Since the first successful transcatheter aortic valve replacement (TAVR) was performed globally in 2002, the TAVR technology has become increasingly mature. With more than a decade of development in China, its application experience, device research and development, procedural improvements, evidence-based medicine, and guideline updates have continuously progressed, leading to a significant increase in the number of procedures conducted. Compared to traditional surgical operations, TAVR has different postoperative monitoring points and principles for the prevention and management of complications, necessitating the formulation of corresponding monitoring and treatment protocols that align with the technical characteristics of the procedure. This guideline is based on clinical practice and incorporates both domestic and international literature as well as the experiences of Fuwai Hospital. It distills and organizes routine postoperative monitoring practices, process optimization, and complication management for TAVR, establishing a set of practical guidelines for postoperative monitoring in China. These guidelines have strong practical value for optimizing postoperative management strategies and preventing and managing complications, which is beneficial for early functional recovery of patients, shortening hospital stays, and reducing complication rates. They provide guidance and reference for domestic peers and support the standardized development and quality improvement of postoperative management for TAVR in China.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

ObjectiveTo assess the predictive value of the bladder deformation index (BDI) in determining upper urinary tract (UUT) damage among patients with neurogenic bladder (NB). MethodsClinical data of 132 NB patients admitted to Beijing Bo'ai Hospital from January, 2015 to December, 2018 were retrospectively analyzed. Patients were divided into UUT damage group and normal UUT group according to the presence or absence of hydronephrosis. The demographics, biochemical parameters and video-urodynamics (VUDS) findings were collected, and BDI was calculated. Receiver operating characteristic (ROC) curves were utilized to evaluate the predictive capability. ResultsThere were 54 patients in UUT damage group and 33 in normal UUT group. The course of disease, creatinine level and BDI were siginificantly different between two groups (P < 0.05), while the area under the curve were 0.686, 0.836 and 0.928, respectively. ConclusionCourse of disease, creatinine level and BDI are associated with UUT damage in NB patients, and BDI demonstrates the highest sensitivity and specificity, which may play a role in diagnosis of UUT damage.

Objective:To develop a pre-processing workflow of real world data (RWD) derived from multicenter clinical laboratories so that the level of data standardization can be improved, and subsequently to produce more robust real world evidence (RWE).Methods:Purpose sampling was used to invite senior experts with experience in clinical research utilizing RWD, covering the fields of clinical laboratory, epidemiology, biostatistics, and clinical medicine. In-depth, semi-structured individual interviews were conducted and thematic analysis was used to analyze the collected data.Results:The in-depth interviews were completed in 16 experts. The experts unanimously agreed that pre-processing RWD derived from multicenter clinical laboratories is necessary prior to its application in research. Based on experts' insights, a comprehensive pre-processing workflow for RWD was constructed, comprising six key steps: ①developing a variable list based on research questions and distributing it to each clinical laboratory; ②conducting an initial quality assessment of RWD based on existing quality control results in clinical laboratories; ③cleaning the data; ④determining whether RWD (including categorical and continuous variables) is heterogeneity among different clinical laboratories; ⑤exploring potential sources of heterogeneity;⑥pre-processing RWD based on identified causes contributing to heterogeneity.Conclusion:The pre-processing workflow of RWD was established, to provide a methodological reference for controlling systematic errors in RWD derived from multicenter clinical laboratories, thereby enhancing the validity of RWE.

Objective:To develop a pre-processing workflow of real world data (RWD) derived from multicenter clinical laboratories so that the level of data standardization can be improved, and subsequently to produce more robust real world evidence (RWE).Methods:Purpose sampling was used to invite senior experts with experience in clinical research utilizing RWD, covering the fields of clinical laboratory, epidemiology, biostatistics, and clinical medicine. In-depth, semi-structured individual interviews were conducted and thematic analysis was used to analyze the collected data.Results:The in-depth interviews were completed in 16 experts. The experts unanimously agreed that pre-processing RWD derived from multicenter clinical laboratories is necessary prior to its application in research. Based on experts' insights, a comprehensive pre-processing workflow for RWD was constructed, comprising six key steps: ①developing a variable list based on research questions and distributing it to each clinical laboratory; ②conducting an initial quality assessment of RWD based on existing quality control results in clinical laboratories; ③cleaning the data; ④determining whether RWD (including categorical and continuous variables) is heterogeneity among different clinical laboratories; ⑤exploring potential sources of heterogeneity;⑥pre-processing RWD based on identified causes contributing to heterogeneity.Conclusion:The pre-processing workflow of RWD was established, to provide a methodological reference for controlling systematic errors in RWD derived from multicenter clinical laboratories, thereby enhancing the validity of RWE.

The management of antibiotic-resistant, bacterial biofilm infections in skin wounds poses an increasingly challenging clinical scenario. Pseudomonas aeruginosa infection is difficult to eradicate because of biofilm formation and antibiotic resistance. In this study, we identified a new benzothiazole derivative compound, SN12 (IC₅₀ = 43.3 nmol/L), demonstrating remarkable biofilm inhibition at nanomolar concentrations in vitro. In further activity assays and mechanistic studies, we formulated an unconventional strategy for combating P. aeruginosa-derived infections by targeting the two-component (Gac/Rsm) system. Furthermore, SN12 slowed the development of ciprofloxacin and tobramycin resistance. By using murine skin wound infection models, we observed that SN12 significantly augmented the antibacterial effects of three widely used antibiotics-tobramycin (100-fold), vancomycin (200-fold), and ciprofloxacin (1000-fold)-compared with single-dose antibiotic treatments for P. aeruginosa infection in vivo. The findings of this study suggest the potential of SN12 as a promising antibacterial synergist, highlighting the effectiveness of targeting the two-component system in treating challenging bacterial biofilm infections in humans.