[摘 要] 目的：探讨绿茶单体表没食子儿茶素没食子酸酯（EGCG）对大鼠肝癌发生过程中肠道菌群结构变化的影响。方法：建立二乙基亚硝胺（DEN）诱导的肝癌大鼠模型。将26只SD大鼠随机分成3组，分别为正常对照组、肝癌组和EGCG干预组。从实验开始第1天起，EGCG干预组每日给予EGCG（40 mg/kg）灌胃，正常对照组和肝癌组给予等量生理盐水灌胃，1次/日，持续至第20周。灌胃结束后，采集大鼠粪便样本，提取DNA，进行高通量16S rRNA V3-V4区测序；处死大鼠，取肝，观察肿瘤形成情况，并计算肝癌发生率。对测序数据进行生物信息学分析：经质控、聚类获得操作分类单元（OTU）表，据此计算α多样性指数（包括Observed species、Chao1、Shannon和Simpson指数），并进行β多样性分析。同时，对物种进行分类学注释，比较各组间菌群组成与丰度差异。结果： EGCG干预组大鼠（8只）肝脏肿瘤形成率明显低于肝癌组（10只）（50% vs 100%， P = 0.023），正常对照组（8只）大鼠无肿瘤发生。在肠道菌群方面，肝癌组操作分类单元（OTU）数量远低于正常对照组（P <0.001），而EGCG干预组OTU数量总体上高于肝癌组（P = 0.021）。α多样性分析显示，肝癌组Shannon指数低于正常对照组（P < 0.05）；此外，与肝癌组相比，EGCG干预组的Observed species指数、Chao1指数、Shannon指数和Simpson指数均显著提高（P < 0.05）。β多样性分析及主坐标分析（PCoA）表明，三组肠道菌群结构存在显著分离（PERMANOVA， R² = 0.3918， P = 0.001），其中EGCG干预组群落结构介于肝癌组与正常对照组之间，并更接近于正常对照组。肝癌组大鼠较正常大鼠肠道菌群中链球菌等潜在致病菌富集，丁酸产生相关菌（如丁酸球菌属、瘤胃球菌属等）丰度显著降低（P < 0.05）。相比之下，在EGCG干预肝癌发生过程中，大鼠肠道菌群结构相对稳定。厚壁菌门/拟杆菌门比值较肝癌组显著提高（P < 0.05），益生菌（如双歧杆菌属、乳杆菌属等）和丁酸产生相关菌（如丁酸球菌属）富集。结论：EGCG干预可降低DEN诱导的大鼠肝癌发生率，并有助于稳定肠道菌群结构，其作用可能与增加菌群多样性、促进益生菌及丁酸产生菌富集、恢复菌群平衡有关。

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

OBJECTIVE: To evaluate the effect and safety of Chinese medicine (CM) Fuzheng Huazhuo Decoction (FHD) in treating patients with coronavirus disease 2019 (COVID-19) who persistently tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: This retrospective cohort study was conducted at Shanghai New International Expo Center shelter hospital in China between April 1 and May 30, 2022. Patients diagnosed as COVID-19 with persistently positive SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) test results for ⩾8 days after diagnosis were enrolled. Patients in the control group received conventional Western medicine (WM) treatment, while those in the FHD group received conventional WM plus FHD for at least 3 days. The primary outcome was viral clearance time. Secondary outcomes included negative conversion rate within 14 days, length of hospital stay, cycle threshold (Ct) values of the open reading frame 1ab (ORF1ab) and nucleocapsid protein (N) genes, and incidence of new-onset symptoms during hospitalization. Adverse events (AEs) that occurred during the study period were recorded. RESULTS: A total of 1,765 eligible patients were enrolled in this study (546 in the FHD group and 1,219 in the control group). Compared with the control group, patients receiving FHD treatment showed shorter viral clearance time for nucleic acids [hazard ratio (HR): 1.500, 95% confidence interval (CI): 1.353-1.664, P<0.001] and hospital stays (HR: 1.371, 95% CI: 1.238-1.519, P<0.001), and a higher negative conversion rate within 14 days (96.2% vs. 82.6%, P<0.001). The incidence of new-onset symptoms was 59.5% in the FHD group, similar to 57.8% in the control group (P>0.05). The Ct values of ORF1ab and N genes increased more rapidly over time in the FHD group than those in the control group post-randomization (ORF1ab gene: β =0.436±0.053, P<0.001; N gene: β =0.415 ±0.053, P<0.001). The incidence of AEs in the FHD group was lower than that in the control group (24.2% vs. 35.4%, P<0.001). No serious AEs were observed. CONCLUSION FHD was effective and safe for patients with persistently positive SARS-CoV-2 PCR tests. (Registration No. ChiCTR2200063956).
Humans ; Drugs, Chinese Herbal/adverse effects* ; Retrospective Studies ; Male ; Female ; Middle Aged ; COVID-19 Drug Treatment ; SARS-CoV-2/drug effects* ; COVID-19/virology* ; Adult ; Aged ; Treatment Outcome

OBJECTIVE: To identify prognostic genes associated with lysosome-dependent cell death (LDCD) in patients with gastric cancer (GC). METHODS: Differentially expressed genes (DEGs) were identified using The Cancer Genome Atlas - Stomach Adenocarcinoma. Weighted gene co-expression network analysis was performed to identify the key module genes associated with LDCD score. Candidate genes were identified by DEGs and key module genes. Univariate Cox regression analysis, and least absolute shrinkage and selection operator regression and multivariate Cox regression analyses were performed for the selection of prognostic genes, and risk module was established. Subsequently, key cells were identified in the single-cell dataset (GSE183904), and prognostic gene expression was analyzed. Cell proliferation and migration were assessed using the Cell Counting Kit-8 assay and the wound healing assay. RESULTS: A total of 4,465 DEGs, 95 candidate genes, and 4 prognostic genes, including C19orf59, BATF2, TNFAIP2, and TNFSF18, were identified in the analysis. Receiver operating characteristic curves indicated the excellent predictive power of the risk model. Three key cell types (B cells, chief cells, and endothelial/pericyte cells) were identified in the GSE183904 dataset. C19orf59 and TNFAIP2 exhibited predominant expression in macrophage species, whereas TNFAIP2 evolved over time in endothelial/pericyte cells and chief cells. Functional experiments confirmed that interfering with C19orf59 inhibited proliferation and migration in GC cells. CONCLUSION C19orf59, BATF2, TNFAIP2, and TNFSF18 are prognostic genes associated with LDCD in GC. Furthermore, the risk model established in this study showed robust predictive power.
Stomach Neoplasms/pathology* ; Humans ; Prognosis ; Lysosomes/physiology* ; RNA-Seq ; Cell Death ; Single-Cell Analysis ; Gene Expression Regulation, Neoplastic ; Cell Proliferation ; Single-Cell Gene Expression Analysis

Since the promulgation of the first Drug Administration Law of the People's Republic of China 40 years ago in 1984, China has undergone four main stages in the traditional Chinese medicine(TCM) regulation: the initial establishment of TCM regulation rules(1984-1997), the formation of a modern TCM regulatory system(1998-2014), the reform of the review and approval system for new TCM drugs(2015-2018), and the construction of a scientific regulation system for TCM(2019-2024). Over the past five years, a series of milestone achievements of TCM regulation in China have been achieved in the six aspects, including its strategic objectives and the establishment of a science-based regulatory system, the reform of the review and approval system for new TCM drugs, the optimization and improvement of the TCM standard system and its formation mechanism, comprehensive enhancement of regulatory capabilities for TCM safety, international harmonization of TCM regulation and its role in promoting innovation. Looking ahead, centered on advancing TCMRS to establish a sound regulatory framework tailored to the unique characteristics of TCM, TCM regulation will evolve into new reform patterns, advancing and extending across eight critical fronts, including the legal framework and policy architecture, the review and approval system for new TCM drugs, the quality standard and management system of TCM, the comprehensive quality & safety regulation and traceability system, the research and transformation system for TCMRS, AI-driven innovations in TCM regulation, the coordination between high-quality industrial development and high-level regulation, and the leadership in international cooperation and regulatory harmonization. In this way, a unique path for the development of modern TCM regulation with Chinese characteristics will be pioneered.
Humans ; China ; Drugs, Chinese Herbal/standards* ; History, 20th Century ; History, 21st Century ; Medicine, Chinese Traditional/trends*

OBJECTIVE To establish a comprehensive evaluation system for lipid-lowering drugs aligned with the clinical reality of Liaoning province， and to assess the commonly used lipid-lowering drugs in medical institutions within Liaoning province. METHODS A comprehensive evaluation system for lipid-lowering drugs of Liaoning province was established using literature analysis and the Delphi expert consultation method. A calculation method of “indicator weighted score” to “step-by-step aggregation” to “percentile standardization” was employed to enable horizontal comparison of 49 commonly used lipid-lowering drugs. RESULTS This study successfully established a comprehensive clinical evaluation system for lipid-lowering drugs of Liaoning province， comprising 7 first-level indicators， 35 second-level indicators， and 116 scoring criteria. Among the first-level indicators， effectiveness （23%） had the highest weight， followed sequentially by safety （22%）， innovativeness （15%）， economy （13%）， suitability （13%）， accessibility （10%）， and other attributes （4%）. Among the 49 evaluated lipid-lowering drugs， the top 10 in overall score were all statins， with rosuvastatin calcium tablet B having the highest total score of 79.22. Inclisiran sodium injection had a total score of 57.14， performed outstandingly in safety （16.27） and innovativeness （11.27）. CONCLUSIONS This study established a relatively comprehensive and detailed clinical evaluation system for lipid-lowering drugs of Liaoning province. Statins remain the cornerstone of lipid management. Most patients can primarily choose drugs like rosuvastatin tablet B， while PCSK9 inhibitors and fixed-dose combinations can be considered for patients with conventional treatment failure or statin intolerance.

With the widespread application of ionizing radiation in many industries and the construction of nuclear power plants, the potentials for nuclear accidents is also increasing. In the event of a nuclear accident, rapid classification of a large population is generally involved, so accurate estimation of the radiation dose to the exposed population is the primary task of nuclear emergency response. Based on this need, World Health Organization and International Atomic Energy Agency have each established a worldwide network of biological dosimetry laboratories. In addition, regional networks of biological dosimetry laboratories have been established in the European Union, North America, Latin America and Asia. Based on the long-term organization of national training and assessment of biological dose estimation technology, China will also establish its own network of biological dosimetry laboratories in the future to cope with the emergency disposal needs of potential nuclear accidents. In this paper, the international biodosimetry network and related work will be reviewed, and the idea of establishing biodosimetry laboratory network in China will be elaborated.

Objective:To investigate the changes in the expression levels of long non-coding ribonucleic acids (lncRNAs) in human lymphocytes induced by low-dose ionizing radiation (LDIR) and the potential of lncRNAs as radiation biomarkers.Methods:Human immortalized lymphocytes (AHH-1) were irradiated with 0, 0.05, and 0.1 Gy of γ-rays at 24 h to extract RNAs for whole transcriptome sequencing. The sequencing was performed based on the 0, 0.05, and 0.1 Gy groups. The differentially expressed lncRNAs induced by LDIR were identified. The molecular functions, biological processes, and signaling pathway enrichment of differentially expressed genes were analyzed through the Gene Ontology (GO) analysis. Candidate lncRNAs were preliminarily validated using the qRT-PCR method. AHH-1 cells were irradiated with 0, 0.02, 0.05, 0.075, 0.1, and 0.2 Gy to extract the total RNAs at 4, 24, 48, 72, 96, and 120 h. The dose-response relationship of candidate lncRNAs was detected and analyzed. Peripheral blood sampled from eight healthy persons was irradiated with 0, 0.02, 0.05, 0.075, 0.1, and 0.2 Gy in vitro, followed by culturing for 24 h and 48 h to further verify the changes in the expression levels of radiation-responsive lncRNAs at the cellular level. Results:A total of 44 lncRNAs that were significantly up- or down-regulated after 0.05 and 0.1 Gy irradiation were initially identified through transcriptome sequencing. Among them, lncRNAs with over two-fold differential expression included SNHG1, SNHG15, NEAT1, and PRC1-AS1. At the cellular level, compared to 0 Gy, the relative expression level of PRC1-AS1 after 4 h to 48 h of γ-ray irradiation, was significantly elevated at 0.05, 0.075, and 0.1 Gy( t= -3.11 to 1.23, P < 0.05). In contrast, the relative expression level of NEAT1 was significantly up-regulated in a dose range of 0.02 to 0.1 Gy ( t=-2.47 to 2.10, P < 0.05). At the level of human peripheral blood, the relative expression levels of PRC1-AS1 and NEAT1 were significantly increased at 24 h after 0 to 0.2 Gy irradiation ( t=-3.79 to -1.96, P < 0.05). Conclusion:The PRC1-AS1 and NEAT1 with significant changes in expression levels serve as potential LDIR biomarkers.