Methods: Cervical radiographs were analyzed. EOP hyperplasia was classified into three subtypes with standardized length measurements. Variables encompassed demographics, ONL-related indices, and sagittal parameters. Subtype comparisons and multivariate regression analyses (with EOP length as dependent variable) were conducted. Results: Analysis of 187 CSM patients (64.2% male) identified gender-specific patterns: males exhibited greater EOP length (9.4±6.8 mm vs. 4.6±3.4 mm, p<0.001). Type III EOP demonstrated male predominance (82.4% vs. type I 31.8%, type II 51.4%; p<0.001), with associated longer hyperplasia length (11.6±6.6 mm vs. type II 5.1±1.9 mm, p<0.001). Type III EOP was associated with higher ONL prevalence (type III 64.8% vs. type I 45.5%, type II 41.9%; p=0.010) and longer ONL osteophyte length (type III 18.8±9.8 mm vs. type I 14.2±8.1 mm, type II 14.2±9.4 mm; p=0.046). Multivariate regression confirmed male sex (β=–3.82, p=0.009), ONL osteophyte length (β=0.16, p=0.017), T1 slope (β=0.27, p=0.041), and spino-cranial angle (β=–0.19, p=0.009) as factors independently associated with EOP length (adjusted R²=0.382). Conclusions Severe EOP hyperplasia exhibits a male-predominant distribution pattern and demonstrates significant radiological associations with ONL and cervical sagittal imbalance in CSM patients. These findings advocate for EOP evaluation in clinical evaluations to identify high-risk biomechanical profiles.

Objective To investigate the medication characteristics of Professor Gu Shizh in the treatment of lumbar disc herniation based on various data mining techniques.Methods The outpatient case records of Professor Gu was collected from January 2018 to September 2024 in the Guoyitang outpatient department of Beijing University of Chinese Medicine(BUCM)and BUCM Famous Elderly TCM Inheritance Research Integrated Platform.Patients with lumbar disc herniation mentioned in Professor Gu Shizhe's book of Zhi Zhen Zhi Yao were also be screened.R Studio 2024.4.3.2 was used for descriptive analysis and Apriori was used for association rule analysis.SPSS Statistics 27.0 was used for clustering analysis,and chiplot website was used for visualize the properties,tastes and meridians of drugs.Cytoscape 3.10.2 was used to build a network of"high frequency drug association",and combined with the TCM theories characteristics and medicine law used by professor Gu Shine in the treatment of lumbar disc herniation were summarized.Results A total of 63 cases were collected,involving 194 prescriptions and 187 kinds of Chinese materia medica.The top 30 drugs with the highest frequency were extracted,and the top 6 drugs were Angelicae Pubescentis Radix,Taxilii Herba,salt Eucommiae Cortex,Glycyrrhizae Radix et Rhizoma Praeparata cum Melle,Chuanxiong Rhizoma,Gentianae Macrophyllae Radix.The main tastes were sweet,bitter and pungent,the main properties were warm and neutral.The main meridians were liver,spleen,lung,kidney and stomach meridians.Three core medicinal combinations were obtained by clustering analysis,including Duhuo Jisheng Decoction,Gegeng Decoction,Zhijing Powder and Xuduan Pill.Conclusion The focus of Professor Gu's treatment for lumbar disc herniation is to nourish the liver and kidneys,warm and replenish qi and blood,reflecting the characteristics of"qi and blood are in harmony,essence and blood are equally emphasized"in medication

Objective This study aimed to develop an automated method for syndrome element differenti-ation in Traditional Chinese Medicine(TCM).Methods We first constructed and trained an Instruction-tuned Multi-Task TCM text embedding model(Instr-MT-TCM)using four distinct TCM task datasets,including domain knowledge,synonymous terminology,syndrome differentiation and treatment,and TCM case labels.Subsequently,five TCM diagnostics experts holding master's degrees or higher were organized to screen a real-world TCM case dataset and annotate symptoms and signs.The purpose was to evaluate the F1-score of the proposed method—the combination of Instr-MT-TCM and a Large Language Model(LLM)—by comparing its performance against the manual annotation result on the syndrome element differentiation task.Finally,to validate its feasibility in real-world clinical settings,the method was applied to 48 prostate cancer cases to calculate the syndrome element scores.Results The Instr-MT-TCM model showed rapid performance improvement in its early training phase,achieving a Recall@1(R@1)of 0.848.Experts curated a dataset of 1,793 real-world clinical cases,covering 34 common diseases and 66 syndrome patterns.In the syndrome element differentiation task,the collaborative framework of LLM and Instr-MT-TCM achieved a mean F1-score of 0.927,outperforming the 0.512 from manual annota-tion.The syndrome element analysis revealed that the predominant elements of disease nature were fire(heat)and yin deficiency,while the main elements of disease location were bladder and kidney.Conclusion This study proposes and validates a novel method for automated TCM syndrome element dif-ferentiation based on the synergy between LLM and our custom Instr-MT-TCM model.Achieving a high F1-score(0.927)on real-world data,the method demonstrates excellent accuracy and generalization ability.Its application in prostate cancer analysis highlights its significant clinical potential,offering effective technical support,and a new research direction for intelligent TCM syndrome element differentiation.

Idiopathic Pulmonary Fibrosis(IPF)is a progressive disease characterized by declining respiratory function and high mortality.Macrophages play a pivotal role in its pathogenesis.Through polarization into pro-inflammatory M1 and pro-fibrotic M2 phenotypes,they contribute to a complex immunoregulatory network.In the early disease stages,M1 macrophage-mediated inflammation causes lung tissue injury,while M2 macrophages drive fibrogenesis by releasing pro-fibrotic factors such as TGF-β.Recent research has revealed that exosomes derived from macrophages serve as carriers for miRNAs,with specific miRNAs(e.g.,miR-328,miR-142-3p)demonstrating significant roles in pulmonary fibrosis.miR-328 promotes fibroblast proliferation and accelerates collagen deposition,whereas miR-142-3p attenuates fibrosis by modulating the TGF-β signaling pathway.Targeted intervention against these macrophage-associated miRNAs shows potential for clinical translation,potentially offering novel approaches for the early diagnosis and targeted therapy of IPF.However,translating these strategies into clinical practice requires overcoming challenges related to production and delivery systems.In conclusion,a deeper understanding of the mechanisms and translational applications of macrophage-derived miRNAs in IPF holds promise for ultimately improving patient prognosis and clinical outcomes.

Idiopathic Pulmonary Fibrosis(IPF)is a progressive disease characterized by declining respiratory function and high mortality.Macrophages play a pivotal role in its pathogenesis.Through polarization into pro-inflammatory M1 and pro-fibrotic M2 phenotypes,they contribute to a complex immunoregulatory network.In the early disease stages,M1 macrophage-mediated inflammation causes lung tissue injury,while M2 macrophages drive fibrogenesis by releasing pro-fibrotic factors such as TGF-β.Recent research has revealed that exosomes derived from macrophages serve as carriers for miRNAs,with specific miRNAs(e.g.,miR-328,miR-142-3p)demonstrating significant roles in pulmonary fibrosis.miR-328 promotes fibroblast proliferation and accelerates collagen deposition,whereas miR-142-3p attenuates fibrosis by modulating the TGF-β signaling pathway.Targeted intervention against these macrophage-associated miRNAs shows potential for clinical translation,potentially offering novel approaches for the early diagnosis and targeted therapy of IPF.However,translating these strategies into clinical practice requires overcoming challenges related to production and delivery systems.In conclusion,a deeper understanding of the mechanisms and translational applications of macrophage-derived miRNAs in IPF holds promise for ultimately improving patient prognosis and clinical outcomes.

Objective This study aimed to develop an automated method for syndrome element differenti-ation in Traditional Chinese Medicine(TCM).Methods We first constructed and trained an Instruction-tuned Multi-Task TCM text embedding model(Instr-MT-TCM)using four distinct TCM task datasets,including domain knowledge,synonymous terminology,syndrome differentiation and treatment,and TCM case labels.Subsequently,five TCM diagnostics experts holding master's degrees or higher were organized to screen a real-world TCM case dataset and annotate symptoms and signs.The purpose was to evaluate the F1-score of the proposed method—the combination of Instr-MT-TCM and a Large Language Model(LLM)—by comparing its performance against the manual annotation result on the syndrome element differentiation task.Finally,to validate its feasibility in real-world clinical settings,the method was applied to 48 prostate cancer cases to calculate the syndrome element scores.Results The Instr-MT-TCM model showed rapid performance improvement in its early training phase,achieving a Recall@1(R@1)of 0.848.Experts curated a dataset of 1,793 real-world clinical cases,covering 34 common diseases and 66 syndrome patterns.In the syndrome element differentiation task,the collaborative framework of LLM and Instr-MT-TCM achieved a mean F1-score of 0.927,outperforming the 0.512 from manual annota-tion.The syndrome element analysis revealed that the predominant elements of disease nature were fire(heat)and yin deficiency,while the main elements of disease location were bladder and kidney.Conclusion This study proposes and validates a novel method for automated TCM syndrome element dif-ferentiation based on the synergy between LLM and our custom Instr-MT-TCM model.Achieving a high F1-score(0.927)on real-world data,the method demonstrates excellent accuracy and generalization ability.Its application in prostate cancer analysis highlights its significant clinical potential,offering effective technical support,and a new research direction for intelligent TCM syndrome element differentiation.

ObjectiveTo perform a genome-wide association study of rubella virus vaccine strain BRD-Ⅱ, so as to fully grasp the sequence characteristics of this genome. MethodsSecond-generation sequencing method was used to conduct the whole-genome sequencing on the vaccine strain BRD-Ⅱ, and the affinity tree of this genome with some vaccine strains and wild-type rubella virus strains was analyzed using the maximum likelihood method. The average genetic distance of nucleic acid sequence of each vaccine strain protein was determined. And homology comparison of structural proteins of each rubella vaccine strain, plus the comparison between this genome with the AY258323.1 genome sequence, were conducted to analyze the homology of E1 protein between the wild-type rubella virus reference strain and vaccine strain BRD-Ⅱ. ResultsThe sequencing results showed that the BRD-Ⅱ strain was a single-molecule single-stranded positive-strand ribonucleic acid (RNA), composed of 9 778 nucleotides, with a GC content of 69.35 %. The C protein was composed of 300 amino acids, the E2 glycoprotein was composed of 282 amino acids, and the E1 glycoprotein was composed of 481 amino acids. The results of preliminary analysis showed that the average genetic distances of nucleic acid sequences were 0.066 700 for the P150 protein, 0.061 933 for the P90 protein, 0.057 850 for the C protein, 0.068 167 for the E2 protein, and 0.068 833 for the E1 protein, respectively. The amino acid sequences in the E2 protein and E1 protein regions of the two BRD-Ⅱ strains did not change, confirming the conserved regions of the E1 protein by comparison. ConclusionThe sequence characteristics of the genome are clarified, which have laid a broad foundation for the subsequent detection of the genetic stability of the main antigen genes.

Objective To investigate the medication characteristics of Professor Gu Shizh in the treatment of lumbar disc herniation based on various data mining techniques.Methods The outpatient case records of Professor Gu was collected from January 2018 to September 2024 in the Guoyitang outpatient department of Beijing University of Chinese Medicine(BUCM)and BUCM Famous Elderly TCM Inheritance Research Integrated Platform.Patients with lumbar disc herniation mentioned in Professor Gu Shizhe's book of Zhi Zhen Zhi Yao were also be screened.R Studio 2024.4.3.2 was used for descriptive analysis and Apriori was used for association rule analysis.SPSS Statistics 27.0 was used for clustering analysis,and chiplot website was used for visualize the properties,tastes and meridians of drugs.Cytoscape 3.10.2 was used to build a network of"high frequency drug association",and combined with the TCM theories characteristics and medicine law used by professor Gu Shine in the treatment of lumbar disc herniation were summarized.Results A total of 63 cases were collected,involving 194 prescriptions and 187 kinds of Chinese materia medica.The top 30 drugs with the highest frequency were extracted,and the top 6 drugs were Angelicae Pubescentis Radix,Taxilii Herba,salt Eucommiae Cortex,Glycyrrhizae Radix et Rhizoma Praeparata cum Melle,Chuanxiong Rhizoma,Gentianae Macrophyllae Radix.The main tastes were sweet,bitter and pungent,the main properties were warm and neutral.The main meridians were liver,spleen,lung,kidney and stomach meridians.Three core medicinal combinations were obtained by clustering analysis,including Duhuo Jisheng Decoction,Gegeng Decoction,Zhijing Powder and Xuduan Pill.Conclusion The focus of Professor Gu's treatment for lumbar disc herniation is to nourish the liver and kidneys,warm and replenish qi and blood,reflecting the characteristics of"qi and blood are in harmony,essence and blood are equally emphasized"in medication

Objective To discuss the medication law of Professor Gu Shizhe in the treatment of insomnia based on various data mining techniques.Methods Clinical information of patients of Professor Gu was collected from the hospital management information system of outpatient clinic of Beijing University of Chinese Medicine(BUCM)and BUCM Famous Elderly TCM Inheritance Research Integrated Platform from January 2018 to October 2022.Professor Gu's books,such as Zhi Zhen Zhi Yao,and Gu Jisheng,Gu Shizhe Yi An Yi Hua Jing Cui,and the medical records from CNKI were screened.WPS Office 5.2.1 was used to build a database of medicine data for Professor Gu's TCM prescriptions,and R 4.2.1 was used for descriptive analysis and Apriori association rule analysis.SPSS Statistics 26.0 was used for clustering analysis,combined with theoretical analysis,to extract Professor Gu's medication law and characteristics in the treatment of insomnia.Results A total of 307 prescriptions were included,involving 250 kinds of Chinese materia medica,among which 30 were high-frequency drugs.The top 5 most frequently used were Glycyrrhizae Radix et Rhizoma Praeparata Cum Melle,Fossilizid,Poria,Bupleuri Radix,and Ziziphi Spinosae Semen.The meridians were mainly liver and lung meridian,and the properties were mainly warm and cold,and the tastes were mainly sweet and bitter.Five core drug groups were obtained by clustering analysis,including Xiaochaihu Decoction,Erchen Decoction,Huanglian Wendan Decoction,Suanzaoren Decoction,Xiaoyao Powder and other prescriptions.Conclusion The characteristics of Professor Gu's treatment of insomnia are to reconcile Shaoyang,clear the heart and soothe the mind;pay attention to yin and yang,regulate the liver;pay attention to the liver,gallbladder,spleen and stomach;the main idea of syndrome differentiation is to reconcile Shaoyang,run the Shaoyang cardinal,and at the same time regulate qi and benefit qi and nourishes yin.

Objective To study the relationship between peripheral blood Toll like receptor 4(TLR4),interleukin-1β(IL-1β)and neutrophil-to-lymphocyte ratio(NLR)and the progression and prognosis of acute pancreatitis(AP).Methods A total of 250 patients with AP were divided into the mild group(121 cases),the moderately severe group(89 cases)and the severe group(40 cases)according to the disease severity.Patients were divided into the poor prognosis group(33 cases)and the good prognosis group(217 cases)based on the prognosis after 5 days of treatment.Peripheral blood levels of TLR4,IL-1β and NLR were compared at different time points,different conditions and different prognosis in patients.The correlation of peripheral blood levels of TLR4,IL-1β and NLR,Ranson score and amylase were analyzed by Pearson correlation analysis.The value of each indicator in the diagnosis of severe AP was analyzed using ROC curve,and multivariate Logistic regression analysis was conducted to identify the prognostic risk factors.Results TLR4,IL-1β and NLR levels in AP patients were higher 24 h after admission than 48 h and 72 h after admission(P＜0.05).Peripheral blood TLR4 and IL-1β levels in AP patients at 24 h after admission increased with the condition became worse.NLR level was higher in the severe group than that in the moderately group or the mild group(P＜0.05).Peripheral blood TLR4,IL-1β and NLR levels in AP patients 24 h after admission were positively correlated with Ranson score at 48 h after admission and amylase level at 24 h after admission(P＜0.05).The area under ROC curve,sensitivity and specificity of the combination of TLR4,IL-1β and NLR at 24 h after admission for predicting severe AP were 0.895,84.53%and 81.69%.Peripheral blood TLR4,IL-1β and NLR levels 24 h after admission were higher in the poor prognosis group than those in the good prognosis group(P＜0.05).Multivariate Logistic regression analysis showed that elevated TLR4,IL-1β and NLR at 24 h after admission were independent risk factors for poor prognosis of patients with AP(P＜0.05).Conclusion Peripheral blood TLR4,IL-1β and NLR in patients with AP are closely related to disease progression and prognosis.Therefore,they can be taken as important monitoring indicators.