ObjectiveThis study aims to explore the mechanism by which Yishen Huoxue Tongqiao Formula improves metabolism-neuroplasticity and treats unilateral vestibular labyrinth destruction by regulating the metabolic balance of glutamate （Glu）/γ-aminobutyric acid （GABA）. Methods48 Sprague-Dawley （SD） adult rats were randomly divided into the sham operation group， model group， Yishen Huoxue Tongqiao Formula groups with low， medium， and high doses （9.20， 18.39， 36.78 g·kg^-1）， and betahistine group （1.62 mg·kg^-1）. A unilateral vestibular labyrinth destruction （vestibular dysfunction） model was established by intratympanic injection of chloroform into the right ear， while the control group received intratympanic injection of normal saline. Drugs were administered once daily for seven consecutive days. During the period， behavioral tests were performed to evaluate the behaviors of rats after unilateral vestibular labyrinth destruction. Hematoxylin-eosin （HE） staining and Nissl staining were used to observe the neuronal morphology in the medial vestibular nucleus. Golgi staining was employed to assess the number of dendritic spines of neurons in the medial vestibular nucleus. Ultra-performance liquid chromatography-tandem mass spectrometry （LC-ESI-MS/MS） was utilized to detect Glu/GABA. Immunofluorescence and immunohistochemistry were used to detect the expressions of neuronal nuclei （NeuN）， growth-associated protein 43 （GAP-43）， and glial fibrillary acidic protein （GFAP）. Western blot and real-time fluorescent quantitative polymerase chain reaction （Real-time PCR） were applied to determine the expressions of glutamate-immunoreactive （Glu-IR）， GABA， GFAP， postsynaptic density protein 95 （PSD-95）， and GAP-43. ResultsCompared with the sham operation group， the model group presented with head deviation， balance disorder， increased tail suspension score， nuclear consolidation of medial vestibular nerve neurons， and decreased Nissl bodies （P<0.01）. The number of dendritic spines in neurons and NeuN-positive cells decreased. The content of Glu decreased. The content of GABA increased （Glu/GABA decreased）. The expression of GAP-43 was down-regulated， and GFAP was up-regulated （P<0.05， P<0.01）. The expressions of Glu-IR， PSD-95， and GAP-43 proteins， as well as Glu-IR mRNA decreased， while the expressions of GABA and GFAP proteins and mRNA increased （P<0.05， P<0.01）. Compared with those in the model group， the head deviation， imbalanced behavior， and tail suspension scores in each treatment group decreased， with alleviated neuronal injury and recovered Nissl bodies （P<0.01）. The number of dendritic spines of neurons increased， and the number of NeuN-positive cells rebounded. The content of Glu increased， and the content of GABA decreased （Glu/GABA increased）. GFAP was down-regulated， and GAP-43 was up-regulated （P<0.05， P<0.01）. The expressions of Glu-IR， PMD-95， and GAP-43 proteins， as well as Glu-IR mRNA increased， while the expressions of GABA and GFAP proteins and mRNA decreased. The effect was more significant in the high-dose group （P<0.01）. ConclusionThe Yishen Huoxue Tongqiao Formula can alleviate vestibular dysfunction， and its mechanism may be associated with regulating the metabolic balance of Glu/GABA， mitigating neural damage， improving synaptic plasticity （promoting GAP-43 expression and inhibiting GFAP expression）， and facilitating vestibular compensation.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective To explore the effects of ultrasound-guided intercostal nerve block(INB)and thoracic paravertebral nerve block(TPVB)on the dosage of anesthetics and the efficacy of analgesia in video-assisted thoracoscopic lobectomy.Methods From October 2019 to October 2023,90 patients undergoing video-assisted thoracoscopic lobectomy at the People's Hospital of Tongling City,Anhui,were selected.They were divided into the INB group(42 cases)and the TPVB group(48 cases).The INB group received ultrasound-guided intercostal nerve block,while the TPVB group was administered ultrasound-guided thoracic paravertebral nerve block.The two groups were compared before anesthesia induction(T0),15 minutes of anesthesia(T1),30 minutes of anesthesia(T2),45 minutes of anesthesia(T3),and after extubation(T4),vita4 signs,anesthetic dosage,analgesic effect,pain stress index and adverse reactions.Results In the TPVB group,systolic blood pressure(SBP)of T1,T2,T3 and T4 were(115.88±9.29)mmHg,(113.58±9.72)mmHg,(117.33±9.17)mmHg and(121.15±10.51)mmHg,respectively;diastolic blood pressure(DBP)were(86.74±7.35)mmHg,(90.83±8.82)mmHg,(90.83±8.82)mmHg and(91.05±8.73)mmHg,respectively;Heart rate(HR)were(79.94±7.46)times/min,(81.97±7.28)times/min,(82.36±7.41)times/min and(85.83±8.32)times/min,respectively.Which were all higher than the INB group[(103.53±8.28)mmHg,(105.40±8.66)mmHg,(109.03±8.13)mmHg,(114.64±9.65)mmHg.(77.68±6.57)mmHg,(79.27±6.69)mmHg,(83.21±7.37)mmHg,(85.83±8.21)mmHg,(71.17±6.21)times/min,(75.18±6.47)times/min,(74.82±6.12)times/min and(79.35±7.12)times/min,respectively],there were statistical significance between the two groups(P＜0.05).Postoperatively,the TPVB group had lower 24-hour sufentanil consumption[(27.68±2.64)μg]and fewer presses of the analgesia pump[(5.16±0.38)times]compared to the INB group[(36.22±3.36)μg and(6.87±0.42)times,(P＜0.05)].Visual analogue scale(VAS)scores for pain at rest and during coughing at 2,24,and 48 hours in group TPVB were 2.44±0.27,3.55±0.42,2.81±0.34 and 3.36±0.23,4.13±0.33,3.80±0.25,respectively,which were also lower than the INB group(2.83±0.44,3.98±0.55,3.33±0.46 and 3.87±0.30,4.59±0.47,4.17±0.29,respectively)(P＜0.05).Levels of prostaglandin E2(PGE2)(1.53±0.28 μg/L),norepinephrine(NE)(362.25±33.85 ng/L),and cortisol(Cor)(278.72±25.13 ng/L)in the TPVB group were lower than those in the INB group(2.71±0.32 μg/L,425.67±38.37 ng/L,315.68±29.21 ng/L)(P＜0.05).Adverse reactions such as nausea and vomiting,and dizziness were less frequent in the TPVB group[1(2.1％),1(2.1％)]compared to the INB group[6(12.5％),5(10.4％)](P＜0.05).Conclusion Ultrasound-guided thoracic paravertebral nerve block is superior to intercostal nerve block in terms of anesthetic dosage and analgesic efficacy in video-assisted thoracoscopic lobectomy.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective To explore the effects of ultrasound-guided intercostal nerve block(INB)and thoracic paravertebral nerve block(TPVB)on the dosage of anesthetics and the efficacy of analgesia in video-assisted thoracoscopic lobectomy.Methods From October 2019 to October 2023,90 patients undergoing video-assisted thoracoscopic lobectomy at the People's Hospital of Tongling City,Anhui,were selected.They were divided into the INB group(42 cases)and the TPVB group(48 cases).The INB group received ultrasound-guided intercostal nerve block,while the TPVB group was administered ultrasound-guided thoracic paravertebral nerve block.The two groups were compared before anesthesia induction(T0),15 minutes of anesthesia(T1),30 minutes of anesthesia(T2),45 minutes of anesthesia(T3),and after extubation(T4),vita4 signs,anesthetic dosage,analgesic effect,pain stress index and adverse reactions.Results In the TPVB group,systolic blood pressure(SBP)of T1,T2,T3 and T4 were(115.88±9.29)mmHg,(113.58±9.72)mmHg,(117.33±9.17)mmHg and(121.15±10.51)mmHg,respectively;diastolic blood pressure(DBP)were(86.74±7.35)mmHg,(90.83±8.82)mmHg,(90.83±8.82)mmHg and(91.05±8.73)mmHg,respectively;Heart rate(HR)were(79.94±7.46)times/min,(81.97±7.28)times/min,(82.36±7.41)times/min and(85.83±8.32)times/min,respectively.Which were all higher than the INB group[(103.53±8.28)mmHg,(105.40±8.66)mmHg,(109.03±8.13)mmHg,(114.64±9.65)mmHg.(77.68±6.57)mmHg,(79.27±6.69)mmHg,(83.21±7.37)mmHg,(85.83±8.21)mmHg,(71.17±6.21)times/min,(75.18±6.47)times/min,(74.82±6.12)times/min and(79.35±7.12)times/min,respectively],there were statistical significance between the two groups(P＜0.05).Postoperatively,the TPVB group had lower 24-hour sufentanil consumption[(27.68±2.64)μg]and fewer presses of the analgesia pump[(5.16±0.38)times]compared to the INB group[(36.22±3.36)μg and(6.87±0.42)times,(P＜0.05)].Visual analogue scale(VAS)scores for pain at rest and during coughing at 2,24,and 48 hours in group TPVB were 2.44±0.27,3.55±0.42,2.81±0.34 and 3.36±0.23,4.13±0.33,3.80±0.25,respectively,which were also lower than the INB group(2.83±0.44,3.98±0.55,3.33±0.46 and 3.87±0.30,4.59±0.47,4.17±0.29,respectively)(P＜0.05).Levels of prostaglandin E2(PGE2)(1.53±0.28 μg/L),norepinephrine(NE)(362.25±33.85 ng/L),and cortisol(Cor)(278.72±25.13 ng/L)in the TPVB group were lower than those in the INB group(2.71±0.32 μg/L,425.67±38.37 ng/L,315.68±29.21 ng/L)(P＜0.05).Adverse reactions such as nausea and vomiting,and dizziness were less frequent in the TPVB group[1(2.1％),1(2.1％)]compared to the INB group[6(12.5％),5(10.4％)](P＜0.05).Conclusion Ultrasound-guided thoracic paravertebral nerve block is superior to intercostal nerve block in terms of anesthetic dosage and analgesic efficacy in video-assisted thoracoscopic lobectomy.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To investigate the biliary complications and recovery of liver function after liver transplantation from citizen's deceased donors (DCD) versus standard criteria donors (SCD).Method The clinical data of 269 patients who underwent orthotopic liver transplantation from January 2009 to December 2016 at the Fuzhou General Hospital were collected.197 livers were from SCD and 72 from DCD.Propensity score matching (PSM) was used to compare the biliary complications and recovery of liver function after liver transplantation in the two groups.Results PSM matched 61 pairs of patients.There were 10 (16.4％) and 8 (13.1％) biliary complications in the DCD and the SCD groups,respectively,with no significant difference between them (P ＞ 0.05).The recovery of liver function was significantly delayed in the DCD group when compared with the SCD group.The levels of ALT,AST,GGT and AKP in the DCD group were significant different on the postoperative first,third,fifth,seventh and fourteenth day (P ＜ 0.05).At 30 days after surgery,there was no significant difference in liver function between the two groups.Conclusions Liver grafts from DCD had a significant impact on the recovery of liver function.When compared with the SCD group,the DCD group recovered significantly slower in liver function.There was no significant increase in the incidence of biliary complications.

Objective To investigate the continuous ambulatory peritoneal dialysis (CAPD) and daytime ambulatory peritoneal dialysis (DAPD) on sleep and quality of life. Methods In this is a multi-center cross-sectional survey, we used the Pittsburgh Sleep Index Scale (PSQI) and the Chinese version of peritoneal dialysis patient quality of life scale, the unified investigating time, the organized trained peritoneal dialysis nurses qualified to conduct research full-time. Survey content includes general information, sleep and quality of life, laboratory tests, dialysis adequacy and other indicators, and the results were pooled analysis. Results A total of 325 hospitalized peritoneal dialysis patients were included in the study, including 177 patients in CAPD, 148 patients in DAPD. The total scores of QOL, physical function, physical factors, psychological status, social function and life satisfaction were (72.61± 10.94), (19.44±3.65), (59.69±7.31), (32.65±5.65), (24.82±6.03), (15.42±5.47)and (74.06±13.59), (20.16± 2.95), (58.99 ± 5.86), (34.58 ± 6.17), (25.10 ± 5.01), (15.36 ± 4.28). The score of CAPD in the mental state dimension was significantly lower than that in DAPD patients (t=-3.715, P<0.01). Else, there was no significant difference between the two groups (P> 0.05). Conclusions Sleep and quality of life in peritoneal dialysis patients are lower than normal adults. Dialysis modes have an impact on sleep andquality of life of patients, health care workers should fully assess the physical and mental state of the patients in order to select the appropriate mode of dialysis.