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 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 analyze the effects of DHT on the proliferation and migration of endothelial progenitor cells (EPCs) and the role of RhoA/ROCK pathway in this process.Methods:Early EPCs were isolated from peripheral blood of healthy adults, and cultured in serum-free EBM-2 medium for 24 h before incubation with various concentrations of DHT (1, 10, and 100 nmol/L). EPCs proliferative and migrative capacities were measured. The adherent cells were collected and randomLy divided into: control group, DHT group, C3 exoenzyme+DHT, Y-27632+DHT group. EPCs proliferation and migration were assayed by MTT assay and modified Boyden chamber assay respectively.Results:DHT significantly increased the proliferation and migration ability of EPCs in a dose- and time-dependent manner, maximum at 10 nmol/L, 24 h ( P<0.05). C3 exoenzyme [(0.22±0.02) vs (0.26±0.05), P>0.05] and Y-27632 [(0.21±0.04) vs (0.26±0.05), P>0.05] can attenuate the proliferative capacities of EPCs induced by DHT compared with the DHT group, but there was no statistical significance. The influence of DHT on EPCs migrative capacities can be abolished by C3 exoenzyme [(35.26±4.27) vs (46.92±5.46), P<0.05] and Y-27632 [(33.61±5.33) vs (46.92±5.46), P<0.01]. C3 exoenzyme [(116.75±7.42) vs (156.80± 21.74), P<0.05] and Y-27632 [(121.73±5.33) vs (156.80 ±21.74), P<0.01] could noticeably attenuate DHT-induced EPCs secretion of VEGF respectively. Conclusions:DHT can modulate EPCs proliferation, migration and the RhoA/ROCK pathway plays an important role in this process.

Objective: To analyze the epidemiological characteristics of human brucellosis and trace back source of infection of human brucellosis in Hunan province during 2010-2018, and provide evidence for the prevention and control of human brucellosis. Methods: The surveillance data of human brucellosis in Hunan during 2010-2018 were analyzed with software Excel 2016 and ArcGIS 10.5, the epidemic characteristics were described using cases number, constituent ratio and rate. The conventional biotype methods were used for the identification of Brucella species, UTS-PCR was applied to further confirm the results from conventional biotype detections, then six virulence genes of two clinical Brucella strains were detected by PCR assay. Cluster analysis of two Brucella strains were performed with Multiple locus variable-number tandem repeat analysis (MLVA) for the investigation of the infection source of human brucellosis. Results: From 2010 to 2018, a total of 728 human brucellosis cases were reported in Hunan with the annual incidence rate of 0.12/100 000. The incidence rate was 2.50/100 000 in Chenzhou and 1.90/100 000 in Yongzhou, higher than those in other areas. The number of counties reporting cases increased from 5 in 2010 to 69 in 2018. Most cases were reported in age group 45-54 years, accounting for 38.32% (279/728). The cases in farmers accounted for 59.07% (430/728) of the total. The male to female ratio of the cases was 2.75 ∶ 1. The reported case number was highest during May-July, accounting for 45.33% (330/728). The incidence was high in summer and autumn, and the peak was in May. The conventional identification showed that two strains were all Brucella melitensis biovar 1, consistent with UTS-PCR amplification results. Six virulence genes were found in two isolated strains, suggesting that the Brucella melitensis strains in this study had strong virulence. MLVA results confirmed that two strains detected in Hunan had complete identical MLVA-16 genotype with strains isolated from goat and camel in Inner Mongolia Autonomous Region, indicating that there was molecular epidemiology relationship between these strains and the source of infection were originated from Inner Mongolia. Conclusions The epidemic of human brucellosis in Hunan is becoming serious, and disease has spread to general population and non-epidemic areas. Two Brucella melitensis strains detected in Hunan were originated from Inner Mongolia. The quarantine and inspection in animal transportation should be strengthened to prevent human outbreaks of brucellosis.

Objective To investigate the risk factors of polytrauma combined with multiple organ dysfunction syndrome (MODS).Methods A retrospective case control study was performed on the clinical data of 299 polytrauma patients admitted to the Affiliated Hospital of Jiangsu University from December 2011 to June 2017.The collected information included gender,age,length of hospital stay,number of injured parts,injury severity scores (ISS),neutrophil count,leukocyte level,hemoglobin level,platelet count,activated partial thromboplastin time (APTI),and D-dimer level within 24 hours since admission.In addition,shock within 24 hours since admission,infection after 3 days since admission,damage control surgery,underlying diseases and prognostic outcomes were also recorded.All the patients were divided into MODS group (94 patients) and non-MODS group (205 patients).Univariate and multivariate logistic regression analyses were used to determine the risk factors of polytrauma combined with MODS.Receiver operating characteristic (ROC) curve was applied to further analyze those risk factors identified by the former analyses.Results In the univariate analysis,there were statistically significant differences between the two groups in the number of injured parts,ISS,hemoglobin level,platelet count,APTT,D-dimer level within 24 hours since admission,shock within 24 hours since admission,infection after 3 days since admission,damage control surgery and prognostic outcomes (P ＜ 0.05).No significant differences were found in gender,age,underlying disease,length of hospital stay,neutrophil level,the leukocyte level within 24 hours since admission between the two groups (P ＞ 0.05).The multivariate logistic regression analysis showed that ISS (OR =1.048),shock within 24 hours since admission (OR =3.913),infection after 3 days since admission (OR =27.715),and D-dimer level within 24 hours since admission (OR =1.015) were significantly associated with polytrauma combined with MODS (P ＜ 0.05).In addition,the area under ROC curve of ISS was 0.726 (95 ％ CI 0.667-0.784),and the area under ROC curve of D-dimer was 0.638 (95％ CI 0.571-0.706).Conclusions The risk factors of polytrauma patients combined with MODS include ISS,infection after 3 days since admission,D-dimer level and shock within 24 hours since admission.In the treatment of polytrauma patients,attention should be paid to assessment of injury severity and coagulation function,active resuscitation to correct shock,prevent and control infection,which can reduce and prevent the risks for polytrauma patients combined with MODS.

The unexpected discovery of cervical cancer ( UDCC) rarely occurs in clinics .Because it is di-agnosed by pathology after surgery ,the therapies are different from ordinary cervical cancer .In addition,the choice of the timing of surgery and other issues are also involved .The patient received the hysterectomy due to cervical intraepithelial neoplasia III and uterine fibroids .Postoperative pathology examination showed cervical microinva-sive carcinoma,stage IA1,and pathology without lymph -vessel invasion involvement ,the patient needs to be fol-lowed up closely postopertation .One case of the unexpected discovery of cervical cancer was reported in this pa -per.The aim is to provide a reference for clinical management .