AIM:To investigate the effects of Auricularia auricular-judae polysaccharide ( AAP) on pulmonary tissues of rats with LPS-induced acute lung injury (ALI) and its mechanisms.METHODS: Adult Sprague-Dawley rats were randomly divided into control group , LPS group, low-dose AAP group, middle-dose AAP group, high-dose APP group, and dexamethasone group.The rats were injected with LPS (8 mg/kg, ip) to induce ALI.The rats in the AAP groups were treated with AAP for 7 d before the induction of ALI .The protein concentration in the bronchoalveolar lavage fluid (BALF) was measured.The lung edema degree was measured by detecting the wet /dry weight ratio.The myeloper-oxidase ( MPO) , total antioxidant capacity ( T-AOC) , total superoxide dismutase ( T-SOD) , nitric oxide synthase ( NOS) and malondialdehyde ( MDA) levels were determined .The pathological changes of the lung tissues were evaluated by HE staining.RESULTS:Treatment with AAP significantly improved LPS-induced lung pathological changes , attenuated the protein concentration in the BALF and wet/dry weight ratio , inhibited the activities of MPO and NOS , reduced MDA level and increased the activities of T-AOC and T-SOD.CONCLUSION: AAP protects against LPS-induced acute lung injury in rats.

Objective:To explore the nursing work items and the technical difficulty in post anesthesia care unit, so as to provide the basis for accurate calculation of anesthesia nursing workload and reasonable matching of human resources.Methods:The primary and secondary indicators of the nursing working system in post anesthesia care unit were determined through the literature review and group discussion. Delphi method was used to revise the indicators and evaluate the importance and difficulty degree of the indicators. Finally, analytic hierarchy process and linear weighted sum method were used to calculate the weight value and difficulty coefficient of each index.Results:The positive coefficients of the experts in the two rounds were 100.00%, the authority coefficients of the experts were 0.90 and 0.96, the variation coefficients ranged from 0.000 to 0.342 and 0.042 to 0.307, and the Kendall coefficients were 0.239 and 0.273 (both P<0.01). The evaluation index system of the difficulty of nursing work in post anesthesia care unit was finally composed of 10 primary indicators and 85 secondary indicators. The weight of the primary index ranged from 0.016 4 to 0.186 4, and the weight of the secondary index ranged from 0.000 8 to 0.064 3. The standardized difficulty coefficient of the secondary index ranged from 1.02 to 1.59. Conclusions:The evaluation index system of the difficulty of nursing work items in post anesthesia care unit was comprehensive and the difficulty coefficient was in line with the actual clinical work in this study, which provides reference for the follow-up scientific calculation of nursing workload and human resources matching in post anesthesia care unit.

Objective:To evaluate the value of mechanical ventilation combined with ultrasound in evaluating the volume reactivity of patients with septic shock.Methods:A prospective study was performed, and 59 patients with septic shock who were admitted to the Emergency Intensive Care Unit of the First Affiliated Hospital of Xinjiang Medical University from October 2016 to February 2018 were included according to the established inclusion and exclusion criteria. First, end-expiratory block test (EEO) and end-inhalation block test (EIO) were performed, followed by volume expansion test (VE) (intravenous infusion of 250 mL saline for 10 min), with cardiac index (CI) change value after VE (ΔCI)≥15% for volume-responsive group (37 cases), ΔCI<15% for volume-free group (22 cases), Vigileo-FloTrac system was used to continuously monitor EEO, EIO, and VE before and after changes in hemodynamic parameters, such as central venous pressure (CVP), mean arterial pressure (MAP), stroke volume variation (SVV), CI, and improved inferior vena cava diameter (IVCD) and respiratory variability index (RVI). The values of predicted capacity reactivity such as changes in CVP, MAP, SVV, CI, and RVI before and after EIO were evaluated, and the relationship between EEO, EIO, and capacity reactivity was analyzed by ROC curve.Results:There was no significant difference between MAP and CI in the response group and non-response group after EEO, EIO and VE intervention ( P>0.05). EEO-ΔSVV, EEO-ΔRVI, EEO-ΔCVP, EIO-ΔSVV and EIO-ΔRVI were compared between the reaction group and the non-reaction group, and the difference was statistically significant ( P<0.05); In the correlation analysis, EEO-ΔRVI and EIO-ΔRVI were correlated with VE-ΔRVI ( r=0.695, P<0.01; r=-0.489, P<0.01); EEO-ΔCVP and VE-ΔCVP were correlated ( r=0.566, P<0.01); EEO-ΔSVV, EIO-ΔSVV are related to VE-ΔSVV ( r=0.842, P<0.01; r= -0.727, P<0.01), and the ROC curve showed ( AUCEEO-ΔSVV=0.890, 95% CI: 0.792-0.988), showed AUCEEO-ΔSVV> AUCEEO-ΔRVI> AUCEIO-ΔSVV> AUCEIO-ΔRVI> AUCEEO-ΔCVP. Conclusions:EEO and EIO combined with ultrasound have certain clinical value in the evaluation of volume responsiveness in patients with septic shock, and the evaluation value of SVV and RVI is superior to CVP, MAP, and CI.

Objective:The predictive model of cardiac arrest in the emergency room was constructed and validated based on Logistic regression.Methods:This study was a retrospective cohort study. Patients admitted to the emergency room of the First Affiliated Hospital of Xinjiang Medical University from January 2020 to July 2021 were included. The general information, vital signs, clinical symptoms, and laboratory examination results of the patients were collected, and the outcome was cardiac arrest within 24 hours. The patients were randomly divided into modeling and validation group at a ratio of 7:3. LASSO regression and multivariable logistic regression were used to select predictive factors and construct a prediction model for cardiac arrest in the emergency room. The value of the prediction model was evaluated using the area under the receiver operator characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).Results:A total of 784 emergency room patients were included in the study, 384 patients occurred cardiac arrest. The 10 variables were ultimately selected to construct a risk prediction model for cardiac arrest: Logit( P)= -4.503+2.159×modified early warning score (MEWS score)+2.095×chest pain+1.670×abdominal pain+ 2.021×hematemesis+2.015×cold extremities+5.521×endotracheal intubation+0.388×venous blood lactate-0.100×albumin+0.768×K ++0.001×D-dimer. The AUC of the model group was 0.984 (95% CI: 0.976-0.993) and that of the validation group was 0.972 (95% CI: 0.951-0.993). This prediction model demonstrates good calibration, discrimination, and clinical applicability. Conclusions:Based on the MEWS score, chest pain, abdominal pain, hematemesis, cold extremities, tracheal intubation, venous blood lactate, albumin, K +, and D-dimer, a predictive model for cardiac arrest in the in-hospital emergency room was constructed to predict the probability of cardiac arrest in emergency room patients and adjust the treatment strategy in time.