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.

Objective To set up the reference standard of cyclovirobuxine D.Methods Thermal analysis,HPLC/MS,HPLC with terminal wavelength,HPLC with fluorescence derivation and with ultraviolet derivation,TLC and nonaqueous titration methods were applied to determine the content of cyclovirobuxine D control.Results Thermal analysis can not be used to analyse the purity of cyclovirobuxine D ,and HPLC/MS,HPLC with terminal wavelength,HPLC with fluorescence derivation and HPLC with ultraviolet derivation can obtain the same purity.Conclusion The methods used for the assay of cyclovirobuxine D control were practical.

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.

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.