The Pharmacovigilance Guideline for Clinical Application of Chinese Patent Medicine for External Use （T/CACM 1563.5—2024）， the first guideline in China specializing for the clinical safety of Chinese patent medicines for external use， was led by the Institute of Basic Research in Clinical Medicine，China Academy of Chinese Medical Sciences，and jointly developed by more than 30 research institutions of medical sciences across the country. Aiming to standardize the pharmacovigilance activities in the clinical application of Chinese patent medicines for external use，the guideline systematically categorizes potential risks and proposes prevention and control measures that cover 11 core sections of risk monitoring and reporting， signal identification，as well as assessment and control， addressing the gap in domestic and international standardization of this field. The compilation of this guideline strictly adhered to international norms and domestic regulations， involving multiple rounds of expert consultations，hybrid interviews， and evidence integration （covering literature，medical insurance，essential medicine，pharmacopoeia data， and regulatory information）. With the scope of application defined to include medical institutions， pharmaceutical manufacturers and distribution enterprises，as well as regulatory authorities， the guideline focuses on key issues such as inherent medicine risks，quality risks，off-label use，risks of combination therapy，and the safety in special populations. During the compilation，core discrepancies such as the definition of application scope and quality risk control were addressed to ensure alignment with regulations such as the Drug Administration Law of the People's Republic of China and the Good Pharmacovigilance Practice. The guideline is registered internationally （PREPARE—2022CN463）. In the future，the implementation of the guideline will be promoted through hierarchical dissemination，dynamic revision，and post-effectiveness evaluation， contributing to rational clinical use and improved patient safety.

The Pharmacovigilance Guideline for Clinical Application of Chinese Patent Medicine for External Use （T/CACM 1563.5—2024）， the first guideline in China specializing for the clinical safety of Chinese patent medicines for external use， was led by the Institute of Basic Research in Clinical Medicine，China Academy of Chinese Medical Sciences，and jointly developed by more than 30 research institutions of medical sciences across the country. Aiming to standardize the pharmacovigilance activities in the clinical application of Chinese patent medicines for external use，the guideline systematically categorizes potential risks and proposes prevention and control measures that cover 11 core sections of risk monitoring and reporting， signal identification，as well as assessment and control， addressing the gap in domestic and international standardization of this field. The compilation of this guideline strictly adhered to international norms and domestic regulations， involving multiple rounds of expert consultations，hybrid interviews， and evidence integration （covering literature，medical insurance，essential medicine，pharmacopoeia data， and regulatory information）. With the scope of application defined to include medical institutions， pharmaceutical manufacturers and distribution enterprises，as well as regulatory authorities， the guideline focuses on key issues such as inherent medicine risks，quality risks，off-label use，risks of combination therapy，and the safety in special populations. During the compilation，core discrepancies such as the definition of application scope and quality risk control were addressed to ensure alignment with regulations such as the Drug Administration Law of the People's Republic of China and the Good Pharmacovigilance Practice. The guideline is registered internationally （PREPARE—2022CN463）. In the future，the implementation of the guideline will be promoted through hierarchical dissemination，dynamic revision，and post-effectiveness evaluation， contributing to rational clinical use and improved patient safety.

Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

Objective To discuss the method of constructing TCM word network and the subsequent optimization method;To solve the problems of natural language processing in the TCM-clinical decision support system using TCM terminology.Methods This study was based on several national standards and terminology dictionaries of TCM,such as Zhong Yi Lin Chuang Zhen Liao Shu Yu Ji Bing Part and Zhong Yi Yao Xue Zhu Ti Ci Biao,supplemented with related corpus on the Internet,and the framework of TCM word network was constructed with ontology as the guide.Word2Vec was used to load pre-trained models for similarity calculation,statistical and rule-based new word discovery,bibliometric analysis,and other methods to add or delete concept words and synonyms such as symptom terms and prescription terms.Results The TCM word network contained 55 semantic types and 13 semantic relationships,more than 110 000 concept words,and more than 300 000 synonyms.In addition to classifying concept words by semantic types,more detailed semantic classification was carried out in the upper and lower tree structures,in which more than 30 000 concept words had superordinate words,and nearly 2 600 concept words had subordinate words.At the same time,a process-based terminology supplementation mechanism,update review mechanism and cleaning mechanism were created.Conclusion TCM word network can not only be applied to the natural language processing process of TCM-clinical decision support system,but also can be applied to the normalization of data and the automatic construction of knowledge graph.

OBJECTIVE To investigate the characteristics of influenza A and influenza B viruses infections in the children aged between 0 and 14 years old after COVID-19 was downgraded to category B management of infectious diseases.METHODS From Jan.2023 to Feb.2024,a total of 2349 children aged between 0 and 14 years old who were treated in Beijing Hospital of Traditional Chinese Medicine,Capital Medical University due to influenza-like symptoms of infection and received nucleic acid testing for influenza A and influenza B viruses were recruited as the research subjects.The gender and age of the children as well as the seasons were observed by chi-square test.RESULTS Totally 2349 children were included in the study,and the total positive rate of influenza was 49.85％(1171/2349);the positive rate of influenza A virus was 36.36％(854/2349),the positive rate of influenza B virus was 13.92％(327/2349),and the positive rate of the mixed infections of influenza A virus and influenza B virus was 0.43％(10/2349).The positive rate of influenza A of the girls was the highest(44.17％)(x2=8.980,P=0.011)among the children aged less than 5 years old;the positive rate of influenza B of the boys was the highest(17.19％)(x2=8.378,P=0.015)among the children aged between 5 and 10 years old.There was significant difference in the positive rate of influenza A virus among the seasons in 2023 to 2024(x2=268.12,P＜0.001);the prevalence rate was 60.93％in spring,44.40％in autumn,22.01％in winter.There was significant difference in the positive rate of influenza B virus among the seasons in 2023 to 2024(x2=373.16,P＜0.001),and the preva-lence rate was 25.44％in winter.CONCLUSIONS The influenza viruses are prevalent in spring,autumn and winter from 2023 to 2024,and the influenza A is dominant.The positive rate of influenza viruses shows an upward trend among the children aged between 0 and 14 years old after the COVID-19 is downgraded to category B management of infectious diseases,with the peak of prevalence lagging behind.

Objective We propose a method for multi-label classification of traditional Chinese medicine(TCM)literature using graph neural networks to enhance sentence embeddings.This approach can effectively capture the relationships between similar articles.By integrating with the semantic information of the text,it improves classification performance.Methods Sentence embedding data of papers are obtained,and a heterogeneous network of traditional Chinese medicine literature is established.The representation information of papers on the heterogeneous network and their own sentence embedding information are learned through the GraphSAGE model of graph neural networks.The feature vectors obtained are then input into the model for multi-label classification.Results In a TCM literature dataset,the multi-label classification model based on graph neural networks achieved precision and F1 scores of 0.83 and 0.72,respectively,outperforming mainstream baseline models.Conclusion The effectiveness of the proposed method in the multi-label classification task for TCM journals.

Medical Immunology,characterized by its fundamental and cutting-edge nature,not only lays a solid foundation for students to learn other basic medical courses and clinical medicine curricula,but also serves as a powerful platform for cultivating deep learning and scientific thinking.This study aims to leverage Medical Immunology as a knowledge platform,adopt an AI+en-hanced flipped classroom approach,and build a thought-oriented classroom to enhance students'thinking skills,thereby meeting the high-quality requirements of the new medical education initiative for medical students.

Objective To investigate the clinical application value of blood routine parameters such as white blood cells(WBC),neu-trophils(NEU),lymphocytes(LYM),monocytes(MON),platelets(PLT),neutrophil to lymphocyte ratio(NLR),and platelet to lymphocyte ratio(PLR)and C-reactive protein(CRP)in children with influenza A(Flu A)virus infection.Methods 317 children with upper respiratory tract symptoms visited Department of Pediatrics,Affiliated Jiangning Hospital of Nanjing Medical University from September 2023 to May 2024 were retrospectively analyzed.According to the six kinds of nucleic acid test results of upper respiratory tract,the children were divided into the Flu A group(n=150)and disease control group(n=167).In addition,97 healthy children who underwent physical examination in our hospital during the same period were selected as the healthy control group.The finger blood of the children was collected for the detection of blood routine parameters and CRP.The Kruskal-Wallis H non-parametric test was used to compare the differences of these parameters among the three groups.The receiver operating characteristic(ROC)curve was drawn,and the area under the ROC curve(AUCROC)was used to evaluate the diagnostic value of each indicator for influenza A virus infection.Results The NLR and PLR in the Flu A group were significantly higher than those in the disease control group and healthy control group,while the levels of WBC and LYM were significantly lower than those in the disease control group and healthy control group(P＜0.05).The level of CRP in the Flu A group was higher than that in the healthy control group but lower than that in the disease control group(P＜0.05).The indicator with the best diagnostic efficacy for influenza A virus infection was LYM,whose AUCROC was 0.79.When the optimal diagnostic threshold was 1.55×109/L,its sensitivity and specificity were 0.620 and 0.837,respectively.Next were PLR and NLR,with AUCROC of 0.735 and 0.723,specificity of 0.754 and 0.826,and sensitivity of 0.693 and 0.553,respectively.The diagnostic efficacy of MON,PLT,and CRP was relatively low.The AUCROC of the combined detection of LYM,NLR,PLR,MON,PLT,and CRP for the diagnosis of influenza A virus infection could increase to 0.838.Conclusion The blood routine parameters of children with influenza A virus infection show a decrease in the level of LYM and an increase in NLR and PLR.The combined detection of LYM,NLR,PLR,MON,PLT,and CRP can improve the accuracy of early diagnosis of influenza A virus infection in children.

Medical Immunology,characterized by its fundamental and cutting-edge nature,not only lays a solid foundation for students to learn other basic medical courses and clinical medicine curricula,but also serves as a powerful platform for cultivating deep learning and scientific thinking.This study aims to leverage Medical Immunology as a knowledge platform,adopt an AI+en-hanced flipped classroom approach,and build a thought-oriented classroom to enhance students'thinking skills,thereby meeting the high-quality requirements of the new medical education initiative for medical students.

Objective:To investigate the radon exposure levels and health effects among non-uranium miners in Chongqing.Methods:From June 2022 to December 2023, a survey was carried out on basic information, radon exposure and occupational health status of 110 male miners with good cooperation attitudes in 6 non-uranium metal mines in normal operation in Chongqing. The logistic regression model was utilized to statistically analyze the impact of miners′ individual radon doses on abnormalities in chromosomal aberration analysis, micronucleus examination, tumor marker tests, and pulmonary CT imaging.Results:Monitoring results from passive integrated radon personal dosimeters indicated that three miners had an annual dose exceeding 2 mSv. Logistic regression analysis revealed that abnormalities in pulmonary CT imaging were associated with miners′ age, tenure, and alcohol consumption ( χ2=12.18, 9.63, 4.87, P<0.05), with no significant correlation with smoking or individual radon dose. There was a significant correlation between individual radon dose and abnormalities in tumor markers; for every additional 1 mSv of effective dose from radon and its progeny, the risk of abnormality increased by 2.25 times ( χ2=4.57, P < 0.05). Conclusions:Some miners in Chongqing are exposed to an annual dose exceeding 2 mSv, and there is a correlation between individual radon doses and abnormalities in lung cancer serum tumor markers. It is necessary to focus on monitoring the radon exposure risks for miners, carry out regular health checks, and enhance the health protection for the miner population.