Objective:To screen the risk factors of acute kidney injury (AKI) in patients with multiple trauma, construct a prediction model accordingly, and evaluate its predictive value.Methods:A retrospective cohort study was performed to analyze the clinical data of 560 multiple trauma patients who were admitted to while Affiliated Hospital of Jiangsu University from January 2017 to June 2023, including 424 males and 136 females, aged 18-91 years [(55.5±15.0)years]. The patients were randomly divided into a training set ( n=392) and validation set ( n=168) with a ratio of 7∶3. Of all, 77 patients were combined with AKI in the training set, while 33 patients combined with AKI in the validation set. The AKI group and non-AKI group in the training set were compared in terms of gender, age, hypertension, diabetes, cause of injury, abbreviated injury scale (AIS) score of head and neck injury, AIS score of maxillofacial injury, AIS score of chest injury, AIS score of abdominal injury, AIS score of extremities and pelvic injury, AIS score of body surface injury, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, Glasgow coma score (GCS) on admission, revised trauma score (RTS) on admission, acute physiology and chronic health assessment II (APACHE II) on admission, injury severity score (ISS) on admission, and laboratory test results on admission including white blood cell count, neutrophil count, lymphocyte count, C-reactive protein, hemoglobin, platelet count, activated partial thromboplastin time (APTT), prothrombin (PT), fibrinogen (FIB), thrombin time (TT), international normalized ratio (INR), D-dimer, blood lactate, base excess, total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, globulin, urea nitrogen, serum creatinine, blood glucose, potassium, sodium and chloronium. In the training set, univariate analysis and Lasso regression analysis were used to screen the risk factors of AKI in patients with multiple trauma, which were then included into multivariate logistic regression analysis to identify the independent risk factors. A nomogram prediction model was constructed using the R software based on the above independent risk factors. Hosmer-Lemeshow (H-L) goodness-of-fit test was performed to evaluate the fitting degree of the prediction model in the training set and the validation set, and the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve (DCA) were plotted in the training set and the validation set to evaluate the predictive performance of the prediction model. Results:There were statistically significant differences in AIS score of abdominal injury, heart rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, GCS on admission, RTS on admission, APACHE II on admission, ISS on admission as well as hemoglobin, platelet count, APTT, PT, FIB, TT, INR, blood lactate, base excess, AST, albumin, globulin, urea nitrogen, serum creatinine, blood glucose and sodium on admission between the AKI group and the non-AKI group ( P<0.05 or 0.01). The characteristic variables screened by Lasso regression analysis included AIS score of abdominal injury, red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drugs therapy, blood lactate on admission, blood creatinine on admission, AST on admission, and blood sodium on admission. Multivariate logistic regression analysis showed that red blood cell transfusion volume within 24 hour following admission ( OR=1.09, 95% CI 1.01, 1.18), mechanical ventilation ( OR=2.49, 95% CI 1.06, 5.85), vasoactive drug therapy ( OR=2.04, 95% CI 1.03, 4.03), blood lactate on admission ( OR=1.10, 95% CI 1.01, 1.21) and serum creatinine on admission ( OR=1.02, 95% CI 1.01, 1.03) were independent risk factors for AKI in patients with multiple trauma ( P<0.05). The regression equation was constructed: Logit[ P/(1- P)]=0.086 2×"red blood cell transfusion volume within 24 hour following admission"+0.912 7×"mechanical ventilation"+0.713 2×"vasoactive drug therapy"+0.098 9×"blood lactate on admission"+0.019 2×"serum creatinine on admission" -4.822 3. H-L goodness-of-fit test showed χ2 value of 9.50 in the training set ( P>0.05) and 6.43 in the validation set ( P>0.05). The results of the ROC curve indicated that the area under the curve (AUC) was 0.84 (95% CI 0.78, 0.89) in the training set and 0.80 (95% CI 0.72, 0.88) in the validation set. The calibration curves showed good agreement with the actual curves, with the predicted probability consistent with the actual probability in both training set and validation set. DCA analysis showed that the threshold probability ranged from 2% to 70% with the net benefit rate of the prediction model greater than 0 in the training set, while the threshold probability ranged from 3% to 69% with the net benefit rate of the prediction model greater than 0 in the validation set. Conclusions:Red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drug therapy, lactate and serum creatinine on admission are independent risk factors for AKI in patients with multiple trauma. The nomogram prediction model based on the above 5 predictive variables of AKI in patients with multiple trauma shows good predictive efficacy and clinical application value.

Objective:To screen the risk factors of acute kidney injury (AKI) in patients with multiple trauma, construct a prediction model accordingly, and evaluate its predictive value.Methods:A retrospective cohort study was performed to analyze the clinical data of 560 multiple trauma patients who were admitted to while Affiliated Hospital of Jiangsu University from January 2017 to June 2023, including 424 males and 136 females, aged 18-91 years [(55.5±15.0)years]. The patients were randomly divided into a training set ( n=392) and validation set ( n=168) with a ratio of 7∶3. Of all, 77 patients were combined with AKI in the training set, while 33 patients combined with AKI in the validation set. The AKI group and non-AKI group in the training set were compared in terms of gender, age, hypertension, diabetes, cause of injury, abbreviated injury scale (AIS) score of head and neck injury, AIS score of maxillofacial injury, AIS score of chest injury, AIS score of abdominal injury, AIS score of extremities and pelvic injury, AIS score of body surface injury, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, Glasgow coma score (GCS) on admission, revised trauma score (RTS) on admission, acute physiology and chronic health assessment II (APACHE II) on admission, injury severity score (ISS) on admission, and laboratory test results on admission including white blood cell count, neutrophil count, lymphocyte count, C-reactive protein, hemoglobin, platelet count, activated partial thromboplastin time (APTT), prothrombin (PT), fibrinogen (FIB), thrombin time (TT), international normalized ratio (INR), D-dimer, blood lactate, base excess, total bilirubin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, globulin, urea nitrogen, serum creatinine, blood glucose, potassium, sodium and chloronium. In the training set, univariate analysis and Lasso regression analysis were used to screen the risk factors of AKI in patients with multiple trauma, which were then included into multivariate logistic regression analysis to identify the independent risk factors. A nomogram prediction model was constructed using the R software based on the above independent risk factors. Hosmer-Lemeshow (H-L) goodness-of-fit test was performed to evaluate the fitting degree of the prediction model in the training set and the validation set, and the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve (DCA) were plotted in the training set and the validation set to evaluate the predictive performance of the prediction model. Results:There were statistically significant differences in AIS score of abdominal injury, heart rate, body temperature, red blood cell and plasma transfusion volume within 24 hours following admission, emergency surgery, mechanical ventilation, vasoactive drug therapy, GCS on admission, RTS on admission, APACHE II on admission, ISS on admission as well as hemoglobin, platelet count, APTT, PT, FIB, TT, INR, blood lactate, base excess, AST, albumin, globulin, urea nitrogen, serum creatinine, blood glucose and sodium on admission between the AKI group and the non-AKI group ( P<0.05 or 0.01). The characteristic variables screened by Lasso regression analysis included AIS score of abdominal injury, red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drugs therapy, blood lactate on admission, blood creatinine on admission, AST on admission, and blood sodium on admission. Multivariate logistic regression analysis showed that red blood cell transfusion volume within 24 hour following admission ( OR=1.09, 95% CI 1.01, 1.18), mechanical ventilation ( OR=2.49, 95% CI 1.06, 5.85), vasoactive drug therapy ( OR=2.04, 95% CI 1.03, 4.03), blood lactate on admission ( OR=1.10, 95% CI 1.01, 1.21) and serum creatinine on admission ( OR=1.02, 95% CI 1.01, 1.03) were independent risk factors for AKI in patients with multiple trauma ( P<0.05). The regression equation was constructed: Logit[ P/(1- P)]=0.086 2×"red blood cell transfusion volume within 24 hour following admission"+0.912 7×"mechanical ventilation"+0.713 2×"vasoactive drug therapy"+0.098 9×"blood lactate on admission"+0.019 2×"serum creatinine on admission" -4.822 3. H-L goodness-of-fit test showed χ2 value of 9.50 in the training set ( P>0.05) and 6.43 in the validation set ( P>0.05). The results of the ROC curve indicated that the area under the curve (AUC) was 0.84 (95% CI 0.78, 0.89) in the training set and 0.80 (95% CI 0.72, 0.88) in the validation set. The calibration curves showed good agreement with the actual curves, with the predicted probability consistent with the actual probability in both training set and validation set. DCA analysis showed that the threshold probability ranged from 2% to 70% with the net benefit rate of the prediction model greater than 0 in the training set, while the threshold probability ranged from 3% to 69% with the net benefit rate of the prediction model greater than 0 in the validation set. Conclusions:Red blood cell transfusion volume within 24 hours following admission, mechanical ventilation, vasoactive drug therapy, lactate and serum creatinine on admission are independent risk factors for AKI in patients with multiple trauma. The nomogram prediction model based on the above 5 predictive variables of AKI in patients with multiple trauma shows good predictive efficacy and clinical application value.

Objective To investigate the relationship between the expression of serum angiopoietin-like protein 8(ANGPTL8)and Kruppel-like factor 2(KLF2)and the degree of coronary artery disease and the occurrence of major adverse cardiac events(MACE)in patients with acute myocardial infarction(AMI).Methods A total of 106 patients with AMI who were hospitalized in our hospital from January 2021 to June 2023 were selected as the research objects.According to the degree of coronary artery disease,the patients were grouped into mild group(52 cases)and severe group(54 cases).According to the occurrence of MACE,the patients were grouped into MACE group(18 cases)and non-MACE group(88 cases).General patient data were collected.Serum ANGPTL8 and KLF2 levels were detected by enzyme-linked immunosorbent assay(ELISA).Spearman correlation analysis was applied to analyze the correlation between serum ANGPTL8 and KLF2 levels and Gensini score in AMI patients.Multivariate logistic regression was applied to analyze the influencing factors of coronary artery disease degree in patients with AMI.Receiver operating characteristic(ROC)curves were drawn to analyze the value of serum ANGPTL8 and KLF2 levels in predicting the occurrence of MACE in AMI patients.Results The proportions of patients with history of hypertension and hyperlipidemia,systolic blood pressure,diastolic blood pressure,levels of triacylglycerol(TG),N-terminal pro-B-type natriuretic peptide(NT-proBNP),cardiac troponin I(cTnI),Gensini score,and level of serum ANGPTL8 in the severe group were higher than those in the mild group(P<0.05).The level of high-density lipoprotein cholesterol(HDL-C)and serum KLF2 in the severe group were lower than those in the mild group(P<0.05).The number of lesions in the mild group and the severe group was statistically obvious(P<0.05).Serum ANGPTL8 level in AMI patients was positively correlated with Gensini score(r=0.638,P<0.05),and serum KLF2 level was negatively correlated with Gensini score(r=-0.612,P<0.05).History of hypertension,hyperlipidemia,cTnI and ANGPTL8 were risk factors for the progression of severe coronary artery disease in patients with AMI(P<0.05),while HDL-C and KLF2 were protective factors(P<0.05).The serum level of ANGPTL8 in AMI patients in the MACE group was higher than that in the non-MACE group(P<0.05),and the serum KLF2 level in the MACE group was lower than that in the non-MACE group(P<0.05).The area under the curve of serum ANG-PTL8 and KLF2 levels and their combination in predicting the occurrence of MACE in AMI patients was 0.740(95%CI:0.646～0.820),0.799(95%CI:0.710～0.870),and 0.806(95%CI:0.717～0.876),respectively.Conclusion The expressions of serum ANGPTL8 and KLF2 are closely related to the degree of coronary artery disease in patients with AMI,and have certain predictive value for the occurrence of MACE.

Objective:To investigate the effect of tumor necrosis factor-α (TNF-α) on the differentiation of orbital fibroblasts (OF) in thyroid-associated ophthalmopathy (TAO) and its regulation mechanism.Methods:Six patients (six eyes) diagnosed with TAO were collected in Tianjin Medical University Eye Hospital from December 2019 to August 2020.Adipose connective tissue was collected during the orbital decompression surgery.OF was isolated and cultured using the tissue block method and vimentin was identified by immunofluorescence.Lipogenic differentiation of OF was induced and identified by oil red O staining.Complete culture medium containing 0, 0.1, 1.0 and 10.0 μg/L TNF-α was used to induce the dedifferentiation of orbital mature adipocytes.Primary culturing cells, 14-day differentiation cells and 20-day dedifferentiation cells were collected.The relative mRNA expression levels of peroxisomal proliferation-activated receptor (PPARγ), extracellular regulatory protein kinase1 (ERK1), ERK2 and fat-coated protein1 (perilipin1) were detected by real-time fluorescent quantitative PCR.The relative protein expression levels of PPARγ, P-ERK1/2 and perilipin1 were detected by Western blot.Results:Human TAO-derived OF were successfully cultured in vitro, spindle-shaped or polygonal, tightly arranged in a vortex pattern, and immunofluorescence staining for vimentin was positive.After OF adipogenic differentiation, lipid droplet structures could be seen in the cytoplasm of some cells, and the stained lipid droplet structures in the cytoplasm could be seen by oil red O staining, which confirmed that the cells obtained after differentiation were adipocytes.Dedifferentiation of adipocytes was induced by 0.1, 1.0, and 10.0 μg/L TNF-α.With the extension of induction time, the volume of lipid droplets in the cytoplasm and the number of cells containing lipid droplets decreased.Lipid droplets disappeared in the cytoplasm on the 20th day of dedifferentiation, and the cells became long spindle-shaped and tightly arranged, dedifferentiated into fibroblast-like cells.Real-time fluorescence quantitative PCR detection results showed that the relative expression levels of PPARγ, ERK1, ERK2 and perilipin1 mRNA in 14-day differentiation group were 4.26±0.09, 2.01±0.09, 3.23±0.10 and 8.69±0.33, respectively, which were significantly higher than 1.00±0.09, 1.05±0.19, 1.00±0.10 and 1.05±0.07 in primary group, and 1.06±0.03, 1.15±0.11 and 6.27±0.09 in 20-day dedifferentiation group (all at P<0.05). Western blot analysis showed that the expression levels of PPARγ, ERK1/2 and perilipin1 proteins in 14-day differentiation group were 1.07±0.03, 1.00±0.03 and 1.13±0.02, respectively, which were significantly higher than 0.37±0.02, 0.29±0.02 and 0.00±0.00 in primary group, and 0.20±0.02, 0.38±0.06 and 0.00±0.00 in 20-day dedifferentiation group (all at P<0.001). Conclusions:TNF-α has a dedifferentiation effect on TAO orbital adipocytes.The mechanism may be related to the downregulation of ERK1/2-PPARγ-perilipin1 signaling pathway.

Objective:To explore the independent risk factor for in-hospital mortality of patients with multiple trauma, and to construct a prediction model of risk of death and validate its efficacy.Methods:A retrospective cohort study was performed to analyze the clinical data of 1 028 patients with multiple trauma admitted to Affiliated Hospital of Jiangsu University from January 2011 to December 2021. There were 765 males and 263 females, aged 18-91 years[(53.8±12.4)years]. The injury severity score (ISS) was 16-57 points [(26.3±7.6)points]. There were 153 deaths and 875 survivals. A total of 777 patients were enrolled as the training set from January 2011 to December 2018 for building the prediction model, while another 251 patients were enrolled as validation set from January 2019 to December 2021. According to the outcomes, the training set was divided into the non-survival group (115 patients) and survival group (662 patients). The two groups were compared in terms of the gender, age, underlying disease, injury mechanism, head and neck injury, maxillofacial injury, chest injury, abdominal injury, extremity and pelvis injury, body surface injury, damage control surgery, pre-hospital time, number of injury sites, Glasgow coma score (GCS), ISS, shock index, and laboratory test results within 6 hours on admission, including blood lactate acid, white blood cell counts, neutrophil to lymphocyte ratio (NLR), platelet counts, hemoglobin, activated partial thromboplastin time (APTT), fibrinogen, D-dimer and blood glucose. Univariate analysis and multivariate Logistic regression analysis were performed to determine the independent risk factors for in-hospital mortality in patients with multiple trauma. The R software was used to establish a nomogram prediction model based on the above risk factors. Area under the receiver operating characteristic (ROC) curve (AUC), calibration curve and clinical decision curve analysis (DCA) were plotted in the training set and the validation set, and Hosmer-Lemeshow goodness-of-fit test was performed.Results:Univariate analysis showed that abdominal injury, extremity and pelvis injury, damage control surgery, GCS, ISS, shock index, blood lactic acid, white blood cell counts, NLR, platelet counts, hemoglobin, APTT, fibrinogen, D-dimer and blood glucose were correlated with in-hospital mortality in patients with multiple trauma ( P<0.05 or 0.01). Logistic regression analysis showed that GCS≤8 points ( OR=1.99, 95% CI 1.12,3.53), ISS>25 points ( OR=7.39, 95% CI 3.50, 15.61), shock index>1.0 ( OR=3.43, 95% CI 1.94,6.08), blood lactic acid>2 mmol/L ( OR=9.84, 95% CI 4.97, 19.51), fibrinogen≤1.5 g/L ( OR=2.57, 95% CI 1.39,4.74) and blood glucose>10 mmol/L ( OR=3.49, 95% CI 2.03, 5.99) were significantly correlated with their in-hospital mortality ( P<0.05 or 0.01). The ROC of the nomogram prediction model indicated that AUC of the training set was 0.91 (95% CI 0.87, 0.93) and AUC of the validation set was 0.90 (95% CI 0.84, 0.95). The calibration curve showed that the predicted probability was consistent with the actual situation in both the training set and validation set. DCA showed that the nomogram prediction model presented excellent performance in predicting in-hospital mortality. In Hosmer-Lemeshow goodness-of-fit test, χ2 value of the training set was 9.69 ( P>0.05), with validation set of 9.16 ( P>0.05). Conclusions:GCS≤8 points, ISS>25 points, shock index>1.0, blood lactic acid>2 mmol/L, fibrinogen≤1.5 g/L and blood glucose>10 mmol/L are independent risk factors for in-hospital mortality in patients with multiple trauma. The nomogram prediction model based on these 6 predictive variables shows a good predictive performance, which can help clinicians comprehensively assess the patient′s condition and identify the high-risk population.

Objective:To investigate the risk factors for overall survival in operable hepatocellular carcinoma with portal vein tumor thrombus (PVTT-HCC) patients and establish a scoring system.Methods:Survival data in 253 PVTT-HCC patients were retrospectively analyzed in Guangxi Medical University Affiliated Tumor Hospital. Survival curves were analyzed using the Kaplan-Meier method and log-rank test. Cox stepwise regression analysis was used to identify independent preoperative risk factors affecting overall survival. A prognostic scoring system based on independent risk factors and their relative coefficients was established to screen patients with greater hepatic resection benefits, and the identification ability of the model was based on ROC.Results:A total of 253 patients with PVTT-HCC were enrolled in this study, there were 222 males and 31 females, with a median age 44 years. The median survival time in all patients was (13.00±2.15) months. Rate of overall survival was 51.8% at 1 year, 25.0% at 3 years and 17.7% at 5 years. Multivariable Cox regression analyses showed four risk factors including: AST≥40 U/L, ALP (≥80 U/L), tumor number (>1), and incomplete tumor capsule. A prognostic scoring system was established based on these variables. The area under curve of the scoring system was 0.780 (95% CI: 0.715-0.845). Patients were classified as low- or high-risk group for hepatic resection depending on whether their score was <3 ( n=77) or ≥3 ( n=176), respectively. High-risk patients had a median survival of 10 months, compared to 29 months in low-risk patients. Low-risk patients also had better survival rates at 1 year (75.3% vs 41.5%), 3 years (47.6% vs 15.2%), and 5 years (34.7% vs 10.5%), P<0.05. Conclusion:A prognostic scoring system for hepatic resection in PVTT-HCC patients has been developed based entirely on preoperative variables. Using this system, patients belong to the low risk group have better prognosis after surgery, which can provide a basis for surgical treatment of PVTT-HCC patients.

Objective:To explore the effects and the mechanism of metformin combined with celecoxib on the proliferation and apoptosis of hepatoma HepG2 and Huh7 cells.Methods:Hepatoma cells HepG2 and Huh7 were divided into control group, metformin group, celecoxib group and combination medication group, CCK-8 assay was used to detect cell proliferation; Hoechst33258 staining method was used to investigate the cell apoptosis; wound healing test was used to detect cells migration ability; Transwell invasion chamber test was used to detect cell invasion ability; Western blotting was used to detect the expression of AMPK, PI3K, Akt, mTOR.Results:After metformin and celecoxib treatment, HepG2 and Huh7 cells were gradually contracted, disintegrated and more apoptotic cells were noticed, and cell proliferation was significantly inhibited. The wound healing test results showed that the cell migration was significantly decreased ( P<0.05) under metformin and celecoxib treatment. The results of the transwell invasion chamber test showed that the metformin and celecoxib treatment inhibited the invasion of HepG2 and Huh7 cells ( P<0.05). The expression levels of AKT, AMPK, and mTOR were decreased in HepG2 cells in the combinational treatment group, and the expression level of PI3K was decreased and then increased; the expression levels of AKT, AMPK, PI3K, and mTOR in Huh7 cells were decreased. Conclusions:Metformin can cooperate with celecoxib to enhance the inhibitory effect on the proliferation, migration and invasion of HepG2 and Huh7 cells. The mechanism may be related to the inhibition of the expression of mTOR signaling pathway.

Objective: To compare the prognosis of radiofrequency ablation (RFA) for postoperative recurrent hepatocellular carcinoma and primary hepatocellular carcinoma(HCC). Methods: The clinical data of 179 patients with recurrent HCC (recurrent group) and primary HCC (primary group) treated by RFA from 2009 to 2015 were retrospectively analyzed. Overall survival rate (OS) and disease-free survival rate (DFS) were analyzed by Kaplan-meier log-rank test. The prognostic factors of RFA for recurrent HCC were analyzed by COX proportional hazard regression. Results: The 1, 3 and 5year′s OS of the recurrent group were 93%, 73%, 61%, respectively and 85%, 75%, 61% for the primary group(χ²=0.017, P=0.896). The corresponding 1, 3 and 5year′s DFS were 61%, 39%, 21% and 79%, 64%, 46% respectively (χ²=3.899, P=0.048). The independent risk factors affecting the OS of the recurrent group were the interval between hepatectomy to recurrence≤12 months (HR=0.264, 95% CI=0.077-0.901, P=0.033) and the Child-Pugh grading B before RFA (HR=4.501, 95% CI=1.426-14.208, P=0.01). Conclusions The DFS of patients with recurrent HCC were shorter than that with primary HCC treated by RFA. The interval between hepatectomy to recurrence and the Child-Pugh grading before RFA were independent risk factors for OS of the recurrent group.

Objective To establish a preoperative nomogram model in predicting microvascular invasion (MVI) and to test its predictive effectiveness in hepatocellular carcinoma (HCC).Methods This retrospective study was conducted on 798 patients with HCC,including 690 males and 108 females,aged (49.8± 10.9) years old who underwent curative hepatectomy in the Guangxi Medical University Affiliated Tumor Hospital between January 2014 and December 2017 were retrospectively analyzed.The patients were divided into the model group (n=579) and the validation group (n=219) according to the periods of the operation time.Independent risk factors of MVI were identified by univariate and multivariate logistic regression analysis in the model group,and a nomogram model was established according to the independent risk factors.The accuracy of the nomogram model in predicting MVI was detected in the two groups by the computer consistency coefficient (C-index) and calibration graph method.The predictive value was evaluated by receiver operating characteristic curve.Results Histopathological diagnosis revealed 278 patients with MVI and no MVI in the 301 patients of HCC out of the 579 patients in the model group.In the validation group,there were 119 patients with MVI and 100 patients with no MVI out of the 219 patients.Total bilirubin ＞15 μmol/L(OR=1.519,95％ CI:1.041 ～ 2.217),alkaline phosphatase ＞60 U/L(OR =1.681,95％CI:1.059～2.670),alpha-fetoprotein ＞200 ng/L (OR=2.192,95％CI:1.531 ～3.134) and tumor maximum diameter (OR =1.120,95％CI:1.057 ～ 1.187) were the independent risk factors of MVI on multivariate analysis.After establishment of the nomogram model using the independent risk factors,the C-indexes were 0.680 and 0.773 respectively in the model group and the validation group.In the calibration graph,the standard curve properly fitted with the predicting calibration curve.The predicted value of MVI obtained was in good agreement with the observed value.The ROC curve analysis nomogram model predicted the low performance of MVI.Conclusion The nomogram model in predicting MVI in patients with HCC was successfully established.The model offered certain guiding significance in the clinical treatment of HCC.

Objective To retrospectively study the prognostic impact of paraneoplastic erythrocytosis (PE) on patients with hepatocellular carcinoma (HCC) after liver resection.Methods 713 patients with HCC who underwent partial hepatic resection in The Affiliated Cancer Hospital of Guangxi Medical University were divided into two groups:the PE group (n =81) and the non-PE group (n =632).The overall survival between the two groups were compared after reducing confounding bias by using propensity score matching (PSM).Independent prognostic predictors were determined by the Cox proportional hazards model.Results 80 pairs of patients were matched using PSM.In the matched cohort,the PE group exhibited significantly longer overall survival (OS) compared to the NPE group of patients without erythrocytosis.The 1-,3-,and 5-year overall survival rates were 88.6％,74.2％,69.0％ in the PE group,and 91.0％,60.1％,41.6％ in the non-PE group,respectively (P ＜ 0.05).Using the log-rank test,tumor size ≥ 10cm,macrovascular invasion,Barcelona Clinic Liver Cancer (BCLC) stage C,PE and complete tumor encapsulation were significantly associated with OS in patients with HCC after liver resection.The Cox regression analysis indicated that tumor size ≥ 10 cm,and Barcelona Clinic Liver Cancer (BCLC) stage C were independent prognostic factors of poor prognosis,while complete tumor encapsulation and paraneoplastic erythrocytosis were independent predictors of good prognosis.Conclusions For patients with HCC who underwent surgical resection,patients with PE had better prognosis than those without PE under the condition of similar tumor burden.PE was an independent predictor of good prognosis.