Objective The pathophysiological process of acute high-altitude hypoxia-induced lung injury affects protein expression levels,which are mainly evaluated by Western blot.No systematic study has investigated changes in internal control proteins as calibration loading amounts.Methods Lung injury at an altitude of 6000 m was induced in a low-pressure,low-oxygen chamber for 8,24,and 72 h using C57BL/6J mice.Establishment of the model was confirmed by hematoxylin and eosin staining.Expression levels of various internal control proteins,including vinculin,α-tubulin,eukaryotic translation initiation factor 5(EIF5),β-actin,and glyceraldehyde 3-phosphate dehydrogenase(GAPDH)were detected by Western blot,and total protein expression was detected by Coomassie blue staining.Furthermore,the lung injury model in vitro was establised by using,Bronchial epithelial cell(BZAS-2B)andhunman umbilical vein endothelial cells(HUVECS)confirmed by TUNEL staining.Expression levels of internal control proteins were detected by Western blot,and total protein expression was detected by Coomassie Blue staining.Results Acute 8,24,and 72 h hypoxic models were successfully established in lung tissue,demonstrating consistent total protein expression and stable levels of the internal reference proteins vinculin,α-tubulin,EIF5,andβ-actin.GAPDH expression was elevated in the HH8 h,HH24 h,and HH72 h groups compared with the normoxia(Nor)group,but only the increase at HH72 h groups was significant.Similarly,8,24,and 48 h hypoxic models were successfully established in BEAS-2B cells and HUVECs,with consistent total protein expression.In BEAS-2B cells,expression levels of the internal reference proteins β-actin and GAPDH were consistent with the normoxic control(NC)group,while vinculin,α-tubulin,and EIF5 expression levels were significantly reduced under hypoxic conditions for up to 24 h.In HUVECs,vinculin and α-tubulin expression levels were also consistent with the NC group,while EIF5,β-actin,and GAPDH expression levels were significantly reduced at 8 h and increased at 48 h.Conclusions Acute hypoxia induces lung tissue injury,and protein expression levels of the internal reference proteins vinculin,α-tubulin,EIF5,and β-actin are stable,making them suitable internal references for Western blot.Additionally,Western blot detected differential expression levels of the internal reference proteins vinculin,α-tubulin,EIF5,β-actin,and GAPDH in BEAS-2B cells and HUVECs,as the most important in vitro lung tissue models of hypoxia-induced injury.

Objective The myocardial injury was induced by hypobaric hypoxia through regulating the expression of various proteins.The expression of proteins was mainly detected by western blot,but the selection of internal reference proteins and their variations have not been systematically studied.Methods Myocardial injury was induced in a low-pressure,low-oxygen chamber simulating an altitude of 6000 m,for 24 and 72 h.Establishment of the myocardial injury model was confirmed by hematoxylin eosin(HE)staining.Expression levels of internal control proteins,including vinculin,α-tubulin,eukaryotic translation initiation factor-5(EIF5),β-actin,glyceraldehyde-3-phosphate dehydrogenase(GAPDH),cyclophilin B,and cofilin,were detected by Western Blot and total protein expression was detected by Ponceau S and Coomassie Blue staining.An adult mouse cardiomyocytes(AMCMs)injury model was induced by hypoxia for 12 and 24 h and confirmed by terminal deoxynucleotidyl transferase dUTP nick end labeling(TUNEL staining).Internal control proteins were detected by Western Blot,as in the in vivo model,and total protein expression was detected by Ponceau S and Coomassie Blue staining.Results A myocardial injury model was established by hypobaric hypoxia for 24 and 72 h,the total protein expression levels remained consistent.The expression of internal control proteins including vinculin,EIF5,β-actin,cyclophilin B,and cofilin was consistent between the control and model groups.Expression levels of α-tubulin were similar in the plain control and 24 h hypobaric hypoxia group,but were significantly lower in the 72 h hypobaric hypoxia group compared with the plain control group.GAPDH expression was significantly higher in the 24 and 72 h hypobaric hypoxia groups than in the plain control group.An AMCM injury model was established by hypoxia for 12 and 24 h.Total protein levels and expression levels of the internal control proteins EIF5 and β-actin were consistent,but vinculin,α-tubulin,GAPDH,cyclophilin B,and cofilin expression levels were higher in both hypoxia groups compared with the normoxic control group.Conclusions EIF5 and β-actin may be the suitable loading control proteins for studies of hypobaric hypoxia-induced myocardial injury using Western Blot.Total protein is also a good choice for hypobaric hypoxia studies.

Objective The pathophysiological process of acute high-altitude hypoxia-induced lung injury affects protein expression levels,which are mainly evaluated by Western blot.No systematic study has investigated changes in internal control proteins as calibration loading amounts.Methods Lung injury at an altitude of 6000 m was induced in a low-pressure,low-oxygen chamber for 8,24,and 72 h using C57BL/6J mice.Establishment of the model was confirmed by hematoxylin and eosin staining.Expression levels of various internal control proteins,including vinculin,α-tubulin,eukaryotic translation initiation factor 5(EIF5),β-actin,and glyceraldehyde 3-phosphate dehydrogenase(GAPDH)were detected by Western blot,and total protein expression was detected by Coomassie blue staining.Furthermore,the lung injury model in vitro was establised by using,Bronchial epithelial cell(BZAS-2B)andhunman umbilical vein endothelial cells(HUVECS)confirmed by TUNEL staining.Expression levels of internal control proteins were detected by Western blot,and total protein expression was detected by Coomassie Blue staining.Results Acute 8,24,and 72 h hypoxic models were successfully established in lung tissue,demonstrating consistent total protein expression and stable levels of the internal reference proteins vinculin,α-tubulin,EIF5,andβ-actin.GAPDH expression was elevated in the HH8 h,HH24 h,and HH72 h groups compared with the normoxia(Nor)group,but only the increase at HH72 h groups was significant.Similarly,8,24,and 48 h hypoxic models were successfully established in BEAS-2B cells and HUVECs,with consistent total protein expression.In BEAS-2B cells,expression levels of the internal reference proteins β-actin and GAPDH were consistent with the normoxic control(NC)group,while vinculin,α-tubulin,and EIF5 expression levels were significantly reduced under hypoxic conditions for up to 24 h.In HUVECs,vinculin and α-tubulin expression levels were also consistent with the NC group,while EIF5,β-actin,and GAPDH expression levels were significantly reduced at 8 h and increased at 48 h.Conclusions Acute hypoxia induces lung tissue injury,and protein expression levels of the internal reference proteins vinculin,α-tubulin,EIF5,and β-actin are stable,making them suitable internal references for Western blot.Additionally,Western blot detected differential expression levels of the internal reference proteins vinculin,α-tubulin,EIF5,β-actin,and GAPDH in BEAS-2B cells and HUVECs,as the most important in vitro lung tissue models of hypoxia-induced injury.

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 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 The myocardial injury was induced by hypobaric hypoxia through regulating the expression of various proteins.The expression of proteins was mainly detected by western blot,but the selection of internal reference proteins and their variations have not been systematically studied.Methods Myocardial injury was induced in a low-pressure,low-oxygen chamber simulating an altitude of 6000 m,for 24 and 72 h.Establishment of the myocardial injury model was confirmed by hematoxylin eosin(HE)staining.Expression levels of internal control proteins,including vinculin,α-tubulin,eukaryotic translation initiation factor-5(EIF5),β-actin,glyceraldehyde-3-phosphate dehydrogenase(GAPDH),cyclophilin B,and cofilin,were detected by Western Blot and total protein expression was detected by Ponceau S and Coomassie Blue staining.An adult mouse cardiomyocytes(AMCMs)injury model was induced by hypoxia for 12 and 24 h and confirmed by terminal deoxynucleotidyl transferase dUTP nick end labeling(TUNEL staining).Internal control proteins were detected by Western Blot,as in the in vivo model,and total protein expression was detected by Ponceau S and Coomassie Blue staining.Results A myocardial injury model was established by hypobaric hypoxia for 24 and 72 h,the total protein expression levels remained consistent.The expression of internal control proteins including vinculin,EIF5,β-actin,cyclophilin B,and cofilin was consistent between the control and model groups.Expression levels of α-tubulin were similar in the plain control and 24 h hypobaric hypoxia group,but were significantly lower in the 72 h hypobaric hypoxia group compared with the plain control group.GAPDH expression was significantly higher in the 24 and 72 h hypobaric hypoxia groups than in the plain control group.An AMCM injury model was established by hypoxia for 12 and 24 h.Total protein levels and expression levels of the internal control proteins EIF5 and β-actin were consistent,but vinculin,α-tubulin,GAPDH,cyclophilin B,and cofilin expression levels were higher in both hypoxia groups compared with the normoxic control group.Conclusions EIF5 and β-actin may be the suitable loading control proteins for studies of hypobaric hypoxia-induced myocardial injury using Western Blot.Total protein is also a good choice for hypobaric hypoxia studies.

The development of longitudinal cohorts has made the identification and surveillance of multiple biological markers and behavioral factors which influence disease course or health status become possible. However, traditional statistical methods typically use univariate longitudinal data for research, failing to fully exploit the information from multivariate longitudinal data. The group-based multi-trajectory model (GBMTM) emerged as a method to study the developmental trajectory of multivariate data in recent years. GBMTM has distinct advantages in analyzing multivariate longitudinal data by identifying potential subgroups of populations following similar trajectories by multiple indicators that influence the outcome of interest. In this study, we introduced the application of GBMTM by explaining the fundamental principles and using the data from a health management study in the elderly by using smart wearing equipment to investigate the relationship between multiple life-related variables and hypertension to promote the wider use of GBMTM in longitudinal cohort studies.

The incidence of falls, as well as the consequent fear of falling among aged hemodialysis patients, is notably high. Such fear contributes to both psychosomatic damage and a diminished quality of life, constituting a significant risk factor for mortality in this demographic. This article comprehensively reviews the overview of fear of falling in aged hemodialysis patients, its effects on these individuals, the current prevalence, influencing factors, and intervention measures. It aims to provide a scientific foundation for formulating effective strategies to counteract the fear of falling in aged hemodialysis patients.

Objective To construct models of viral transfection and hypoxia/reoxygenation induced cellular injury in adult mouse cardiomyocytes(AMCMs)isolated using a non-Langendorff method.Methods AMCMs were isolated,extracted,sedimented,and plated using a non-Langendorff method.The morphology and survival rate of the isolated cells were evaluated 2,24,48 and 72 h after plating,and their integrity was observed by immunofluorescence staining for α-actinin.The isolated AMCMs were infected with adenoviruses carrying an RFP-expressing vector and fluorescence images were obtained at 36 and 48 h post-infection and used to calculate transfection efficiency.The cells were cultured under hypoxic conditions for 45 min,reoxygenated for 24 h,and then stained with propidium iodide(PI)to verify establishment of the hypoxia/reoxygenation injury model.Results The survival rates of AMCMs at 2,24 and 48 h after plating were comparable,but survival was significantly reduced at 72 h.The integrity of the AMCMs was good and＞80%of the cells were transfected with adenovirus at 48 h.After hypoxia/reoxygenation treatment,42%of cells were stained by PI,suggesting successful establishment of the AMCM injury model.Conclusions In this study,we developed a non-Langendorff method for the fast and easy isolation of AMCMs with high cell viability.The isolated cells can be efficiently infected with adenovirus and respond to hypoxia/reoxygenation injury.These findings provide a systematic method for isolating AMCMs and for applying gene modification and hypoxia/reoxygenation injury in these cells.

Objective:To investigate the clinical value of abdominal adipose volume in predicting early tumor recurrence after resection of hepatocellular carcinoma (HCC).Methods:The retrospective case-control study was conducted. The clinicopathological data of 132 HCC patients with tumor diameter ≤5 cm who were admitted to The First Affiliated Hospital of Army Medical University from December 2017 to October 2019 were collected. There were 110 males and 22 females, aged (51±4)years. All patients underwent resection of HCC. Preoperative computer tomography scanning was performed and the visceral and subcutaneous fats of patients were quantified using the Mimics Research 21.0 software. Based on time to postoperative tumor recurrence patients were divided to two categories: early recurrence and non-early recurrence. Observation indicators: (1) consistency analy-sis; (2) analysis of factors influencing early tumor recurrence after resection of HCC and construction of prediction model. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the t test. Measurement data with skewed distribu-tion were represented as M( Q1,Q3) or M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were expressed as absolute numbers, and comparison between groups was conducted using the chi-square test or Fisher exact probability. Consistency analysis was conducted using the intragroup correlation coefficient (ICC) test. Multivariate analysis was performed using the binary Logistic regression model forward method. Independent risk factors influencing early tumor recurrence after resection of HCC were screened. The area under curve (AUC) of receiver operating characteristic (ROC) curve was applied to select the optimal cut-off value to classify high and low risks of recurrence. The Kaplan-Meier method was used to draw survival curve and calculate survival time. The Log-Rank test was used for survival analysis. Results:(1) Consistency analysis. The consistency ICC of abdominal fat parameters of visceral fat volume (VFV), subcutaneous fat volume, visceral fat area, and subcutaneous fat area measured by 2 radiologists were 0.84, 1.00, 0.86, and 0.94, respectively. (2) Analysis of factors influencing early tumor recurr-ence after resection of HCC and construction of prediction model. All 132 patients were followed up after surgery for 662(range, 292-1 111)days. During the follow-up, there were 52 patients with non-early recurrence and 80 patients with early recurrence. Results of multivariate analysis showed that VFV was an independent factor influencing early tumor recurrence after resection of HCC ( odds ratio=4.07, 95% confidence interval as 2.27-7.27, P<0.05). The AUC of ROC curve based on VFV was 0.78 (95% confidence interval as 0.70-0.85), and the sensitivity and specificity were 72.2 % and 77.4 %, respectively. The optimal cut-off value of VFV was 1.255 dm 3, and all 132 patients were divided into the high-risk early postoperative recurrence group of 69 cases with VFV >1.255 dm 3, and the low-risk early postoperative recurrence group of 63 cases with VFV ≤1.255 dm 3. The disease-free survival time of the high-risk early postoperative recurrence group and the low-risk early post-operative recurrence group were 414(193,702)days and 1 047(620,1 219)days, showing a significant difference between them ( χ2=31.17, P<0.05). Conclusions:VFV is an independent factor influen-cing early tumor recurrence of HCC after resection. As a quantitative indicator of abdominal fat, it can predict the prognosis of HCC patients.