OBJECTIVE To synthesize a polymer PEI-ss-PEG2000-DSPE containing disulfide bond and to prepare as cationic micelle(P-ss-PD) based on branched polyethyleneimine(PEI). To investigate the ability of P-ss-PD micelle to reduce cytotoxicity and improve the transfection efficiency of antisense oligonucleotide(ASO) in human breast cancer cell lines, and to study the anti-tumor effect of P-ss-PD micelle in nude mice. METHODS PEI-ss-PEG2000-DSPE was synthesized by grafting PEG2000-DSPE onto branched PEI with disulfide bond as a connecting arm. P-ss-PD micelle was prepared by ethanol injection method and P-ss-PD/ASO nanocomplex was obtained by combining P-ss-PD micelle with ASO. The particle size and zeta potential of P-ss-PD/ASO nanocomplex at various mass ratios were determined by laser particle size analyzer. Agarose gel retardation assay was used to investigate the binding degree of P-ss-PD/ASO nanocomplex and determine the optimal mass ratio. At the same time, the reduction responsive ability of P-ss-PD micelle was investigated. The cytotoxicity of P-ss-PD micelle was detected by CCK8 kit. The transfection efficiency of P-ss-PD micelle was investigated by flow cytometry and high content cell imaging analysis system in MDA-MB-231 cells. The anti-tumor effect of P-ss-PD micelle was investigated by tumor-bearing nude mice models. RESULTS When the mass ratio was 300∶1, the particle size of P-ss-PD/ASO nanocomplex was the smallest and had a good stability. The average particle size was (58.90 ± 4.08)nm, the average zeta potential was (16.80 ± 1.23)mV, and the morphology was uniform spherical. P-ss-PD/ASO nanocomplex had the reduction responsive ability and could release ASO under highly reductive conditions.In vitro, compared with unmodified branched PEI, the cytotoxicity of P-ss-PD micelle was significantly reduced and the transfection efficiency was significantly increased.In vivo, the tumor growth inhibition rate of P-ss-PD/ASO nanocomplex in tumor-bearing nude mice was more than 50%. CONCLUSION The P-ss-PD micelle prepared in this study is a kind of low toxicity and high transfection efficiency non-viral vector, which has the characteristics of reduction responsive releasing, and shows a promising application in ASO drug delivery.

Objective Stereotactic body radiotherapy(SBRT)and radiofrequency ablation(RFA)are primary treatments for hepatocellular carcinoma(HCC)at present.However,the effect of these treatments in clinical trails are rather controversial.The purpose of this paper is to conduct a meta-analysis on the clinical effect and related complications of SBRT and RFA for HCC.Methods A computerized retrieval of academic papers concerning the treatment effect of SBRT and RFA for HCC from the databases of PubMed,Embase,Cochrane Library,Scopus,Web of Science,CBM,CNKI,Wanfang database,VIP was conducted.The retrieval time period was from the establishment of the database to June 2022.Stata14.0 software was used to make meta-analysis.Results A total of 14 retrospective studies including 6 806 patients were included in this analysis.The results of combined hazard ratio(HR)based on overall survival(OS)showed that the OS rate of SBRT was lower than that of RFA(HR=1.25,95％CI=1.10-1.43,12=0％,P=0.000 9),while the results of combined HR of local control(LC)rate indicated that SBRT had a better therapeutic effect(HR=0.61,95％CI=0.47-0.78,I2=0％,P=0.000 1).Subgroup analysis revealed that the combined HR of LC rate favored the performance of SBRT for patients with tumor diameter larger than 2 cm(HR=2.64,95％CI=1.56-4.48,I=0％,P=0.000 3).No statistically significant difference in the incidence of late serious adverse reactions existed between SBRTgroup and RFA group(OR=1.01,95％CI=0.59-1.73,I2=30％,P=0.97).Conclusion SBRT is superior to RFA in controlling local HCC lesions,especially in patients whose tumor diameter is larger than 2 cm,although it does not show certain advantages in the survival benefit.(J Intervent Radiol,2023,32:1233-1241)

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

Objective:To investigate the effect of functional movement assessment on the recurrence of patients with ankylosing spondylitis (AS) after treat-to-target therapy.Methods:The clinical data of 61 patients with AS in Chengdu were collected including clinical symptoms and AS disease activity (ASDAS). After 24 weeks adalimumab treatment, motor function score of AS patients(ASDAS<1.3) was assessed by functional movement screen (FMS), then adalimumab was discontinued and the rest of the concurrent drugs were continued until the disease relapse or up to 1 year. The data of the two groups were compared using t-test analysis and Cox proportionate hazard model. Results:① The recurrence rate of patients with AS after treat-to-target therapy within 1 year follow-up was 57.4%; ② The recurrence group was younger [(27±7) vs (31±6), t=5.96, P=0.02], the ASADAS value was at the high end when adalimumab was withdrawal [(1.29±0.07) vs (0.87±0.16), t=177.31, P<0.01], and the FMS value was lower after treat-to-target [(12.9±2.7) vs (16.2±1.9), t=29.23, P<0.01], The time to reaching the treatment target was longer [(2.9±1.2) month vs (1.7±0.6) month, t=19.89, P<0.01] than the stable group; ③ The cut-off value of the FMS test of AS patients after treat-to-target therapy was 14.25 (sensitivity was 84.6%, specificity was 80%) . The time to treat-to-target was a risk factor for recurrence ( RR=2.285, P<0.05), and the FMS value after treat-to-target was a protective factor ( RR=0.625, P<0.05). Conclusion:After discontinuing the adalimumab, about half of the patients relapse. The time reaching the treatment target and the FMS value after treat-to-target therapy are the risk factors for disease recurrence.

ObjectiveTo explore the relationship between occupational stress， psychological capital and insomnia among nurses， and to test the mediating effect of psychological capital on the relationship between nurses' occupational stress and insomnia. MethodsStratified random sampling method was utilized in selecting 810 nurses from a tertiary A-level hospital from March to May 2021. The Effort-Reward Imbalance questionnaire （ERI）， Psychological Capital Questionnaire （PCQ） and Athens Insomnia Scale （AIS） were used to evaluate the occupational stress， psychological capital and insomnia of nurses， respectively. Then the mediation effect of psychological capital on the relationship between occupational stress and insomnia in nurses was tested by PROCESS macro program. ResultsA total of 658 （81.23%） questionnaires were effectively collected. Analysis found that nurses' effort-reward ratio was positively correlated with AIS score （r=0.379， P<0.01）， and negatively correlated with PCQ score （r=-0.275， P<0.01）. Nurses' PCQ score was negatively correlated with AIS score （r=-0.402， P<0.01）. Nurses' occupational stress could negatively predict psychological capital （β=-11.024， t=-7.324， P<0.01）， and positively predict insomnia （β=4.117， t=10.478， P<0.01）. Psychological capital could negatively predict insomnia （β=-0.087， t=-9.083， P<0.01）. The predictive effect of occupational stress on insomnia was statistically significant with psychological capital introduced as a mediating variable （β=3.158， t=8.185， P<0.01）. ConclusionPsychological capital plays a partial mediating role in the relationship between occupational stress and insomnia in nurses.

Objective:Based on 108, 591 cases of pediatric emergency visits in a Level Ⅲ Grade A women and children′s hospital in Guangzhou area, we analyze the disease spectrum and epidemiological characteristics, and summarize the characteristics of patient flow changes.These investigations will provide an basis for scientific decision-making for manpower and material resource management of pediatric emergency and hospital workflow design.Methods:The children admitted to the Pediatric Emergency Department of the Zhujiang New Town District of Guangzhou Women and Children′s Medical Center from October 2016 to September 2018, including night emergency and inpatient observations, were analyzed according to the admission date, admission time, gender, age, initial diagnosis and etc.Results:There were more boys than girls in the emergency department, whose ratio was 1.46∶1 (64 480∶44 111 cases). The age of children ranged from 0 to 17 years old, with a median of 11 (23, 48) months.The age distribution was mainly under 5 years old, accounting for 84.14% (91 336/108 591). During the whole year, the number of children in July was the most, accounting for 10.53% (11 433/108 591), and the children in February were the least, accounting for 6.04% (6 555/108 591). The highest visit time of the whole day was 22-23 pm, accounting for 18.83% (20 443/108 591). The most of the diagnosis was respiratory disease, accounting for 53.83% (66 522/123 576). A total of 1 057 critically ill children were received, accounting for 0.97% (1 057/108 591). A total of 911 accidental injuries were received, accounting for 0.84% (911/108 591). Acute upper respiratory tract infection was the most among all diagnoses, accounting for 34.47% (42 541/123 576).Conclusion:Children in the pediatric emergency department of Guangzhou Women and Children′s Medical Center are mainly under 5 years old, and the number of children is the most in July of the year.The main disease is respiratory diseases.Medical staff can be trained according to the actual situation, and the disease spectrum can be updated in time to provide convenience for emergency rescue and improve service level.

Objective:To explore the value of different radiomics models based on machine learning in predicting the risk of distant recurrence and metastasis of triple-negative breast cancer after neoadjuvant therapy.Methods:The clinical and imaging data of 150 patients with triple-negative breast cancer (TNBC) confirmed by histopathology were retrospectively analyzed. All patients underwent neoadjuvant chemotherapy and surgical resection from August 2011 to May 2017 in Fudan University Shanghai Cancer Center and Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. One hundred and nine patients from Shanghai Fudan University Shanghai Cancer Center were used as the training group, and 41 patients from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine were used as the validation group. The features were extracted from dynamic contrast-enhanced MRI (DCE-MRI) before treatment and were added with time domain features innovatively. Least absolute shrinkage and selection operator cross validation and recursive feature elimination were applied to select features. Six different supervised machine learning algorithms (logistic regression, linear discriminant analysis, k-nearest neighbor, naive bayesian, decision tree, support vector machine) were used to predict the prognosis. ROC curve, accuracy and F1 measure were used to evaluate the performance of the six algorithms, and also verified by the validation group.Results:The support vector machine algorithm had the best predictive effect in the recurrence and metastasis model based on 15 features, with the highest area under curve (training group was 0.917, validation group was 0.859), and the highest accuracy rate (training group was 87.5%, validation group was 82.9%) and the highest F1 measure (training group was 0.800, validation group was 0.741). In addition, of the 15 imaging features, 12 were the time domain features and 3 were spatial features.Conclusion:With the help of the time domain features and machine learning algorithms, radiomics signatures based on preoperative DCE-MRI can help predict the distant prognosis for TNBC after neoadjuvant chemotherapy and provide support for clinical decision making and follow-up management.

During a radiological or nuclear emergency, iodine thyroid blocking is an urgent protection action to prevent or reduce the absorption of radioactive iodine by thyroid. Although potassium iodide (KI) administration is recommended by WHO guidelines for iodine prophylaxis following nuclear accidents and is also widely implemented in most national guidelines, the scientific evidence for the guidelines lacks as the guidelines are mostly based on expert opinions and recommendations. This paper introduces the development and revision of WHO guidelines for iodine thyroid blocking published in 1989 and 1999, as well as the revision methods and main recommendations of the guidelines published in 2017, which supports the use of iodine thyroid blocking during a radiological or nuclear emergency.

This paper summarizes the training methods of clinical nursing decision-making ability at home and abroad, analyzes the connotation of clinical nursing decision-making ability and the existing problems in the current training methods and puts forward suggestions, in order to provide valuable reference for nursing managers and educators to train nurses' clinical decision-making ability, and then improve the decision-making ability of nursing staff and improve the quality of nursing.

ObjectiveTo explore the in-depth cognition and experience of nursing procedure in the course of clinical practice in undergraduatenursing interns. MethodsBy purposive sampling, 15 undergraduate nursing interns who were at the late stage of clinical practice in a ClassⅢ Grade A general hospital in Shanghai were selected from March to May in 2018. Semi-structured interview was adopted to interview them and Colaizzi seven-step method was used to analyze the data. ResultsTotally four themes were extracted, namely, the gradual dilution of theoretical knowledge of nursing procedures, the different understanding of the role of nursing procedures, different views on whether nursing procedures are applied in nursing work, and various deficiencies in the application of nursing procedures in nursing work. ConclusionsStrengthening nursing students＇ study of theoretical knowledge of nursing process,clarifying the important role and application of nursing process,encouraging nursing students to apply nursing procedures in a coordinated and standardized manner ,strengthening the holistic concept of nursing students and improving the consistency of nursing students＇ learning and application of nursing process in different departments are the key points for the continuous improvement of clinical intern teaching.