Objective To study the physical and chemical stability of biapenem and parenteral nutrient solutions when they are used together through a Y-type infusion pathway,and to evaluate the rationality and feasibility of clinical compatibility.Methods Based on the actual clinical infusion rates of biapenem solution and parenteral nutrition solution in ICU,under room temperature and light conditions,simulate a Y-shaped pathway to mix biapenem solution and four types of parenteral nutrition solutions in three volume ratios(1∶1,2∶1,3∶1),and collect the compatible solutions at 0,1,2,4,and 6 hours,observing the appearance,pH value,osmolality,insoluble particles,Zeta potential,particle size and the change of biapenem content of the compatible solution,to investigate the potential interaction between them.Results Within 6 hours,the appearance,pH value,osmotic pressure,insoluble particles,particle size,and Zeta potential did not significantly change.Compared with zero time,the content of the biapenem fluctuated between 93.68%and 100.86%,and there was no impurity peak interference in the chromatogram.Conclusion The physico-chemical properties of biapenem were stable within 6 hours under the condition of room temperature and no light exposure through Y-type infusion pathway and parenteral nutrient solutions.

Objective To study the physical and chemical stability of biapenem and parenteral nutrient solutions when they are used together through a Y-type infusion pathway,and to evaluate the rationality and feasibility of clinical compatibility.Methods Based on the actual clinical infusion rates of biapenem solution and parenteral nutrition solution in ICU,under room temperature and light conditions,simulate a Y-shaped pathway to mix biapenem solution and four types of parenteral nutrition solutions in three volume ratios(1∶1,2∶1,3∶1),and collect the compatible solutions at 0,1,2,4,and 6 hours,observing the appearance,pH value,osmolality,insoluble particles,Zeta potential,particle size and the change of biapenem content of the compatible solution,to investigate the potential interaction between them.Results Within 6 hours,the appearance,pH value,osmotic pressure,insoluble particles,particle size,and Zeta potential did not significantly change.Compared with zero time,the content of the biapenem fluctuated between 93.68%and 100.86%,and there was no impurity peak interference in the chromatogram.Conclusion The physico-chemical properties of biapenem were stable within 6 hours under the condition of room temperature and no light exposure through Y-type infusion pathway and parenteral nutrient solutions.

Objective:To investigate the status of medical care seeking delay and diagnosis delay of pulmonary tuberculosis (PTB) patients in Tongzhou District and Changping District of Beijing, analyze the related factors and put forward suggestions for early detection and scientific management of PTB patients.Methods:A retrospective epidemiological survey was conducted to collect the incidence data of PTB registered in Tongzhou and Changping from January 1 to December 31, 2021 by using the Chinese Tuberculosis Information Management System, and telephone interview were used for information supplement. Multivariate logistic regression model and decision tree model were used to analyze the influencing factors of medical care seeking delay and diagnosis delay of PTB patients.Results:In 2021, the medical care seeking delay time M( Q1, Q3) in the PTB patients in Tongzhou and Changping was 11 (5, 26) days, with a delay rate of 41.71%. Results from multivariate logistic regression model analysis revealed that factors influencing the medical care seeking delay included regular health check-up ( OR=0.033, 95% CI: 0.008-0.147), coughing for less than 2 weeks or showing any symptom of PTB before medical care seeking ( OR=0.378, 95% CI: 0.215-0.665), showing other symptoms before medical care seeking( OR=2.791, 95% CI: 1.710-4.555), no work or school in medical care seeking ( OR=2.990, 95% CI: 1.419-6.298). The diagnosis delay time M( Q1, Q3) in the PTB patients was 8 (0, 18) days, with a delay rate of 35.20%. Multivariate logistic regression model analysis revealed that the factors influencing the diagnosis delay of PTB included being diagnosed at a specialized tuberculosis hospital ( OR=0.426, 95% CI: 0.236-0.767) or a tuberculosis prevention and control institution ( OR=1.843, 95% CI: 1.061-3.202) and being traced as a source of infection ( OR=2.632, 95% CI: 1.062-6.521). The overall performance of the multivariate logistic regression model was comparable to that of the decision tree model, with the decision tree model exhibiting higher sensitivity but lower specificity. Conclusions:The medical care seeking delay rate and diagnosis delay rate of tuberculosis in Tongzhou and Changping were at low levels in 2021. However, it is still necessary to strengthen the health education and active screening, improve the public awareness of PTB prevention and control, and further improve the level of medical services and medical access to reduce the medical care seeking delay and diagnosis delay of PTB patients.

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Objective:To explore the technical focus of robotic-assisted laparoscopic surgery for the treatment of horseshoe kidney combined with renal tumor.Methods:The clinical data of a patient with horseshoe kidney combined with renal tumor treated by robot-assisted laparoscopic partial nephrectomy in the Second Hospital of Dalian Medical University in September 2021 were retrospectively analyzed. PubMed, CNKI, Wanfang and VIP databases were searched for all the literature on the use of robot-assisted laparoscopic nephrectomy or partial nephrectomy for the treatment of horseshoe kidney combined with renal tumor from the time of establishment to December 2022.Results:A total of 11 patients from 10 articles were retrieved and 12 patients were enrolled. Among the 12 patients, 4 cases used the retroperitoneal approach and 8 cases used the transperitoneal approach. Two cases were operated by traditional laparoscope, and the arteries were searched for and controlled before the robotic arm was placed to perform the partial nephrectomy and suture; and 10 cases were operated with the robotic-assisted laparoscopic approach throughout the whole procedure. Five cases of nephrectomy were performed on one side, and 7 cases were performed in the partial nephrectomy. Postoperative pathological diagnosis was clear cell carcinoma in 8 cases, chromophobe cell carcinoma in 1 case, eosinophilic cell carcinoma in 1 case, renal cell carcinoma in 1 case, and renal abscess in 1 case. The patient in the Second Hospital of Dalian Medical University was 38 years old female who was admitted to the hospital with a fever. After CT arteriography and three-dimensional reconstruction, robotic-assisted laparoscopic partial nephrectomy of right kidney and isthmus dissecting was performed. During the operation, tumor trophoblast vessels were ligated and dissected one by one by using single-use tissue closure clips, and the isthmus was dissected using endoscopic cutting anastomosis on the left side of the tumor, with the tumor edges sharply resected and completely dissected. The operation time was 240 min, without thermal ischemia time, and the bleeding volume was about 300 ml. The patient recovered well after the operation, and the postoperative pathological diagnosis was renal abscess.Conclusions:Robot-assisted laparoscopic treatment of horseshoe kidney combined with renal tumor is safe and effective, and has more advantages than traditional laparoscopic surgery. Preoperative CT arteriography or three-dimensional reconstruction examination should be applied to fully evaluate the variant vessels. The surgical access and plan should be decided according to the size and location of the tumor. The variant vessels should be properly handled during operation. The use of endoscopic cutting anastomosis to deal with the isthmus can be more conducive to the surgical operation.

Objective To explore the role of β-1,3-galactosyltransferase 2(B3galt2)in mice with cerebral ischemic injury.Methods Adult male C57BL/6J mice were randomly divided into the sham,suture-occluded middle cerebral artery occlusion(MCAO)model,MCAO model+lentiviral vector control(LV-GFP),and MCAO model+lentiviral vector overexpression B3galt2(LV-B3galt2)groups,with six mice in each group.Neurological deficit scoring and rotating rod experiments were performed 24 h after ischemia in each group,and 2,3,5-triphenyltetrazolium chloride(TTC)staining was used to determine the infarction volume.The number of neurons in the ischemic cerebral cortex was determined in each group using Nissl staining.The levels of oxidative stress-related factors in the brain tissues were detected using the relevant kits.Results Compared with the sham group,the MCAO model group showed increased infarct volume and neurological deficits(P<0.05),significantly decreased number of neurons in the ischemic cerebral cortex and levels of super-oxide dismutase(SOD)and glutathione peroxidase(GSH)(all P<0.05),and significantly increased levels of reactive oxygen species(ROS)and malondialdehyde(MDA)(all P<0.05).Compared with the MCAO model group,the LV-B3galt2 group had reduced volume of cerebral infarction,significantly improved neurological deficits(all P<0.05),significantly increased number of neurons in the ischemic cerebral cortex of mice,significantly decreased levels of ROS and MDA(P<0.05),and significantly elevated levels of SOD and GSH(all P<0.05).Conclusion B3galt2 overexpression can reduce brain injury in an ischemic damage mouse model,and its mechanism may be through the inhibition of oxidative stress reactions.

Objective:To investigate the delay in medical care-seeking and diagnosis of human brucellosis cases in Daxing District, Beijing City, and analyze the relevant influencing factors.Methods:Retrospective analysis method was used to collect the information on brucellosis cases reported by the Infectious Disease Reporting Information Management System of the China Disease Prevention and Control Information System in Daxing District, Beijing City from January 1, 2013 to December 31, 2022. And the case information was reviewed, including demographic information, clinical manifestations, medical care-seeking, diagnosis, etc. Binary logistic regression was used to analyze the influencing factors of delayed diagnosis.Results:A total of 120 cases of brucellosis were included, including 91 males and 29 females, with a male-to-female ratio of 3.14 ∶ 1.00. The majority were aged 40 - 79 years, accounting for 81.7% (98/120). Farmers and unemployed household workers were the main occupations, accounting for 67.5% (81/120). The main clinical manifestations were fever (85.8%, 103/120), hyperhidrosis (65.8%, 79/120), fatigue (61.7%, 74/120), and muscle soreness (60.0%, 72/120). The delay rate of medical care-seeking was 33.3% (40/120), with a median medical care-seeking time of 6 days (interquartile range: 2 - 22 days, range: 0 - 727 days). The diagnosis delay rate was 50.8% (61/120), with a median diagnosis time of 10 days (interquartile range: 3 - 22 days, range: 0 - 1 530 days). The results of binary logistic regression analysis showed that the reasons for initial diagnosis were severe or recurrent symptoms ( OR = 6.76, 95% CI: 2.40 - 19.01), serological monitoring or other diseases ( OR = 36.00, 95% CI: 3.57 - 362.72), and delayed medical care-seeking ( OR = 4.30, 95% CI: 1.68 - 11.01) were risk factors for delayed diagnosis of brucellosis cases. The location of the diagnostic hospitals outside the city ( OR = 0.27, 95% CI: 0.08 - 0.92) was a protective factor for delayed diagnosis of brucellosis cases. Conclusions:The medical care-seeking and diagnosis of brucellosis cases in Daxing District, Beijing City have been delayed to varying degrees. The reasons of initial diagnosis are severe or recurrent symptoms, serological monitoring or other diseases, and medical care-seeking delay are the risk factors for the delayed diagnosis of brucellosis cases. The location of the diagnostic hospitals outside the city is a protective factor for delayed diagnosis of brucellosis cases.

Objective:To investigate the delay in medical care-seeking and diagnosis of human brucellosis cases in Daxing District, Beijing City, and analyze the relevant influencing factors.Methods:Retrospective analysis method was used to collect the information on brucellosis cases reported by the Infectious Disease Reporting Information Management System of the China Disease Prevention and Control Information System in Daxing District, Beijing City from January 1, 2013 to December 31, 2022. And the case information was reviewed, including demographic information, clinical manifestations, medical care-seeking, diagnosis, etc. Binary logistic regression was used to analyze the influencing factors of delayed diagnosis.Results:A total of 120 cases of brucellosis were included, including 91 males and 29 females, with a male-to-female ratio of 3.14 ∶ 1.00. The majority were aged 40 - 79 years, accounting for 81.7% (98/120). Farmers and unemployed household workers were the main occupations, accounting for 67.5% (81/120). The main clinical manifestations were fever (85.8%, 103/120), hyperhidrosis (65.8%, 79/120), fatigue (61.7%, 74/120), and muscle soreness (60.0%, 72/120). The delay rate of medical care-seeking was 33.3% (40/120), with a median medical care-seeking time of 6 days (interquartile range: 2 - 22 days, range: 0 - 727 days). The diagnosis delay rate was 50.8% (61/120), with a median diagnosis time of 10 days (interquartile range: 3 - 22 days, range: 0 - 1 530 days). The results of binary logistic regression analysis showed that the reasons for initial diagnosis were severe or recurrent symptoms ( OR = 6.76, 95% CI: 2.40 - 19.01), serological monitoring or other diseases ( OR = 36.00, 95% CI: 3.57 - 362.72), and delayed medical care-seeking ( OR = 4.30, 95% CI: 1.68 - 11.01) were risk factors for delayed diagnosis of brucellosis cases. The location of the diagnostic hospitals outside the city ( OR = 0.27, 95% CI: 0.08 - 0.92) was a protective factor for delayed diagnosis of brucellosis cases. Conclusions:The medical care-seeking and diagnosis of brucellosis cases in Daxing District, Beijing City have been delayed to varying degrees. The reasons of initial diagnosis are severe or recurrent symptoms, serological monitoring or other diseases, and medical care-seeking delay are the risk factors for the delayed diagnosis of brucellosis cases. The location of the diagnostic hospitals outside the city is a protective factor for delayed diagnosis of brucellosis cases.

【Objective】 To explore the factors influencing the survival and prognosis of patients with bladder urothelial carcinoma (BUC) after surgical treatment, and to establish an artificial intelligence algorithm to predict the effects of different surgical regimens. 【Methods】 BUC patients treated with surgery during Jan.2007 and Jan.2019 in The Second Hospital of Dalian Medical University and Nanfang Hospital of Southern Medical University were enrolled. The complete clinical and follow-up data were collected. Deep neural network (DNN) was used to establish an artificial intelligence algorithm model. A prediction model of survival and prognosis was established, and the influencing factors of survival were explored and ranked by the artificial intelligence algorithm. 【Results】 A total of 832 patients were involved, including 438 (52.64%) treated in The Second Hospital of Dalian Medical University, and 394 (47.36%) treated in Nanfang Hospital of Southern Medical University. Of all cases, 579 (69.6%) were non-muscle invasive bladder cancer, and 253 (30.4%) were muscle invasive bladder cancer. Transurethral resection of bladder tumor was conducted in 539 (64.8%) cases, partial cystectomy in 66 (7.9%) cases, and total cystectomy in 227 (27.3%) cases. The data of patients treated in Second Hospital of Dalian Medical University were used for DNN modeling, and the data of patients treated in Nanfang Hospital of Southern Medical University were used for external verification after modeling. Finally, it was concluded that the factors affecting survival and prognosis were T stage, pathological grade, hypertension or cardiovascular and cerebrovascular disease, hemoglobin, blood calcium, smoking, albumin, lymphocytes, age, ratio of albumin/globulin, operation method, N stage, and creatinine clearance rate in descending order. The model could be used for preoperative prediction. 【Conclusion】 Through DNN modeling and external verification, the influencing factors of postoperative survival can be predicted for patients with bladder cancer, and the surgical effects can also be predicted before operation. The model can provide artificial intelligence algorithm support for the selection of surgical methods and postoperative follow-up plans.