Liver transplantation is the most effective treatment for end-stage liver disease and can significantly prolong patient survival. However, patients usually have poor physiological and psychological conditions before and after undergoing liver transplantation surgery, which affects prognosis and reduces quality of life. Preoperative prehabilitation, through intervention modes such as exercise, nutrition and psychology, can improve patients' preoperative functional reserve, alleviate perioperative stress reactions, reduce postoperative infection risks and be beneficial for postoperative recovery after liver transplantation. Therefore, this article reviews the latest research progress on the timing and location of prehabilitation, the necessity of prehabilitation and intervention models for preoperative prehabilitation of liver transplant patients. The aim is to deepen the understanding and application of preoperative prehabilitation for liver transplant patients in clinical practice, in order to provide theoretical and practical basis for preoperative prehabilitation of liver transplant patients and improve their prognosis.

Objective To investigate the distribution characteristics of Oncomelania hupensis snails in Yunnan Province fol-lowing interruption of schistosomiasis transmission, so as to provide the evidence for assessing the risk of schistosomiasis transmission and scientifically formulating the schistosomiasis surveillance program. Methods According to the requirements of the National Schistosomiasis Surveillance Scheme (2020 Edition), O. hupensis snail surveillance data were collected from 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province from 2020 to 2024, including area of snail survey, area of snail habitats, area of re-emerging snail habitats, number of frames surveyed, number of frames with O. hupensis snails, number of O. hupensis snails captured, and number of living snails, and the occurrence of frames with snails and mean density of living snails were calculated. Changes in snail status over the 5-year period from 2020 to 2024 and the differences in snail distributions specified by epidemic intensity, environmental type, and vegetation type were analyzed. Results The areas of snail survey increased from 1 727.96 hm² in 2020 to 3 894.45 hm² in 2024 (peak) across 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province during the period from 2020 through 2024. The areas of snail habitats increased from 70.36 hm² in 2020 to a peak in 2023 (172.04 hm²), followed by a reduction to 132.36 hm² in 2024, and the areas of re-emerging snail habitats increased from 42.71 hm² in 2020 to a peak in 2022 (78.43 hm²), followed by a reduction to 40.21 hm² in 2024. The occurrence of frames with snails and mean density of living snails increased from 1.24% (3 025/244 404) and (0.033 2 ± 0.038 7) snails/0.1 m² in 2020 to peaks at 2.03% (6 231/307 563) and (0.066 9 ± 0.068 4) snails/0.1 m² in 2023, followed by reductions to 1.04% (5 829/559 941) and (0.032 6 ± 0.057 7) snails/0.1 m² in 2024, respectively. There was a significant difference in the occurrence of frames with snails over the 5-year study period (χ² = 1 962.95, P < 0.05), and the occurrence of frames with snails reduced by 48.71% in 2024 relative to in 2023 (χ² = 1 411.05, P < 0.005); however, there was no significant difference in the mean density of living snails over the 5 years (H = 5.310, P > 0.05). There were significant differences in the occurrence of frames with snails (χ² = 481.27, P < 0.05) and mean density of living snails (H = 6.872, P < 0.05) in schistosomiasis-endemic areas with different epidemic intensities. The occurrence of frames with snails (χ² = 25.32 and 38.70, both P values < 0.017) and mean density of living snails (Z = 28.55 and 49.96, both P values < 0.017) were higher in schistosomiasis transmission-interrupted and eliminated areas with snails than in schistosomiasis-eliminated areas without snails, and the occurrence of frames with snails (χ² = 453.54, P < 0.017) and mean density of living snails (Z = −56.97, P < 0.017) were higher in schistosomiasis-eliminated areas with snails than in schistosomiasis transmission-interrupted areas with snails. O. hupensis snails were mainly distributed in paddy fields, dry farmlands and ditches; however, the occurrence of frames with snails (13.40%, 424/3 164) and mean density of living snails [(0.252 8 ± 0.158 7) snails/0.1 m²] were higher in ponds/weirs than in other types of environments (both P values < 0.05). Rice, dry farmland crops and weeds were main vegetations in which O. hupensis snails were distributed, and the occurrence of frames with snails (2.29%, 7 111/310 140) and mean density of living snails [(0.072 3 ± 0.018 9) snails/0.1 m²] were higher in weeds than in other types of environments (both P values < 0.05). Conclusions O. hupensis snails have been effectively controlled in Yunnan Province following implementation of integrated schistosomiasis control measures; however, there are still risk factors for schistosomiasis transmission, including reduced attention to schistosomiasis control and snail re-emergence. Improved control efforts and surveillance system construction and timely identification of risk factors of snail status and timely management are recommended to ensure the achievement of the target of schistosomiasis elimination as scheduled.

This consensus focuses on the clinical application of domestic ultra-high-definition (UHD) medical endoscopes in cardiovascular surgery, addressing their clinical value, key technical aspects, and strategies for broader implementation. It traces the evolution of domestic endoscopic platforms from high-resolution imaging toward integrated advanced functionalities such as fluorescence imaging, three-dimensional visualization, and intelligent assistance. The document outlines standardized procedural steps and perioperative management pathways for minimally invasive endoscopic cardiovascular surgery and summarizes specific application scenarios and technical considerations in congenital heart disease, valvular disorders, coronary artery disease, arrhythmias, and cardiac tumors. Furthermore, recommendations are provided regarding professional training, multicenter collaboration, the establishment of clinical evaluation systems, and coordinated policy-industry support to promote standardized adoption and high-quality dissemination of domestic medical devices. Based on available evidence and expert agreement, 16 consensus statements are presented, aiming to offer authoritative and actionable guidance for clinical practice.

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Objective To investigate the correlation between peripheral blood CD14+CD16+monocytes and IgG N-glycosyl levels and disease activity in patients with systemic lupus erythematosus(SLE).Methods A total of 109 SLE patients admitted to Xingtai Cental Hospital from August 2021 to November 2024 were retrospectively selected as the study objects.According to SLE disease activity index(SLE-DAI),the patients were divided into active group(n=52)and stable group(n=57).In addition,56 patients who underwent physical examination during the same period were selected as the control group.Clinical data of patients were collected,CD14+CD16+mononuclear cells were detected by flow cytometry(FCM),and IgG N-glycosyl levels were detected by hydrophilic interaction chromatography-mass spectrometry.Logistic regression analysis was performed to analyze the influencing factors of SLE-DAI,and multicollinearity test[variance inflation factor(VIF)]was performed for independent variables.The prediction model of disease activity was constructed.The effectiveness of the predictive model was evaluated by describing the recviver operator characteristic(ROC)curve and calculating the area under the curve(AUC)value.Hosmer-Lemeshow goodness-of-fit test predicted the calibration degree of the model.Results The levels of WBC,Hb,PLT,ALB,complement C3 and complement C4 in control group were higher than those in SLE group(t=8.917～22.171),and the levels of CRP were lower than those in SLE group(t=-17.359),with differences were statistical significance(all P<0.05).The CRP level and the proportion of CD14+CD16+mononuclear cells in the active group were higher than those in the stable group,and the differences were statistically significant(t=5.449,11.112,all P<0.05).The IgG glycosylation characteristics of galactosylation,sialylation and N-acetylglucosamine modification were lower than those in the stable group,and the differences were statistical significance(Z=-2.432～-0.158,all P<0.05).Spearman correlation analysis showed that the proportion of CD14+CD16+monocytes was significantly negatively correlated with IgG galactosylation,sialylation level and bisection N-acetylglucosamine modification(r=-0.656,-0.531,-0.608,all P<0.01).CD14+CD16+monocyte ratio was positively correlated with SLE-DIA(r=0.581,all P<0.01).IgG galactosylation,sialylation levels and bisection N-acetylglucosamine modification were negatively correlated with SLE-DIA(r=-0.645,-0.609,-0.503,all P<0.01).Logistic regression analysis showed that CRP>8.21mg/L,CD14+CD16+≥16.17%,sialylation<22.05%and isotropic N-acetylglucosamine modification<16.53%were independent risk factors for disease activity in SLE patients(Wald χ2=4.471～12.811,all P<0.05).The VIF values of the above independent variables were all less than 10.By establishing the Logistic regression prediction model and drawing the ROC curve,the AUC value for diagnosing SLE disease activity was 0.821(95%CI:0.733～0.905),the sensitivity,specificity and the Yodon index were 85.37%,75.67%,0.677,respectively.and the P values of Hosmer-Lemeshow goodness-of-fit test models were 0.568,respectively.Conclusion The proportion of CD14+CD16+monocytes in peripheral blood of SLE patients increase significantly,and the level of IgG glycosylation characteristics decrease,both of which are correlated with SLE-DIA.The predictive model constructed based on the two had a good ability to distinguish SLE-DIA from inactive state,with high sensitivity and moderate specificity conducive to early clinical recognition,and the model fitting effect is good.SLE-DIA can be evaluated more accurately.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Objective To develop dynamic prediction models based on multiple postnatal time points to support early diagnosis and individualized intervention for bronchopulmonary dysplasia(BPD)in preterm infants with gestational age<32 weeks.Methods Clinical data of 472 preterm infants with gestational age<32 weeks admitted to the Second Xiangya Hospital of Central South University between January 2016 and November 2020 were retrospectively analyzed.Multivariable logistic regression was applied to develop five independent prediction models at postnatal days 1,7,14,21,and 28.The performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and the Hosmer-Lemeshow test.Results Baseline characteristics such as gestational age and birth weight differed significantly between the BPD group(n=147)and the non-BPD group(n=325)(P<0.05).Predictors of BPD evolved across time points:on day 1,key predictors included gestational age,birth weight,Score for Neonatal Acute Physiology II(SNAP-II),invasive mechanical ventilation,and fraction of inspired oxygen>30%;by day 7,additional variables emerged,including fasting duration>2 days,mean feeding advancement rate<8.5 mL/(kg·d),neonatal respiratory distress syndrome,apnea of prematurity,and positive sputum culture;from day 14 onward,nutrition-and treatment-related indicators were incorporated additionally.The models demonstrated good discrimination at postnatal days 1,7,14,21,and 28,with AUCs of 0.917,0.927,0.939,0.944,and 0.968,respectively,and good calibration(Hosmer-Lemeshow P>0.05).Internal validation showed AUCs ranging from 0.899 to 0.958,indicating robust performance.Conclusions Dynamic postnatal prediction models incorporating indicators spanning perinatal factors,respiratory support,nutritional management,and therapeutic interventions demonstrate high predictive performance and facilitate dynamic risk assessment for BPD in preterm infants with gestational age<32 weeks.

Objective To develop dynamic prediction models based on multiple postnatal time points to support early diagnosis and individualized intervention for bronchopulmonary dysplasia(BPD)in preterm infants with gestational age<32 weeks.Methods Clinical data of 472 preterm infants with gestational age<32 weeks admitted to the Second Xiangya Hospital of Central South University between January 2016 and November 2020 were retrospectively analyzed.Multivariable logistic regression was applied to develop five independent prediction models at postnatal days 1,7,14,21,and 28.The performance of the models was assessed using the area under the receiver operating characteristic curve(AUC)and the Hosmer-Lemeshow test.Results Baseline characteristics such as gestational age and birth weight differed significantly between the BPD group(n=147)and the non-BPD group(n=325)(P<0.05).Predictors of BPD evolved across time points:on day 1,key predictors included gestational age,birth weight,Score for Neonatal Acute Physiology II(SNAP-II),invasive mechanical ventilation,and fraction of inspired oxygen>30%;by day 7,additional variables emerged,including fasting duration>2 days,mean feeding advancement rate<8.5 mL/(kg·d),neonatal respiratory distress syndrome,apnea of prematurity,and positive sputum culture;from day 14 onward,nutrition-and treatment-related indicators were incorporated additionally.The models demonstrated good discrimination at postnatal days 1,7,14,21,and 28,with AUCs of 0.917,0.927,0.939,0.944,and 0.968,respectively,and good calibration(Hosmer-Lemeshow P>0.05).Internal validation showed AUCs ranging from 0.899 to 0.958,indicating robust performance.Conclusions Dynamic postnatal prediction models incorporating indicators spanning perinatal factors,respiratory support,nutritional management,and therapeutic interventions demonstrate high predictive performance and facilitate dynamic risk assessment for BPD in preterm infants with gestational age<32 weeks.

Objective To investigate the correlation between peripheral blood CD14+CD16+monocytes and IgG N-glycosyl levels and disease activity in patients with systemic lupus erythematosus(SLE).Methods A total of 109 SLE patients admitted to Xingtai Cental Hospital from August 2021 to November 2024 were retrospectively selected as the study objects.According to SLE disease activity index(SLE-DAI),the patients were divided into active group(n=52)and stable group(n=57).In addition,56 patients who underwent physical examination during the same period were selected as the control group.Clinical data of patients were collected,CD14+CD16+mononuclear cells were detected by flow cytometry(FCM),and IgG N-glycosyl levels were detected by hydrophilic interaction chromatography-mass spectrometry.Logistic regression analysis was performed to analyze the influencing factors of SLE-DAI,and multicollinearity test[variance inflation factor(VIF)]was performed for independent variables.The prediction model of disease activity was constructed.The effectiveness of the predictive model was evaluated by describing the recviver operator characteristic(ROC)curve and calculating the area under the curve(AUC)value.Hosmer-Lemeshow goodness-of-fit test predicted the calibration degree of the model.Results The levels of WBC,Hb,PLT,ALB,complement C3 and complement C4 in control group were higher than those in SLE group(t=8.917～22.171),and the levels of CRP were lower than those in SLE group(t=-17.359),with differences were statistical significance(all P<0.05).The CRP level and the proportion of CD14+CD16+mononuclear cells in the active group were higher than those in the stable group,and the differences were statistically significant(t=5.449,11.112,all P<0.05).The IgG glycosylation characteristics of galactosylation,sialylation and N-acetylglucosamine modification were lower than those in the stable group,and the differences were statistical significance(Z=-2.432～-0.158,all P<0.05).Spearman correlation analysis showed that the proportion of CD14+CD16+monocytes was significantly negatively correlated with IgG galactosylation,sialylation level and bisection N-acetylglucosamine modification(r=-0.656,-0.531,-0.608,all P<0.01).CD14+CD16+monocyte ratio was positively correlated with SLE-DIA(r=0.581,all P<0.01).IgG galactosylation,sialylation levels and bisection N-acetylglucosamine modification were negatively correlated with SLE-DIA(r=-0.645,-0.609,-0.503,all P<0.01).Logistic regression analysis showed that CRP>8.21mg/L,CD14+CD16+≥16.17%,sialylation<22.05%and isotropic N-acetylglucosamine modification<16.53%were independent risk factors for disease activity in SLE patients(Wald χ2=4.471～12.811,all P<0.05).The VIF values of the above independent variables were all less than 10.By establishing the Logistic regression prediction model and drawing the ROC curve,the AUC value for diagnosing SLE disease activity was 0.821(95%CI:0.733～0.905),the sensitivity,specificity and the Yodon index were 85.37%,75.67%,0.677,respectively.and the P values of Hosmer-Lemeshow goodness-of-fit test models were 0.568,respectively.Conclusion The proportion of CD14+CD16+monocytes in peripheral blood of SLE patients increase significantly,and the level of IgG glycosylation characteristics decrease,both of which are correlated with SLE-DIA.The predictive model constructed based on the two had a good ability to distinguish SLE-DIA from inactive state,with high sensitivity and moderate specificity conducive to early clinical recognition,and the model fitting effect is good.SLE-DIA can be evaluated more accurately.