Background/Aims: The World Health Organization set the goal of eliminating hepatitis C virus (HCV) by 2030, with 80% and 65% reductions in HCV incidence and mortality rates, respectively. We aimed to evaluate the health benefits, cost-effectiveness and return on investment (ROI) of HCV elimination. Methods: Using an HCV transmission compartmental model, we evaluated the benefits and costs of different strategies combining screening and treatment for Chinese populations. We identified strategies to achieve HCV elimination and calculated the incremental cost-effectiveness ratios (ICERs) per disability-adjusted life year (DALY) averted for 2022–2030 to identify the optimal elimination strategy. Furthermore, we estimated the ROI by 2050 by comparing the required investment with the economic productivity gains from reduced HCV incidence and deaths. Results: The strategy that results in the most significant health benefits involves conducting annual primary screening at a rate of 14%, re-screening people who inject drugs annually and the general population every five years, and treating 95% of those diagnosed (P14-R4-T95), preventing approximately 5.75 and 0.44 million HCV infections and deaths, respectively, during 2022–2030. At a willingness-to-pay threshold of $12,615, the P14-R4-T95 strategy is the most cost-effective, with an ICER of $5,449/DALY. By 2050, this strategy would have a net benefit of $120,997 million (ROI=0.868). Conclusions Achieving HCV elimination in China by 2030 will require significant investment in large-scale universal screening and treatment, but it will yield substantial health and economic benefits and is cost-effective.

Objective To explore the relationship between epstein-barr viral(EBV)load and T lymphocyte subsets distribution in peripheral blood of children with systemic lupus erythematosus(SLE)and its synergistic effect on liver injury.Methods A total of 212 children with SLE admitted to the Xinjiang Uygur Autonomous Region Children's Hospital from October 2021 to September 2023 were selected as the study subjects.The SLE disease activity index(SLEDAI)score was divided into active group(n=58)and inactive group(n=154).The clinical data and laboratory indexes of the two groups were compared.The curve relationship between EBV load and T lymphocytes was analyzed by local weighted scatterplot smoothing(LOWESS)method.According to the occurrence of liver injury,212 children with SLE were divided into two groups(n=166)without liver injury and(n=46)with liver injury.The clinical data of the two groups were compared.Multifactor Logistics regression analysis was used to screen the influencing factors of liver injury in SLE children.Restricted cubic spline(RCS)model was used to analyze the relationship between EBV load,T lymphocytes and liver injury.The unconditioned Logistic regression model was used to analyze the interaction between EBV load and T lymphocytes on liver injury.Results Compared with the inactive group,the active group had younger age(t=9.265),higher hormone dosage(t=2.369),decreased ALB,CD4+,CD4+/CD8+levels(t=4.870,3.463,5.820),and increased EBV load,CD3+,CD8+levels(t=40.830,12.719,5.048),and the differences were statistically significant(all P<0.05).LOWESS analysis showed that EBV load had a nonlinear relationship with CD3+,CD4+,CD8+and CD4+/CD8+.The proportion of children with liver injury who had a disease course of≤3 months,EBV load,CD3+and CD8+levels,and the proportion of active children were lower than those without liver injury(t/χ2=10.749～30.378);the levels of ALB,CD4+and CD4+/CD8+indexes were higher than those without live injury(t=13.561,3.117,13.204),and the differences were statistically significantly(all P<0.05).Multivariate Logistic regression analysis showed that disease duration≤3 months,EBV load,CD3+,ALB,CD4+/CD8+and active SLE were influential factors for liver injury in SLE children(Wald χ2=1.020～1.932,all P<0.05).The results of RCS model analysis showed that EBV load,CD3+and CD8+were positively correlated with liver injury in SLE children,and CD4+,CD4+/CD8+were negatively correlated with liver injury in SLE children.Interaction analysis results showed that EBV load≥450 IU/ml had a multiplicative and additive interaction with CD3+≥60%,CD4+<30%,CD8+≥37%and CD4+/CD8+<0.94,and the risk of liver injury in SLE children was higher when the two factors co-existed.Conclusion There is a nonlinear relationship between EBV load in peripheral blood of SLE children and CD3+,CD4+,CD8+and CD4+/CD8+.Peripheral blood EBV load and abnormal expression of CD3+,CD4+,CD8+and CD4+/CD8+are all factors affecting liver injury in children with SLE,and there is a multiplicative and additive interaction between the above factors.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To describe the positive rates of high-risk human papillomavirus (HR-HPV) DNA and serum-neutralizing antibody in cervical intraepithelial neoplasia (CIN) tissues of rural women in Xiangyuan County, Shanxi Province, and evaluate the association of HR-HPV DNA and neutralizing antibody positive status with the occurrence of CIN.Methods:In a cohort of 1 897 women aged 35-45 years established by the Shanxi Province Cervical Cancer Screening StudyⅠ, DNA typing (SPF10 PCR-DEIA-LiPA25) was performed by using tissue samples of women with positive HR-HPV test results [Hybrid CaptureⅡ(HC2)] or abnormal cytological or pathological results. Serum HR-HPV neutralizing antibody detection was conducted with multicolor pseudovirion-based neutralization assay. Cochran-Armitage trend test was used to analyze the changing trend of the positive rate of HR-HPV DNA and neutralizing antibody with the progression of CIN. Multivariate logistic regression models were used to evaluate the influence and multiplicative interaction of HR-HPV DNA and neutralizing antibody positive status on the occurrence of CIN. The relative excess risk ( RERI), attributable proportion of interaction ( AP), and the synergy index ( SI) of the interaction were calculated to evaluate the additive interaction of HR-HPV DNA and neutralizing antibody on the occurrence of CIN. Results:The positive rate of any type of HR-HPV DNA (HPV16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68) in 479 women who were HC2 positive or had abnormal cytological or pathological detection results was 37.16%. In normal, CIN1, CIN2, and CIN3+ groups, the HR-HPV DNA positive rates were 18.03%, 49.53%, 90.24% and 94.59%, respectively. The positive rate of any type of HR-HPV neutralizing antibody was 63.88%. In normal, CIN1, CIN2, and CIN3+ groups, the positive rates of HR-HPV neutralizing antibody were 63.95%, 57.94%, 70.73%, and 72.97%, respectively. The positive rate of any type of HR-HPV neutralizing antibody was 53.31% in 1 418 women who were HC2 negative and had normal cytopathology, and the most common types were HPV51 (27.36%) and HPV39 (24.96%). Multivariate logistic regression analysis showed that any type of HR-HPV DNA positive status ( OR=9.15, 95% CI: 5.99-14.20, P<0.001) was the independent factor for the occurrence of CIN, HR-HPV neutralizing antibody positive status was not associated with the occurrence of CIN ( OR=0.95, 95% CI: 0.61-1.48, P=0.815). The OR value of the multiplication of HR-HPV DNA and neutralizing antibody positive status of the occurrence of CIN was 1.63 (95% CI: 0.67-3.95), P=0.283. Quantitative analysis of interaction showed that RERI was 1.65 (95% CI:-3.56-6.86), SI was 1.28 (95% CI: 0.58-2.82), and AP was 0.19 (95% CI:-0.36-0.75). Conclusions:HR-HPV DNA positive status was a risk factor for the occurrence of CIN, but neutralizing antibody positive status was not associated with the occurrence of CIN. They had no significant multiplicative or additive interaction with the occurrence of CIN.

Background/Aims: The World Health Organization set the goal of eliminating hepatitis C virus (HCV) by 2030, with 80% and 65% reductions in HCV incidence and mortality rates, respectively. We aimed to evaluate the health benefits, cost-effectiveness and return on investment (ROI) of HCV elimination. Methods: Using an HCV transmission compartmental model, we evaluated the benefits and costs of different strategies combining screening and treatment for Chinese populations. We identified strategies to achieve HCV elimination and calculated the incremental cost-effectiveness ratios (ICERs) per disability-adjusted life year (DALY) averted for 2022–2030 to identify the optimal elimination strategy. Furthermore, we estimated the ROI by 2050 by comparing the required investment with the economic productivity gains from reduced HCV incidence and deaths. Results: The strategy that results in the most significant health benefits involves conducting annual primary screening at a rate of 14%, re-screening people who inject drugs annually and the general population every five years, and treating 95% of those diagnosed (P14-R4-T95), preventing approximately 5.75 and 0.44 million HCV infections and deaths, respectively, during 2022–2030. At a willingness-to-pay threshold of $12,615, the P14-R4-T95 strategy is the most cost-effective, with an ICER of $5,449/DALY. By 2050, this strategy would have a net benefit of $120,997 million (ROI=0.868). Conclusions Achieving HCV elimination in China by 2030 will require significant investment in large-scale universal screening and treatment, but it will yield substantial health and economic benefits and is cost-effective.

AIM: To delineate the specific mutation responsible for retinitis pigmentosa(RP)in a Yi pedigree, and to analyze the correlation of RHO gene mutation with clinical phenotype.METHODS:A comprehensive clinical evaluation was conducted on the proband diagnosed with RP and other familial members, complemented by a thorough ophthalmic examination. Peripheral blood samples were obtained from the proband and familial members, from which genomic DNA was extracte. Subsequent whole exome sequencing(WES)was employed to identify the variant genes in the proband. The identified variant gene was validated through Sanger sequencing, then an in-depth analysis of the mutation genes was carried out using genetic databases to ascertain the pathogenic mutation sites. Furthermore, an exhaustive analysis was performed to delineate the genotype and phenotype characteristics.RESULTS:The RP pedigree encompasses 5 generations with 42 members, including 19 males and 23 females. A total of 13 cases of RP were identified, consisting of 4 males and 9 females, which conforms to the autosomal dominant inheritance pattern. The clinical features of this family include an early onset age, rapid progression, and a more severe condition. The patients were found to have night blindness around 6 years old, representing the earliest reported case of night blindness in RP families. The retina was manifested by progressive osteocytoid pigmentation of the fundus, a reduced visual field, and significantly decreased or even vanished a and b amplitudes of ERG. The combined results of WES and Sanger sequencing indicated that the proband had a heterozygous missense mutation of the RHO gene c.1040C>T:p.P347L, where the 1 040 base C of cDNA was replaced by T, causing codon 347 to encode leucine instead of proline. Interestingly, this mutation has not been reported in the Chinese population.CONCLUSION:This study confirmed that the mutant gene of RP in a Yi nationality pedigree was RHO(c.1040C>T). This variant leads to the change of codon 347 from encoding proline to encoding leucine, resulting in a severe clinical phenotype among family members. This study provides a certain molecular, clinical, and genetic basis for genetic counseling and gene diagnosis of RHO.

AIM: To delineate the specific mutation responsible for retinitis pigmentosa(RP)in a Yi pedigree, and to analyze the correlation of RHO gene mutation with clinical phenotype.METHODS:A comprehensive clinical evaluation was conducted on the proband diagnosed with RP and other familial members, complemented by a thorough ophthalmic examination. Peripheral blood samples were obtained from the proband and familial members, from which genomic DNA was extracte. Subsequent whole exome sequencing(WES)was employed to identify the variant genes in the proband. The identified variant gene was validated through Sanger sequencing, then an in-depth analysis of the mutation genes was carried out using genetic databases to ascertain the pathogenic mutation sites. Furthermore, an exhaustive analysis was performed to delineate the genotype and phenotype characteristics.RESULTS:The RP pedigree encompasses 5 generations with 42 members, including 19 males and 23 females. A total of 13 cases of RP were identified, consisting of 4 males and 9 females, which conforms to the autosomal dominant inheritance pattern. The clinical features of this family include an early onset age, rapid progression, and a more severe condition. The patients were found to have night blindness around 6 years old, representing the earliest reported case of night blindness in RP families. The retina was manifested by progressive osteocytoid pigmentation of the fundus, a reduced visual field, and significantly decreased or even vanished a and b amplitudes of ERG. The combined results of WES and Sanger sequencing indicated that the proband had a heterozygous missense mutation of the RHO gene c.1040C>T:p.P347L, where the 1 040 base C of cDNA was replaced by T, causing codon 347 to encode leucine instead of proline. Interestingly, this mutation has not been reported in the Chinese population.CONCLUSION:This study confirmed that the mutant gene of RP in a Yi nationality pedigree was RHO(c.1040C>T). This variant leads to the change of codon 347 from encoding proline to encoding leucine, resulting in a severe clinical phenotype among family members. This study provides a certain molecular, clinical, and genetic basis for genetic counseling and gene diagnosis of RHO.

Background/Aims: The World Health Organization set the goal of eliminating hepatitis C virus (HCV) by 2030, with 80% and 65% reductions in HCV incidence and mortality rates, respectively. We aimed to evaluate the health benefits, cost-effectiveness and return on investment (ROI) of HCV elimination. Methods: Using an HCV transmission compartmental model, we evaluated the benefits and costs of different strategies combining screening and treatment for Chinese populations. We identified strategies to achieve HCV elimination and calculated the incremental cost-effectiveness ratios (ICERs) per disability-adjusted life year (DALY) averted for 2022–2030 to identify the optimal elimination strategy. Furthermore, we estimated the ROI by 2050 by comparing the required investment with the economic productivity gains from reduced HCV incidence and deaths. Results: The strategy that results in the most significant health benefits involves conducting annual primary screening at a rate of 14%, re-screening people who inject drugs annually and the general population every five years, and treating 95% of those diagnosed (P14-R4-T95), preventing approximately 5.75 and 0.44 million HCV infections and deaths, respectively, during 2022–2030. At a willingness-to-pay threshold of $12,615, the P14-R4-T95 strategy is the most cost-effective, with an ICER of $5,449/DALY. By 2050, this strategy would have a net benefit of $120,997 million (ROI=0.868). Conclusions Achieving HCV elimination in China by 2030 will require significant investment in large-scale universal screening and treatment, but it will yield substantial health and economic benefits and is cost-effective.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective To investigate the relationship between serum cyclophilin A(CypA),ischemia modi-fied albumin(IMA),galectin-3(Gal-3)levels and oxidative stress and cognitive impairment(CI)in patients with acute cerebral infarction(ACI)complicated with obstructive sleep apnea hypopnea syndrome(OSAHS).Methods A total of 168 patients with ACI complicated with OSAHS who were admitted to the hospital from December 2020 to October 2023 were selected,and the levels of serum CypA,IMA,Gal-3 and oxidative stress indexes[superoxide dismutase(SOD),malondialdehyde(MDA)]in patients with different severity(mild group,moderate group,severe group)were compared.The correlation between serum CypA,IMA,Gal-3 lev-els and oxidative stress indexes were analyzed by Pearson method.The incidence of CI in patients with ACI complicated with OSAHS were statistically analyzed,the levels of serum CypA,IMA and Gal-3 were com-pared between normal cognitive group and CI group,and the risk factors of CI in patients with ACI complicat-ed with OSAHS were analyzed by multivariate Logistic regression model.Results The levels of serum CypA,IMA,Gal-3 and MDA in mild group,moderate group and severe group increased in turn(P<0.05),and ser-um SOD level decreased in turn(P<0.05).Serum CypA,IMA and Gal-3 in ACI patients complicated with OSAHS were negatively correlated with SOD(P<0.05),and positively correlated with MDA(P<0.05).Of 168 ACI patients complicated with OSAHS,79 cases(47.02%)had CI and were included in the CI group.Compared with normal cognitive group,the serum levels of CypA,IMA and Gal-3 in CI group were signifi-cantly increased(P<0.05).Multivariate Logistic regression model analysis showed that apnea hypopnea in-dex(AHI)≥20 times/h,frontal and temporal lobe infarction,elevated CypA level,elevated IMA level,and el-evated Gal-3 level were risk factors for CI in ACI patients complicated with OSAHS(P<0.05).Conclusion The high expression of serum CypA,IMA and Gal-3 in patients with ACI complicated with OS-AHS is significantly correlated with oxidative stress and CI.The increase of serum CypA,IMA and Gal-3 lev-els indicates an increased risk of CI in patients with ACI complicated with OSAHS,and early detection of the three has good clinical value in evaluating the condition of patients with ACI complicated with OSAHS.