Puji Xiaoduyin， a specialized formula for the swollen-head epidemic， was recorded in the Catalogue of Ancient Classical Formula （the Second Batch）-Han Medicine， published in September 2023. It had been inherited and developed by medical experts of successive generations and passed down to this day. This paper sorted out the historical evolution of this formula using bibliometric methods. It also comprehensively analyzed key information on the formula name， historical origin， drug dosage， herb origin， processing methods， decocting methods， function， and clinical applications. Additionally， this paper analyzed the application of this formula in both modern and ancient times. Results showed that the formula was first recorded as "Puji Xiaodu Yinzi" in LI Dongyuan's Proven Formulas written by LI Gao from the Jin dynasty. The medicinal composition and dosage were： Scutellariae Radix and Coptidis Rhizoma （20.65 g each）， Ginseng Radix et Rhizoma 12.39 g， Scrophulariae Radix， Citri Reticulatae Pericarpium， and Glycyrrhizae Radix et Rhizoma （8.26 g each）， Forsythiae Fructus， Arctii Fructus， Isatidis Radix， and Lasiosphaera Calvatia （4.13 g each）， Bombyx Batryticatus and Cimicifugae Rhizoma （2.891 g each）， Bupleuri Radix and Platycodonis Radix （8.26 g each）. These medicines were grounded to fine powder. One dose， including 20.65 g of the powder， was mixed with 600 mL of water and decocted to 300 mL. After abandoning slag， the medicine should be taken warm frequently. In the formula， Bombyx Batryticatus is stir-fired. With the effect of dispersing wind and clearing heat， removing stagnation and dissipating mass， the formula is specialized in swollen-head epidemic， pestilence， red and swelling head， face， and neck， dry mouth and tongue， as well as other diseases resulting from toxic heat stagnated in the upper jiao. The formula is widely used in treating diseases involving the respiratory， dermal， ophthalmologic， otolaryngologic， and nervous systems. The formula is most frequently used for respiratory diseases， with a wide range of symptoms including parotitis/mumps （66 times）， followed by tonsillitis （28 times）. In conclusion， the broadly applied formula has accurate efficacy and great development value.

Puji Xiaoduyin， a specialized formula for the swollen-head epidemic， was recorded in the Catalogue of Ancient Classical Formula （the Second Batch）-Han Medicine， published in September 2023. It had been inherited and developed by medical experts of successive generations and passed down to this day. This paper sorted out the historical evolution of this formula using bibliometric methods. It also comprehensively analyzed key information on the formula name， historical origin， drug dosage， herb origin， processing methods， decocting methods， function， and clinical applications. Additionally， this paper analyzed the application of this formula in both modern and ancient times. Results showed that the formula was first recorded as "Puji Xiaodu Yinzi" in LI Dongyuan's Proven Formulas written by LI Gao from the Jin dynasty. The medicinal composition and dosage were： Scutellariae Radix and Coptidis Rhizoma （20.65 g each）， Ginseng Radix et Rhizoma 12.39 g， Scrophulariae Radix， Citri Reticulatae Pericarpium， and Glycyrrhizae Radix et Rhizoma （8.26 g each）， Forsythiae Fructus， Arctii Fructus， Isatidis Radix， and Lasiosphaera Calvatia （4.13 g each）， Bombyx Batryticatus and Cimicifugae Rhizoma （2.891 g each）， Bupleuri Radix and Platycodonis Radix （8.26 g each）. These medicines were grounded to fine powder. One dose， including 20.65 g of the powder， was mixed with 600 mL of water and decocted to 300 mL. After abandoning slag， the medicine should be taken warm frequently. In the formula， Bombyx Batryticatus is stir-fired. With the effect of dispersing wind and clearing heat， removing stagnation and dissipating mass， the formula is specialized in swollen-head epidemic， pestilence， red and swelling head， face， and neck， dry mouth and tongue， as well as other diseases resulting from toxic heat stagnated in the upper jiao. The formula is widely used in treating diseases involving the respiratory， dermal， ophthalmologic， otolaryngologic， and nervous systems. The formula is most frequently used for respiratory diseases， with a wide range of symptoms including parotitis/mumps （66 times）， followed by tonsillitis （28 times）. In conclusion， the broadly applied formula has accurate efficacy and great development value.

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 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.

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 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 understand the reliability of caloric test results through the analysis of the pod shape dia-gram types of caloric test and to provide a more accurate and reliable basis for vestibular system function evaluation.Meth-ods The results of caloric test in 528 patients from July 2021 to May 2022 in vertigo diagnosis and treatment center were analyzed.According to pod pattern and unilateral weakness(UW),the patients were divided into five groups:cold and warm test symmetrical group,left and right side asymmetry group,single value significant abnormal group,bilateral hypo-plasia group and pod morphology unable to be classified group.Unreliable data can be identified by comparing,explaining,and summarizing the results of UW of different groups,so as to reduce the interference of unreliable data to clinical diagno-sis and treatment.Results Cold and warm test symmetric group(n=439)included patients with symmetry of bilateral vestibular function(n=273)and decreased vestibular function of one side(n=166).Left and right symmetrical groups in-eluded pationts with bilateral symmetric vestibular function(n=11)and unilateral reduction(n=13).Single value signifi-cant abnormal group showed cases with unilateral vestibular function reduction(n=25)and bilateral symmetric vestibular function(n=11).Bilateral hypolasion group showed cases with bilateral symmetric vestibular function(n=25)and unilat-eral reduction(n=9).Pod morphology unclassified group(n=5)suggested varions interference factors.Conduson When the function of the peripheral vestibular is symmetrical and normal,we should be alert to the existence of temperature effect,and should consider the inspection of the perfusion apparatus and the calibration of the irrigation temperature.When the function of the peripheral vestibular shows unilateral weakness,repeating the irrigation is recommended when the single value of the caloric test significantly increases or decreases.If there is no change,repeating the irrigation at the same tem-perature in the other ear or even repeating the whole caloric test are recommended.The UW value could not truly reflect the state of peripheral vestibular function when the caoric test produces bilateral weakness.Therefore,water irrigation could be used to make a correct judgment based on the medical history and other auxiliary examinations.It is of great significance to judge the effectiveness of caloric test comprehensively by combining the morphology of pod diagram with UW value.

Objective To analyze the clinicopathological characteristics and prognosis of oligodendroglioma with IDH mutation and 1p/19q codeletion. Methods We collected the data of 54 oligodendroglioma patients with IDH mutation and 1p/19q codeletion.The patients'clinicopathological data, including age, histological grade, and tumor site, were analyzed for the effects on progression-free and overall survival. Results Among the 54 patients, 46 cases were with tumor sites in one lobe, and eight cases involved tumor sites in more than two lobes.A total of 12 and 42 cases had WHO grades 2 and 3 oligodendroglioma, respectively.Detection by fluorescence in situ hybridization showed 1p/19q co-deletion in all cases.Immunohistochemical tests revealed diffuse and strong positive results for Olig2.All glial fibrillary acidic proteins were positive.p53 was strongly positive in six cases.ATRX was expressed in all 48 cases.Ki-67 proliferation index ranged from 5% to 60%.Sanger sequencing showed that all 54 cases had IDH gene mutations (40 cases were IDH1 mutations, and 14 were IDH2 mutations), and 33 cases had telomerase reverse transcriptase promoter mutations.Relapse and metastasis occurred in 16 patients during treatment.Univariate analysis indicated that the postoperative recurrence and metastasis interval of more than two years can prolong the progression-free and overall survival of patients.All 54 patients had a mean progression-free survival of 33.5 months and the mean overall survival of 40.7 months. Conclusion For oligodendroglioma with IDH mutation and 1p/19q codeletion, precision chemoradiotherapy after surgery can reduce the risk of progression, and the postoperative recurrence and metastasis interval is associated with the prognosis.

The lung is one of the most common sites for cancer metastasis. Collagens in the lung provide a permissive microenvironment that supports the colonization and outgrowth of disseminated tumor cells. Therefore, down-regulating the production of collagens may contribute to the inhibition of lung metastasis. It has been suggested that miR-29 exhibits effective anti-fibrotic activity by negatively regulating the expression of collagens. Indeed, our clinical lung tumor data shows that miR-29a-3p expression negatively correlates with collagen I expression in lung tumors and positively correlates with patients' outcomes. However, suitable carriers need to be selected to deliver this therapeutic miRNA to the lungs. In this study, we found that the chemotherapy drug cisplatin facilitated miR-29a-3p accumulation in the exosomes of lung tumor cells, and this type of exosomes exhibited a specific lung-targeting effect and promising collagen down-regulation. To scale up the preparation and simplify the delivery system, we designed a lung-targeting liposomal nanovesicle (by adjusting the molar ratio of DOTAP/cholesterol-miRNAs to 4:1) to carry miR-29a-3p and mimic the exosomes. This liposomal nanovesicle delivery system significantly down-regulated collagen I secretion by lung fibroblasts in vivo, thus alleviating the establishment of a pro-metastatic environment for circulating lung tumor cells.

Objective: To investigate the role of body fat ratio in the evaluation of obstructive sleep apnea(OSA). Methods: A retrospective analysis was made on 174 cases (between November, 2017 and April, 2018 showed that) of sleep monitoring in the Department of Otorhinolaryngology in Peking University Third Hospital. The data included the gender, age, body fat rate, body mass index (BMI), neck circumference, and apnea-hypopnea index (AHI). The above data were analyzed by non parametric correlation analysis, receiver operating characterristic (ROC) curve analysis and multiple factor Logistic regression analysis to study the relationship between the gender,age,body fat rate,BMI,neck circumference and other indexes of the patients with AHI. Results: Nonparametric correlation analysis showed that the correlation from strong to weak to AHI among women was BMI (r=0.621, P<0.001),body fat rate (r=0.602, P<0.001), age (r=0.570, P<0.001), neck circumference (r=0.402, P=0.014), respectively. BMI (r=0.599, P<0.001), neck circumference (r=0.493, P<0.001), body fat rate (r=0.318, P<0.001), and age (r=0.256, P=0.003) among men. ROC curve analysis showed that the strong to weak index (area under curve,AUC) of the AHI>15/h among women was the body fat rate (AUC=0.884, P=0.001), BMI(AUC=0.810, P=0.008), neck circumference (AUC=0.759, P=0.027), age (AUC=0.750, P=0.033), and the male was BMI (AUC=0.765,P<0.001), neck circumference (AUC=0.720, P<0.001), age (AUC=0.634, P=0.008), and body fat rate (AUC=0.632, P=0.010), respectively. Multifactor Logistic regression analysis showed that the body fat rate (OR=1.704,95%CI=1.012-2.870) in women was an independent risk factor for AHI greater than 15/h; the age of male (OR=1. 044, 95%CI=1.005-1.085) and BMI (OR=1.285, 95%CI=1.056-1.562) were independent risk factors for AHI greater than 15/h. Conclusion Body fat rate can be used as a new indicator for predicting the severity of OSA,especially in adult female population. In adult female moderate to severe OSA patients (AHI>15/h), compared with BMI,neck circumference and age,the body fat rate has the greatest correlation with AHI. Compared with BMI,neck circumference and age,the body fat rate has a decisive role in predicting moderate to severe OSA (AHI>15/h).