OBJECTIVE To analyze the changing trend of hospital-acquired infections(HAIs)in hospitals from 2017 to 2023,and identify the distribution of different departments,infection sites and pathogens,so as to provide a scientific basis for the prevention and control of HAI.METHODS During the study period,the HAI data were col-lected and classified by year,department and infection site.Joinpoint regression model was used to analyze the trend of HAI rate,calculate the average annual percentage change(AAPC),and evaluate the distribution of infec-tion sites and pathogenic bacteria.RESULTS The overall infection situation showed that the average HAI rate was 1.19％,with a trend of increasing first and then decreasing during the study period,and AAPC was 8.33％(95％CI:0.025-0.173).The infection rate was the highest in the emergency department with an AAPC of 13.51％(95％CI:0.074-0.250),while the infection rates in the department of traditional Chinese medicine and orthope-dics were relatively stable.Lower respiratory tract infections accounted for the major proportion,followed by uri-nary tract infections and bloodstream infections.The AAPC for lower respiratory tract infections was 18.64％.The pathogenic analysis showed that bacterial infections were mainly gram-negative bacteria,with Kleb-siella pneumoniae,Pseudomonas aeruginosa,and Acinetobacter baumannii being the most common pathogens causing HAI.CONCLUSIONS The results of the study reveal that significant differences in HAI rates among dif-ferent departments and infection sites,especially the increasing infection rate in the emergency department should be highly concerned.In view of the persistent high incidence of respiratory and urinary tract infections,it is recom-mended to strengthen the relevant prevention and control measures.To effectively reduce the incidence of HAIs,it is necessary to focus on the monitoring and management of drug-resistant pathogens in the future.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. 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. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Malignant hyperthermia(MH)is a rare perioperative disease with autosomal dominant inheritance,and its pathogenesis involves specific gene mutations.Its clinical feature is that conventional anesthetics can trigger abnormally high metabolic reactions in skeletal muscles.Although the incidence of this disease is low,the condition is dangerous,progresses rapidly,and has a high mortality rate;Its treatment relies on early diagnosis,timely application of the specific drug Dantrolene Sodium,and rapid and orderly comprehensive symptomatic supportive treatment.MH is a critical perioperative emergency that can occur during surgery.It presents with symptoms such as hyperpyrexia,metabolic acidosis,rhabdomyolysis,and dysfunction of multiple organ systems.If not treated promptly,it can quickly lead to life-threatening arrhythmias and cardiac arrest.This condition serves as an essential teaching example in anesthesia crisis resource management.As an effective teaching method,scenario simulation exercises can comprehensively enhance medical staff's personal technical,non-technical,and teamwork abilities through simulating emergency scenarios,teaching assessments,and retrospective discussions,especially suitable for comprehensive management training of fatal diseases.Many countries internationally have incorporated simulation exercises for MH into their routine teaching and training systems.The effectiveness of teaching and training for anesthesiologists in MH and their ability to handle anesthesia crisis events have been continuously improved through a periodic training model.This article systematically reviews the research progress and practical experience of scenario simulation exercises in emergency training for MH,with a focus on exploring how to establish a scenario simulation exercise plan for emergency application and comprehensive symptomatic support treatment of Dantrolene Sodium based on the actual situation in China,providing reference for improving the teaching and training quality of MH and other clinical crisis events.

To understand the current epidemiological status and biological characteristics of avian paramyxoviruses(APMV)in wild birds in China,a total of 1 384 fecal samples of wild birds were collected in eight provinces(autonomous regions),including Ningxia,in 2023,to detect avian pa-ramyxovirus infections by viral isolation and RT-PCR.Positive samples were subjected to F gene sequence amplification and genetic evolutionary analyses.The results showed that 10 strains of APMV were isolated and identified from 1 384 wild bird feces samples with a positive rate of 0.72％.Out of the 10 strains,4 strains were APMV-1,which was in the same branch to the Ameri-can goose APMV-1 strain and had the homology ranging from 93％to 97.3％.Three strains of APMV-4 were in the same branch with the Russian duck APMV-4 strain and the Russian pintail APMV-4 strain,with homology ranging from 99.1％to 99.5％.Three strains were APMV-6,they were in the same branch with the Russian ruddy bladdered duck APMV-6 strain,with homology ranging from 98.7％to 99.20％.The intracerebral inoculatable pathogenicity index(ICPI)of the four strains for 1-day-old chicks ranged from 0 to 0.48,which was low in pathogenicity for chick-ens.The above results enriches the epidemiological information and the biological characteristics of avian paramyxovirus in wild birds in China,which provides a reference for the early warning,scien-tific prevention and control of this disease.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. 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. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

To understand the current epidemiological status and biological characteristics of avian paramyxoviruses(APMV)in wild birds in China,a total of 1 384 fecal samples of wild birds were collected in eight provinces(autonomous regions),including Ningxia,in 2023,to detect avian pa-ramyxovirus infections by viral isolation and RT-PCR.Positive samples were subjected to F gene sequence amplification and genetic evolutionary analyses.The results showed that 10 strains of APMV were isolated and identified from 1 384 wild bird feces samples with a positive rate of 0.72％.Out of the 10 strains,4 strains were APMV-1,which was in the same branch to the Ameri-can goose APMV-1 strain and had the homology ranging from 93％to 97.3％.Three strains of APMV-4 were in the same branch with the Russian duck APMV-4 strain and the Russian pintail APMV-4 strain,with homology ranging from 99.1％to 99.5％.Three strains were APMV-6,they were in the same branch with the Russian ruddy bladdered duck APMV-6 strain,with homology ranging from 98.7％to 99.20％.The intracerebral inoculatable pathogenicity index(ICPI)of the four strains for 1-day-old chicks ranged from 0 to 0.48,which was low in pathogenicity for chick-ens.The above results enriches the epidemiological information and the biological characteristics of avian paramyxovirus in wild birds in China,which provides a reference for the early warning,scien-tific prevention and control of this disease.

OBJECTIVE To analyze the changing trend of hospital-acquired infections(HAIs)in hospitals from 2017 to 2023,and identify the distribution of different departments,infection sites and pathogens,so as to provide a scientific basis for the prevention and control of HAI.METHODS During the study period,the HAI data were col-lected and classified by year,department and infection site.Joinpoint regression model was used to analyze the trend of HAI rate,calculate the average annual percentage change(AAPC),and evaluate the distribution of infec-tion sites and pathogenic bacteria.RESULTS The overall infection situation showed that the average HAI rate was 1.19％,with a trend of increasing first and then decreasing during the study period,and AAPC was 8.33％(95％CI:0.025-0.173).The infection rate was the highest in the emergency department with an AAPC of 13.51％(95％CI:0.074-0.250),while the infection rates in the department of traditional Chinese medicine and orthope-dics were relatively stable.Lower respiratory tract infections accounted for the major proportion,followed by uri-nary tract infections and bloodstream infections.The AAPC for lower respiratory tract infections was 18.64％.The pathogenic analysis showed that bacterial infections were mainly gram-negative bacteria,with Kleb-siella pneumoniae,Pseudomonas aeruginosa,and Acinetobacter baumannii being the most common pathogens causing HAI.CONCLUSIONS The results of the study reveal that significant differences in HAI rates among dif-ferent departments and infection sites,especially the increasing infection rate in the emergency department should be highly concerned.In view of the persistent high incidence of respiratory and urinary tract infections,it is recom-mended to strengthen the relevant prevention and control measures.To effectively reduce the incidence of HAIs,it is necessary to focus on the monitoring and management of drug-resistant pathogens in the future.

Malignant hyperthermia(MH)is a rare perioperative disease with autosomal dominant inheritance,and its pathogenesis involves specific gene mutations.Its clinical feature is that conventional anesthetics can trigger abnormally high metabolic reactions in skeletal muscles.Although the incidence of this disease is low,the condition is dangerous,progresses rapidly,and has a high mortality rate;Its treatment relies on early diagnosis,timely application of the specific drug Dantrolene Sodium,and rapid and orderly comprehensive symptomatic supportive treatment.MH is a critical perioperative emergency that can occur during surgery.It presents with symptoms such as hyperpyrexia,metabolic acidosis,rhabdomyolysis,and dysfunction of multiple organ systems.If not treated promptly,it can quickly lead to life-threatening arrhythmias and cardiac arrest.This condition serves as an essential teaching example in anesthesia crisis resource management.As an effective teaching method,scenario simulation exercises can comprehensively enhance medical staff's personal technical,non-technical,and teamwork abilities through simulating emergency scenarios,teaching assessments,and retrospective discussions,especially suitable for comprehensive management training of fatal diseases.Many countries internationally have incorporated simulation exercises for MH into their routine teaching and training systems.The effectiveness of teaching and training for anesthesiologists in MH and their ability to handle anesthesia crisis events have been continuously improved through a periodic training model.This article systematically reviews the research progress and practical experience of scenario simulation exercises in emergency training for MH,with a focus on exploring how to establish a scenario simulation exercise plan for emergency application and comprehensive symptomatic support treatment of Dantrolene Sodium based on the actual situation in China,providing reference for improving the teaching and training quality of MH and other clinical crisis events.

Objective To observe the efficacy of hepatic artery infusion chemotherapy(HAIC)combined with lenvatinib for treating Barcelona clinic liver cancer(BCLC)stage B or C hepatocellular carcinoma(HCC),and to explore the impact factors of patients'survival time.Methods Data of 104 patients with BCLC stage B or C HCC were retrospectively analyzed.The patients were divided into observation group(n=46,underwent HAIC combined with lenvatinib)and control group(n=58,underwent HAIC alone).The clinical efficacy and adverse reactions of treatments,as well as patients'overall survival(OS)and progression free survival(PFS)were recorded and compared between groups.Cox regressions were used to explore the impact factors of patients'survival time.Results Three months and 6 months after HAIC,the results of modified response evaluation criteria in solid tumors(mRECIST)in observation group were both better than those in control group(both P＜0.05),while no significant difference was found between groups one year after HAIC(P＞0.05).The overall survival rate in observation group was higher than that in control group(P＜0.05),while there was no significant difference of progression free survival rate between groups(P＞0.05).The incidence of rash in observation group was higher than that in control group(P＜0.05).Multiple Cox regression showed prolonged OS in HCC patients in observation group(hazard ratio[HR]=0.425,95%CI[0.255,0.791])compared with that in control group.Compared with pre-treatment Eastern Cooperative Oncology Group(ECOG)score 1,AFP≥400 μg/ml,the number of tumor foci≥3 and BCLC stage C,pre-treatment ECOG score 0,AFP＜400 μg/ml,the number of tumor foci≤2 and BCLC stage B were all independent protective factors of OS in HCC patients(all P＜0.05).Conclusion HAIC combined with lenvatinib was safe and effective for treating BCLC stage B or C HCC.Pre-treatment ECOG score,serum AFP level,the number of tumor foci and BCLC stage were all independent impact factors of OS.