Objective:To investigate the changes of oxygen metabolic parameters and blood glucose concentrations during perioperative period of orthotopic liver transplantation.Methods: The blood gas,cardiac index(CI),mixed venous oxygen saturation(SvO_(2)),oxygen delivery(DO_(2)),oxygen consumption(VO_(2)),oxygen extraction rate(O_(2)ER),and arterial blood glucose were measured at defined time points(before anesthesia induction,30 min before anhepatic period,5 min after anhepatic period,30 min after anhepatic period,5 min of neohepatic period,30 min of neohepatic period,and postoperation) in 50 patients receiving liver transplantation.Results: Compared with those before anesthesia induction,no significant changes in CI,SvO_(2),DO_(2),VO_(2),and O_(2)ER were found in pre-anhepatic period.CI,VO_(2),and DO_(2) decreased significantly at the early 5 min of the anhepatic period(P

Objective To investigate the effects of transient plateau factor on acute lung injury induced by phosgene poisoning in rabbits.Methods Forty New Zealand white rabbits of both sexes,aged 2.0-2.5 kg,were randomly divided into 4 groups (n =10 each) using a random number table:control group (group C),plateau factor group (group H),phosgene poisoning group (group P),and phosgene poisoning and plateau factor group (group HP).In group H,the rabbits were exposed to a simulated altitude of 33000 m for 2 h.In group P,the rabbits were exposed to phosgene for 3 min only.In group HP,the rabbits were exposed to phosgene for 33 min and then to a simulated altitude of 3000 m for 2 h.Respiratory rate (RR) was recorded and blood samples were taken before exposure to phosgene (T1),after exposure to phosgene (T2),and at 0,1 and 6 h after onset of exposure to a simulated altitude of 33000 m (T3-5) for determination of PaO2 and oxygenation index (OI) was calculated.The chests were opened at T5 and lungs removed for determination of lung water content (LC) and for microscopic examination.Lung coefficient (LC) was calculated.Results Compared with C group,RR was significantly increased at T3 in group H (P ＜ 0.05),and RR was increased and OI was decreased at T2-5 in P and HP groups (P ＜ 0.05 or 0.01).Compared with P group,RR was increased and OI was decreased at T3-5 in HP group (P ＜ 0.05 or 0.01).LW and LC were significantly higher in P and HP groups than in group C,and in HP group than in group P (P ＜ 0.05 or 0.01).The microscopic examination showed that pathological changes were observed in P and HP groups,however,the changes were severer in HP group.Conclusion Transient plateau factor can obviously aggravate the degree of acute lung injury induced by phosgene poisoning in rabbits.

China ; Humans ; Streptococcus pneumoniae

Objective:To evaluate the feasibility and clinical value of pre-treatment non-enhanced chest CT radiomics features and machine learning algorithm to predict the mutation status and subtype (19Del/21L858R) of epidermal growth factor receptor (EGFR) for patients with non-small cell lung cancer (NSCLC).Methods:This retrospective study enrolled 280 NSCLC patients from first and second affiliated hospital of University of South China who were confirmed by biopsy pathology, gene examination, and have pre-treatment non-enhanced CT scans. There are 136 patients were confirmed EGFR mutation. Primary lung gross tumor volume was contoured by two experienced radiologists and oncologists, and 851 radiomics features were subsequently extracted. Then, spearman correlation analysis and RELIEFF algorithm were used to screen predictive features. The two hospitals were training and validation cohort, respectively. Clinical-radiomics model was constructed using selected radiomics and clinical features, and compared with models built by radiomics features or clinical features respectively. In this study, machine learning models were established using support vector machine (SVM) and a sequential modeling procedure to predict the mutation status and subtype of EGFR. The area under receiver operating curve (AUC-ROC) was employed to evaluate the performances of established models.Results:After feature selection, 21 radiomics features were found to be efffective in predicting EGFR mutation status and subtype and were used to establish radiomics models. Three types models were established, including clinical model, radiomics model, and clinical-radiomics model. The clinical-radiomics model showed the best predictive efficacy, AUCs of predicting EGFR mutation status for training dataset and validation dataset were 0.956 (95% CI: 0.952-1.000) and 0.961 (95% CI: 0.924-0.998), respectively. The AUCs of predicting 19Del/L858R mutation subtype for training dataset and validation dataset were 0.926 (95% CI: 0.893-0.959), 0.938 (95% CI: 0.876-1.000), respectively. Conclusions:The constructed sequential models based on integration of CT radiomics, clinical features and machine learning can accurately predict the mutation status and subtype of EGFR.