Community-acquired pneumonia （CAP） refers to an infectious inflammation of the lung parenchyma （including the alveolar wall，that is，the broad pulmonary interstitium） acquired outside the hospital. Its common pathogens include streptococcus pneumoniae，respiratory viruses， mycoplasma pneumoniae， and so on. The related factors for the occurrence and development of CAP include patient characteristics （immune function，mucus production and clearance function，coagulation function，physical condition， and comorbidity） and pathogen characteristics （susceptibility，virulence，and antibiotic resistance）. The pathogenesis of CAP lies in immune deficiency，pathogen invasion，inflammatory response disorder，mucus production and clearance disorder， coagulation disorder， and so on. The pathogenesis of CAP in traditional Chinese medicine can be described as "vital Qi deficiency and toxic stasis". Vital Qi deficiency （lack of immunity） is the potential pathogenesis of the disease and easy to be invaded by external pathogens （respiratory pathogens）. Toxic stasis （inflammatory disorder，mucus production and clearance disorder，and coagulation dysfunction） is the key pathogenic factor. Vital Qi deficiency and toxic stasis are intermingled in a state of deficiency and excess，which suggests that the treatment of CAP lies in strengthening vital Qi and eliminating pathogenic factors. This involves strengthening vital Qi in the whole process to consolidate body resistance and nourish promordial Qi. It also involves clearing heat，eliminating phlegm，removing dampness，and dispelling stasis to dispel pathogenic toxins based on the syndrome differentiation. Its action mechanism is to regulate immune and inflammatory responses，resist pathogens，and improve mucus production and clearance， as well as coagulation disorders. Starting from the key pathogenesis of CAP，"vital Qi deficiency and toxic stasis"， this paper discussed the pathogenesis of CAP and summarized the action mechanism of vital Qi strengthening for detoxification in its treatment. It is intended to complement the theoretical system by identifying "vital Qi deficiency and toxic stasis" as the key pathogenesis underlying CAP and the scientific connotation of treating CAP with vital Qi strengthening for detoxification，thereby providing insights for its clinical application.

Objective To investigate the application effect of high flow humidified oxygen therapy combined with tracheotomy in elderly patients with cerebral hemorrhage and analyze the risk factors of pulmonary infection.Methods A total of 140 elderly patients with cerebral hemorrhage who underwent tracheotomy in our hospital from 2019 to 2023 were selected as the study objects,of which 93 patients receiving high-flow humidified oxygen therapy were selected as the observation group,and 47 patients receiving conventional low-flow oxygen therapy were selected as the control group during the same period.The changes of PaO2,SpO2,heart rate and mean arterial pressure were compared,and patients in the observation group were divided into infected group(n=26)and non-infected group(n=67)according to whether pulmonary infection occurred during hospitalization.The risk factors affecting pulmonary infection during hospitalization in elderly patients with cerebral hemorrhage were analyzed,and a nematographic prediction model was built to predict the risk of pulmonary infection.Results The PaO2 and SpO2 levels in observation group were higher than those in control group at 24 h and 72 h,but the respiratory rate was lower than that in control group(P＜0.05).The improvement of sputum viscosity in the observation group(68 cases of grade Ⅰ sputum and 25 cases of grade Ⅱ sputum)was better than that in the control group(16 cases of grade Ⅰ sputum,17 cases of grade Ⅱ sputum and 14 cases of gradeⅢ sputum).The rate of phlegm scab formation(0)and the number of sputum aspiration(5.15±1.08)times were lower than those in the control group[14.87％,(8.17±1.82)times](P＜0.05).There were significant differences in tracheotomy time,smoking history,bed rest time,mechanical ventilation time and nasal feeding tube retention time between infected and non-infected groups(P＜0.05).Logistic regression analysis showed that tracheotomy time＞5 d,smoking history,bed time＞10 d,mechanical ventilation time ≥24 h,nasal feeding tube retention time≥10 d were the risk factors for pulmonary infection in elderly patients with cerebral hemorrhage during treatment(P＜0.05).The AUC,sensitivity and specificity were 0.696,0.565 and 0.889 in elderly patients with cerebral hemorrhage complicated by pulmonary infection during treatment.Conclusion High-flow humidification oxygen therapy combined with tracheotomy can improve the oxygenation status in elderly patients with cerebral hemorrhage,but the time of tracheotomy,smoking history,bed rest time,mechanical ventilation time,and nasal feeding tube retention time will affect the pulmonary infection during treatment.The construction of a nomogram model based on these risk factors has higher predictive efficacy in evaluating the pulmonary infection.

The emergence of artificial intelligence(AI)has had a significant impact on medical research and practice,both in terms of the number of studies and research paradigms,and has become an important tool for the development of pharmacoepidemiology.However,traditional AI has faced many challenges,while facilitating pharmacoepidemiology research,such as complex data processing,difficulty in identifying drug exposures and potential outcomes,and time-consuming and laborious study design and implementation.The rapid development of generative AI,represented by large language models(LLMs),has demonstrated a unique potential to enhance research efficiency,shift research paradigms,and facilitate knowledge discovery.LLMs are equipped with natural language understanding and generation capabilities.Through deep mining of multi-dimensional data resources,LLMs can quickly and accurately extract,analyze,summarize,and present the required information,which can not only help drug discovery,drug repurposing,pharmacovigilance and other pharmacoepidemiological tasks,but also provide powerful support for the whole process of research protocol design,data analysis,result interpretation and paper publication.Driven by LLMs,pharmacoepidemiology research is gradually moving into a new stage based on big data and automated analysis.Of course,LLMs also have problems of data bias,"illusion"of results,and ethical and legal regulation.By strengthening interdisciplinary cooperation,establishing a standardized evaluation system,improving ethical and regulatory guidance,enhancing data quality,strengthening practitioner training and capacity building,and promoting human-machine collaborative research modes,it is expected that the potential of LLMs in pharmacoepidemiology will be fully released,and it will provide a more scientific,rapid,and efficient technological support for drug regulation and public health decision-making.

Objective:To describe the epidemiological characteristics of 22 common respiratory patho-gens in patients with pneumonia in Yinzhou,Ningbo,from January 1,2015 to December 21,2024.Methods:The test data of 22 common respiratory pathogens in patients diagnosed with pneumonia or lung infection in the Yinzhou Regional Health Information Platform from January 1,2015 to December 21,2024 were collected.The positive cases,positive rates,and positive proportions were calculated.The epidemiological characteristics were described by the year,sex,age group,season,and coronavirus disease 2019(COVID-19)pandemic period.Results:A total of 77 531 pneumonia patients were included,with 492 696 respiratory pathogen tests performed.The number of respiratory pathogen tests and positive cases of pneumonia patients in Yinzhou showed an upward trend.In the study,34.63％of the pneumo-nia patients tested positive for at least one pathogen,and the pathogen non-detection rate decreased from 79.44％in 2015 to 58.38％in 2024.The overall pathogen positive rate was 9.12％,which decreased during the COVID-19 pandemic and had not returned to the historical level after the COVID-19 pande-mic.The positive rate was highest in children aged 6-17 years(13.99％),and lowest in the elderly over 60 years(4.16％).The top 3 highest number of positive cases was Mycoplasma pneumoniae,influenza A virus,and influenza B virus;the top 3 highest positive rates of pathogen tests were Mycoplasma pneu-moniae(25.26％),rhinovirus(12.02％),and Bordetella pertussis(11.66％).The pathogen spectrum proportion in men was similar to that in women,only showing a higher ratio of Mycobacterium tuberculosis and a slightly lower ratio of Mycoplasma pneumoniae(P＜0.001).Mycoplasma pneumoniae,respiratory syncytial virus,and rhinovirus infections were more common in children,while influenza virus,Mycobac-terium tuberculosis,and Streptococcus pyogenes infections were more common in adults and the elderly(P＜0.001).Influenza virus and human metapneumovirus infections were more common in winter,rhi-novirus and Bordetella pertussis infections were more common in spring,and Mycoplasma pneumoniae in-fections were relatively more common in fall(P＜0.001).After the COVID-19 pandemic,the propor-tions of rhinovirus,respiratory syncytial virus,and human metapneumovirus infections in the pneumonia patients increased signi-ficantly,reaching 7.53％,4.26％,and 2.25％,respectively,while the propor-tions of influenza B virus and Mycobacterium tuberculosis infections decreased to 4.14％and 2.80％,re-spectively(P＜0.001).Conclusion:In the past decade,the scale of respiratory pathogen infection in the pneumonia population in Yinzhou had expanded significantly,and there were differences in distribu-tion by the year,gender,age group,and season.The respiratory pathogen spectrum in pneumonia pa-tients after the COVID-19 pandemic had a trend of diversification.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.

Dipsaci Radix is a commonly used yang tonifying medicine in clinical practice.Ancient books record that its preparation methods are diverse,mainly concentrated in the Ming and Qing dynasties,including wine soaking,wine washing,wine baking,wine stir frying,stir frying,wine mixing,and salt water stir frying.Wine roasting can promote blood circulation,dispel cold stagnation,and has been used throughout history;salt roasting has been seen in modern times,which can induce Chinese materia medica to descend and enhance liver and kidney tonifying effects;at present,it is mainly used for slicing raw materials,but there are also processed products such as wine fried products,salt fried products,stir fried slices,and charcoal slices.This article reviewed the herbal monographs,TCM ancient books,processing standards and modern literature,and combed the related elaboration of the processing history and modern processing research of Dipsaci Radix in the literature,so as to provide references for the processing mechanism,method research,clinical application and resource development and utilization of Dipsaci Radix.

Objective:To describe the epidemiological characteristics of 22 common respiratory patho-gens in patients with pneumonia in Yinzhou,Ningbo,from January 1,2015 to December 21,2024.Methods:The test data of 22 common respiratory pathogens in patients diagnosed with pneumonia or lung infection in the Yinzhou Regional Health Information Platform from January 1,2015 to December 21,2024 were collected.The positive cases,positive rates,and positive proportions were calculated.The epidemiological characteristics were described by the year,sex,age group,season,and coronavirus disease 2019(COVID-19)pandemic period.Results:A total of 77 531 pneumonia patients were included,with 492 696 respiratory pathogen tests performed.The number of respiratory pathogen tests and positive cases of pneumonia patients in Yinzhou showed an upward trend.In the study,34.63％of the pneumo-nia patients tested positive for at least one pathogen,and the pathogen non-detection rate decreased from 79.44％in 2015 to 58.38％in 2024.The overall pathogen positive rate was 9.12％,which decreased during the COVID-19 pandemic and had not returned to the historical level after the COVID-19 pande-mic.The positive rate was highest in children aged 6-17 years(13.99％),and lowest in the elderly over 60 years(4.16％).The top 3 highest number of positive cases was Mycoplasma pneumoniae,influenza A virus,and influenza B virus;the top 3 highest positive rates of pathogen tests were Mycoplasma pneu-moniae(25.26％),rhinovirus(12.02％),and Bordetella pertussis(11.66％).The pathogen spectrum proportion in men was similar to that in women,only showing a higher ratio of Mycobacterium tuberculosis and a slightly lower ratio of Mycoplasma pneumoniae(P＜0.001).Mycoplasma pneumoniae,respiratory syncytial virus,and rhinovirus infections were more common in children,while influenza virus,Mycobac-terium tuberculosis,and Streptococcus pyogenes infections were more common in adults and the elderly(P＜0.001).Influenza virus and human metapneumovirus infections were more common in winter,rhi-novirus and Bordetella pertussis infections were more common in spring,and Mycoplasma pneumoniae in-fections were relatively more common in fall(P＜0.001).After the COVID-19 pandemic,the propor-tions of rhinovirus,respiratory syncytial virus,and human metapneumovirus infections in the pneumonia patients increased signi-ficantly,reaching 7.53％,4.26％,and 2.25％,respectively,while the propor-tions of influenza B virus and Mycobacterium tuberculosis infections decreased to 4.14％and 2.80％,re-spectively(P＜0.001).Conclusion:In the past decade,the scale of respiratory pathogen infection in the pneumonia population in Yinzhou had expanded significantly,and there were differences in distribu-tion by the year,gender,age group,and season.The respiratory pathogen spectrum in pneumonia pa-tients after the COVID-19 pandemic had a trend of diversification.

Objective:To describe the hotspots and application trends of artificial intelligence (AI) in epidemiology in the past decade and analyze its advantages and challenges.Methods:The literatures with AI and epidemiology related keywords were systematically retrieved from Web of Science and China National Knowledge Infrastructure from 2014 to 2024. CiteSpace was used for bibliometric analysis of publication volume, keyword co-occurrence, clustering, emergence and cited literature co-occurrence analysis.Results:A total of 5 389 English papers and 1 659 Chinese papers were included, showing an increasing publication trend. High-frequency Chinese keywords included prediction, influencing factor, and machine learning, while English keywords frequently used were machine learning, prediction, and artificial intelligence. The Chinese keywords formed 14 clusters such as epidemiological characteristic, dietary pattern, and elderly individual, and the English keywords formed 21 clusters including prediction model, risk factor, and adult. In international studies, health policy, COVID-19, and digital health were the emerging frontier keywords. Eleven core papers were selected, covering key areas like traffic accident risk assessment, public health big data application, and deep learning in medical diagnosis.Conclusions:This study systematically summarized the research hotspots and development trends of AI applications in epidemiology over the past decade by using bibliometric methods, which indicated that current AI-based epidemiological studies are still in the exploratory phase, with the coexisting of both advantages and challenges. Continued attention should be paid to the future development of this field.

Objective·To develop a graph neural network(GNN)-based auxiliary diagnostic model for gallbladder cancer on CT images,and validate its accuracy and feasibility.Methods·From January 2010 to November 2023,1 774 contrast-enhanced CT arterial-phase images were acquired from 887 patients with normal gallbladder,benign gallbladder disease,or gallbladder cancer at Xinhua Hospital and Renji Hospital,Shanghai Jiao Tong University School of Medicine.These images were randomly divided into training and testing sets at a 4∶1 ratio to develop a hybrid GNN-convolutional neural network(CNN)model,named VJK-GIN.The model constructed a pixel-level graph in which each pixel served as a node,and spatial adjacency defined the edges,enabling extraction of local texture features.In the model architecture design,VJK-GIN integrated a three-layer graph isomorphism network,augmented with virtual nodes and jump-knowledge connections;global pooling compressed node features into a graph-level representation,which was classified by a multi-layer perceptron head.Five-fold cross-validation was used to compare VJK-GIN with GNN baselines(GCN,GraphSAGE,GAT,and GIN)and CNN baselines(ViT,EfficientNetV2,and ConvNeXt)in terms of accuracy,precision,recall,F1-score,and area under the receiver operating characteristic curve(AUC).Results·The results of five-fold cross-validation showed that VJK-GIN achieved an F1-score of 0.799(95%CI 0.775?0.823),recall of 0.795(95%CI 0.773?0.817),precision of 0.799(95%CI 0.775?0.823),AUC of 0.812(95%CI 0.792?0.832),and accuracy of 0.773(95%CI 0.748?0.798),surpassing all competing models across every metric.Conclusion·The VJK-GIN model exhibits high stability and accuracy in identifying contrast-enhanced CT images of normal,benign,and malignant gallbladder conditions.

Objective·To develop a graph neural network(GNN)-based auxiliary diagnostic model for gallbladder cancer on CT images,and validate its accuracy and feasibility.Methods·From January 2010 to November 2023,1 774 contrast-enhanced CT arterial-phase images were acquired from 887 patients with normal gallbladder,benign gallbladder disease,or gallbladder cancer at Xinhua Hospital and Renji Hospital,Shanghai Jiao Tong University School of Medicine.These images were randomly divided into training and testing sets at a 4∶1 ratio to develop a hybrid GNN-convolutional neural network(CNN)model,named VJK-GIN.The model constructed a pixel-level graph in which each pixel served as a node,and spatial adjacency defined the edges,enabling extraction of local texture features.In the model architecture design,VJK-GIN integrated a three-layer graph isomorphism network,augmented with virtual nodes and jump-knowledge connections;global pooling compressed node features into a graph-level representation,which was classified by a multi-layer perceptron head.Five-fold cross-validation was used to compare VJK-GIN with GNN baselines(GCN,GraphSAGE,GAT,and GIN)and CNN baselines(ViT,EfficientNetV2,and ConvNeXt)in terms of accuracy,precision,recall,F1-score,and area under the receiver operating characteristic curve(AUC).Results·The results of five-fold cross-validation showed that VJK-GIN achieved an F1-score of 0.799(95%CI 0.775?0.823),recall of 0.795(95%CI 0.773?0.817),precision of 0.799(95%CI 0.775?0.823),AUC of 0.812(95%CI 0.792?0.832),and accuracy of 0.773(95%CI 0.748?0.798),surpassing all competing models across every metric.Conclusion·The VJK-GIN model exhibits high stability and accuracy in identifying contrast-enhanced CT images of normal,benign,and malignant gallbladder conditions.