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.

AIM　To study the phase I metabolites of phenoprolamine hydrochloride (DDPH) in rat bile. METHODS　DDPH was administered ip to bile duct-cannulated rats. Bile samples were collected before administration and up to 12 h after administration. After being treated with β-glucuronidase, the bile samples were purified and enriched with C-18 SPE columns, and then were analyzed by LC/DAD/MSD. The samples containing synthesized reference standards of DDPH metabolite 1-(2,6-dimethylphenoxy)-2-(3-methoxy-4-hydroxyphenylethylamino)-propane (M1), 1-(2,6-dimethyl-3-hydroxyphenoxy)-2-(3,4-methoxy-phenylethylamino)-propane (M2), 1-(2,6-dimethyl-4-hydroxyphenoxy)-2-(3,4-methoxyphenylethylamino)-propane (M3), 1-(2,6-dimethyl-4-hydroxyphenoxy)-2-(3-hydroxy-4-methoxyphenylethylamino)-propane (M4), 1-(2,6-dimethyl-3-hydroxyphenoxy)-2-(3-hydroxy-4-methoxyphenylethylamino)-propane (M5) and 1-(2,6-dimethyl-4-hydroxyphenoxy)-2-(3-methoxy-4-hydroxyphenylethylamino)-propane (M6) were analyzed by LC/DAD/MSD under identical conditions. RESULTS　The retention times, UV spectra, molecular weights and production spectra (obtained by collision-induced dissociation)of the apparent ions of peak A, B, C, D, E and F in the total ion chromatogram of DDPH treated rat bile sample were consistent with those of M1, M2, M3, M5, M4 and M6, respectively. CONCLUSION　M1, M2, M3, M4, M5 and M6 were identified as the phase I metabolites of DDPH in the rat.

AIM　To explore the biotransformation of compound 7-(4-chlorbenzyl)-7,8,13,13a-tetrahydroberberine in the rabbit. METHODS　Analyze the rabbit bile sample with HPLC, LC/MS and LC/NMR. RESULTS　A metabolite and unchanged 7-(4-chlorbenzyl)-7,8,13,13a-tetrahydroberberine were found in the rabit bile, the metabolite was characterized and its structure was elucidated. CONCLUSION　Compound 7-(4-chlorbenzyl)-7,8,13,13a-tetrahydroberberine is metabolized by demethylation at 10-OCH3 position.

AIM　To characterize the primary structure of recombinant L-asparaginase II product. METHODS　The molecular weight of the protein was measured by pneumatically-assisted electrospray ionization mass spectrometry with flow injection mode. Subsequently, tryptic peptide mapping was performed by high performance liquid chromatography on a C8 column with tandem UV and MS detection. An easy-to-use and simple denaturation process with trichloroacetic acid was conducted prior to tryptic digest so as to release the digest resistance from the protein structure. The amino acid sequences of the tryptic peptides were elucidated based on their in-source collision-induced dissociation spectra. RESULTS　The measured molecular mass was different from the theoretical value. Three amino acid variations were unambiguously detected along the peptide backbone derived from the gene-encoding sequence. CONCLUSION　This paper revealed that LC/ESI/MS had provided a promising and robust technique in primary structure analysis and quality control of DNA-derived recombinant protein pharmaceuticals.

Object To develop a new method for the determination of curcumol in essential oil from rhizoma Curcumae L.. Methods The contents of curcumol were determined by high performance capillary gas chromatography with sequential increase of temperature on a HEWLETT PACKARD 5890A gas chromatograph. Results The method can be used to determine curcumol with accuracy at a recovery of 101.4% and RSD of 0.40%. Conclusion The present study provided a satisfactory method for the determination of curcumol, and it was found that its contents in four different species (C. wenyujin, C. longa, C. aeruginose, and C. kwangsiensis) were markedly different.

Object To establish the standardization and digit ization methods for gas chromatographic fingerprint chromatograms of the essenti al oil of Curcuma longa L. Methods A polynomial regression analysis technique was estab lished for the calculation and prediction of the gas chromatographic retention i ndices by using a series of normal aliphatic hydrocarbons as the reference stand ards. And it was used for the characterization of the features of the gas chroma tographic fingerprint spectra of the essential oil of C. longa. Results It was approved that retention indices of the gas chrom atographic fingerprint spectra obtained at a variety of conditions were stable and reliable with excellent reproducibility, and fairly good ruggedness. It was also much better than the relative retention time indices. Conclusion The fingerprint spectra standard established on t he multiple references basis are much more reasonable and useful for the practic al quality assurance and validation of Chinese herbals.