Objective:To construct a risk prediction model for Vaginal Intraepithelial Neoplasia Grade 2 or Worse(VaIN 2+)lesions,and to establish a nomogram for individual diagnosis of VaIN 2+and risk stratification,so as to provide guidance for the treatment of vaginal lesions.Methods:A total of 248 women diagnosed with VaIN through colposcopic biopsy at the Center for Gynecologic and Cervical Diseases,First Affiliated Hospital of Nanjing Medical University,from January 2021 to January 2024 were included in this study.Based on the gold standard established by histological and pathological findings,these patients were categorized into a lower VaIN 2 group and a VaIN 2+group.Univariate comparative analysis was performed on the two groups.Multivariate Logis-tic regression analysis was used to determine the risk factors of VaIN 2+and to construct a diagnostic model.The nomogram model was established by using R Studio software.The discrimination,calibration and clinical practical value of the model were evaluated by the area under the receiver operating characteristic(ROC)curve and cali-bration curve.Results:Univariate analysis identified that HPV type,cervical lesion grade,acetowhite change,vagi-nal lesion duration,vaginal lesion location,and cervical lesion duration as influencing factors for diagnosing VaIN 2+(P<0.1).Multivariate binary Logistic regression analysis indicated that HPV16/18 positivity,cervical lesion grade≥CIN 2,thick acetowhite change,vaginal lesion duration≥5 years,and vaginal lesion location at the upper 1/3 of the vagina were independent risk factors for diagnosing VaIN 2+(OR>1,P<0.05),while cervical lesion duration<3 years was a protective factor(OR<1,P<0.05),with acetowhite change having the greatest impact(OR4.54).A regression model was established based on the multivariate binary Logistic regression analysis,with an AUC of 0.813.A nomogram model was constructed and internally validated,yielding a consistency index(C-index)of 0.81.Patients were stratified into risk groups using the X-tile software,with higher total scores indi-cating a greater risk of developing VaIN 2+.Conclusions:The nomogram model constructed in this study can in-dividually predict the risk of VaIN 2+lesions in patients,with high accuracy and clinical practicability.

Objective:To construct a risk prediction model for Vaginal Intraepithelial Neoplasia Grade 2 or Worse(VaIN 2+)lesions,and to establish a nomogram for individual diagnosis of VaIN 2+and risk stratification,so as to provide guidance for the treatment of vaginal lesions.Methods:A total of 248 women diagnosed with VaIN through colposcopic biopsy at the Center for Gynecologic and Cervical Diseases,First Affiliated Hospital of Nanjing Medical University,from January 2021 to January 2024 were included in this study.Based on the gold standard established by histological and pathological findings,these patients were categorized into a lower VaIN 2 group and a VaIN 2+group.Univariate comparative analysis was performed on the two groups.Multivariate Logis-tic regression analysis was used to determine the risk factors of VaIN 2+and to construct a diagnostic model.The nomogram model was established by using R Studio software.The discrimination,calibration and clinical practical value of the model were evaluated by the area under the receiver operating characteristic(ROC)curve and cali-bration curve.Results:Univariate analysis identified that HPV type,cervical lesion grade,acetowhite change,vagi-nal lesion duration,vaginal lesion location,and cervical lesion duration as influencing factors for diagnosing VaIN 2+(P<0.1).Multivariate binary Logistic regression analysis indicated that HPV16/18 positivity,cervical lesion grade≥CIN 2,thick acetowhite change,vaginal lesion duration≥5 years,and vaginal lesion location at the upper 1/3 of the vagina were independent risk factors for diagnosing VaIN 2+(OR>1,P<0.05),while cervical lesion duration<3 years was a protective factor(OR<1,P<0.05),with acetowhite change having the greatest impact(OR4.54).A regression model was established based on the multivariate binary Logistic regression analysis,with an AUC of 0.813.A nomogram model was constructed and internally validated,yielding a consistency index(C-index)of 0.81.Patients were stratified into risk groups using the X-tile software,with higher total scores indi-cating a greater risk of developing VaIN 2+.Conclusions:The nomogram model constructed in this study can in-dividually predict the risk of VaIN 2+lesions in patients,with high accuracy and clinical practicability.

Objective To evaluate the effect and mechanism of rivaroxaban, an inhibitor of coagulation factor Ⅹa (FⅩa), on endotoxin-induced injury to human umbilical vein endothelial cells (HUVEC). Methods When cultured HUVEC grow to 80% fusion, they were divided into four groups according to the random number method: blank control group (DMEM medium), lipopolysaccharide (LPS) group (cells were challenged by 100 μg/L LPS for 16 hours), FⅩa+LPS group (cells were challenged by LPS for 16 hours after they were cultured with 100 nmol/L FⅩa for 24 hours), and FⅩa+RIV+LPS group (cells were challenged by LPS for 16 hours after they were cultured with 100 nmol/L FXa and 1 μmol/L rivaroxaban for 24 hours). After each group of cells were challenged with LPS, the cell activity was detected by the cell proliferation and toxicity kit (CCK-8); the cell migration ability was detected by cell scratch experiments;the abilities of cells migration were measured by scratch-wound-healing assay; the apoptosis of cells were evaluated using flow cytometry; the endothelial barrier of cells was assessed by Transwell and Evans blue; the levels of tumor necrosis factor-α(TNF-α), interleukin (IL-1β, IL-6) were detected by the enzyme linked immunosorbent assay (ELISA); the expressions of nuclear factor-κB (NF-κB) and mitogen activated protein kinase (MAPK) signaling pathway were detected by Western Blot. Results Compared with blank control group, the cell viability in LPS group was significantly decreased, and the migration ability, number of apoptotic cells, and barrier permeability of endothelial cells was significantly increased, the levels of TNF-α, IL-1β and IL-6 were significantly increased, and the expressions of phosphorylation of c-Jun N-terminal kinase (p-JNK), phosphorylation of p38MAPK (p-p38MAPK), phosphorylation of transforming growth factor kinase 1 (p-TAK1) and phosphorylation of NF-κBp65 (p-NF-κBp65) were significantly increased. It indicated that LPS could stimulate the inflammatory response of vascular endothelial cells, and had a significant impact on cell activity, apoptosis and function. There was no significant difference in above indexes between FⅩa+LPS group and LPS group, except for the level of IL-6 being higher in FⅩa+LPS group. Compared with FⅩa+LPS group, in FⅩa+RIV+LPS group, the cell activity was significantly increased (A value: 0.42±0.02 vs. 0.33±0.02), and migration ability was significantly decreased (folds: 1.78±0.17 vs. 2.24±0.20), the number of apoptotic cells was significantly decreased [(11.30±0.70)% vs. (21.03±0.19)%], and permeability of monolayers endothelial cells was significantly decreased [(149±12)% vs. (253±15)%], the levels of inflammatory cytokines were significantly decreased [IL-1β(ng/L): 163.2±20.7 vs. 477.8±20.2, IL-6 (ng/L): 69.3±0.5 vs. 238.0±24.1, TNF-α(ng/L): 117.0±13.1 vs. 196.2±4.5], the expressions of p-TAK1 and p-NF-κBp65 were significantly decreased (p-TAK1/TAK1: 0.74±0.09 vs. 1.85±0.15, p-NF-κBp65/NF-κBp65: 1.15±0.17 vs. 2.36±0.20), with statistically significant differences (all P <0.05). There was no significant difference in the p-JNK, p-p38MAPK expressions between FⅩa+RIV+LPS group and FⅩa+LPS group (p-JNK/JNK: 1.64±0.12 vs. 1.65±0.15, p-p38MAPK/p38MAPK: 2.31±0.32 vs. 2.35±0.20, both P > 0.05). Conclusion Rivaroxaban can effectively relieve the inflammatory response of HUVEC stimulated by LPS, which may be related to the inhibition of NF-κB signaling pathway activation rather than MAPK signaling pathway.