In recent decades, the global community has encountered several significant viral outbreaks, including the Ebola epidemic in West Africa, the Zika virus epidemic in South America, and the recent worldwide COVID-19 pandemic. In these instances, the deployment of effective vaccines has been instrumental in protecting public health. Nevertheless, as new challenges emerge in the prevention and management of infectious diseases, the traditional model of global vaccine development confronts both unprecedented opportunities and challenges. These circumstances underscore the limitations inherent in conventional vaccine development, particularly the protracted timelines and substantial costs involved. This article examines innovative approaches in contemporary vaccine clinical trials, investigates randomization techniques specific to vaccine studies, and delineates essential statistical considerations pertinent to vaccine trial design. The objective is to provide scientific support for vaccine development and to foster ongoing innovation and optimization within the realm of vaccine research and development.

Clinical research on rare diseases has always faced multiple challenges in clinical research design and implementation due to small sample sizes of patients,high heterogeneity,and limited research resources.The rapid development of digital intelligence technology has provided innovative solutions for rare disease research.This article systematically explores the current status and response strategies of clinical research on rare diseases in the digital intelligence age.On the one hand,the efficiency of rare disease research has been optimized through adaptive design,mixed trial mode,and precision medicine stratification methods.On the other hand,solutions based on digital technology have been proposed to address the practical challenges of recruitment difficulties and underrepresentation of rare disease clinical research patients,data management and technical barriers,and insufficient coverage of natural medical history and baseline databases through digital intelligence technology.By combining international collaboration,intelligent screening,and remote experiments,a multidisciplinary collaboration and international cooperation,adaptive design,digital data platform,and patient-centered remote research model have been constructed as the core implementation strategies.Typical cases demonstrate that digital intelligence technology not only effectively shortens the drug development cycle,but also significantly enhances patient benefits,providing a replicable practical paradigm for global rare disease research.The practice of digital platforms represented by the International Rare Disease Research Alliance and the China Rare Disease Diagnosis and Treatment Collaboration Network has further verified the feasibility and promotional value of the digitalization path.In summary,digital intelligence technology has shown considerable promise in overcoming the clinical research challenges of rare diseases and accelerating the development of treatment plans,providing systematic references for researchers,regulatory agencies,and patient organizations.It is expected to drive the clinical research of rare diseases towards a more efficient and accurate future.

In recent decades, the global community has encountered several significant viral outbreaks, including the Ebola epidemic in West Africa, the Zika virus epidemic in South America, and the recent worldwide COVID-19 pandemic. In these instances, the deployment of effective vaccines has been instrumental in protecting public health. Nevertheless, as new challenges emerge in the prevention and management of infectious diseases, the traditional model of global vaccine development confronts both unprecedented opportunities and challenges. These circumstances underscore the limitations inherent in conventional vaccine development, particularly the protracted timelines and substantial costs involved. This article examines innovative approaches in contemporary vaccine clinical trials, investigates randomization techniques specific to vaccine studies, and delineates essential statistical considerations pertinent to vaccine trial design. The objective is to provide scientific support for vaccine development and to foster ongoing innovation and optimization within the realm of vaccine research and development.

Clinical research on rare diseases has always faced multiple challenges in clinical research design and implementation due to small sample sizes of patients,high heterogeneity,and limited research resources.The rapid development of digital intelligence technology has provided innovative solutions for rare disease research.This article systematically explores the current status and response strategies of clinical research on rare diseases in the digital intelligence age.On the one hand,the efficiency of rare disease research has been optimized through adaptive design,mixed trial mode,and precision medicine stratification methods.On the other hand,solutions based on digital technology have been proposed to address the practical challenges of recruitment difficulties and underrepresentation of rare disease clinical research patients,data management and technical barriers,and insufficient coverage of natural medical history and baseline databases through digital intelligence technology.By combining international collaboration,intelligent screening,and remote experiments,a multidisciplinary collaboration and international cooperation,adaptive design,digital data platform,and patient-centered remote research model have been constructed as the core implementation strategies.Typical cases demonstrate that digital intelligence technology not only effectively shortens the drug development cycle,but also significantly enhances patient benefits,providing a replicable practical paradigm for global rare disease research.The practice of digital platforms represented by the International Rare Disease Research Alliance and the China Rare Disease Diagnosis and Treatment Collaboration Network has further verified the feasibility and promotional value of the digitalization path.In summary,digital intelligence technology has shown considerable promise in overcoming the clinical research challenges of rare diseases and accelerating the development of treatment plans,providing systematic references for researchers,regulatory agencies,and patient organizations.It is expected to drive the clinical research of rare diseases towards a more efficient and accurate future.

Objective This study aims to extract medical events from the ancient Chinese medical book"Treatise on Febrile Diseases"and explore their internal connections.By constructing an event evolution graph,this study visualizes the progression of diseases related to the three Yang and three Yin,provides new ideas for the digitization of ancient Chinese medical literature,and offers more intuitive learning and reference material for modern clinical practice and education in Traditional Chinese Medicine(TCM).Methods Taking the classic TCM literature"Treatise on Febrile Diseases"as the research subject,we initially used a combination of the BERT model and LSTM-CRF model to identify medical events and their argument constituents in the ancient text.Then,an improved SpERT model was employed to identify multi-event relationships.Finally,we constructed an event evolution graph of"Treatise on Febrile Diseases"with medical events as nodes and event relationships as edges,which represents the internal connections among medical events.Results The models mentioned above achieved a precision rate of 0.768,a recall rate of 0.761,and an F1 score of 0.772 for identifying medical events and their argument constituents.Additionally,achieving a precision rate of 0.736,a recall rate of 0.682,and an F1 score of 0.687 for recognizing complex event relationships.Through the above model,the text of Treatises of Febrile Diseases was extracted,and finally the theory graph was constructed by Neo4j,which contained 3518 medical events and 5294 event relationships.Conclusion The event evolution graph organizes medical events in a cohesive manner,facilitating understanding of the relationships among diseases,patterns,treatments,prescriptions,and outcomes.Therefore,it provides a multidimensional approach for learning and guiding clinical practice in TCM.

Objective This study aims to extract medical events from the ancient Chinese medical book"Treatise on Febrile Diseases"and explore their internal connections.By constructing an event evolution graph,this study visualizes the progression of diseases related to the three Yang and three Yin,provides new ideas for the digitization of ancient Chinese medical literature,and offers more intuitive learning and reference material for modern clinical practice and education in Traditional Chinese Medicine(TCM).Methods Taking the classic TCM literature"Treatise on Febrile Diseases"as the research subject,we initially used a combination of the BERT model and LSTM-CRF model to identify medical events and their argument constituents in the ancient text.Then,an improved SpERT model was employed to identify multi-event relationships.Finally,we constructed an event evolution graph of"Treatise on Febrile Diseases"with medical events as nodes and event relationships as edges,which represents the internal connections among medical events.Results The models mentioned above achieved a precision rate of 0.768,a recall rate of 0.761,and an F1 score of 0.772 for identifying medical events and their argument constituents.Additionally,achieving a precision rate of 0.736,a recall rate of 0.682,and an F1 score of 0.687 for recognizing complex event relationships.Through the above model,the text of Treatises of Febrile Diseases was extracted,and finally the theory graph was constructed by Neo4j,which contained 3518 medical events and 5294 event relationships.Conclusion The event evolution graph organizes medical events in a cohesive manner,facilitating understanding of the relationships among diseases,patterns,treatments,prescriptions,and outcomes.Therefore,it provides a multidimensional approach for learning and guiding clinical practice in TCM.

It is of great significance to analyze the configuration of programs funded by the National Natural Science Foundation of China using funding data in the field of preventive medicine from 2007 to 2021. The analysis mainly focused on the funding status of the program, funding amount, funded institutions and personnel. A total of 5 349 programs in the discipline of preventive medicine were funded over the last 15 years. The funding amount in this discipline accounted for a relatively low proportion in the total funding amount of the Department of Medical Sciences and this proportion also showed a decreasing trend. Non-infectious disease epidemiology, human nutrition, and health toxicology were the top three subdisciplines of all funded programs in preventive medicine. The proportion of programs led by young scholars was gradually rising over the years, and young scholars were playing an increasingly influential role in scientific research. The funding status among each subdiscipline and institution also varied. The results of this study reflected the thriving of preventive medicine. Meanwhile, some problems and dilemmas were also revealed in its current development. Hopefully, this study could provide valuable information for institutions and preventive medicine researchers to apply for National Natural Science Foundation, and promote the long-term development of preventive medicine in the future.

It is of great significance to analyze the configuration of programs funded by the National Natural Science Foundation of China using funding data in the field of preventive medicine from 2007 to 2021. The analysis mainly focused on the funding status of the program, funding amount, funded institutions and personnel. A total of 5 349 programs in the discipline of preventive medicine were funded over the last 15 years. The funding amount in this discipline accounted for a relatively low proportion in the total funding amount of the Department of Medical Sciences and this proportion also showed a decreasing trend. Non-infectious disease epidemiology, human nutrition, and health toxicology were the top three subdisciplines of all funded programs in preventive medicine. The proportion of programs led by young scholars was gradually rising over the years, and young scholars were playing an increasingly influential role in scientific research. The funding status among each subdiscipline and institution also varied. The results of this study reflected the thriving of preventive medicine. Meanwhile, some problems and dilemmas were also revealed in its current development. Hopefully, this study could provide valuable information for institutions and preventive medicine researchers to apply for National Natural Science Foundation, and promote the long-term development of preventive medicine in the future.

Objective:In the context of COVID-19 pandemic, the epidemic severities, non-pharmaceutical intervention intensities, individual behavior patterns and vaccination coverage vary with countries in the world. China has experienced a long period without indigenous cases, unfortunately, multi local outbreaks caused by imported cases and other factors have been reported, posing great challenges to COVID-19 prevention and control in China. Thus it is necessary to explore the mechanisms of the re-emerged COVID-19 epidemics and their differences.Methods:Based on susceptible exposed infectious recovered (SEIR) epidemic dynamics model, we developed a set of novel evolution equations which can describe the dynamic processes of integrated influence of interventions, vaccination coverage and individual behavior changes on the re-emergency of COVID-19 epidemic. We developed methods to calculate the optimal intervention intensity and vaccination rate at which the size of susceptible population can be reduced to less than threshold for the re-emergency of COVID-19 epidemic.Results:If strong interventions or super interventions are lifted too early, even a small cause can lead to the re-emergence of COVID-19 epidemic at different degrees. Moreover, the stronger the early control measures lifted are, the more severe the epidemic is. The individual behavior changes for the susceptibility to the epidemic and the enhancement or lifting of prevention and control measures are key factors to influence the incidence the multi outbreaks of COVID-19. The optimist early intervention measures and timely optimization of vaccination can not only prevent the re-emergency of COVID-19 epidemic, but also effectively lower the peak of the first wave of the epidemic and delay its arrival.Conclusion:The study revealed that factors for the re-emergence of COVID-19 epidemics included the intensity and lifting of interventions, the change of individual behavior to the response of the epidemic, external incentives and the transmissibility of COVID-19.

COVID-19 has brought a significant impact to the global health system, and also opportunities and challenges to epidemiological researches. Theoretical epidemiological models can simulate the process of epidemic in scenarios under different conditions. Therefore, modeling researches can analyze the epidemical trend of COVID-19, predict epidemical risks, and evaluate effects of different control measures and vaccine policies. Theoretical epidemiological modeling researches provide scientific advice for the prevention and control of infectious diseases, and play a crucial role in containing COVID-19 over the past year. In this study, we review the theoretical epidemiological modeling researches on COVID-19 and summarize the role of theoretical epidemiological models in the prevention and control of COVID-19, in order to provide reference for the combination of mathematical modeling and epidemic control.
COVID-19 ; Communicable Diseases/epidemiology* ; Humans ; Models, Theoretical ; SARS-CoV-2