333 hypomethylated) exhibited differential m6A modifications in MDD patients. GO and KEGG analysis revealed that hypermethylated mRNAs were primarily enriched in neuroactive ligand-receptor interactions, while hypomethylated mRNAs were associated with the AMP-activated protein kinase (AMPK) signaling pathway. Additionally, a total of 350 differentially expressed mRNAs were identified (171 upregulated and 179 downregulated), enriched in the cyclic adenosine monophosphate (cAMP) and tumor necrosis factor (TNF) signaling pathways. MeRIP-qPCR results demonstrated that the m6A methylation level of GRM4 in MDD patients (25.40±2.38) was significantly higher than that in healthy controls (9.40±1.00) ( t=13.88, P<0.05), whereas the methylation level of CAMKK2 (19.63±6.60) was significantly lower than that in healthy controls (30.51±7.20) ( t=2.48, P<0.05). Conclusion:The m6A modification expression profile is abnormal in patients with major depressive disorder, which may be involved in the pathogenesis and development of MDD, and the identification of key pathways may provide new clues and evidence for the development of more effective therapeutic targets for MDD.

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 chain mediating effect of anxiety and flow experience on perceived stress and mobile phone addiction in nursing college students.Methods:In December 2021, a cross-sectional design survey was conducted on 4 179 freshmen and sophomores in a nursing college in Heilongjiang Province. The Chinese perceived stress scale, generalized anxiety disorder-7, flow state scale, and mobile phone addiction tendency scale were selected separately to assess perceived stress, anxiety symptoms, flow experience and mobile phone addiction. SPSS 26.0 software was used for descriptive analysis, independent sample t-test, Spearman correlation analysis, and AMOS 24.0 software was used for mediating effect test. Results:(1) Among the 3 050 nursing students, there were 714(23.41%) students who were addicted to mobile phones. (2) Spearman correlation analysis indicated that perceived stress(27.31±9.56) was positively correlated with anxiety(7.00(1.00, 10.00), r=0.441, P<0.05), flow experience((12.00±3.40), r=0.517, P<0.05), and mobile phone addiction((42.42±13.05), r=0.476, P<0.05).Anxiety was positively correlated with flow experience ( r=0.430, P<0.01) and mobile phone addiction ( r=0.538, P<0.01).Flow experience was positively correlated with mobile phone addiction ( r=0.490, P<0.01). (3) Anxiety and flow experience played seperate mediating and chain mediating roles between perceived stress and mobile phone addiction, accounting for 26.06%(0.165/0.633), 23.54%(0.149/0.633) and 3.48%(0.022/0.633) of the total effect. Conclusion:Perceived stress not only directly affects the mobile phone addiction of nursing students, but also indirectly affects mobile phone addiction through the independent and chain mediating effects of anxiety and flow experience.

Objective This study aimed to clarify the anti-influenza virus activity of Bingyanqing(BYQ),as well as to explore the mechanism of BYQ in treating influenza through network pharmacology and experimental verification.Methods The impact of BYQ on mortality,lung index,and viral load in an influenza mouse model was detected.We collected the ingredients and targets of BYQ formula by searching databases including TCMSP,Swiss Target Prediction and consulting the literature.The"ingredients-common target"network for anti-influenza effect of BYQ was constructed using Cytoscape software.The protein-protein interaction(PPI)network was constructed using the STRING database and Cytoscape software,and the core targets were screened.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses were performed for common targets.The effect of BYQ on reducing influenza-induced oxidative damage,the expression of antioxidant and pro-oxidant enzyme,P65 phosphorylation and nuclear factor E2-related factor 2(Nrf2)nuclear translocation in vivo were investigated.Results BYQ significantly reduced mortality,lung index,pulmonary viral load and lung injury in a mouse model of influenza.We obtained one hundred and ninety-three of BYQ active compounds,which corresponded to three hundred and thirty-eight targets.There are 180 influenza-related targets among them.Nine targets,including IκBα kinase α(CHUK),IκBα kinase γ(IKBKG),NF-κB p65(RELA),tumor necrosis factor(TNF)and interleukin 6(IL6),were identified as potential core targets.GO analysis indicated that BYQ is involved in several biological functions,including antibacterial and antioxidant stress responses.KEGG analysis revealed the involvement of several viral and immune-related pathways for BYQ in treating influenza,including herpes simplex virus,influenza A virus,TNFα and toll-like receptor pathways.In vivo studies showed that high-dose BYQ significantly reduced pulmonary malondialdehyde(MDA)levels(P＜0.01)and increased total superoxide dismutase(SOD)activity(P＜0.001)in a mouse mode of influenza compared to oseltamivir phosphate.The treatment group with the combination of BYQ&oseltamivir phosphate had lower levels of NADPH oxidase 2(NOX2)and 4(NOX4)(P＜0.001),and higher levels of catalase(CAT)and glutathione peroxidase 4(GPX4)(P＜0.001)in the lungs than oseltamivir phosphate group.The combined treatment group showed more significant Nrf2 nuclear translocation(P＜0.05)than the oseltamivir phosphate group.However,there was no significant difference in P65 phosphorylation levels between the combination treatment group and the oseltamivir phosphate group(P＜0.05),but P65 phosphorylation levels in both groups were lower than in the model group(P＜0.05).Conclusion BYQ exhibits significant anti-influenza virus activity,manifests a dual effect by inhibiting the synthesis of pro-oxidant enzymes and promoting the antioxidant system,thereby alleviates the oxidative stress damage caused by influenza.