ObjectiveTo explore the changes in color， odor and chemical components during wine-processing of Chuanxiong Rhizoma（CR）， identify differential markers， and provide a basis for standardizing the process and establishing quality standards. MethodsFifteen batches of CR samples from 4 producing areas were collected. Colorimeter and electronic nose were used to detect the color changes and odor components of CR before and after wine-processing. Multivariate statistical methods including partial least squares-discriminant analysis（PLS-DA）， principal component analysis（PCA）， discriminant factor analysis（DFA） and Fisher discriminant analysis were applied to identify wine-processed CR at different processing stages and establish discriminant models， and differential components were screened out based on variable importance in the projection（VIP） value1. Then， high performance liquid chromatography（HPLC） was employed to detect the content changes of four components（ferulic acid， senkyunolide I， senkyunolide A and ligustilide） during the processing stages. ResultsThe differences of wine-processed CR at various stages were primarily reflected in color parameters L^*（brightness value）， a^*（red-green value） and b^*（yellow-blue value）. Based on chromaticity differences， the color reference ranges were established for moderately processed CR， including L^* of 46.75-48.24， a^* of 5.37-6.07 and b^* of 20.32-21.70. In odor analysis， DFA revealed significant differences among processing stages， and 11 odor markers were identified， with four differential markers（4-hydroxy-3-butylphthalide， isopropyl butyrate， L-limonene and 1-methoxyhexane） based on VIP values. HPLC results showed that there was no significant difference of the four components except for ligustilide in wine-processed CR at different stages. ConclusionThis study achieved rapid identification of wine-processed CR with different processing degrees by electronic sensory technology and differential component content detection， with discrimination accuracy rates of 92.4% and 93.272% for color and odor， respectively. This paper also established the reference ranges of main colorimetric parameters for wine-processed CR at different stages， and four differential components were screened out， providing a basis for standardizing the processing of wine-processed CR and establishing quality standards for this decoction pieces.

In this paper， by referring to ancient and modern literature， the textual research of Inulae Flos has been conducted to clarify the name， origin， production area， quality evaluation， harvesting， processing and others， so as to provide reference and basis for the development and utilization of famous classical formulas containing this herb. After textual research， it could be verified that the medicinal use of Inulae Flos was first recorded in Shennong Bencaojing of the Han dynasty. In successive dynasties， Xuanfuhua has been taken as the official name， and it also has other alternative names such as Jinfeicao， Daogeng and Jinqianhua. The period before the Song and Yuan dynasties， the main origin of Inulae Flos was the Asteraceae plant Inula japonica， and from the Ming and Qing dynasties to the present， I. japonica and I. britannica are the primary source. In addition to the dominant basal species， there are also regional species such as I. linariifolia， I. helianthus-aquatili， and I. hupehensis. The earliest recorded production areas in ancient times were Henan， Hubei and other places， and the literature records that it has been distributed throughout the country since modern times. The medicinal part is its flower， the harvesting and processing method recorded in the past dynasties is mainly harvested in the fifth and ninth lunar months， and dried in the sun， and the modern harvesting is mostly harvested in summer and autumn when the flowers bloom， in order to remove impurities， dry in the shade or dry in the sun. In addition， the roots， whole herbs and aerial parts are used as medicinal materials. In ancient times， there were no records about the quality of Inulae Flos， and in modern times， it is generally believed that the quality of complete flower structure， small receptacles， large blooms， yellow petals， long filaments， many fluffs， no fragments， and no branches is better. Ancient processing methods primarily involved cleaning， steaming， and sun-drying， supplemented by techniques such as boiling， roasting， burning， simmering， stir-frying， and honey-processing. Modern processing focuses mainly on cleaning the stems and leaves before use. Regarding the medicinal properties， ancient texts describe it as salty and sweet in taste， slightly warm in nature， and mildly toxic. Modern studies characterize it as bitter， pungent， and salty in taste， with a slightly warm nature. Its therapeutic effects remain consistent across eras， including descending Qi， resolving phlegm， promoting diuresis， and stopping vomiting. Based on the research results， it is recommended that when developing famous classical formulas containing Inulae Flos， either I. japonica or I. britannica should be used as the medicinal source. Processing methods should follow formula requirements， where no processing instructions are specified， the raw products may be used after cleaning.

ObjectiveTo explore the changes in color， odor and chemical components during wine-processing of Chuanxiong Rhizoma（CR）， identify differential markers， and provide a basis for standardizing the process and establishing quality standards. MethodsFifteen batches of CR samples from 4 producing areas were collected. Colorimeter and electronic nose were used to detect the color changes and odor components of CR before and after wine-processing. Multivariate statistical methods including partial least squares-discriminant analysis（PLS-DA）， principal component analysis（PCA）， discriminant factor analysis（DFA） and Fisher discriminant analysis were applied to identify wine-processed CR at different processing stages and establish discriminant models， and differential components were screened out based on variable importance in the projection（VIP） value1. Then， high performance liquid chromatography（HPLC） was employed to detect the content changes of four components（ferulic acid， senkyunolide I， senkyunolide A and ligustilide） during the processing stages. ResultsThe differences of wine-processed CR at various stages were primarily reflected in color parameters L^*（brightness value）， a^*（red-green value） and b^*（yellow-blue value）. Based on chromaticity differences， the color reference ranges were established for moderately processed CR， including L^* of 46.75-48.24， a^* of 5.37-6.07 and b^* of 20.32-21.70. In odor analysis， DFA revealed significant differences among processing stages， and 11 odor markers were identified， with four differential markers（4-hydroxy-3-butylphthalide， isopropyl butyrate， L-limonene and 1-methoxyhexane） based on VIP values. HPLC results showed that there was no significant difference of the four components except for ligustilide in wine-processed CR at different stages. ConclusionThis study achieved rapid identification of wine-processed CR with different processing degrees by electronic sensory technology and differential component content detection， with discrimination accuracy rates of 92.4% and 93.272% for color and odor， respectively. This paper also established the reference ranges of main colorimetric parameters for wine-processed CR at different stages， and four differential components were screened out， providing a basis for standardizing the processing of wine-processed CR and establishing quality standards for this decoction pieces.

In this paper， by referring to ancient and modern literature， the textual research of Inulae Flos has been conducted to clarify the name， origin， production area， quality evaluation， harvesting， processing and others， so as to provide reference and basis for the development and utilization of famous classical formulas containing this herb. After textual research， it could be verified that the medicinal use of Inulae Flos was first recorded in Shennong Bencaojing of the Han dynasty. In successive dynasties， Xuanfuhua has been taken as the official name， and it also has other alternative names such as Jinfeicao， Daogeng and Jinqianhua. The period before the Song and Yuan dynasties， the main origin of Inulae Flos was the Asteraceae plant Inula japonica， and from the Ming and Qing dynasties to the present， I. japonica and I. britannica are the primary source. In addition to the dominant basal species， there are also regional species such as I. linariifolia， I. helianthus-aquatili， and I. hupehensis. The earliest recorded production areas in ancient times were Henan， Hubei and other places， and the literature records that it has been distributed throughout the country since modern times. The medicinal part is its flower， the harvesting and processing method recorded in the past dynasties is mainly harvested in the fifth and ninth lunar months， and dried in the sun， and the modern harvesting is mostly harvested in summer and autumn when the flowers bloom， in order to remove impurities， dry in the shade or dry in the sun. In addition， the roots， whole herbs and aerial parts are used as medicinal materials. In ancient times， there were no records about the quality of Inulae Flos， and in modern times， it is generally believed that the quality of complete flower structure， small receptacles， large blooms， yellow petals， long filaments， many fluffs， no fragments， and no branches is better. Ancient processing methods primarily involved cleaning， steaming， and sun-drying， supplemented by techniques such as boiling， roasting， burning， simmering， stir-frying， and honey-processing. Modern processing focuses mainly on cleaning the stems and leaves before use. Regarding the medicinal properties， ancient texts describe it as salty and sweet in taste， slightly warm in nature， and mildly toxic. Modern studies characterize it as bitter， pungent， and salty in taste， with a slightly warm nature. Its therapeutic effects remain consistent across eras， including descending Qi， resolving phlegm， promoting diuresis， and stopping vomiting. Based on the research results， it is recommended that when developing famous classical formulas containing Inulae Flos， either I. japonica or I. britannica should be used as the medicinal source. Processing methods should follow formula requirements， where no processing instructions are specified， the raw products may be used after cleaning.

OBJECTIVE To construct an intelligent pharmaceutical Q&A platform for precision medication with low “artificial intelligence （AI） hallucination”， aiming to enhance the accuracy， consistency， and traceability of medication consultations. METHODS Medication package inserts were batch-processed and converted into structured data through Python programming to build a local pharmaceutical knowledge base. The retrieval and question-answering processes were designed based on large language models， and system integration and localized deployment were completed on Dify platform. By designing typical clinical medication questions and comparing the output of the intelligent pharmaceutical Q&A platform with the online version of DeepSeek across dimensions such as peak time retrieval， half-life， and dosage adjustment reasoning for patients with renal impairment， the accuracy and reliability of its retrieval and reasoning results were evaluated. RESULTS The intelligent pharmaceutical Q&A platform， constructed based on local drug package inserts， achieved 100% accuracy in retrieval and reasoning for peak time， half-life， and dosage adjustment schemes. In comparison， the online version of DeepSeek demonstrated accuracies of 30%（6/20）， 50%（10/20）， and 38%（23/60） across these three dimensions， respectively. CONCLUSIONS The constructed intelligent pharmaceutical Q&A platform is capable of accurately retrieving and extracting information from the local knowledge base based on clinical inquiries， thereby avoiding the occurrence of AI hallucinations and providing reliable medication decision support for healthcare professionals.

Objective: To investigate the factors influencing the co-prevalence of allergic rhinitis and obesity among primary and secondary school students in Inner Mongolia, so as to provide a data foundation and theoretical basis for developing targeted intervention measures. Methods: In September and October 2024, a stratified cluster random sampling method was employed to select 139 102 students from 539 schools across 12 leagues/cities and 103 banners/counties in Inner Mongolia Autonomous Region. Participants who were diagnosed with allergic rhinitis by a doctor at least once within one year and had a body mass index ≥ 28 kg/m 2 were considered to have comorbid conditions. Results: The coprevalence rate of allergic rhinitis and obesity among primary and secondary school students in Inner Mongolia was 6.4% (8 931 cases). Lasso-Logistic regression revealed that nonboarding status, higher maternal education, consuming high protein foods ≥1 time daily, occasionally or never eating breakfast, engaging in moderate to vigorous physical activity for ≥60 minutes on fewer than half of holidays, and having been exposed to second hand smoke in person within the past seven days were associated with higher odds ratios for co-prevalence of allergic rhinitis and obesity( OR = 1.23 , 1.22-1.63, 1.20, 1.19, 1.38, 1.35); being female, higher grade level, residence in flag/county/district areas, non only child status, never having consumed a full glass of alcohol, non hypertensive status, and households without pets were associated with lower co-prevalence risks ( OR =0.65, 0.67-0.77, 0.81, 0.87, 0.73, 0.41, 0.68) (all P <0.05). The ROC curve indicated an area under the curve of 0.64 for the predictive model, demonstrating satisfactory discriminatory ability. The calibration curve showed consistency between predicted and actual occurrence probabilities. Conclusions The co-prevalence of allergic rhinitis and obesity among primary and secondary school students in Inner Mongolia is closely associated with demographic characteristics, dietary behaviours, and lifestyle habits. Future prevention and control strategies should prioritize these factors to implement targeted interventions.

ObjectiveTo observe the effect of Yifei Jianpi prescription on the of signal transducer and activator of transcription protein 1 （STAT1）/interferon regulatory factor 3 （IRF3） signaling pathway in a pneumonia model induced by lipopolysaccharide （LPS） and to explore the mechanism of Yifei Jianpi prescription in improving lung immune and inflammatory responses. MethodsSixty male SPF SD rats were used in this study. Ten rats were randomly assigned to the normal control group， and the remaining 50 were instilled with LPS in the trachea to establish a pneumonia model. After successful modeling， the rats were randomly divided into the model group， dexamethasone group （0.5 mg·kg^-1）， and Yifei Jianpi prescription high-dose （12 mg·kg^-1）， medium-dose （6 mg·kg^-1）， and low-dose （3 mg·kg^-1） groups， with 10 rats in each group. Treatment was administered once daily， and the normal control and model groups received the same volume of normal saline. After 14 days， flow cytometry was used to detect the classification of whole blood lymphocytes. Enzyme-linked immunosorbent assay （ELISA） was used to measure serum levels of immunoglobulin G （IgG）， immunoglobulin A （IgA）， immunoglobulin M （IgM）， and the content of tumor necrosis factor-α （TNF-α）， interleukin-8 （IL-8）， interleukin-6 （IL-6）， and interleukin-10 （IL-10） in alveolar lavage fluid （BALF）. Hematoxylin-eosin （HE） staining was used to observe lung tissue pathology and score the damage. Thymus weight， spleen weight， and wet-to-dry weight ratio （W/D） were recorded. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression of STAT1， IRF3， IL-6， and interferon-alpha （IFN-α） in lung tissues， while Western blot was performed to assess the protein expression of STAT1， IRF3， IL-6， and IFN-α. ResultsCompared with the normal control group， the model group showed significantly increased proportion of B lymphocytes in peripheral blood， decreased proportions of NK cells and CD4⁺/CD8⁺ （P<0.05， P<0.01）， decreased serum levels of IgG and IgA， significantly increased IgM levels （P<0.01）， significantly elevated content of TNF-α， IL-6， and IL-8 in BALF， and significantly decreased IL-10 levels （P<0.01）. Lung tissue damage was evident， with significant increases in thymus and spleen weights and a higher W/D ratio （P<0.01）. The mRNA and protein expression of STAT1， IRF3， IFN-α， and IL-6 in lung tissues was significantly upregulated （P<0.05，P<0.01）. Compared with the model group， the Yifei Jianpi prescription groups showed significantly reduced proportions of B lymphocytes in peripheral blood， increased proportions of NK cells and CD4⁺/CD8⁺ ratios （P<0.05， P<0.01）， significantly increased serum levels of IgG and IgA， significantly decreased IgM levels （P<0.05， P<0.01）， significantly reduced levels of TNF-α， IL-6， and IL-8 in BALF， and significantly increased IL-10 levels （P<0.01）. Lung tissue damage was alleviated， thymus and spleen weights were significantly reduced， and the W/D ratio was markedly decreased （P<0.01）. The mRNA and protein expression of STAT1， IRF3， IFN-α， and IL-6 in lung tissues was significantly downregulated （P<0.05， P<0.01）. ConclusionYifei Jianpi prescription can alleviate lung tissue damage and improve immune and inflammatory responses in LPS-induced pneumonia rats. The mechanism may be related to the inhibition of STAT1/IRF3 signaling pathway activation.

AIM:To analyze differential proteins associated with the pathogenesis of dry eye(DE)using bioinformatics methods, in order to reveal their potential molecular mechanisms.METHODS: Articles published in PubMed and EMBASE databases from the inception of the database to August 31, 2023, that used proteomic methods to detect protein expression in clinical samples of dry eye were searched. Differential proteins were selected and further analyzed using the STRING database and Cytoscape software for hub gene screening and module analysis. Protein-protein interaction(PPI)analysis, gene ontology(GO)functional annotation, and Kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analysis were performed.RESULTS: A total of 21 articles were included, identifying 74 differentially expressed proteins. The most frequently occurring differential proteins were calgranulin A(SA1008), lipocalin-1(LCN1), lysozyme C(LYZ), mammaglobin-B(SCGB2A1), proline-rich protein 4(PRR4), transferrin(TF), and calgranulinB(S100A9). The top 10 hub genes were serum albumin(ALB), tumor necrosis factor(TNF), interleukin 6(IL6), IL1B, IL8, matrix metalloproteinase 9(MMP9), alpha-1-antitrypsin(SERPINA1), IL10, complement component 3(C3), and lactotransferrin(LTF). Module analysis suggested MMP9 and PRR4 as seed genes. KEGG analysis showed that differential proteins were mainly enriched in the IL17 signaling pathway(61.9%).CONCLUSION: The results reveal potential molecular targets and pathways for DE and confirm the association between the pathogenesis of DE and inflammation. Further in-depth research is needed to confirm the significance of these biomarkers in clinical practice.

Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.

Objective: To explore the plasma metabonomic characteristics of patients with type 2 diabetes mellitus and turbid toxin accumulation syndrome. Methods: One hundred and three patients with type 2 diabetes mellitus and turbid toxin accumulation syndrome were enrolled from November 2023 to February 2024 in the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine and 54 healthy individuals were recruited. The general data of the two groups were analyzed, and the plasma metabolite content was detected using ultra-high performance liquid chromatography-Orbitrap mass spectrometry. Construct an orthogonal partial least squares discriminant analysis model to screen metabolites with significant intergroup changes. The variable importance in projection≥ 1, |log2FC|>1, and P<0.05 were used as the criteria for the screening of differential metabolites. Annotate differential metabolites using internal databases and the human metabolome database, and perform pathway analysis using MetaboAnalyst website. Results: There was no statistically significant difference in gender and age between the two groups.Seventeen potential differential metabolites were identified. The D-4′-phosphopantothenate, 2, 6-dichloroindophenol, 4-methylphenol, hypoxanthine, 11, 12-epoxyeicosatrienoic acids, oleamide, 3-phenyllactic acid contents were higher in patients with type 2 diabetes mellitus and turbid toxin accumulation syndrome than in healthy individuals(P<0.05); 3-anisic acid, 3-iodo-octadecanoic acid, mebendazole, β-alanine, citric acid, trans-aconitic acid, geranyl diphosphate, lysophosphatidylcholine(18∶2), phosphatidylethanolamine(18∶1), and caprolactam contents were lower in patients with type 2 diabetes mellitus and turbid toxin accumulation syndrome than in healthy individuals(P<0.05). Ten metabolic pathways were identified, including the key metabolic pantothenate and coenzyme A biosynthesis pathways. Conclusion Metabolic differences were observed between patients with type 2 diabetes mellitus and turbid toxin accumulation syndrome and healthy individuals, and the underlying mechanism may involve the pantothenate and coenzyme A biosynthesis pathways, jointly mediated by D-4′-phosphopantothenate and β-alanine.