T7 RNA polymerase (T7 RNAP) is one of the simplest known RNA polymerases. Its unique structural features make it a critical model for studying the mechanisms of RNA synthesis. This review systematically examines the static crystal structure of T7 RNAP, beginning with an in-depth examination of its characteristic “thumb”, “palm”, and “finger” domains, which form the classic “right-hand-like” architecture. By detailing these structural elements, this review establishes a foundation for understanding the overall organization of T7 RNAP. This review systematically maps the functional roles of secondary structural elements and their subdomains in transcriptional catalysis, progressively elucidating the fundamental relationships between structure and function. Further, the intrinsic flexibility of T7 RNAP and its applications in research are also discussed. Additionally, the review presents the structural diagrams of the enzyme at different stages of the transcription process, and through these diagrams, it provides a detailed description of the complete transcription process of T7 RNAP. By integrating structural dynamics and kinetics analyses, the review constructs a comprehensive framework that bridges static structure to dynamic processes. Despite its advantages, T7 RNAP has a notable limitation: it generates double-stranded RNA (dsRNA) as a byproduct. The presence of dsRNA not only compromises the purity of mRNA products but also elicits nonspecific immune responses, which pose significant challenges for biotechnological and therapeutic applications. The review provides a detailed exploration of the mechanisms underlying dsRNA formation during T7 RNAP catalysis, reviews current strategies to mitigate this issue, and highlights recent progress in the field. A key focus is the semi-rational design of T7 RNAP mutants engineered to minimize dsRNA generation and enhance catalytic performance. Beyond its role in transcription, T7 RNAP exhibits rapid development and extensive application in fields, including gene editing, biosensing, and mRNA vaccines. This review systematically examines the structure-function relationships of T7 RNAP, elucidates the mechanisms of dsRNA formation, and discusses engineering strategies to optimize its performance. It further explores the engineering optimization and functional expansion of T7 RNAP. Furthermore, this review also addresses the pressing issues that currently need resolution, discusses the major challenges in the practical application of T7 RNAP, and provides an outlook on potential future research directions. In summary, this review provides a comprehensive analysis of T7 RNAP, ranging from its structural architecture to cutting-edge applications. We systematically examine: (1) the characteristic right-hand domains (thumb, palm, fingers) that define its minimalistic structure; (2) the structure-function relationships underlying transcriptional catalysis; and (3) the dynamic transitions during the complete transcription cycle. While highlighting T7 RNAP’s versatility in gene editing, biosensing, and mRNA vaccine production, we critically address its major limitation—dsRNA byproduct formation—and evaluate engineering solutions including semi-rationally designed mutants. By synthesizing current knowledge and identifying key challenges, this work aims to provide novel insights for the development and application of T7 RNAP and to foster further thought and progress in related fields.

In recent years, the reform of the registration, evaluation, and approval system for traditional Chinese medicine(TCM) has been promoted at the national level, with establishment of an evaluation evidence system for TCM registration that combines TCM theory, human use experience, and clinical trials(known as the "three-combined" evaluation evidence system). This system, which aligns with the characteristics of TCM clinical practice and the laws of TCM research and development, recognizes the unique value of human use experience in medicine and returns to the essence of medicine as an applied science, thus receiving widespread recognition from both academia and industry. However, it meanwhile poses new and higher challenges. This article delves into the value and challenges faced by the "three-combined" evaluation evidence system from three perspectives: registration management, medical institutions, and the TCM industry. Furthermore, it discusses how the China Association of Chinese Medicine, leveraging its academic platform advantages and leading roles, has made exploratory and practical efforts to facilitate the research and development of new TCM drugs and the implementation of the "three-combined" evaluation evidence system.
Drugs, Chinese Herbal/standards* ; Humans ; Medicine, Chinese Traditional/standards* ; China ; Drug Development

Objective:To assess the association between diabetes mellitus and the risk of sudden cardiac death (SCD), and to identify potential contributing factors.Methods:This meta-analysis was an updated version of the original study Diabetes mellitus and the risk of sudden cardiac death： a systematic review and meta-analysis of prospective studies. The original review included all eligible case-control and cohort studies published in PubMed and Embase up to 2017 that investigated the association between diabetes and SCD risk. In this updated study, newly published studies were added, including those available in PubMed, Embase, China National Knowledge Infrastructure (CNKI), and WANFANG MED ONLINE up to December 3, 2023. Search terms included "diabetes""glucose""sudden cardiac death" "cardiac arrest" and their Chinese equivalent. The primary outcome was the risk of SCD, while factors such as country, ethnicity, skin color, follow-up duration, left ventricular ejection fraction (LVEF), baseline comorbidities, and other relevant variables were analyzed as potential influencing factors. Relative risk ( RR) was used as the summary measure. A random-effects model was used when significant heterogeneity was detected, otherwise a fixed-effects model was used. Cochran′s Q test was used for subgroup analysis to assess the influence of factors such as region, baseline diseases, LVEF, and ethnicity (based on skin color) on the outcomes. Results:A total of 32 cohort/case-control studies with a combined sample size of 3 252 954 individuals were included. The meta-analysis showed that the risk of SCD in patients with diabetes was double that of non-diabetics ( RR=2.00, 95% CI: 1.83-2.19, P<0.001). In Asian populations, the risk of SCD in diabetic patients was 1.78 times that of non-diabetic individuals ( RR=1.78, 95% CI: 1.51-2.10), 2.05 times that of in European populations ( RR=2.05, 95% CI: 1.79-2.34), and 2.12 times that of in American populations ( RR=2.12, 95% CI: 1.82-2.47), with no statistically significant heterogeneity between regions ( P=0.287). Among individuals without other baseline comorbidities, the risk of SCD was 2.12 times higher in diabetic patients than in those without diabetes ( RR=2.12, 95% CI: 1.89-2.38). In patients with baseline coronary heart disease, the risk was 1.75 times that of non-diabetics ( RR=1.75, 95% CI: 1.45-2.11). In those with baseline heart failure, the risk was 1.92 times that of non-diabetics ( RR=1.92, 95% CI: 1.51-2.43). In patients with baseline atrial fibrillation, the risk was 4.00 times that of non-diabetic individuals ( RR=4.00, 95% CI: 1.38-11.56). In patients undergoing hemodialysis due to renal failure, the risk was 1.76 times that of non-diabetic individuals ( RR=1.76, 95% CI: 1.25-2.48), with no statistically significant heterogeneity between groups ( P=0.262). In cardiac patients with LVEF>50%, the risk of SCD in diabetic patients was 2.08 times that of non-diabetic individuals ( RR=2.08, 95% CI: 1.57-2.75), and in those with LVEF<50%, the risk was 1.69 times that of non-diabetic individuals ( RR=1.69, 95% CI: 1.30-2.18), with no statistically significant heterogeneity between groups ( P=0.277). In yellow-skinned populations, the risk of SCD in diabetic patients was 1.80 times that of healthy individuals ( RR=1.80, 95% CI: 1.73-1.87), and in white-skinned populations, it was 2.18 times that of healthy individuals ( RR=2.18, 95% CI: 1.88-2.54), with statistically significant heterogeneity between groups ( P=0.014). Conclusions:Diabetes mellitus significantly increased the risk of SCD, and this effect may be more pronounced in white-skinned populations, while region, baseline comorbidities, and LVEF had no further effect.

Objective To explore the clinical efficacy of repeated transcranial magnetic stimulation(rTMS)and acupuncture therapy in the treatment of chronic insomnia disorder(CID)patients.Methods A total of 80 patients with CID,who were treated at Nanchang First Hospital from January 2022 to December 2023,were selected for the study.The patients were randomly divided into control group and treatment group,with 40 cases in each group.The control group patients were treated with dexmedetomidine,while the treatment group patients received rTMS and acupuncture therapy in addition to control group.The treatment course was 4 weeks,and the sleep quality,sleep related indicators,and psychological condition improvement of both groups of patients were observed before and after treatment.Results After treatment,the Pittsburgh sleep quality index scores of both groups of patients decreased(P＜0.05);The sleep latency and number of awakenings were lower than before treatment(P＜0.05),and the total sleep time,sleep efficiency,and proportion of rapid eye movement sleep were higher than before treatment,treatment group showed more significant improvement than control group(P＜0.05).After treatment,the Hamilton anxiety and depression scale scores of both groups of patients decreased compared to before treatment,but there was no statistically significant difference in control group before and after treatment(P＞0.05).However,there was a statistically significant difference in treatment group before and after treatment(P＜0.05).Conclusion The combination of rTMS and acupuncture treatment can significantly improve the sleep quality of CID patients,while also reducing the accompanying symptoms of anxiety and depression.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective To evaluate the efficacy and safety of the new left ventricular circulation assist device iVAC 2L in high-risk percutaneous coronaryintervention(HR-PCI)in Chinese patients.Methods We reported 6 PCIs in 5 patients supported by iVAC 2L,a new left ventricular circulation assist device,performed in Macao from September 2022 to March 2023.All patients were assessed by heart team and categorize to be high-risk for procedure.Clinical and intra-procedural data were analyzed.iVAC 2L-related complications and 30-day results were also documented.Results Insertion and removement of iVAC 2L successfully performed in all the 5 patients.Three of them underwent complete revascularization in the index procedure;one failed for the first time due to intolerance of the prolonged procedure,but succeeded for the reattempt of complete revascularization a month later,with the support of iVAC 2L.PCI was abandoned due to poor vessel condition.iVAC 2L,the new left ventricular circulation assist device,supported effectively during the 6 procedures.The patients were stable during the procedure.The success rate of hemodynamic support was 100％.No iVAC 2L-related complications and 30-day major adverse cardiac and cerebral events occurred,the 30-day survival was 100％.Conclusions Initial experience suggested that the new left ventricular circulation assist device iVAC 2L could provide effective and safe support in high-risk PCI.