OBJECTIVES: To compare the commonly used methods for analyzing treatment switching in clinical trials to facilitate selection of optimal methods in different scenarios. METHODS: Based on the data characteristics of patient conversion in oncology clinical trials, we simulated the survival time of patients across different scenarios and compared the bias, mean square error and coverages of the treatment effects derived from different methods. RESULTS: The sample size had an almost negligible impact on the outcomes of the various methods. Compared to conventional methods, more complex methods (RPSFTM, IPCW, TSE, and IPE) resulted in lower errors across different scenarios. The IPCW method could cause a significant increase in errors in cases where the probability of conversion was high. The TSE method had the lowest error and mean squared error when the risk was low and the probability of conversion was high. The IPE method had an obvious advantage in the scenario with a low probability of conversion, but it may slightly underestimate the treatment effect when the inflation factor was small. CONCLUSIONS The choice of a specific method for analyzing cohort transition should be made based on considerations of both the probability of conversion and inflation factor in different scenarios.
Humans ; Clinical Trials as Topic/methods* ; Neoplasms/therapy*

The overall health condition of patients significantly affects the diagnosis,treatment,and prognosis of endodontic diseases.A systemic consideration of the patient's overall health along with oral conditions holds the utmost importance in determining the necessity and feasibility of endodontic therapy,as well as selecting appropriate therapeutic approaches.This expert consensus is a collaborative effort by specialists from endodontics and clinical physicians across the nation based on the current clinical evidence,aiming to provide general guidance on clinical procedures,improve patient safety and enhance clinical outcomes of endodontic therapy in patients with compromised overall health.

Objective:To construct a risk prediction model for the inter-hospital transfer of critically ill children using machine learning methods, identify key medical features affecting transfer outcomes, and improve the success rate of transfers.Methods:A prospective study was conducted on critically ill children admitted to the pediatric transfer center of Hunan Children's Hospital from January 2020 to January 2021. Medical data on critical care features and relevant data from the Pediatric Risk of Mortality (PRISMⅢ) scoring system were collected and processed. Three machine learning models, including logistic regression, decision tree, and Relief algorithm, were used to construct the risk prediction model. A back propagation neural network was employed to build a referral outcome prediction model to verify and analyze the selected medical features from the risk prediction model, exploring the key medical features influencing inter-hospital transfer risk.Results:Among the 549 transferred children included in the study, 222 were neonates (40.44%) and 327 were non-neonates (59.56%). There were 50 children in-hospital deaths, resulting in a mortality rate of 9.11%. After processing 151 critical care medical feature data points, each model selected the top 15 important features influencing transfer outcomes, with a total of 34 selected features. The decision tree model had an overlap of 72.7% with PRISMⅢ indicators, higher than logistic regression (36.4%) and Relief algorithm (27.3%). The training prediction accuracy of the decision tree model was 0.94, higher than the accuracy of 0.90 when including all features, indicating its clinical utility. Among the top 15 important features selected by the decision tree model, the impact on transfer outcomes was ranked as follows based on quantitative feature violin plots: base excess, total bilirubin, ionized calcium, total time, arterial oxygen pressure, blood parameters (including white blood cells, platelets, prothrombin time/activated partial thromboplastin time), carbon dioxide pressure, blood glucose, systolic blood pressure, heart rate, organ failure, lactate, capillary refill time, temperature, and cyanosis. Eight of these important features overlapped with PRISMⅢ indicators, including systolic blood pressure, heart rate, temperature, pupillary reflex, consciousness, acidosis, arterial oxygen pressure, carbon dioxide pressure, blood parameters, and blood glucose. The decision tree was used to select the top 15 medical features with high impact on the neonatal and non-neonatal datasets, respectively. A total of 19 features were selected, among which there were 8 differences and 11 overlap terms between the important features of the neonatal and non-neonatal.Conclusions:Machine learning models could serve as reliable tools for predicting the risk of inter-hospital transfer of critically ill children. The decision tree model exhibits superior performance and helps identify key medical features affecting inter-hospital transfer risk, thereby improving the success rate of inter-hospital transfers for critically ill children.

Previous studies mainly used β-amyloid and α-synuclein as the biomarkers for the diagnosis of neurodegenerative diseases. In recent years，studies have shown that telomeres at the end of chromosome can be used as an index to measure the degree of biological aging，and telomere length and telomerase activity may also be used as the blood markers to evaluate the risk，progression，and poor prognosis of neurodegenerative diseases in the elderly；however，there is still a lack of consistency between the research findings in China and globally. Understanding the role of telomere-related biomarkers in age-related diseases can help clinicians learn more about the mechanism of disease development and progression. This article reviews the latest research advances in the telomere-telomerase system in neurodegenerative diseases，in order to introduce the influence of telomere length and telomerase activity on neurodegenerative diseases and their potential mechanisms of action.
Telomere ; Telomerase ; Neurodegenerative Diseases ; Alzheimer Disease ; Parkinson Disease

Objective:To explore the clinical value of serum insulin combined with cardiac-related markers in evaluating the severity of sepsis associated encephalopathy (SAE).Methods:The clinical data of 130 children with sepsis who admitted to the Pediatric Intensive Care Unit of Hunan Children's Hospital from January 2018 to December 2021 were analyzed retrospectively, and the differences of serum insulin and cardiac-related markers in children with sepsis and SAE were compared.Results:The levels of serum insulin, creatine kinase isoenzyme, hypersensitive troponin T, and N-terminal cerebral urine peptide in the SAE group were significantly higher than those in the non-SAE group ( P<0.05), but there was no significant difference in heart rate and lactic acid ( P>0.05). The levels of serum insulin, creatine kinase isoenzyme, hypersensitive troponin T, N-terminal cerebral urine peptide and lactic acid in the death group were significantly higher than those in the survival group ( P<0.05), while the heart rate was not significantly different ( P>0.05). The area under ROC curve of serum insulin, creatine kinase isoenzyme, hypersensitive troponin T, and N-terminal cerebral urine peptide in predicting SAE were 0.841, 0.599, 0.700, and 0.667, respectively; in terms of judging the prognosis of sepsis, the area under ROC curve were 0.647, 0.669, 0.645, and 0.683, respectively; and in terms of judging the prognosis of children with SAE, the areas under the ROC curve were 0.509, 0.682, 0.666 and 0.555, respectively. Binary logistic regression equation was established with serum insulin, creatine kinase isoenzyme, hypersensitive troponin T, and N-terminal cerebral urine peptide: Y=8.153×NT-proBNP+1.704×CTnT-hs+27.121×insulin+0.946×CK-MB+1.573. The area under the ROC curve of the new variable Y in predicting sepsis SAE, evaluating the prognosis of sepsis, and predicting the prognosis of children with sepsis and SAE was 0.890, 0.756, and 0.729, respectively. Conclusions:Serum insulin, creatine kinase isoenzyme, hypersensitive troponin T, and N-terminal cerebral urine peptide can be used alone to determine the severity of sepsis and sepsis in children with SAE. The combined value of the four indicators is obviously better than that of the single indicator. The combined application of the four indicators may better evaluate the severity of sepsis and SAE.

Objective:To develop a questionnaire on knowledge, attitude and practice of Helicobacter pylori (Hp) infection among physical examination population, and to verify its reliability and validity.Methods:This study was a cross-sectional survey. Based on the theory of knowledge, attitude and practice, the first draft of the questionnaire on knowledge, attitude and practice of Hp infection in physical examination population was designed by means of retrospective literature research, qualitative interview, discussion in the research group. The structure and items of the questionnaire were consulted and revised by the expert consultation method (Delphi method). Through pre investigation of 186 physical examination personnel from May to June in 2021, the final version of the "knowledge, attitude and practice questionnaire on Helicobacter pylori infection among physical examination population" was formed after adjustment and verification of the reliability and validity of the questionnaire.Results:The questionnaire of knowledge, attitude and practice of Hp infection in physical examination population included 3 dimensions and 28 items, and the cumulative variance contribution rate was 56.271%. The content validity index of each item level of the questionnaire was 0.75-1.00, and the content validity index of the total questionnaire was 0.94. The Cronbach alpha of knowledge, attitude and practice dimensions in this questionnaire were 0.862, 0.901 and 0.798 respectively. The overall Cronbach alpha of the questionnaire was 0.890, and the half reliability was 0.698. The test-retest reliability of the questionnaire was 0.919, and the test-retest reliability of each dimension was 0.924, 0.917 and 0.845.Conclusions:The questionnaire has good reliability and validity, and can be used to measure the level of knowledge, attitude and practice of Hp infection in physical examination population.

Objective To investigate the relationship between sleep duration and obesity, and the risk of common chronic diseases in the occupational population in Shanghai City. Methods A total of 18 775 occupational individuals were selected as the study subjects using convenience sampling method in Shanghai City. Data on personal lifestyle behaviors and medical examination results were collected. The relationship between sleep duration and different types of obesity with dyslipidemia, hyperuricemia, hypertension, and hyperglycemia was analyzed. Results The incidence of dyslipidemia, hyperuricemia, hypertension, and hyperglycemia among the study subjects was 24.9%, 16.2%, 11.5%, and 7.3%, respectively. The incidence of these four chronic diseases were higher in individuals with central obesity and suboptimal sleep compared to the control group (all P<0.01). Multivariate logistic regression analysis showed that suboptimal sleep combined with general obesity/overweight increased the risk of dyslipidemia, hyperuricemia, hypertension, and hyperglycemia in the study subjects ［odds ratio (OR) were 2.40, 3.47, 3.30, and 2.79, respectively; all P<0.01］, after adjusting for age, gender, education level, marital status, occupation type, labor intensity, smoking, and drinking. Suboptimal sleep combined with central obesity also potentially increased the risk of these four chronic diseases (OR were 2.25, 3.09, 3.09, and 2.98, respectively; all P<0.01). Conclusion The incidence of common chronic diseases is relatively high in the occupational population in Shanghai City. Suboptimal sleep combined with different types of obesity increases the risk of common chronic diseases.

Objective:To provide evidence for the selection of fixation devices and CTV to PTV margins (M ptv) in precision radiotherapy for pelvic tumors by analyzing three fixation devices in precision radiotherapy for prostate cancer. Methods:From April 2015 to December 2020, 133 prostate cancer patients treated with pelvic drainage area irradiation in our center were retrospectively analyzed. The patients were fixed with 1.2m vacuum bag (n=39), 1.8m vacuum bag (n=44) and personalized prone plate by our center (n=50). Each patient was asked to complete our bowel and bladder preparation process before positioning and radiotherapy. The registration of CBCT to planned CT before each treatment adopted the same registration box and algorithm. Setup errors in the SI, LR and AP directions under qualified bowel and bladder conditions were recorded. Setup errors in three directions under three fixation devices and corresponding M ptv values were analyzed. The correlation between setup errors with age and body mass index (BMI) was analyzed. Results:Analysis of 3333 setup errors data showed: in the SI and LR directions, the mean setup errors of 1.2m vacuum bag (3.26mm, 2.34mm) were greater than those of 1.8m vacuum bag (2.51mm, P<0.001; 1.90mm, P<0.001), and personalized prone plate (3.07mm, P=0.066; 2.10 mm, P=0.009). In the AP direction, the mean setup errors of 1.2m vacuum bag (supine)(2.20mm) were smaller than those of 1.8m vacuum bag (3.33mm, P<0.001) and personalized prone plate (3.61mm, P<0.001). The setup errors of 1.8m vacuum bag in all directions were smaller than those of personalized prone plate (P≤0.028). According to Van Herk's expansion formula, the M ptv of 1.2m vacuum bag in three directions was approximately 4 mm. The M ptv of 1.8m vacuum bag and personalized prone plate in the SI and LR directions was approximately 3 mm, and more than 5 mm in the AP direction. The setup errors were not correlated with age or BMI. Conclusions:From the setup errors results of three devices, 1.8m vacuum bag is the best, followed by personalized prone plate. And supine position is better than prone position in the AP direction.

Objective: To investigate the clinicopathological and radiological features of benign fibro-osseous lesion (BFOL). Methods: Sixty-five cases of craniofacial BFOL, eight cases of peripheral ossifying fibroma (POF) and one case of low-grade central osteosarcoma diagnosed at Sichuan Provincial People′s Hospital between January 2010 and March 2019 were collected. The clinicopathologic features, hematoxylin-eosin and immunohistochemical (IHC) staining and radiographic features were analyzed. MDM2 gene amplification was detected by FISH in difficult borderline cases. Results: This cohort of BFOLs included 50 cases of fibrous dysplasia (FD), 12 cases of ossifying fibroma (OF), and three cases of juvenile psammomatoid ossifying fibroma (JPOF). The average ages of patients with FD,OF and JPOF were 31.7, 39.2 and 26.0 years respectively. The male to female ratio was 1.0∶1.8.The average age of POF was 47.0 years, with male to female ratio of 1∶7. Patient of low-grade central osteosarcoma was a 48-year-old man. Twenty-seven cases of FD were located in the jaw, and 23 cases were in other craniofacial bones. Nine cases of OF were located in the jaw, and three cases were in the nasal cavity. Two cases of JPOF were in the nasal sinus, and one was in the jaw. All POF were located in the gingiva, and low-grade central osteosarcoma was located in the mandible. The imaging features of FD were luffa-like or ground-glass like signal shadows with poorly defined borders with expansion. OF had clear borders or sclerosing margins. Both JOF and low-grade central osteosarcoma were expansile intraosseously and with focally invasive nodular masses with ground-glass like signal shadows; and POF showed soft tissue mass with bone formation. Histological features of BFOLs showed mixed fibrous and irregular osteoid lesions. FD had no clear relationship with the host bone and no osteoblasts surrounded the bone trabeculae. Osteoblasts rimming was found in OF, and the boundaries of the host bone were clear. JPOF and low-grade central osteosarcoma infiltrated the host bone focally, and the latter showed mild cellular atypia. MDM2 amplification was detected in low-grade central osteosarcoma. Conclusions BFOLs are a group of fibro-osseous lesions with similar morphology in the head and neck and face, but their clinical features and prognosis are different; and their imaging and histological characteristics are also slightly different. Attentions should be given to the combination of clinical, imaging and pathologic features of BFOLs, especially the differential diagnosis between BFOLs and low-grade central osteosarcoma. Molecular detection could be used to assist the diagnosis in difficult cases.

Objectives To determine the risk factors of ventilator-associated pneumonia (VAP) in the pediatric intensive care unit and to explore effective strategies to reduce the morbidity of VAP. Methods A retrospective analysis was conducted on 455 children admitted into the PICU of Hunan Children's Hospital from June 2014 to June 2017. The 455 children were divided into VAP group (n=43) and non-VAP group (n=412). The incidence of VAP was identified and risk factors were compared using the logistic regression analysis via SPSS 19.0 software.Results There were 311 males and 144 females with a median age of 11 months old (29 days to 9 years and 4 months). The incidence of VAP was 9.45% (43/455). Congenital laryngeal and trachea malformation with pulmonary infection was the first reason for the occurrence of VAP (23.3%), followed by congenital heart diseases with pulmonary infection (18.6%). Via univariate analysis, types of endotracheal intubation (χ2=45.33, P<0.001), duration of mechanical ventilation (Z=1.21, P=0.034), re-intubation (χ2=20.22, P=0.004), early usage of antibiotics (χ2=4.98, P=0.026),and methods of nutritional support(χ2=10.15,P=0.006)were identified as risk factors of VAP in the pediatric intensive care unit patients (P<0.05). Based on the multivariate logistic regression analysis, the followings were all independent predictor for VAP:types of endotracheal intubation(OR=1.87,95%CI:1.48~9.75),duration of mechanical ventilation(OR=1.14, 95%CI:1.08~2.35), re-intubation (OR=3.42, 95%CI:1.26~5.57), early usage of antibiotics (OR=4.55, 95%CI:2.21~8.77). Conclusions Many risk factors were found related with the occurrence of VAP. A comprehensive analysis of the host factors and iatrogenic factors should be conducted. Rational use of antibiotics and daily assessment of extubation might help reduce the incidence of VAP.