[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 investigate the application and effect of a competency-oriented teaching model in standardized nutritional diagnosis and treatment training for clinical physicians.Methods:A blended teaching method combining online and offline lectures was used to teach core knowledge and skills of clinical nutrition among clinical physicians by implementing a step-by-step "popularization-strengthening-deepening" strategy. The number of nutritional consultations, the improvement in nutritional diagnosis and treatment among clinical physicians, and the degree of satisfaction after training were used as assessment indices.Results:Compared with the data in 2021, the number of annual nutritional consultations was increased by 21.41% in 2022 and 53.18% in 2023. A total of 281 clinical physicians received online deepening course training, among whom 237 (84.34%) completed the online clinical nutrition knowledge test, with a mean score of (86.17±5.48) points and a pass rate of 81.86% (194/237). The online training received a satisfaction rate of 80.39%.Conclusions:The training program designed with competency-based objectives, systematic content, and diverse methodologies can significantly enhance the standardized thinking and capabilities of clinical physicians in nutritional diagnosis and treatment.

Objective To investigate the awareness of female patients with pelvic floor dysfunction regarding minimally invasive laser treatment of the reproductive tract and analyze the factors influencing their decision-making intentions,this study aims to provide a foundation for early treatment of pelvic floor dysfunction and further development in reproductive health management.Methods A convenience sampling method was employed to select 164 female patients with pelvic floor dysfunction who sought treatment at the Pelvic Rehabilitation Center of Dongguan Maternal and Child Health Care Hospital between June 2023 and August 2024.The study utilized the Female Sexual Function Index,Incontinence Quality of Life Questionnaire,and Family Support Self-Assessment Scale to conduct a survey.Binary logistic stepwise regression analysis was conducted to investigate the factors influ-encing patients'inclination towards undergoing genital laser minimally invasive treatment.Results Among the 164 female patients,143(87.2%)expressed an intention to receive treatment,with 22.6%demonstrating a rela-tively clear understanding of genital laser minimally invasive treatment.Logistic regression analysis revealed that occupation significantly influenced treatment intention(P<0.05).Compared to healthcare professionals,individuals in the teaching profession(OR=10.81,95%CI:1.04～112.21),self-employed individuals(OR=20.34,95%CI:3.46～119.43),and those in other professions(OR=16.26,95%CI:4.05～65.29)were more inclined to express willingness for undergoing treatment.Furthermore,a lower score on the Incontinence Quality of Life scale was found to positively correlate with treatment intention(OR=0.96,95%CI:0.93～0.99).Conclusion Although patients express a high intention to undergo minimally invasive genital laser treatment,their overall awareness of the procedure remains insufficient.

Objective To investigate the awareness of female patients with pelvic floor dysfunction regarding minimally invasive laser treatment of the reproductive tract and analyze the factors influencing their decision-making intentions,this study aims to provide a foundation for early treatment of pelvic floor dysfunction and further development in reproductive health management.Methods A convenience sampling method was employed to select 164 female patients with pelvic floor dysfunction who sought treatment at the Pelvic Rehabilitation Center of Dongguan Maternal and Child Health Care Hospital between June 2023 and August 2024.The study utilized the Female Sexual Function Index,Incontinence Quality of Life Questionnaire,and Family Support Self-Assessment Scale to conduct a survey.Binary logistic stepwise regression analysis was conducted to investigate the factors influ-encing patients'inclination towards undergoing genital laser minimally invasive treatment.Results Among the 164 female patients,143(87.2%)expressed an intention to receive treatment,with 22.6%demonstrating a rela-tively clear understanding of genital laser minimally invasive treatment.Logistic regression analysis revealed that occupation significantly influenced treatment intention(P<0.05).Compared to healthcare professionals,individuals in the teaching profession(OR=10.81,95%CI:1.04～112.21),self-employed individuals(OR=20.34,95%CI:3.46～119.43),and those in other professions(OR=16.26,95%CI:4.05～65.29)were more inclined to express willingness for undergoing treatment.Furthermore,a lower score on the Incontinence Quality of Life scale was found to positively correlate with treatment intention(OR=0.96,95%CI:0.93～0.99).Conclusion Although patients express a high intention to undergo minimally invasive genital laser treatment,their overall awareness of the procedure remains insufficient.

Objective To construct a prediction model for the length of hospitalization in children with pneumonia based on clinical characteristics.Methods A retrospective analysis of the clinical data of 1 255 children with pneumonia was conducted.The patients were divided into two groups based on the median length of hospitalization:the≤7 days group(628 cases)and the>7 days group(627 cases).The differences between the two groups in demographic characteristics,past medical history,clinical manifestations,laboratory test results,imaging findings,treatment plans and other clinical data were compared.A multivariate stepwise Logistic regression analysis was performed to identify the factors influencing hospitalization for>7 days and to construct a prediction model.The model was evaluated using the receiver operating characteristic(ROC)curve and the clinical decision curve.Results Compared to the≤7 days group,children in the>7 days group were younger in gae,had higher height,a higher proportion of preterm infants,a higher proportion of previous pneumonia history,and a higher body temperatures at admission.Furthermore,in the>7 days group,white blood cell count,neutrophil count,platelet count,C-reactive protein(CRP)and procalcitonin levels were elevated.The proportion of bilateral lesions,oxygen therapy,respiratory support and pleural effusion were higher,while lymphocyte count and hemoglobin levels were lower(P<0.05).The results of the multivariate Logistic regression analysis showed that age(OR=0.979,95%CI:0.972-0.987),history of prematurity(OR=1.751,95%CI:1.216-2.521),previous history of pneumonia(OR=1.520,95%CI:1.037-2.228),admission temperature(OR=1.290,95%CI:1.097-1.518),serum CRP(OR=1.019,95%CI:1.013-1.025),pleural effusion(OR=1.980,95%CI:1.309-2.994)and oxygen therapy(OR=2.849,95%CI:1.851-4.385)were independent risk factors for a hospital stay>7 days in children with pneumonia.The model had an accuracy of 79.2%,and the area under the curve(AUC)was 0.919(95%CI:0.854-0.961).Conclusion The regression model constructed based on clinical characteristics can effectively predict the length of hospitalization in children with pneumonia.It provides scientific evidence for the early identification of high-risk children,optimization of treatment plans and shortening of hospital stays.

Objective To construct a prediction model for the length of hospitalization in children with pneumonia based on clinical characteristics.Methods A retrospective analysis of the clinical data of 1 255 children with pneumonia was conducted.The patients were divided into two groups based on the median length of hospitalization:the≤7 days group(628 cases)and the>7 days group(627 cases).The differences between the two groups in demographic characteristics,past medical history,clinical manifestations,laboratory test results,imaging findings,treatment plans and other clinical data were compared.A multivariate stepwise Logistic regression analysis was performed to identify the factors influencing hospitalization for>7 days and to construct a prediction model.The model was evaluated using the receiver operating characteristic(ROC)curve and the clinical decision curve.Results Compared to the≤7 days group,children in the>7 days group were younger in gae,had higher height,a higher proportion of preterm infants,a higher proportion of previous pneumonia history,and a higher body temperatures at admission.Furthermore,in the>7 days group,white blood cell count,neutrophil count,platelet count,C-reactive protein(CRP)and procalcitonin levels were elevated.The proportion of bilateral lesions,oxygen therapy,respiratory support and pleural effusion were higher,while lymphocyte count and hemoglobin levels were lower(P<0.05).The results of the multivariate Logistic regression analysis showed that age(OR=0.979,95%CI:0.972-0.987),history of prematurity(OR=1.751,95%CI:1.216-2.521),previous history of pneumonia(OR=1.520,95%CI:1.037-2.228),admission temperature(OR=1.290,95%CI:1.097-1.518),serum CRP(OR=1.019,95%CI:1.013-1.025),pleural effusion(OR=1.980,95%CI:1.309-2.994)and oxygen therapy(OR=2.849,95%CI:1.851-4.385)were independent risk factors for a hospital stay>7 days in children with pneumonia.The model had an accuracy of 79.2%,and the area under the curve(AUC)was 0.919(95%CI:0.854-0.961).Conclusion The regression model constructed based on clinical characteristics can effectively predict the length of hospitalization in children with pneumonia.It provides scientific evidence for the early identification of high-risk children,optimization of treatment plans and shortening of hospital stays.

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.