1.Pharmacokinetics and tissue distribution of fluorescent-labeled Astragalus polysaccharides in mice.
Xiao-Huan WANG ; Peng-Xin LI ; Ting-Ting GONG ; Yun-Qian LU ; Bo YANG ; Xiang-Tao WANG
China Journal of Chinese Materia Medica 2025;50(7):1959-1968
In this study, the reductive amination method was used to label IR783 on Astragalus polysaccharides(APS) for the first time, which was verified by ultraviolet-visible spectroscopy and infrared spectroscopy. Quantitative analysis methods of APS-IR783 in plasma and various tissue were established using a multifunctional microplate reader. The pharmacokinetics and tissue distribution of APS-IR783 in mice were investigated after a single intravenous injection of 30 mg·kg~(-1) APS-IR783, and pharmacokinetic parameters were calculated using DAS 2.0 software. The results showed that the APS used had a mass fraction of 93.69%, a relative molecular weight of 1.55×10~5, and a polydispersity index(PDI, M_w/M_n) of 1.73, close to a homogeneous polysaccharide. The IR783 labeling yield reached 86.50%, and the content of IR783 in APS-IR783 was 0.72%. After a single intravenous injection of 30 mg·kg~(-1), the pharmacokinetic parameters of APS in mouse plasma were as follows: T_(max) was(0.67±0.26) h; C_(max) was(1 599.29±159.30) mg·L~(-1); T_(1/2α) and T_(1/2β) were(2.29±3.06) h and(0.44±0.05) h, respectively; AUC_(0-t) was(23 398.91±2 907.03) mg·h·L~(-1); AUC_(0-∞) was(27 710.55±3 506.55) mg·h·L~(-1); MRT_(0-∞) was(34.38±12.59) h; CL was 0.001 L·h~(-1)·kg~(-1); V_z was(0.042±0.017) L·kg~(-1). The in vivo biodistribution study demonstrated that the in vivo exposure ratios of APS in different tissue were in the following order: spleen > liver > kidney > lung > heart > small intestine > muscle > large intestine > brain > stomach, where the top five tissue accounted for 87.54% of the total area under the curve(AUC). This study successfully labeled APS with a water-soluble near-infrared fluorescent probe of IR783 for the first time and revealed the pharmacokinetics and tissue distribution of APS in mice. The paper provides detailed in vivo behavior of APS after intravenous injection, which lays the foundation for the development and utilization of APS and related natural medicines.
Animals
;
Mice
;
Polysaccharides/chemistry*
;
Tissue Distribution
;
Astragalus Plant/chemistry*
;
Male
;
Drugs, Chinese Herbal/chemistry*
;
Fluorescent Dyes/pharmacokinetics*
;
Female
2.Construction and performance evaluation of a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury
Tao MEI ; Zheyong JIA ; Lie CHEN ; Peng CAO ; Wei XIAO ; Weiqiang MAO ; Jianwu GONG ; Lixin XU
Chinese Journal of Trauma 2025;41(11):1048-1058
Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.
3.Reporting Guidelines for Healthcare Guideline Adaptations:An Interpretation of the RIGHT-Ad@pt Checklist
Liyun GONG ; Xiaomei WANG ; Guoqing PENG ; Huan YU ; Xiaoman TAO
Medical Journal of Peking Union Medical College Hospital 2025;16(1):204-215
Clinical practice guideline adaptation(hereinafter referred to as"guideline adaptation")is the consolidation and revision of existing high-quality guidelines so that the recommendations are better suited to the specific needs of different regions,thereby guiding optimal clinical practice.Currently,the guideline adap-tations is increasing in number internationally,but their reporting quality still needs to be improved.In 2022,the RIGHT-Ad@pt guideline adaptation reporting checklist was released.It provides a detailed description of the guideline adaptation process and reporting content,which will significantly enhance the rigor,transparency,and standardization of guideline adaptations.This paper interprets and analyzes the 34 items on the checklist,with the aim of providing reference for guideline adapters to standardize the reporting process.
4.Efficacy and safety of a facilitated percutaneous coronary intervention with half-dose recombinant staphylokinase in ST-segment elevation myocardial infarction
Tian-yu WU ; Wen-hao ZHANG ; Peng-sheng CHEN ; Chen LI ; Tian WU ; Zhan LÜ ; Tong WANG ; Kun LIU ; Zhi-wen TAO ; Xiao-xuan GONG ; Liang YUAN ; Yong LI ; Bo CHEN ; Xin CHEN ; Zeng-guang CHEN ; Nai-quan YANG ; Yuan-yuan SANG ; Xiao-yan WANG ; Bai-hong LI ; Li ZHU ; Guo-yu WANG ; Xin ZHAO ; Chuan LU ; Jun JIANG ; Rui-na HAO ; Chun-jian LI
Chinese Journal of Interventional Cardiology 2025;33(8):431-438
Objective To investigate the clinical efficacy and safety of facilitated percutaneous coronary intervention(PCI)with half-dose recombinant staphylokinase(r-SAK)in patients with ST-segment elevation myocardial infarction(STEMI)who are expected to undergo PCI within 120 minutes.Methods From October 2021 to August 2022,a total of 200 STEMI patients in eight centers were included and randomly assigned in a 1﹕1 ratio to either r-SAK group or control group.Patients received loading doses of aspirin and ticagrelor and intravenous heparin and were randomized to receive an intravenous bolus of either 5 mg r-SAK or normal saline prior to PCI.The outcomes were set as ST-segment resolution(STR)at 60-90 minutes after PCI,the proportion and transition of pathological Q waves on the 5th day after PCI,and the proportion of high-sensitivity cardiac troponin T(hs-cTnT)peaking within 12 hours of onset.The safety outcome was major bleeding events defined as Bleeding Academic Research Consortium(BARC)≥type 3 bleeding during hospitalization.Results Compared with the control group,the r-SAK group had a higher proportion of STR≥70%within 60-90 minutes after PCI(58.3%vs.40.3%,P=0.009);a lower proportion of pathological Q waves(59.1%vs.74.1%,P=0.040);a lower rate of Q wave progression(14.8%vs.43.2%,P<0.001);a higher rate of Q wave disappearance(12.5%vs.3.7%,P=0.027);and a higher proportion of hs-cTnT peaking within 12 hours of symptom onset[31/40(77.5%)vs.17/33(51.5%),P=0.027].Regarding the safety outcome,no significant difference in BARC≥type 3 bleeding was found between the two groups during hospitalization(P>0.05).Conclusions For STEMI patients who were expected to undergo primary PCI within 120 minutes of symptom onset,the facilitated PCI with half-dose r-SAK significantly increased the proportion of STR≥70%at 60-90 minutes after PCI,reduced the formation of pathological Q waves,and shortened the time to peak hs-cTnT,without increasing the risk of bleeding,which should be an alternative reperfusion strategy worthy of further study.
5.Study on the Relationship between the Changes of Four Indexes Related to Plasma Ferroptosis and the Prognosis after TACE in Patients with Hepatocellular Carcinoma
Fei YANG ; Jicheng GAO ; Song LIU ; Huixiao ZUO ; Weiyong GONG ; Zhe ZHANG ; Tao PENG
Journal of Modern Laboratory Medicine 2025;40(5):78-81,87
Objective To analyze the relationship between the expression of ferroptosis markers in the tumor microenvironment(TME)and the prognosis of transcatheter arterial chemoembolization(TACE)for hepatocellular carcinoma(HCC).Methods This prospective observational study included 100 HCC patients who received TACE treatment at Langfang Hospital of Traditional Chinese Medicine from March 2019 to June 2021 as the study subjects.The levels of 8-isoprostaglandin F2α(8-iso-PGF2α),4-hydroxy-2-nonenal(4-HNE),8-hydroxy-2'deoxyguanosine(8-OH-dG)and hepcidin in plasma were evaluated by ELISA kit at baseline(1 day before TACE),1 day after TACE and 4~8 weeks.The changes of ferroptosis related markers during TACE treatment were compared.The difference between the level of 8-iso-PGF2α,4-HNE and the baseline 1 day after TACE treatment was recorded as △8-iso-PGF2α,△4-HNE.Results Compared with the baseline,the levels of 8-iso-PGF2α and 4-HNE increased significantly and the level of hepcidin decreased significantly one day after TACE treatment,and the differences were statistically significant(t=8.03,16.29,2.92,all P<0.05).Compared with 1 day after treatment,the levels of 8-iso-PGF2α,4-HNE decreased and the level of 8-OH-dG increased at 4~8 weeks after TACE treatment,and the differences were statistically significant(t=9.12,17.17,2.63,all P<0.05).Multivariate COX analysis showed that △8-iso-PGF2α,△4-HNE and 8-iso-PGF2α 1 day after TACE treatment were independent factors affecting the overall survival after TACE(Wald χ2=5.205,13.801,6.054,all P<0.05).The survival time of patients with △4-HNE>2.01 μg/ml was significantly longer than that of patients with △4-HNE≤2.01 μg/ml(Log-rank=5.718,P=0.017),and that of patients with△8-iso-PGF2α>1.75ng/ml was sig-nificantly longer than that of patients with△8-iso-PGF2≤1.75ng/ml(Log-rank=4.163,P=0.041).Conclusion The prognosis of HCC patients who are in a state of high ferroptosis(4-HNE and 8-iso-PGF2 increased)at 1 day after TACE treatment is better,which indicate that ferroptosis mediated HCC death induced by TACE treatment.
6.Efficacy and safety of a facilitated percutaneous coronary intervention with half-dose recombinant staphylokinase in ST-segment elevation myocardial infarction
Tian-yu WU ; Wen-hao ZHANG ; Peng-sheng CHEN ; Chen LI ; Tian WU ; Zhan LÜ ; Tong WANG ; Kun LIU ; Zhi-wen TAO ; Xiao-xuan GONG ; Liang YUAN ; Yong LI ; Bo CHEN ; Xin CHEN ; Zeng-guang CHEN ; Nai-quan YANG ; Yuan-yuan SANG ; Xiao-yan WANG ; Bai-hong LI ; Li ZHU ; Guo-yu WANG ; Xin ZHAO ; Chuan LU ; Jun JIANG ; Rui-na HAO ; Chun-jian LI
Chinese Journal of Interventional Cardiology 2025;33(8):431-438
Objective To investigate the clinical efficacy and safety of facilitated percutaneous coronary intervention(PCI)with half-dose recombinant staphylokinase(r-SAK)in patients with ST-segment elevation myocardial infarction(STEMI)who are expected to undergo PCI within 120 minutes.Methods From October 2021 to August 2022,a total of 200 STEMI patients in eight centers were included and randomly assigned in a 1﹕1 ratio to either r-SAK group or control group.Patients received loading doses of aspirin and ticagrelor and intravenous heparin and were randomized to receive an intravenous bolus of either 5 mg r-SAK or normal saline prior to PCI.The outcomes were set as ST-segment resolution(STR)at 60-90 minutes after PCI,the proportion and transition of pathological Q waves on the 5th day after PCI,and the proportion of high-sensitivity cardiac troponin T(hs-cTnT)peaking within 12 hours of onset.The safety outcome was major bleeding events defined as Bleeding Academic Research Consortium(BARC)≥type 3 bleeding during hospitalization.Results Compared with the control group,the r-SAK group had a higher proportion of STR≥70%within 60-90 minutes after PCI(58.3%vs.40.3%,P=0.009);a lower proportion of pathological Q waves(59.1%vs.74.1%,P=0.040);a lower rate of Q wave progression(14.8%vs.43.2%,P<0.001);a higher rate of Q wave disappearance(12.5%vs.3.7%,P=0.027);and a higher proportion of hs-cTnT peaking within 12 hours of symptom onset[31/40(77.5%)vs.17/33(51.5%),P=0.027].Regarding the safety outcome,no significant difference in BARC≥type 3 bleeding was found between the two groups during hospitalization(P>0.05).Conclusions For STEMI patients who were expected to undergo primary PCI within 120 minutes of symptom onset,the facilitated PCI with half-dose r-SAK significantly increased the proportion of STR≥70%at 60-90 minutes after PCI,reduced the formation of pathological Q waves,and shortened the time to peak hs-cTnT,without increasing the risk of bleeding,which should be an alternative reperfusion strategy worthy of further study.
7.Study on the Relationship between the Changes of Four Indexes Related to Plasma Ferroptosis and the Prognosis after TACE in Patients with Hepatocellular Carcinoma
Fei YANG ; Jicheng GAO ; Song LIU ; Huixiao ZUO ; Weiyong GONG ; Zhe ZHANG ; Tao PENG
Journal of Modern Laboratory Medicine 2025;40(5):78-81,87
Objective To analyze the relationship between the expression of ferroptosis markers in the tumor microenvironment(TME)and the prognosis of transcatheter arterial chemoembolization(TACE)for hepatocellular carcinoma(HCC).Methods This prospective observational study included 100 HCC patients who received TACE treatment at Langfang Hospital of Traditional Chinese Medicine from March 2019 to June 2021 as the study subjects.The levels of 8-isoprostaglandin F2α(8-iso-PGF2α),4-hydroxy-2-nonenal(4-HNE),8-hydroxy-2'deoxyguanosine(8-OH-dG)and hepcidin in plasma were evaluated by ELISA kit at baseline(1 day before TACE),1 day after TACE and 4~8 weeks.The changes of ferroptosis related markers during TACE treatment were compared.The difference between the level of 8-iso-PGF2α,4-HNE and the baseline 1 day after TACE treatment was recorded as △8-iso-PGF2α,△4-HNE.Results Compared with the baseline,the levels of 8-iso-PGF2α and 4-HNE increased significantly and the level of hepcidin decreased significantly one day after TACE treatment,and the differences were statistically significant(t=8.03,16.29,2.92,all P<0.05).Compared with 1 day after treatment,the levels of 8-iso-PGF2α,4-HNE decreased and the level of 8-OH-dG increased at 4~8 weeks after TACE treatment,and the differences were statistically significant(t=9.12,17.17,2.63,all P<0.05).Multivariate COX analysis showed that △8-iso-PGF2α,△4-HNE and 8-iso-PGF2α 1 day after TACE treatment were independent factors affecting the overall survival after TACE(Wald χ2=5.205,13.801,6.054,all P<0.05).The survival time of patients with △4-HNE>2.01 μg/ml was significantly longer than that of patients with △4-HNE≤2.01 μg/ml(Log-rank=5.718,P=0.017),and that of patients with△8-iso-PGF2α>1.75ng/ml was sig-nificantly longer than that of patients with△8-iso-PGF2≤1.75ng/ml(Log-rank=4.163,P=0.041).Conclusion The prognosis of HCC patients who are in a state of high ferroptosis(4-HNE and 8-iso-PGF2 increased)at 1 day after TACE treatment is better,which indicate that ferroptosis mediated HCC death induced by TACE treatment.
8.Construction and performance evaluation of a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury
Tao MEI ; Zheyong JIA ; Lie CHEN ; Peng CAO ; Wei XIAO ; Weiqiang MAO ; Jianwu GONG ; Lixin XU
Chinese Journal of Trauma 2025;41(11):1048-1058
Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.
9.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
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.
10.MOLECULAR EPIDEMIOLOGICAL INVESTIGATION ON CO-INFECTION OF INTESTINAL PROTOZOA IN GASTROINTESTINAL CANCER PATIENTS
Nan ZHANG ; Hong-Bo ZHANG ; Xiu-Yan YU ; Yan-Hui YU ; Peng-Tao GONG ; Jian-Hua LI ; Xiao-Cen WANG ; Xin LI ; Xu ZHANG ; Xi-Chen ZHANG
Acta Parasitologica et Medica Entomologica Sinica 2024;31(2):123-128
Objective The relationship between parasitic infections and cancer has become a research hotpot.Although reports of single intestinal protozoan infection in gastrointestinal cancer patients,co-infections are rare.To investigate co-infections of intestinal protozoa in gastrointestinal cancer patients.Methods The DNA of 195 fecal specimens was amplified using nested PCR and sequenced for the presence of Pentatrichomonas hominis,Giardia duodenalis,Cryptosporidium parvum,Blastocystis hominis,Dientamoeba fragilis,and Enterocytozoon bieneusi.Results An overall infection rate of 48.72%(95/195),with 23 cases(24.21%)co-infected with two parasites,three cases(3.16%)co-infected with three parasites.Additionally,67 cases(70.52%)were infected with one protozoa,including 56 cases with Pentatrichomonas hominis,one with Blastocystis hominis,nine with Cryptosporidium parvum,and one case with Dientamoeba fragilis.No infection with Enterocytozoon bieneusi was detected.Conclusion The results indicated a high rate of intestinal protozoan co-infection among gastrointestinal cancer patients.Through one-way ANOVA analysis,it was observed that cases of individual infection with P.hominis were significantly higher compared to those of co-infection with two or three types of protozoa containing P.hominis(P=0.0022)and cases of co-infection with three types of protozoa(P=0.0019).However,no significant difference in the infection rates was observed between two and three types of protozoa(P=0.2775),suggesting that cases of single infection with P.hominis were higher than cases of co-infection with two or more types of protozoa in gastrointestinal cancer patients.BLAST and single nucleotide polymorphism analysis revealed that gene sequences of different infected protozoa,except for a few with 100%homology to the GenBank reference sequence,exhibited varying degrees of base mutations,insertions,or loss at different loci.This study offers crucial insights for understanding the etiology,diagnosis,and prevention of gastrointestinal cancer.

Result Analysis
Print
Save
E-mail