Objective:This study aims to construct an evaluation index system suitable for the core competency of Clinical Research Coordinators (CRCs) in Investigator-Initiated Trials (IITs) in China.Methods:This study developed a system framework through the Onion Model, literature research, and expert interviews, utilized the Delphi method to build the index system. and analyzed the weight of each indicator through the Analytic Hierarchy Process (AHP).Results:Four first-level indicators were basic knowledge (0.143), job skills (0.300 8), professional quality (0.483 9), and personality traits (0.072 3). Besides, 18 second-level indicators and 49 third-level indicators were developed through the Delphi method. According to the third round expert′s consultation, the average scores of all indexes were >3.50, the authoritative coefficient was 0.86, the coefficient of variation of each index was <0.30, and Kendall coefficients of concordance were 0.183~0.366 ( P<0.001). The consistency ratios of single-sort were<0.1, and the overall sort of all indexes was 0.043 7, which showed good logical reliability. Conclusions:This evaluation index system for Clinical Research Coordinators is of great scientific sense. It provides IIT-conducting investigators in institutions with a proficient assessment tool to help them find qualified and reliable CRCs.

Objective:This study aims to explore the risk factors of Multi-center Investigator-Initiated Clinical Trials (MIITs), and provide a basis for developing study management strategies.Methods:The original draft of MIIT risk evaluation factors was determined through literature analysis and internal discussions of the research group. Thirty five experts were consulted using the Delphi method, and then the MIIT risk evaluation elements were finally determined. Analytic Hierarchy Process (AHP) was used to calculate the weights of each index.Results:The recovery rates of both rounds of expert consultation were 100%, and the degree of expert authority was 0.856. The study ultimately formed an MIIT risk evaluation framework consisting of three first-class indexes, twelve second-class indexes, and thirty-eight third-class indexes. The weight values of the first-class indexes (start-up period, implementation period, and summary period) were 0.209 8, 0.710 6, and 0.079 6, respectively. Meanwhile, the weight values of the second-class indexes and third-class indexes were determined.Conclusions:Exploring the risk evaluation factors of MIIT provides valuable insights into identifying critical risk points, which, in turn, contributes to enhancing MIIT management efficiency, research progress, and quality.

Objective:This study aims to explore the risk factors of Multi-center Investigator-Initiated Clinical Trials (MIITs), and provide a basis for developing study management strategies.Methods:The original draft of MIIT risk evaluation factors was determined through literature analysis and internal discussions of the research group. Thirty five experts were consulted using the Delphi method, and then the MIIT risk evaluation elements were finally determined. Analytic Hierarchy Process (AHP) was used to calculate the weights of each index.Results:The recovery rates of both rounds of expert consultation were 100%, and the degree of expert authority was 0.856. The study ultimately formed an MIIT risk evaluation framework consisting of three first-class indexes, twelve second-class indexes, and thirty-eight third-class indexes. The weight values of the first-class indexes (start-up period, implementation period, and summary period) were 0.209 8, 0.710 6, and 0.079 6, respectively. Meanwhile, the weight values of the second-class indexes and third-class indexes were determined.Conclusions:Exploring the risk evaluation factors of MIIT provides valuable insights into identifying critical risk points, which, in turn, contributes to enhancing MIIT management efficiency, research progress, and quality.

Objective:This study aims to construct an evaluation index system suitable for the core competency of Clinical Research Coordinators (CRCs) in Investigator-Initiated Trials (IITs) in China.Methods:This study developed a system framework through the Onion Model, literature research, and expert interviews, utilized the Delphi method to build the index system. and analyzed the weight of each indicator through the Analytic Hierarchy Process (AHP).Results:Four first-level indicators were basic knowledge (0.143), job skills (0.300 8), professional quality (0.483 9), and personality traits (0.072 3). Besides, 18 second-level indicators and 49 third-level indicators were developed through the Delphi method. According to the third round expert′s consultation, the average scores of all indexes were >3.50, the authoritative coefficient was 0.86, the coefficient of variation of each index was <0.30, and Kendall coefficients of concordance were 0.183~0.366 ( P<0.001). The consistency ratios of single-sort were<0.1, and the overall sort of all indexes was 0.043 7, which showed good logical reliability. Conclusions:This evaluation index system for Clinical Research Coordinators is of great scientific sense. It provides IIT-conducting investigators in institutions with a proficient assessment tool to help them find qualified and reliable CRCs.

Objective:To investigate the factors affecting the delivery of macrosomic infants in pregnant women with gestational diabetes mellitus, and utilize data to construct a nomogram model and validate it.Methods:A total of 1 493 pregnant women with gestational diabetes mellitus who received treatment at Shaanxi Provincial People's Hospital from January 2023 to January 2024 were included in this study. A retrospective study was conducted to analyze the proportion of pregnant women with gestational diabetes mellitus who delivered macrosomic infants. The influential factors in pregnant women with gestational diabetes who delivered macrosomic infants were analyzed using both univariate and multivariate analyses. A predictive model for macrosomia in pregnant women with gestational diabetes mellitus was established, and its predictive efficiency was evaluated.Results:Among the 1 493 pregnant women with gestational diabetes mellitus included in this survey, 51 delivered macrosomic infants, accounting for 3.42%. Univariate analysis revealed that weight gain during pregnancy [(14.11 ± 3.25) kg vs. (10.62 ± 2.72) kg, t = 8.94], pre-pregnancy body mass index [(24.31 ± 2.51) kg/m2 vs. (23.25 ± 2.13) kg/m2, t = 8.94], gestational week [(39.14 ± 0.42) weeks vs. (38.92 ± 0.51) weeks, t = 3.04], fasting blood glucose [(5.15 ± 0.41) mmol/L vs. (4.75 ± 0.35) mmol/L, t = 7.97], blood glucose level during the oral glucose tolerance test at 0 hours [(5.71 ± 0.42) mmol/L vs. (5.49 ± 0.41) mmol/L, t = 3.76], and insulin resistance index [(0.54 ± 0.13) vs. (0.41 ± 0.10), t = 9.02] had a significant impact on the delivery of macrosomic infants (all P < 0.05). Multivariate logistic analysis indicated that weight gain during pregnancy, fasting blood glucose, and insulin resistance index were independent risk factors for delivering macrosomic infants in pregnant women with gestational diabetes mellitus ( OR = 1.685, 27.113, 25.816, all P < 0.05). The multivariate logistic regression model demonstrated good goodness of fit (Hosmer-Lemeshow χ2 = 10.34, P > 0.05). Based on the factors identified through multivariate analysis, a nomogram risk model was constructed, yielding a C-index of 0.742. The independent risk factors from the logistic regression model and their prediction probabilities were utilized to generate the receiver operating characteristic curve for predicting the likelihood of delivering macrosomic infants among pregnant women with gestational diabetes mellitus. The areas under the curve were 0.815, 0.779, 0.795, and 0.938, respectively. Conclusion:The predictive model established based on weight gain during pregnancy, fasting blood glucose levels, and the insulin resistance index demonstrates significant predictive value for the delivery of macrosomic infants in pregnant women with gestational diabetes mellitus.

OBJECTIVE To construct the drug clinical comprehensive evaluation index system and quantitative grading in China， and to provide a reference for scientifically carrying out comprehensive clinical evaluation of drugs. METHODS The analytic hierarchy model was used to establish the drug clinical comprehensive evaluation index system， the weight of the evaluation index and the quantitative grading of each index were determined through expert consultation and model calculation. RESULTS The results of expert consultation were integrated by using the analytic hierarchy model， and the drug clinical comprehensive evaluation index system was obtained： including six first-level indicators of effectiveness， safety， economy， suitability， accessibility and innovation， as well as twenty-three second-level indicators of recommended status， medication for special populations， and drug treatment costs； the weight of each indicator was calculated through estimation-matrix method. CONCLUSIONS The analytic hierarchy model can construct the drug clinical comprehensive evaluation index system and quantitative grading in China， which can provide methodological references for comprehensive analysis and decision-making， thus making the clinical comprehensive evaluation of drugs completer and more scientific.

Humans ; Nitric Oxide ; Carcinoma, Non-Small-Cell Lung/drug therapy* ; Lung Neoplasms ; Lung ; Nitric Oxide Synthase ; Macrophages, Alveolar ; Pulmonary Alveoli

At present， there are still some problems in China’s clinical comprehensive drug evaluation， such as the unscientific design of the evaluation content， the nonstandard evaluation method and organizational process， and the evaluation results not meeting the decision-making needs. It is urgent to carry out quality control over the whole process of the clinical comprehensive drug evaluation project. From the technical point of view， the quality control methods of clinical comprehensive drug evaluation are discussed through three links of the evaluation content and design （giving the quality control key points of the theme selection process and scheme design）， the evaluation method （discussing the quality control elements of two common evaluation methods， i. e. documentary evidence method and real-world research） and result application transformation （giving suggestions on quality control from the comprehensive analysis of evaluation results， transformation of evaluation results and decision-making）， so as to promote the quality improvement of clinical comprehensive drug evaluation.

Aristolochic acids (AAs) have long been considered as a potent carcinogen due to its nephrotoxicity. Aristolochic acid I (AAI) reacts with DNA to form covalent aristolactam (AL)-DNA adducts, leading to subsequent A to T transversion mutation, commonly referred as AA mutational signature. Previous research inferred that AAs were widely implicated in liver cancer throughout Asia. In this study, we explored whether AAs exposure was the main cause of liver cancer in the context of HBV infection in mainland China. Totally 1256 liver cancer samples were randomly retrieved from 3 medical centers and a refined bioanalytical method was used to detect AAI-DNA adducts. 5.10% of these samples could be identified as AAI positive exposure. Whole genome sequencing suggested 8.41% of 107 liver cancer patients exhibited the dominant AA mutational signature, indicating a relatively low overall AAI exposure rate. In animal models, long-term administration of AAI barely increased liver tumorigenesis in adult mice, opposite from its tumor-inducing role when subjected to infant mice. Furthermore, AAI induced dose-dependent accumulation of AA-DNA adduct in target organs in adult mice, with the most detected in kidney instead of liver. Taken together, our data indicate that AA exposure was not the major threat of liver cancer in adulthood.

Corona virus disease 2019 (COVID-19) patients with trauma are at high risk of venous thromboembolism (VTE), which must be taken seriously in the therapeutic processes. Hypercoagulable state is induced by 2019 novel coronavirus (2019-nCoV) in many ways, such as increasing the level of inflammatory factors and fibrinogen, and inducing endothelial cell injury. The venous wall injuries from trauma and operation directly or indirectly trigger off the exogenous coagulation pathway and the microcirculation can be damaged at the same time, which may initiate the exogenous pathway of VTE. Immobilization of limbs and forced bed rest during the treatment of traumatic patients will slow venous blood flow. Chronic non-communicable diseases such as diabetes in the elderly were independent risk factors for VTE. Furthermore, the persistent fever, severe lung disease, respiratory failure, sepsis and invasive technology application add the risk of VTE and the difficulty of treatment. In order to help effective prevention VTE of for COVID-19 patients with trauma, the authors put forward relevant technical suggestions for prevention and nursing of VTE to provide basis for nursing work during pandemic of COVID-19.