Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To analyze the situation of rodents carrying Hantavirus and the genetic and evolutionary characteristics of the virus in Zibo city, Shandong province in 2022, and provide reference for the scientific prevention and control of hemorrhagic fever with renal syndrome (HFRS).Methods:Real-time quantitative PCR (RT-qPCR) was used to detect hantavirus (HV) nucleic acids in rodent lung tissues and identify HV genotypes. Each nucleic acid fragment was designed to amplify various gene fragments by segment, and the whole genome of Hantavirus was sequenced by second generation sequencing. Sequence assembly was performed using SeqMan 7.1.0.44, a subprogram of DNAStar. Sequence alignment and evolutionary analysis were conducted using MEGA 7.0 and BioEdit software.Results:A total of 270 host animals were captured in this survey. Among them, 13 rodent lung samples tested positive for Hantavirus, resulting in a virus-positive rate of 4.8%. The full-genome sequences of four hantavirus strains were successfully obtained, all identified as Seoul virus (SEOV) genotype. Four Hantavirus-positive samples showed high nucleotide sequence homology in the M gene and belonged to the SEOV S3 subtype. These strains exhibited high similarity with those from Hebei, Liaoning, and Beijing. The amino acid sequences of the nucleoprotein and glycoprotein immunogenic epitopes were identical to those of the vaccine strain Z37.Conclusions:This study successfully determined the full genome sequences of four hantavirus strains from Zibo city, Shandong province. The genotypes are primarily SEOV, with the subtype being S3. The homology of genes within the same subtype is high, with no significant variations observed. The alignment of immune epitopes in key proteins suggests that the current vaccine may provide protection against locally circulating strains, but further in-depth research is still required.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective To explore the role of sevoflurane(SEV)in sepsis-induced acute lung injury(ALI)and observe its impact on the Wnt/β-catenin signaling pathway.Methods Forty C57 mice were randomly divided into 4 groups(n=10 each):Sham,CLP,SEV,and SEV+XAV(β-catenin inhibitor).A sepsis model was established via cecal ligation and puncture.Lung injury was evaluated using HE staining,lung wet/dry weight ratio,and TUNEL staining.Levels of inflammatory factors(TNF-α,IL-1β,IL-6)were detected by ELISA.Oxidative stress indices(SOD,MDA,ROS)were measured by colorimetry and flow cytometry.Hindlimb blood perfusion and oxygenation were assessed with laser speckle flowmetry.Expressions of key Wnt pathway molecules and down-stream target genes(c-Myc,Cyclin D1)were detected by RT-qPCR and Western blot.Co-localization of β-catenin and SP-C(a marker of type Ⅱ alveolar epithelial cells)in lung tissues was determined by immunofluorescence staining.Results Compared with the Sham group,the CLP group exhibited significant increases in sepsis severity,lung pathological damage including alveolar structure destruction,inflammatory infiltration,and apoptosis,elevation in pro-inflammatory cytokine levels,and significant decrease in SOD and increase in MDA and ROS.Additionally,lower limb blood flow and oxygenation levels were significantly reduced,while the expression of β-catenin and its downstream target genes,as well as the co-localization signal and fluorescence intensity of β-catenin with SP-C,were significantly downregulated(all P<0.05).Compared with the CLP group,the SEV group showed significant improvements in all these indicators.However,compared with the SEV group,the SEV+XAV group demon-strated a reversed protective effect,with all indicators approaching the levels observed in the CLP group(all P<0.05).Conclusion Sevoflurane alleviates sepsis-induced ALI by activating Wnt/β-catenin signaling pathway,exerting anti-inflammatory and antioxidant effects,and enhancing the expression and localization of β-catenin in type Ⅱ alveolar epithelial cells.

Objective To explore the role of sevoflurane(SEV)in sepsis-induced acute lung injury(ALI)and observe its impact on the Wnt/β-catenin signaling pathway.Methods Forty C57 mice were randomly divided into 4 groups(n=10 each):Sham,CLP,SEV,and SEV+XAV(β-catenin inhibitor).A sepsis model was established via cecal ligation and puncture.Lung injury was evaluated using HE staining,lung wet/dry weight ratio,and TUNEL staining.Levels of inflammatory factors(TNF-α,IL-1β,IL-6)were detected by ELISA.Oxidative stress indices(SOD,MDA,ROS)were measured by colorimetry and flow cytometry.Hindlimb blood perfusion and oxygenation were assessed with laser speckle flowmetry.Expressions of key Wnt pathway molecules and down-stream target genes(c-Myc,Cyclin D1)were detected by RT-qPCR and Western blot.Co-localization of β-catenin and SP-C(a marker of type Ⅱ alveolar epithelial cells)in lung tissues was determined by immunofluorescence staining.Results Compared with the Sham group,the CLP group exhibited significant increases in sepsis severity,lung pathological damage including alveolar structure destruction,inflammatory infiltration,and apoptosis,elevation in pro-inflammatory cytokine levels,and significant decrease in SOD and increase in MDA and ROS.Additionally,lower limb blood flow and oxygenation levels were significantly reduced,while the expression of β-catenin and its downstream target genes,as well as the co-localization signal and fluorescence intensity of β-catenin with SP-C,were significantly downregulated(all P<0.05).Compared with the CLP group,the SEV group showed significant improvements in all these indicators.However,compared with the SEV group,the SEV+XAV group demon-strated a reversed protective effect,with all indicators approaching the levels observed in the CLP group(all P<0.05).Conclusion Sevoflurane alleviates sepsis-induced ALI by activating Wnt/β-catenin signaling pathway,exerting anti-inflammatory and antioxidant effects,and enhancing the expression and localization of β-catenin in type Ⅱ alveolar epithelial cells.

Objective:To analyze the situation of rodents carrying Hantavirus and the genetic and evolutionary characteristics of the virus in Zibo city, Shandong province in 2022, and provide reference for the scientific prevention and control of hemorrhagic fever with renal syndrome (HFRS).Methods:Real-time quantitative PCR (RT-qPCR) was used to detect hantavirus (HV) nucleic acids in rodent lung tissues and identify HV genotypes. Each nucleic acid fragment was designed to amplify various gene fragments by segment, and the whole genome of Hantavirus was sequenced by second generation sequencing. Sequence assembly was performed using SeqMan 7.1.0.44, a subprogram of DNAStar. Sequence alignment and evolutionary analysis were conducted using MEGA 7.0 and BioEdit software.Results:A total of 270 host animals were captured in this survey. Among them, 13 rodent lung samples tested positive for Hantavirus, resulting in a virus-positive rate of 4.8%. The full-genome sequences of four hantavirus strains were successfully obtained, all identified as Seoul virus (SEOV) genotype. Four Hantavirus-positive samples showed high nucleotide sequence homology in the M gene and belonged to the SEOV S3 subtype. These strains exhibited high similarity with those from Hebei, Liaoning, and Beijing. The amino acid sequences of the nucleoprotein and glycoprotein immunogenic epitopes were identical to those of the vaccine strain Z37.Conclusions:This study successfully determined the full genome sequences of four hantavirus strains from Zibo city, Shandong province. The genotypes are primarily SEOV, with the subtype being S3. The homology of genes within the same subtype is high, with no significant variations observed. The alignment of immune epitopes in key proteins suggests that the current vaccine may provide protection against locally circulating strains, but further in-depth research is still required.

Objective:To explore the effect and safety of endoscopic tubular musculoskeletal tumor surgery (ETMS) technology in spinal tumors.Methods:Clinical data were retrospectively collected from 18 spinal tumor patients who were treated with ETMS technology at Tianjin Medical University Cancer Institute and Hospital ( n=16) or the Affiliated Hospital of Qingdao University ( n=2) from November 2022 to December 2023. The total cohort included 11 males and 7 females, with the age at 60.3±8.6 years (range of 41-76). Two cases were diagnosed with benign tumors, four patients were diagnosed with spinal hematologic malignancies while other 12 cases were patients with spinal metastases. After localization under the C-arm X-ray machine, the spinal endoscopic channel is established using dilators. Soft tissue is dissected under endoscopic guidance to create an artificial cavity. Subsequently, the saline medium relied upon by the spinal endoscopic technique is removed, and posterior decompression and tumor curettage are performed using tubular techniques. Frankel grade classification and paraplegia index were used to evaluate the improvement of postoperative function and the VAS score was performed in pain scoring. The surgical complications and tumor evaluation were observed by postoperative outpatient and telephone follow-up. Results:The ETMS technology was successfully completed in all 18 patients with the mean operation time of 240.3±80.2 min. The median of intraoperative bleeding was 200.0(172.5, 350.0) ml and the mean postoperative drainage was 131.4±69.5 ml. The median value of postoperative hospitalization days was 6.0(4.0, 10.25) d. The paraplegia index decreased from 1.5(0, 3.0) preoperatively to 0(0, 1.25) postoperatively ( Z=-2.599, P=0.009). All the patients presented an improvement in Frankel grading after surgery except for one patient (downgrading from grade E to grade D). There was significantly difference in Frankel grading between preoperative and postoperative groups ( Z=2.812, P=0.005). The median value of preoperative VAS score was up to 5.5(4.0, 7.0) while the median value at postoperative, one month after surgery and three months after surgery were 1.5(1.0, 2.25), 1.0(0, 1.0) and 0(0, 1.0), respectively (χ 2=44.641, P<0.001). The 3-month postoperative VAS improvement rate was 91.2% (range 75%-100%). During a mean follow-up period of 7.6±6.2 months, none of the 18 patients presented surgical complications or tumor recurrence at surgical region. Only one patient died at 3.2 months after surgery until the last follow-up due to respiratory failure after lung tumor progression. The mean survival of the total cohort was up to 13.3 [95% CI (11.5, 15.0)] months. The 16 cases with spinal metastases or spinal hematological malignancies had a mean survival of 13.2 [95% CI (11.3, 15.0)] months. Conclusion:The ETMS technology presented good efficacy and safety in treatment of spinal tumors with low blood supply and with diameter less than 5cm.

Objective To investigate the current status of mini-CEX scores among students in the Elderly Service Man-agement program after completing the"Fundamentals of Nursing"course and analyze the influencing factors.Methods A total of 99 students from the Elderly Service Management program at the Wuming campus of Guangxi Medical University were selected as the study subjects.Assessment tools included a general information questionnaire,the Chinese version of the Mini Clinical E-valuation Exercise(mini-CEX),the Self-Rating Scale of Self-Directed Learning(SRSSDL),and a self-learning ability assess-ment scale.Stepwise linear regression analysis was employed to explore the factors affecting mini-CEX scores.Results The total mini-CEX score for the 99 students was 49.00(44.00,55.00).Stepwise linear regression analysis revealed that being a student leader,SRSSDL scores,self-learning ability,and teaching model were significant factors(P＜0.05),explaining 56.8％of the total variance.Conclusion The clinical comprehensive ability of students in the Elderly Service Management program requires enhancement,influenced by multiple factors including teaching model,self-learning ability,and self-directed learning capacity.

Objective:To examine whether the mixed infection rate in pertussis infants is significantly higher than that in non-pertussis infants with respiratory tract infection, to explore the mixed infection pathogen distribution in pertussis infants, and to provide reference for clinical diagnosis and treatment.Methods:A case-control study was conducted on 118 nasopharyngeal swabs collected from infants who applied for clinical pertussis etiological testing (culture and specific nucleic acid detection of Bordetella pertussis) in Beijing Children′s Hospital, Jiaxing Maternity and Child Health Care Hospital and Wuhu No.1 People′s Hospital from August 2018 to January 2021.According to the pertussis etiological testing results, the patients were divided into the pertussis group (65 cases) and non-pertussis group (53 cases). Thirty-three pairs of cases were matched according to age, onset season and city.All nasopharyngeal swabs were tested for infections of other pathogens using FilmArray RP2, which can detect 21 respiratory infection pathogens.The mixed infection rate was compared between groups by Chi- square test. Results:According to the FilmArray RP2 test results, 56.9%(37/65) cases in pertussis group and 15.1%(8/53) cases in the non-pertussis group were positive for multiple pathogens, and the difference was statistically significant ( χ2=21.651, P<0.001). The top 5 mixed infection pathogens in pertussis infants were human rhinovirus/enterovirus (HRV/EV) (38.5%, 25/65), parainfluenza virus (PIV) (18.5%, 12/65), respiratory syncytial virus (RSV) (10.8%, 7/65), coronavirus (Cov) (10.8%, 7/65), and adenovirus (ADV) (7.7%, 5/65). The mixed infection rates of the pertussis group in spring, summer, autumn and winter were 46.2% (6/13), 58.3%(14/24), 55.6%(5/9), and 63.2%(12/19), respectively.Comparison of matched and unmatched cases achieved similar results. Conclusions:Among clinical suspected pertussis infant specimens, the mixed infection rate in confirmed cases is tremendously higher than that in non-pertussis infants.The main mixed infection pathogens in pertussis infants are HRV/EV, PIV, RSV, Cov, and ADV.Mixed infection in pertussis children commonly occurs in four seasons, with the highest incidence in winter.

Objective:To look for the problems faced in the construction of the tele-critical care system, explore the framework of construction of the tele-critical care system, and verify the application effects of the established tele-critical care system.Methods:Through literature review and on-site investigation and demonstration, the causes affecting the construction of the tele-critical care system were explored. Through on-site investigation of the actual situation of the critical care department in relevant hospitals, arguing and choosing intended intensive care unit (ICU) and cooperative third-party communication and equipment companies, and through the Internet of Things and 5G communication technology, a tele-critical care system with the core hospital of the group as the center and the member institutes within the group as the nodes was built. Via the established tele-critical care system, activities such as tele-monitoring, visual remote ward rounds, remote consultation, remote teaching were carried out to verify the functions of the system.Results:The insufficient cognition of relevant personnel, tele-medicine practice certification requirements, information security issues and the barriers of equipment information integration were the main causes affecting the construction of tele-critical care system. There were five parts in the tele-critical care system architecture foundations, including bed unit equipment and audio and video information collection system, lossless and secure transmission of collected information, real-time display of information in the remote center, real-time staff interaction between the centre and the nodal hospitals, and information cloud storage. It has been verified that patients' diagnostic and treatment information can be transmitted safely, losslessly and in real-time by a special line through private 5G network. Through this system, real-time and stable upload of audio and video information of patients and application information of monitors, ventilators and infusion work stations can be achieved; combined with tele-conference connections to conduct two-way communication with local medical staff, real-time tele-monitoring, visual remote ward rounds, remote consultation, remote teaching and other functions can be achieved.Conclusion:The tele-critical care system we established is feasible to construct within the medical group and can safely and effectively realize the functions of real-time tele-monitoring, visual remote ward rounds, remote consultation, and remote teaching.