AIM: To synthesize biodegradable poly lactic acid (PLA) through the optimization and selection of process and catalyst.METHODS: This experiment was performed at the Research Center for Green Chemistry and Technology in the School of Municipal and Environmental Engineering of Harbin Institute of Technology from February 2003 to October 2004. Biodegradable poly L-lactic acid (PLLA) was synthesis by melt polycondensation using L-lactic acid (LA) as material. Firstly, oligo L-lactic acid (OLLA) was prepared by dehydrating aqueous solution of LA without catalyst. And then, the mixture of OLLA and catalyst was heated at a certain temperature and pressure for a period of time to get the product of PLLA. The structure of PLLA was characterized by Fourier transform infrared (FTIR) spectra and 1H- nuclear magnetic resonance (1H-NMR) spectra. The polydispersity was determined by gel permeation chromatography (GPC).RESULTS: OLLA with the viscosity average molecular weight (Mη) of 6 500 g/mol was prepared by the following steps: the solution of LA without catalyst was dehydrated at 140 ℃, first at reduced pressure of 30 kPa for 2 hours, and then at 5 kPa for 4 hours. SnCl2-p-toluenesulfonic acid (TSA) system was the effective catalyst for the polycondensation of OLLA. After the mixture of OLLA and catalysts polycondensed at 165 ℃ and 5 kPA for about 8 hours, PLLA with the Mη of 65 000 g/mol was obtained by using SnCl2-TSA system as catalyst with the SnCl2 amount of 0.4wt% to OLLA and equal mol ratio of SnCl2 and TSA.CONCLUSION:PLLA with a certain practicability was obtained under the optimal process and catalyst. Oligomerization of LA played an important role on improving the molecular weight of PLLA.

Objective:To construct the prediction model of SAP complicated with intra-abdominal hypertension (IAH), and evaluate the prediction efficiency of the model.Methods:The clinical data of 322 SAP patients admitted to the emergency department of Cangzhou Hospital of Integrated Chinese and Western Medicine in Hebei Province from January 2017 to December 2021 were retrospectively analyzed. They were divided into IAH group ( n=153) and control group ( n=169) according to whether they had IAH complications or not. The clinical characteristics and laboratory test results of the two groups were compared. Multifactor logistic step-up regression was used to analyze the risk factors of SAP patients complicated with IAH. A nomogram model for predicting SAP complicated with IAH was established by using R software. The receiver operating characteristic curve (ROC) of the model was plotted, and the area under the curve (AUC) was calculated to evaluate its prediction efficiency. Calibration chart, Hosmer-Lemesshow test and decision curve analysis were used to evaluate the prediction accuracy and clinical application value of the model. The Bootstrap method was applied to verify the model internally. Results:In IAH group, cases with body mass index, CRP, procalcitonin (PCT), WBC, acute physiological and chronic health assessmentⅡ (APACHEⅡ) score, modified CT Severity Index score (MCTSI), incidence of complications (abdominal effusion, abdominal infection, gastrointestinal dysfunction, shock, multiple organ dysfunction syndrome), mechanical ventilation, the number of high-volume fluid reactivation (24 h≥4 L) were more than those in control group; serum albumin and serum calcium in IAH group were lower than those in control group, and the differences were statistically significant (all P value <0.05). Multivariate logistic regression analysis showed that serum albumin ( OR=0.815, 95% CI 0.710-0.937), CRP ( OR=1.005, 95% CI 1.002-1.008), MCTSI ( OR=2.043, 95% CI 1.695-2.463), complication of gastrointestinal dysfunction ( OR=4.179, 95% CI 2.170-8.049), and high-volume fluid resuscitation ( OR=4.265, 95% CI 2.269-8.015) were independent risk factors for IAH in SAP.The Nomogram prediction model was established using the five factors above as parameters, and the AUC value for predicting IAH complication was 0.886. The Hosmer-Lemesshow test showed a high consistency between the prediction results and the actual clinical observation results ( P=0.189). The results of decision curve analysis showed that the prediction probability of the model was between 10% and 85%, which could bring more benefits to patients. Conclusions:The early prediction model of SAP with concurrent IAH is successfully established, which can better predict the risk of SAP with concurrent IAH.

Objective:To construction the risk factors associated with prolonged hospitalization in patients with severe acute pancreatitis (SAP) and develop a prediction model for assessing these risks.Methods:SAP patients admitted to the department of emergency of Hebei Province Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine from January 2015 to December 2023 were retrospectively selected as the study subjects. The 75% of hospital stay was used as the cut-off point, and the patients were categorized into a normal group and an extended group. Clinical indicators of patients were collected, and independent risk factors for prolonged hospital stay in SAP patients were analyzed using multifactor Logistic regression. A prediction model was established, and a nomogram was created. The efficiency of the prediction model was evaluated using a receiver operator characteristic curve (ROC curve). The accuracy of the model was assessed using Hosmer-Lemeshow goodness-of-fit test. Decision curve analysis (DCA) was employed to evaluate the clinical applicability of the model. Finally, internal validation of the model was conducted using Bootstrap method.Results:A total of 510 patients with SAP were included, and the length of hospital stay was 18 (6, 44) days, including 400 cases in the normal group (<24 days) and 110 cases in the extended group (≥24 days). Multivariate Logistic regression analysis showed that abdominal effusion [odds ratio ( OR) = 4.163, 95% confidence interval (95% CI) was 2.105-8.234], acute physiology and chronic health evaluation Ⅱ (APACHEⅡ; OR = 1.320, 95% CI was 1.185-1.470), C-reactive protein (CRP; OR = 1.006, 95% CI was 1.002-1.011), modified CT severity index (MCTSI; OR = 1.461, 95% CI was 1.213-1.758), procalcitonin (PCT; OR = 1.303, 95% CI was 1.095-1.550) and albumin ( OR = 0.510, 95% CI was 0.419-0.622) were independent risk factors for prolonged hospital stay in SAP patients (all P < 0.01). ROC curve analysis showed that the area under the curve (AUC) of the model was 0.922 (95% CI was 0.896-0.947), the optimal cut-off value was 0.726, the sensitivity was 87.3%, and the specificity was 85.3%. Hosmer-Lemeshow test showed that χ 2 = 5.79, P = 0.671. It showed that the prediction model had good prediction efficiency and fit degree. The DCA curve showed that the prediction probability of the model could bring more clinical benefits to patients at 0.1 to 0.7. Bootstrap internal verification showed that the model had a high consistency (AUC = 0.916). Conclusions:Abdominal effusion, high APACHEⅡ score, high CRP, high MCTSI, high PCT and low albumin level are significantly associated with prolonged hospital stay in SAP patients. The prediction model can help clinicians make more scientific clinical decisions for SAP patients.