Objective:To develop and explore the clinical value of a nomogram model for the preoperative diagnosis of proliferative hepatocellular carcinoma (HCC) based on gadoxetate disodium (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI).Methods:The preoperative Gd-EOB-DTPA-enhanced MRI data and clinical pathological data of patients with pathologically confirmed proliferative (178 cases) and non-proliferative type HCC (378 cases) from September 2017 to November 2022 were retrospectively collected. The MRI features and clinicopathological features of proliferative and non-proliferative type HCC were evaluated. Multivariate logistic regression analysis was used to determine the independent predictive factors of proliferative-type HCC. The nomogram prediction model was constructed using R software. The receiver operating characteristic curve (ROC) was used to evaluate its diagnostic efficacy. The calibration curve and decision curve analysis (DCA) were drawn to evaluate the calibration performance and clinical application value of the nomogram model. The optimal threshold for distinguishing high-risk from low-risk was determined using the Youden index. The survival prognosis of proliferative and non-proliferative type HCC was analyzed and compared using the Kaplan-Meier survival curve and the log-rank test. The measurement data were analyzed using the independent sample t-test or the Mann-Whitney U test. The count data were compared using the χ2 test. Results:There were statistically significant differences in alpha-fetoprotein (AFP) levels ( χ2=17.244, P<0.001), tumor morphology ( χ2=13.669, P<0.001), intratumoral fatty degeneration ( χ2=10.495, P=0.001), abnormal enhancement of peritumoral abnormalities during arterial phase ( χ2=37.662, P<0.001), tumor capsule ( χ2=23.961, P<0.001), intratumoral necrosis ( χ2=77.184, P<0.001), intratumoral hemorrhage ( χ2=4.892, P=0.027), peritumoral hypointense in hepatobiliary phase ( χ2=47.675, P<0.001), rim arterial phase hyperenhancement ( χ2=115.976, P<0.001), intratumoral artery ( χ2=15.528, P<0.001) and intravenous tumor thrombus ( χ2=10.532, P=0.001) between proliferative and non-proliferative type HCC groups. Multivariate logistic regression analysis showed that AFP>200 μg/L ( OR=1.561, P=0.044), no intratumoral fatty degeneration ( OR=1.947, P=0.033), intratumoral necrosis ( OR=2.084, P=0.003), peritumoral hypointensity in the hepatobiliary phase ( OR=2.314, P=0.001), and annular hyperenhancement in the arterial phase ( OR=5.557, P<0.001) were independent predictors for preoperative diagnosis of proliferative-type HCC. A nomogram model for preoperative prediction of proliferative type HCC was constructed based on the independent predictors. The area under the ROC curve model for predicting proliferative-type HCC was 0.772 (95% CI: 0.735-0.807), with a sensitivity of 69.1% and a specificity of 75.4%. The calibration curve and DCA curve showed superior calibration performance and clinical applicability of the nomogram model. The Kaplan-Meier curve showed that the recurrence free survival rate after liver resection was significantly lower in patients with proliferative-type HCC than that of non-proliferative-type HCC ( P<0.001), and the high-risk group was significantly lower than the low-risk group ( P<0.001). Conclusions:The construction of a nomogram model based on Gd-EOB-DTPA-enhanced MRI features combined with AFP >200μg/L can accurately diagnose proliferative-type HCC and predict its preoperative prognosis.

Objective:To analyze the protective effect of a respiratory syncytial virus (RSV) vaccine based on bacterial outer membrane vesicle (OMV) in mice.Methods:The pre-fusion protein (preF) of RSV was linked to the surface of OMV through the transmembrane protein cytolysin A (ClyA) to form the nanovaccine OMV-preF. The morphological characteristics of OMV and OMV-preF were observed under a transmission electron microscope. OMV-preF was intramuscularly injected into BALB/c mice and the elicited humoral and cellular immune responses were evaluated. The protective effect of OMV-preF was assessed by challenging the immunized mice with RSV Long strain. One-way analysis of variance and Tukey test were used for statistical analysis.Results:The results showed that preF was stably expressed in OMV, and both OMV-preF and OMV exhibited a double-layer vesicle structures under the microscope. OMV-preF could significantly activate the cellular and humoral immune responses in mice, causing a significant increase in CD8 + T cells and CD19 + B cells as well as a significant increase in the serum level of specific IgG. The neutralizing antibodies produced in the immunized mice could significantly inhibit the replication of RSV Long strain in vivo. Conclusions:The nanovaccine OMV-preF can induce high-level humoral and cellular immune responses, and the antibodies produced following immunization can effectively inhibit viral replication. This study provides a new strategy for RSV subunit vaccines.

Objective To explore the value of combined detection of serum mitogen-activated protein kinase 1(MAPK1)and lysyl oxidase-like protein 2(LOXL2)in early diagnosis of cervical cancer.Methods A total of 218 patients with cervical lesions were selected as study group(103 ca-ses in cervical cancer group,115 cases in benign tumor group).Additionally,100 patients with cer-vical intraepithelial neoplasia grade Ⅱ were selected as precancerous lesion group,and 79 healthy in-dividuals undergoing physical examinations during the same period were selected as control group.Se-rum levels of MAPK1 and LOXL2 were measured in each group.Pearson correlation analysis was used to evaluate the correlations of serum MAPK1 and LOXL2 levels in patients with cervical cancer.Logistic regression analysis was performed to screen influencing factors for the occurrence of cervical cancer.Receiver operating characteristic(ROC)curves were plotted to assess the diagnostic efficacy of serum MAPK1 and LOXL2 for cervical cancer.Results Serum MAPK1 and LOXL2 levels in the study group were higher than those in the precancerous lesion group and the control group,and those in the precancerous lesion group were higher than those in the control group,with statistically signif-icant differences(P＜0.05).The proportion of patients with high-risk human papillomavirus(HPV)infection and serum MAPK1 and LOXL2 levels in the cervical cancer group were higher than those in the benign tumor group,with statistically significant differences(P＜0.05).Serum MAPK1 and LOXL2 levels in patients with stage Ⅲ to Ⅳ cervical cancer were higher than those in patients with stage Ⅰ to Ⅱ cervical cancer,with statistically significant differences(P＜0.05).Pearson correlation analysis showed a positive correlation between serum MAPK1 and LOXL2 levels in patients with cervical cancer(r=0.468,P＜0.001).Logistic regression analysis showed that high-risk HPV infection,MAPK1 and LOXL2 were all influencing factors for the occurrence of cervi-cal cancer(P＜0.05).ROC curve analysis showed that the area under the curve(AUC)for com-bined diagnosis of serum MAPK1 and LOXL2 was 0.911,which was significantly greater than the AUCs for individual diagnoses(0.848 and 0.843,respectively).Conclusion Serum MAPK1 and LOXL2 levels in patients with cervical cancer are significantly upregulated,and the two indicators were positively correlated.High-risk HPV infection,serum MAPK1 and LOXL2 levels were influen-cing factors for the occurrence of cervical cancer.Combined detection of MAPK1 and LOXL2 levels is expected to assist in the diagnosis of cervical cancer.

OBJECTIVE: This study investigates the efficacy and mechanisms of Qingjie Fuzheng Granules (QFG) in inhibiting colitis-associated colorectal cancer (CAC) development via RNA sequencing (RNA-seq) and 16S ribosomal RNA (rRNA) correlation analysis. METHODS: CAC was induced in BALB/c mice using azoxymethane (AOM) and dextran sulfate sodium (DSS), and QFG was administered orally to the treatment group. The effects of QFG on CAC were evaluated using disease index, histology, and serum T-cell ratios. RNA-seq and 16S rRNA analysis assessed the transcriptome and microbiome change. Key pharmacodynamic pathways were identified by integrating these data and confirmed via Western blotting and immunofluorescence. The link between microbiota and CAC-related markers was explored using linear discriminant analysis effect size and Spearman correlation analysis. RESULTS: Long-term treatment with QFG prevented AOM/DSS-induced CAC formation, reduced levels of interleukin (IL)-1β, tumor necrosis factor-alpha (TNF-α), IL-6, and interferon γ (IFN-γ), and increased CD3⁺ and CD4⁺/CD8⁺ T cells ratio, without causing hepatic or renal toxicity. A 16S rRNA analysis revealed that QFG rebalanced the Firmicutes/Bacteroidetes ratio and mitigated AOM/DSS-induced microbiota disturbances. Transcriptomics and Western blotting analysis identified the nucleotide-binding oligomerization domain-containing protein 2 (NOD2)/nuclear factor kappa-B (NF-κB) pathway as key for QFG's treatment against CAC. Furthermore, QFG decreased the abundance of Bacilli, Bacillales, Staphylococcaceae, Staphylococcus, Lactobacillales, Aerococcus, Alloprevotella, and Akkermansia, while increasing Clostridiales, Lachnospiraceae, Lachnospiraceae_NK4A136_group, Ruminococcaceae, and Muribaculaceae, which were highly correlated with CAC-related markers or NOD2/NF-κB pathway. CONCLUSION By mapping the relationships between CAC, immune responses, microbiota, and key pathways, this study clarifies the mechanism of QFG in inhibiting CAC, highlighting its potential for clinical use as preventive therapy.

Objective:To evaluate the efficacy of gadolinium ethoxybenzyl- diethy-lenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI features in the preoperative prediction of tertiary lymphoid structures (TLS) within hepatocellular carcinoma (HCC) lesions.Methods:This retrospective cross-sectional study included clinical and pathological data from 297 HCC patients treated at the Southwest Hospital, Army Medical University between June 2021 and November 2022. Based on postoperative pathology, patients were categorized into TLS-negative ( n=93) and TLS-positive ( n=204) groups. MRI features of HCC lesions using Gd-EOB-DTPA enhancement and relevant clinical data were analyzed. Intergroup comparisons of imaging features and laboratory findings were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher exact test, as appropriate. The logistic regression analysis was conducted to identify independent predictors of TLS positivity. A predictive model was constructed and visualized using a nomogram. The model′s predictive performance and clinical utility were assessed using the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) was compared using the DeLong test. Results:Significant differences were observed between the TLS-negative and TLS-positive groups in alpha-fetoprotein (AFP) levels, intratumoral hemorrhage, and peritumoral satellite nodules in the hepatobiliary phase ( P<0.05). Multivariate logistic regression identified intratumoral hemorrhage ( OR=0.123, 95% CI 0.070-0.216, P<0.001) and peritumoral satellite nodules in the hepatobiliary phase ( OR=0.236, 95% CI 0.093-0.596, P=0.002) as independent predictive factors for TLS-positivity. The imaging model based on these two features yielded an AUC of 0.764 (95% CI 0.709-0.809) for predicting TLS-positivity. When combined with AFP levels, the resulting clinical-imaging model achieved a superior AUC of 0.784 (95% CI 0.732-0.829), which was significantly higher than that of the imaging model alone ( Z=2.20, P=0.028). A nomogram was constructed based on the clinical-imaging model. The calibration curve demonstrated good predictive performance of the nomogram, and the DCA showed that the curve remained above the default line across a range of reasonable threshold probabilities, indicating that patients could derive clinical benefit. Conclusion:A nomogram model based on Gd-EOB-DTPA enhanced MRI features combined with AFP levels can effectively predict the presence of TLS in HCC.

Objective:To investigate the role of adenosine triphosphatase phospholipid transporter 10a(ATP10a)methylation assay in the triage of atypical squamous cell of undetermined significance(ASC-US)and low-grade squamous intraepithelial lesions(LSIL)in cervical cytology.Methods:188 patients with cervical exfolia-ted cells of ASC-US and LSIL were selected,and High-risk human papilloma virus(HR-HPV)typing and cervical biopsy results of the patients were collected at the same time.The cervical biopsy pathology results were used as the gold standard,and they were divided into inflammation,LSIL,high grade squamous intraepithelial lesions(HSIL)and cervical squamous cell carcinoma(SCC).Classify inflammation and LSIL as LSIL-group,HSIL and SCC as HSIL+group to compare the efficacy of ATP10a methylation and HR-HPV testing for diagnosis of HSIL+in this population(including HSIL and SCC).Results:The methylation detection value of ATP10a in the inflam-mation,LSIL,HSIL,and SCC patients were 19.035(16.478,20.823),13.446(5.890,20.674),10.336(4.733,17.336),4.223(1.713,7.754),respectively.The methylation detection value of ATP10a in the LSIL-group was 17.812(10.787,20.686),while that in the HSIL+group was 7.251(3.170,14.194).There was a statistically sig-nificant difference between the groups(Z=-5.824,P＜0.001).The proportion of HR-HPV positivity in the LSIL-group was 70.9％,which was lower than that of the HSIL+group(88.2％),and the difference was statistically significant(Z=-2.887,P=0.004).The specificity,negative predictive value(NPV),and area under the receiver operating characteristic(ROC)curve(AUC)of ATP10A methylation diagnosis of HSIL+(66.0％,79.1％,and 0.747)were higher than those of HR-HPV detection(29.1％,50.7％,0.587).Sensitivity and positive predictive value(PPV)(78.8％and 65.7％)were lower than those of HR-HPV testing(88.2％and 75.0％).when using ATP10a methylation instead of HR-HPV detection for triage of cytological abnormalities(ASC-US,LSIL),the col-poscopy referral rate could be reduced to 54.3％.In the ASC-US population,the AUC(0.683)for the diagnosis of HSIL+by ATP10a methylation test was higher than that of HR-HPV test(0.599),and the difference was statisti-cally significant(P=0.028).In the LSIL population,the AUC(0.828)for the diagnosis of HSIL+by ATP10a methylation test was still higher than that of HR-HPV test(0.563),and the difference was statistically significant(P=0.005).Conclusions:ATP10a methylation levels increased with the severity of cervical lesions,and the di-agnostic efficacy of ATP10a methylation detection for the severity of cervical lesions may not be lower than HR-HPV typing.

Objective To observe the effect of comorbidity for patients with non-small cell lung cancer (NSCLC) on exercise tolerance and cardiopulmonary function. Methods NSCLC patients who underwent cardiopulmonary exercise testing (CPET) before surgery were retrospectively included. According to the Charlson comorbidity index (CCI) score, patients were divided into two groups: a CCI≥3 group and a CCI＜3 group. The patients were matched with a ratio of 1 : 1 by propensity score matching according to the age, body mass index, sex, smoking history, exercise habits, pathological stage and type of surgery. After matching, CPET indexes were compared between the two groups to explore the differences in exercise tolerance and cardiopulmonary function. Results A total of 276 patients were included before matching. After matching, 56 patients were enrolled with 28 patients in each group, including 38 (67.9%) males and 18 (32.1%) females with an average age of (70.7±6.8) years. Compared with the CCI＜3 group, work rate at peak (WR peak), WR peak/predicted value (WR peak%), kilogram oxygen uptake at anaerobic threshold (VO2/kg AT), VO2/kg peak, VO2/kg peak%, peak carbon dioxide output, the minute ventilation to carbon dioxide production slope, O2 pulse peak and O2 pulse peak% of CCI≥3 group were statistically different (P＜0.05). Among them, the rate of postoperative pulmonary complication in the CCI≥3 group was higher than that in the CCI＜3 group (60.7% vs. 32.1%, P=0.032). Conclusion In the NSCLC patients, exercise tolerance and cardiopulmonary function decreased in patients with CCI≥3 compared with those with CCI<3. CPET can provide an objective basis for risk assessment in patients with comorbidity scored by CCI for pulmonary resection.

Purpose To explore the predictive value of CT-enhanced for tumor mutation burden(TMB)in hepatocellular carcinoma(HCC).Materials and Methods A total of 22 patients with pathologically confirmed HCC after undergoing radical resection in the First Affiliated Hospital,Army Medical University(Third Military Medical University)from January 2020 to January 2023 were collected,all of whom were quantified for TMB.Clinical,laboratory tests,CT imaging characteristics and follow-up of patients were recorded.Variables with P<0.2 were screened by stepwise regression analysis for independent risk factors for TMB.The area under the curve of receiver operating characteristic was used to assess the diagnostic efficacy.Results High TMB level was a risk factor for disease-free survival after HCC surgery(HR=1.115,P<0.05).According to the optimal cut-off value,TMB was classified into a high-risk group(>9.25 mutation/Mb)and low-risk group(≤9.25 mutation/Mb).Univariate analysis of intratumor ischemia or necrosis was statistically different between the high-risk and low-risk groups(P=0.005),and only intratumor ischemia or necrosis was an independent risk factor for predicting high TMB level by stepwise regression analysis(P<0.05).The area under the curve for predicting disease-free survival was 0.833(95%CI 0.615-0.956,P<0.001),with a sensitivity of 100.0%and a specificity of 66.7%.Conclusion High TMB level is associated with poor prognosis after HCC resection.Intratumor ischemia or necrosis have certain clinical value in predicting high TMB level,and are expected to provide a reference basis for personalized diagnosis and treatment of HCC patients.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To investigate the role of adenosine triphosphatase phospholipid transporter 10a(ATP10a)methylation assay in the triage of atypical squamous cell of undetermined significance(ASC-US)and low-grade squamous intraepithelial lesions(LSIL)in cervical cytology.Methods:188 patients with cervical exfolia-ted cells of ASC-US and LSIL were selected,and High-risk human papilloma virus(HR-HPV)typing and cervical biopsy results of the patients were collected at the same time.The cervical biopsy pathology results were used as the gold standard,and they were divided into inflammation,LSIL,high grade squamous intraepithelial lesions(HSIL)and cervical squamous cell carcinoma(SCC).Classify inflammation and LSIL as LSIL-group,HSIL and SCC as HSIL+group to compare the efficacy of ATP10a methylation and HR-HPV testing for diagnosis of HSIL+in this population(including HSIL and SCC).Results:The methylation detection value of ATP10a in the inflam-mation,LSIL,HSIL,and SCC patients were 19.035(16.478,20.823),13.446(5.890,20.674),10.336(4.733,17.336),4.223(1.713,7.754),respectively.The methylation detection value of ATP10a in the LSIL-group was 17.812(10.787,20.686),while that in the HSIL+group was 7.251(3.170,14.194).There was a statistically sig-nificant difference between the groups(Z=-5.824,P＜0.001).The proportion of HR-HPV positivity in the LSIL-group was 70.9％,which was lower than that of the HSIL+group(88.2％),and the difference was statistically significant(Z=-2.887,P=0.004).The specificity,negative predictive value(NPV),and area under the receiver operating characteristic(ROC)curve(AUC)of ATP10A methylation diagnosis of HSIL+(66.0％,79.1％,and 0.747)were higher than those of HR-HPV detection(29.1％,50.7％,0.587).Sensitivity and positive predictive value(PPV)(78.8％and 65.7％)were lower than those of HR-HPV testing(88.2％and 75.0％).when using ATP10a methylation instead of HR-HPV detection for triage of cytological abnormalities(ASC-US,LSIL),the col-poscopy referral rate could be reduced to 54.3％.In the ASC-US population,the AUC(0.683)for the diagnosis of HSIL+by ATP10a methylation test was higher than that of HR-HPV test(0.599),and the difference was statisti-cally significant(P=0.028).In the LSIL population,the AUC(0.828)for the diagnosis of HSIL+by ATP10a methylation test was still higher than that of HR-HPV test(0.563),and the difference was statistically significant(P=0.005).Conclusions:ATP10a methylation levels increased with the severity of cervical lesions,and the di-agnostic efficacy of ATP10a methylation detection for the severity of cervical lesions may not be lower than HR-HPV typing.