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 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.

ObjectiveTo reveal the active substances and mechanisms of modified Qing'e formula （MQEF） in delaying skin photoaging by ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry （UPLC-Q-TOF-MS），network pharmacology， and cell experiments. MethodsUPLC-Q-TOF-MS and a literature review were employed to analyze the transdermally absorbed components in mice after the topical application of MQEF. The potential targets of MQEF in treating skin photoaging were retrieved from databases.The compound-potential target network and protein-protein interaction network were constructed to screen the key components and core targets. A photoaging cell model was established by irradiating HaCaT cells with medium-wave ultraviolet B （UVB）. The safe doses of bakuchiol （BAK） and salvianolic acid B （SAB） for treating HaCaT cells and the effects of BAK and SAB on the viability of cells exposed to UVB irradiation were determined by the cell counting kit-8 （CCK-8） method.The reactive oxygen species （ROS） fluorescent probe was used to measure the ROS production in the cells treated with BAK and SAB.The expression levels of genes related to oxidative stress，inflammation，collagen metabolism，and circadian rhythm clock were measured by Real-time PCR. ResultsA total of 24 transdermally absorbed components of MQEF were identified，which acted on 367 potential targets，and 417 targets related to skin photoaging were screened out，among which 47 common targets were predicted as the targets of MQEF in treating skin photoaging. MQEF exerted the anti-photoaging effect via key components such as BAK and SAB，which acted on core proteins such as serine/threonine kinase 1 （Akt1） and mitogen-activated protein kinase 3 （MAPK3） and intervened in core pathways such as the tumor necrosis factor （TNF） and phosphatidylinositol-3-kinase （PI3K）/protein kinase B （Akt） signaling pathways.Compared with the model group，the administration of BAK and SAB increased the survival rate of HaCaT cells （P<0.01），down-regulated the mRNA levels of cyclooxygenase-2 （COX-2），interleukin-6 （IL-6），tumor necrosis factor-α （TNF-α），matrix metalloproteinase-1 （MMP-1），and matrix metalloproteinase-9 （MMP-9） （P<0.01），and up-regulated the mRNA levels of heme oxygenase-1 （HO-1） and NAD（P）H quinone dehydrogenase 1 （NQO-1） （P<0.05，P<0.01） in photoaged HaCaT cells.In addition，it eliminated excess ROS production induced by UVB and up-regulated the mRNA levels of brain and muscle ARNT-like 1 （BMAL1） and circadian locomotor output cycles kaput （CLOCK） associated with circadian clock （P<0.05，P<0.01）. ConclusionMQEF delays skin photoaging through the coordinated effects of various components，multiple targets，and diverse pathways.The key components BAK and SAB in MQEF exhibit anti-photoaging properties，which involve inhibiting oxidative stress，preventing collagen degradation，mitigating inflammation，and maintaining normal skin circadian rhythms by regulating clock gene expression.

Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.

AIM: To delineate the specific mutation responsible for retinitis pigmentosa(RP)in a Yi pedigree, and to analyze the correlation of RHO gene mutation with clinical phenotype.METHODS:A comprehensive clinical evaluation was conducted on the proband diagnosed with RP and other familial members, complemented by a thorough ophthalmic examination. Peripheral blood samples were obtained from the proband and familial members, from which genomic DNA was extracte. Subsequent whole exome sequencing(WES)was employed to identify the variant genes in the proband. The identified variant gene was validated through Sanger sequencing, then an in-depth analysis of the mutation genes was carried out using genetic databases to ascertain the pathogenic mutation sites. Furthermore, an exhaustive analysis was performed to delineate the genotype and phenotype characteristics.RESULTS:The RP pedigree encompasses 5 generations with 42 members, including 19 males and 23 females. A total of 13 cases of RP were identified, consisting of 4 males and 9 females, which conforms to the autosomal dominant inheritance pattern. The clinical features of this family include an early onset age, rapid progression, and a more severe condition. The patients were found to have night blindness around 6 years old, representing the earliest reported case of night blindness in RP families. The retina was manifested by progressive osteocytoid pigmentation of the fundus, a reduced visual field, and significantly decreased or even vanished a and b amplitudes of ERG. The combined results of WES and Sanger sequencing indicated that the proband had a heterozygous missense mutation of the RHO gene c.1040C>T:p.P347L, where the 1 040 base C of cDNA was replaced by T, causing codon 347 to encode leucine instead of proline. Interestingly, this mutation has not been reported in the Chinese population.CONCLUSION:This study confirmed that the mutant gene of RP in a Yi nationality pedigree was RHO(c.1040C>T). This variant leads to the change of codon 347 from encoding proline to encoding leucine, resulting in a severe clinical phenotype among family members. This study provides a certain molecular, clinical, and genetic basis for genetic counseling and gene diagnosis of RHO.