1.A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.
Zihan LU ; Fangjun HUANG ; Guangyao CAI ; Jihong LIU ; Xin ZHEN
Journal of Southern Medical University 2025;45(1):170-178
OBJECTIVES:
To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators.
METHODS:
Tabular data with missing laboratory indicators were collected from 393 patients with ovarian cancer and 1951 control patients. The missing ovarian cancer laboratory indicator features were projected to the latent space to obtain a classification model using the representational learning classification model based on discriminative learning and mutual information coupled with feature projection significance score consistency and missing location estimation. The proposed constraint term was ablated experimentally to assess the feasibility and validity of the constraint term by accuracy, area under the ROC curve (AUC), sensitivity, and specificity. Cross-validation methods and accuracy, AUC, sensitivity and specificity were also used to evaluate the discriminative performance of this classification model in comparison with other interpolation methods for processing of the missing data.
RESULTS:
The results of the ablation experiments showed good compatibility among the constraints, and each constraint had good robustness. The cross-validation experiment showed that for identification of ovarian cancer with missing laboratory indicators, the AUC, accuracy, sensitivity and specificity of the proposed multi-constraints representation-based learning classification model was 0.915, 0.888, 0.774, and 0.910, respectively, and its AUC and sensitivity were superior to those of other interpolation methods.
CONCLUSIONS
The proposed model has excellent discriminatory ability with better performance than other missing data interpolation methods for identification of ovarian cancer with missing laboratory indicators.
Female
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Humans
;
Ovarian Neoplasms/diagnosis*
;
Machine Learning
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ROC Curve
2.Robotic-assisted radical colorectal cancer surgery with the KangDuo surgical robotic system vs . the da Vinci Xi surgical system in elderly patients: A multicenter randomized controlled trial.
Hao ZHANG ; Yuliuming WANG ; Chunlin WANG ; Yunxiao LIU ; Xin WANG ; Xin ZHANG ; Yihaoran YANG ; Junyang LU ; Lai XU ; Zhen SUN ; Zhengqiang WEI ; Yi XIAO ; Guiyu WANG
Chinese Medical Journal 2025;138(11):1384-1386
3.Construction of Saccharomyces cerevisiae cell factory for efficient biosynthesis of ferruginol.
Mei-Ling JIANG ; Zhen-Jiang TIAN ; Hao TANG ; Xin-Qi SONG ; Jian WANG ; Ying MA ; Ping SU ; Guo-Wei JIA ; Ya-Ting HU ; Lu-Qi HUANG
China Journal of Chinese Materia Medica 2025;50(4):1031-1042
Diterpenoid ferruginol is a key intermediate in biosynthesis of active ingredients such as tanshinone and carnosic acid.However, the traditional process of obtaining ferruginol from plants is often cumbersome and inefficient. In recent years, the increasingly developing gene editing technology has been gradually applied to the heterologous production of natural products, but the production of ferruginol in microbe is still very low, which has become an obstacle to the efficient biosynthesis of downstream chemicals, such as tanshinone. In this study, miltiradiene was produced by integrating the shortened diterpene synthase fusion protein,and the key genes in the MVA pathway were overexpressed to improve the yield of miltiradiene. Under the shake flask fermentation condition, the yield of miltiradiene reached about(113. 12±17. 4)mg·L~(-1). Subsequently, this study integrated the ferruginol synthase Sm CYP76AH1 and Sm CPR1 to reconstruct the ferruginol pathway and thereby realized the heterologous synthesis of ferruginol in Saccharomyces cerevisiae. The study selected the best ferruginol synthase(Il CYP76AH46) from different plants and optimized the expression of pathway genes through redox partner engineering to increase the yield of ferruginol. By increasing the copy number of diterpene synthase, CYP450, and CPR, the yield of ferruginol reached(370. 39± 21. 65) mg·L~(-1) in the shake flask, which was increased by 21. 57-fold compared with that when the initial ferruginol strain JMLT05 was used. Finally, 1 083. 51 mg·L~(-1) ferruginol was obtained by fed-batch fermentation, which is the highest yield of ferruginol from biosynthesis so far. This study provides not only research ideas for other metabolic engineering but also a platform for the construction of cell factories for downstream products.
Saccharomyces cerevisiae/genetics*
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Diterpenes/metabolism*
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Metabolic Engineering
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Fermentation
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Abietanes
4.Prognostic Significance of Endothelial Activation and Stress Index in Mantle Cell Lymphoma.
Xin-Yue ZHOU ; Zhi-Qin YANG ; Jin HU ; Feng-Yi LU ; Qian-Nan HAN ; Huan-Huan ZHAO ; Wen-Xia GAO ; Yu-Han MA ; Hu-Jun LI ; Zhen-Yu LI ; Kai-Lin XU ; Wei CHEN
Journal of Experimental Hematology 2025;33(4):1051-1056
OBJECTIVE:
To investigate the predictive value of endothelial activation and stress index (EASIX) for the prognosis of patients with mantle cell lymphoma (MCL).
METHODS:
A retrospective analysis was conducted to assess prognosis and compare the clinical features of patients diagnosed with MCL who were admitted to the Affiliated Hospital of Xuzhou Medical University from January 2010 to June 2023, had therapeutic indications and received standard treatment.
RESULTS:
A total of 66 patients were included and divided into high EASIX group and low EASIX group, according to a cutoff value of 0.97 determined by the receiver operating characteristic (ROC) curve. Multivariate Cox regression analysis showed that prealbumin <0.2 g/L, high EASIX, and ECOG PS score ≥2 were independent risk factors influencing overall survival (OS) in MCL patients. The median OS of patients in the high and low EASIX group was 13.0 and 37.5 months, and the median progression-free survival was 8.8 and 26.0 months, respectively. The proportions of patients with ECOG PS score ≥2 and prealbumin <0.2 g/L at onset significantly increased in the high EASIX group compared to those in the low EASIX group.
CONCLUSION
At the time of initial diagnosis, EASIX can serve as an independent prognostic indicator impacting OS in patients with MCL. Furthermore, patients in the high EASIX group experience a poorer prognosis and shorter survival duration compared with those in the low EASIX group.
Humans
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Lymphoma, Mantle-Cell/pathology*
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Prognosis
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Retrospective Studies
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Male
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Female
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Middle Aged
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Aged
;
ROC Curve
5.Expert consensus on the application of nasal cavity filling substances in nasal surgery patients(2025, Shanghai).
Keqing ZHAO ; Shaoqing YU ; Hongquan WEI ; Chenjie YU ; Guangke WANG ; Shijie QIU ; Yanjun WANG ; Hongtao ZHEN ; Yucheng YANG ; Yurong GU ; Tao GUO ; Feng LIU ; Meiping LU ; Bin SUN ; Yanli YANG ; Yuzhu WAN ; Cuida MENG ; Yanan SUN ; Yi ZHAO ; Qun LI ; An LI ; Luo BA ; Linli TIAN ; Guodong YU ; Xin FENG ; Wen LIU ; Yongtuan LI ; Jian WU ; De HUAI ; Dongsheng GU ; Hanqiang LU ; Xinyi SHI ; Huiping YE ; Yan JIANG ; Weitian ZHANG ; Yu XU ; Zhenxiao HUANG ; Huabin LI
Journal of Clinical Otorhinolaryngology Head and Neck Surgery 2025;39(4):285-291
This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans
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Nasal Cavity/surgery*
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Nasal Surgical Procedures
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China
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Consensus
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Sinusitis/surgery*
;
Dermal Fillers
6.Investigation on the standardization of medication reconciliation service in national medical institutions
Xin TIAN ; Xuelian YAN ; Dan MEI ; Jiancun ZHEN ; Qunhong SHEN ; Jin LU
China Pharmacy 2024;35(10):1163-1167
OBJECTIVE To provide a reference for the implementation and high-quality development of hospital medication reconciliation. METHODS A semi-structured questionnaire was designed to investigate the implementation of drug reconciliation services in medical institutions before and after the release of 5 standards such as Standard for Medication Reconciliation Services in Medical Institutions(“standards” for short,in 2021 and 2022). Descriptive statistical analysis was conducted on the survey results. RESULTS After the promulgation of the standards, the medication reconciliation service rate of all types of medical institutions increased from 15.10% (434/2 874) in 2021 to 27.84%(363/1 304) in 2022. In 2022, in the 363 medical institutions providing drug reconciliation services, the median number of pharmacists involved in drug reconciliation was 6. The participation rate of pharmacists in standardized training for drug reconciliation services was 75.00%, among which the participation rate of third-class hospitals was higher, reaching 85.71%. The main stages covered by medication reconciliation services included patient admission, transfer between departments, and discharge. The main problems found in the service included repeated medication (252, 69.42%), inappropriate usage and dosage (228, 62.81%), drug interactions and adverse reactions (218, E-mail:cputianxin@163.com 60.06%). Only 69 institutions (19.01%) had a separate electronic information recording system, while 48 institutions 58516003。E-mail:zhenjiancun@vip.163.com (13.22%) had established comprehensive quality management and evaluation improvement systems. In terms of value embodiment, 141 institutions (38.84%) did not provide any form of compensation to relevant pharmacists. “Closely linked to enhancing patient satisfaction and improving services” was the most significant experience influencing medication reconciliation work(192, 52.89%), while “the shortage of talent which meet the relevant requirements” stands as the primary challenge faced by medical institutions at all levels(238, 65.56%). CONCLUSIONS The release of the standards has effectively improved the development rate of medication reconciliation in national medical institutions. However, there is still room for improvement in various aspects, including the allocation of personnel for medication reconciliation services, service content, information management, and the construction of quality control and evaluation systems.
7.Species-level Microbiota of Biting Midges and Ticks from Poyang Lake
Jian GONG ; Fei Fei WANG ; Qing Yang LIU ; Ji PU ; Zhi Ling DONG ; Hui Si ZHANG ; Zhou Zhen HUANG ; Yuan Yu HUANG ; Ben Ya LI ; Xin Cai YANG ; Meihui Yuan TAO ; Jun Li ZHAO ; Dong JIN ; Yun Li LIU ; Jing YANG ; Shan LU
Biomedical and Environmental Sciences 2024;37(3):266-277,中插1-中插3
Objective The purpose of this study was to investigate the bacterial communities of biting midges and ticks collected from three sites in the Poyang Lake area,namely,Qunlu Practice Base,Peach Blossom Garden,and Huangtong Animal Husbandry,and whether vectors carry any bacterial pathogens that may cause diseases to humans,to provide scientific basis for prospective pathogen discovery and disease prevention and control. Methods Using a metataxonomics approach in concert with full-length 16S rRNA gene sequencing and operational phylogenetic unit(OPU)analysis,we characterized the species-level microbial community structure of two important vector species,biting midges and ticks,including 33 arthropod samples comprising 3,885 individuals,collected around Poyang Lake. Results A total of 662 OPUs were classified in biting midges,including 195 known species and 373 potentially new species,and 618 OPUs were classified in ticks,including 217 known species and 326 potentially new species.Surprisingly,OPUs with potentially pathogenicity were detected in both arthropod vectors,with 66 known species of biting midges reported to carry potential pathogens,including Asaia lannensis and Rickettsia bellii,compared to 50 in ticks,such as Acinetobacter lwoffii and Staphylococcus sciuri.We found that Proteobacteria was the most dominant group in both midges and ticks.Furthermore,the outcomes demonstrated that the microbiota of midges and ticks tend to be governed by a few highly abundant bacteria.Pantoea sp7 was predominant in biting midges,while Coxiella sp1 was enriched in ticks.Meanwhile,Coxiella spp.,which may be essential for the survival of Haemaphysalis longicornis Neumann,were detected in all tick samples.The identification of dominant species and pathogens of biting midges and ticks in this study serves to broaden our knowledge associated to microbes of arthropod vectors. Conclusion Biting midges and ticks carry large numbers of known and potentially novel bacteria,and carry a wide range of potentially pathogenic bacteria,which may pose a risk of infection to humans and animals.The microbial communities of midges and ticks tend to be dominated by a few highly abundant bacteria.
8.Construction and validation of an in-hospital mortality risk prediction model for patients receiving VA-ECMO:a retrospective multi-center case-control study
Yue GE ; Jianwei LI ; Hongkai LIANG ; Liusheng HOU ; Liuer ZUO ; Zhen CHEN ; Jianhai LU ; Xin ZHAO ; Jingyi LIANG ; Lan PENG ; Jingna BAO ; Jiaxin DUAN ; Li LIU ; Keqing MAO ; Zhenhua ZENG ; Hongbin HU ; Zhongqing CHEN
Journal of Southern Medical University 2024;44(3):491-498
Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.
9.Construction and validation of an in-hospital mortality risk prediction model for patients receiving VA-ECMO:a retrospective multi-center case-control study
Yue GE ; Jianwei LI ; Hongkai LIANG ; Liusheng HOU ; Liuer ZUO ; Zhen CHEN ; Jianhai LU ; Xin ZHAO ; Jingyi LIANG ; Lan PENG ; Jingna BAO ; Jiaxin DUAN ; Li LIU ; Keqing MAO ; Zhenhua ZENG ; Hongbin HU ; Zhongqing CHEN
Journal of Southern Medical University 2024;44(3):491-498
Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.
10.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.

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