ObjectiveTo develop a nomogram-based risk prediction model for chronic obstructive pulmonary disease (COPD) incidence among the community-dwelling population aged 40 years old and above, so as to provide targeted references for the screening and prevention of COPD. MethodsBased on a natural population cohort in suburban Shanghai, a total of 3 381 randomly selected participants aged ≥40 years underwent pulmonary function tests between July and October 2021. Cox stepwise regression analysis was used to develop overall and gender-specific risk prediction models, along with the construction of corresponding risk nomograms. Model predictive performance was evaluated using the C-indice, area under the curve (AUC) values, and Brier score. Stability was assessed through 10-fold cross-validation and sensitivity analysis. ResultsA total of 3 019 participants were included, with a median follow-up duration of 4.6 years. The COPD incidence density was 17.22 per 1 000 person-years, significantly higher in males (32.04/1 000 person-years) than that in females (7.38/1 000 person-years) (P<0.001). The overall risk prediction model included the variables such as gender, age, education level, BMI, smoking, passive smoking, and respiratory comorbidities. The male-specific model incorporated the variables such as age, BMI, respiratory comorbidities, and smoking, while the female-specific model included age, marital status, respiratory comorbidities, and pulmonary tuberculosis history. The C-indices for the overall, male-specific, and female-specific models were 0.829, 0.749, and 0.807, respectively. The 5-year AUC values were 0.785, 0.658, and 0.811, with Brier scores of 0.103, 0.176, and 0.059, respectively. Both 10-fold cross-validated C-indices and sensitivity analysis (excluding participants with a follow-up duration of <6 months) yielded C-indices were above 0.740. ConclusionThis study developed concise and practical overall and gender-specific COPD risk prediction models and corresponding nomograms. The models demonstrated robust performance in predicting COPD incidence, providing a valuable reference for identifying high-risk populations and formulating targeted screening and personalized management strategies.

OBJECTIVES: To classify bladder cancer based on immune cell infiltration score and to construct a risk assessment model for prognosis of patients. METHODS: The transcriptome data and data of breast cancer patients were obtained from the TCGA database. The single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was realized by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were extracted. A risk scoring model and a nomogram for risk assessment of prognosis for bladder cancer patients were constructed and verified. RESULTS: The immune cell infiltration scores of normal tissues and tumor tissues were calculated, and B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. Breast cancer patients were clustered into two groups (Cluster 1 and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). WGCNA screened out 35 genes related to key immune cells, and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients. CONCLUSIONS According to the immune cell infiltration score, bladder cancer patients can be classified. And the bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

BACKGROUND: The application of programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) antibodies has greatly improved the clinical outcomes of lung cancer patients. Here, we retrospectively analyzed the efficacy of PD-1 antibody therapy in locally advanced non-surgical or metastatic lung cancer patients, and preliminarily explored the correlation between peripheral blood biomarkers and clinical responses. METHODS: We conducted a single center study that included 61 IIIA-IV lung cancer patients who received PD-1 antibody treatment from March 2020 to December 2021, and collected the medical record data on PD-1 antibody first-line or second-line treatment. The levels of multiple Th1 and Th2 cytokines in the patient's peripheral blood serum, as well as the phenotype of peripheral blood T cells, were detected and analyzed. RESULTS: All the patients completed at least 2 cycles of PD-1 monoclonal antibody treatment. Among them, 42 patients (68.9%) achieved partial response (PR); 7 patients (11.5%) had stable disease (SD); and 12 patients (19.7%) had progressive disease (PD). The levels of peripheral blood interferon gamma (IFN-γ) (P=0.023), tumor necrosis factor α (TNF-α) (P=0.007) and interleukin 5 (IL-5) (P=0.002) before treatment were higher in patients of the disease control rate (DCR) (PR+SD) group than in the PD group. In addition, the decrease in absolute peripheral blood lymphocyte count after PD-1 antibody treatment was associated with disease progression (P=0.023). Moreover, the levels of IL-5 (P=0.0027) and IL-10 (P=0.0208) in the blood serum after immunotherapy were significantly increased compared to baseline. CONCLUSIONS Peripheral blood serum IFN-γ, TNF-α and IL-5 in lung cancer patients have certain roles in predicting the clinical efficacy of anti-PD-1 therapy. The decrease in absolute peripheral blood lymphocyte count in lung cancer patients is related to disease progression, but large-scale prospective studies are needed to further elucidate the value of these biomarkers.
Humans ; Lung Neoplasms/metabolism* ; Interleukin-5/therapeutic use* ; Tumor Necrosis Factor-alpha/therapeutic use* ; Retrospective Studies ; Programmed Cell Death 1 Receptor ; Biomarkers ; Immunotherapy ; Disease Progression ; B7-H1 Antigen

To explore the mechanism of spleen- were obtained for the treatment of acute-on-chronic livstrengthening and moisture-nourishing liver prescription er failure, and 244 intersecting target genes and 7 core (JPLSYGF) in the treatment of acute-on-chronic liver target genes were screened. Molecular docking showed failure using network pharmacology and the molecular that the core target genes AKT1, SRC, VEGFA, docking. Methods Relying on TCMSP and Gene- STAT3 , EGFR, MAPK3 , HRAS had good affinity with Cards and other databases, the relevant targets of JPL- quercetin, the main active component in the JPLSYGF in the treatment of acute-on-chronic liver failure SYGF, and had strong binding activity. In addition, in were obtained. String and Cytoscape were used to con- vivo tests verified that the JPLSYGF could reduce the struct PPI networks of targets, core targets were expression of HRAS, EGFR, STAT3 , SRC, and VEGscreened out, and DAVID was used for GO function FA, to delay the progression of acute-on-chronic liver annotation and KEGG pathway enrichment analysis. failure. Conclusions JPLSYGF may act on core tar- The main active ingredients of the traditional Chinese gets such as HRAS, EGFR, STAT3, SRC, VEGFA medicine compound formula for JPLSYGF were select- and so on, to achieve the effect of treating acute-oned with a bioavailability OB value of =Э 30% and a chronic liver failure. drug-like DL^O. 18 as the screening conditions, and.

Platycodonis Radix is the dry root of Platycodon grandiflorum of Campanulaceae, which has a variety of pharmacological effects and is a commonly used bulk Chinese medicine. In this study, the chloroplast genome sequences of six P. grandiflorum from different producing areas has been sequenced with Illumina HiSeq X Ten platform. The specific DNA barcodes were screened, and the germplasm resources and genetic diversity were analyzed according to the specific barcodes. The total length of the chloroplast genome of 6 P. grandiflorum samples was 172 260-172 275 bp, and all chloroplast genomes showed a typical circular tetrad structure and encoded 141 genes. The comparative genomics analysis and results of amplification efficiency demonstrated that trnG-UCC and ndhG_ndhF were the potential specific DNA barcodes for identification the germplasm resources of P. grandiflorum. A total of 305 P. grandiflorum samples were collected from 15 production areas in 9 provinces, for which the fragments of trnG-UCC and ndhG_ndhF were amplificated and the sequences were analyzed. The results showed that trnG-UCC and ndhG_ndhF have 5 and 11 mutation sites, respectively, and 5 and 7 haplotypes were identified, respectively. The combined analysis of the two sequences formed 13 haplotypes (named Hap1-Hap13), and Hap4 is the main genotype, followed by Hap1. The unique haplotypes possessed by the three producing areas can be used as DNA molecular tags in this area to distinguish from the germplasm resources of P. grandiflorum from other areas. The haplotype diversity, nucleotide diversity and genetic distance were 0.94, 4.79×10^-3 and 0.000 0-0.020 3, respectively, suggesting that the genetic diversity was abundant and intraspecific kinship was relatively close. This study laid a foundation for the identification of P. grandiflorum, the protection and utilization of germplasm resources, and molecular breeding.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

AIM To investigate the variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip.METHODS UPLC-MS/MS was adopted in the content determination of formononetin,ononin,calycosin,calycosin-7-glucoside,medicarpin,genistein,luteolin,liquiritigenin,isoliquiritigenin,vanillic acid,ferulic acid,γ-aminobutyric acid,adenosine and betaine,after which cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition to explore differential components.RESULTS After rubbing strip,formononetin,calycosin,liquiritigenin and γ-aminobutynic acid demonstrated increased contents,along with decreased contents of ononin,calycosin-7-glucoside and vanillic acid.The samples with and without rubbing strip were clustered into two types,calycosin-7-glucoside,formononetin,γ-aminobutynic acid,vanillic acid,calycosin-7-glucoside and formononetin were differential components.CONCLUSION This experiment clarifies the differences of chemical constituents in Hedysari Radix before and after rubbing strip,which can provide a reference for the research on rubbing strip mechanism of other medicinal materials.

ObjectiveTo evaluate the efficacy of three screening questionnaires for COPD in the community residents of Songjiang District， Shanghai， and to provide a basis for selecting COPD screening questionnaire and process that are more suitable. MethodsCommunity residents aged 40 years or over were randomly selected from the Shanghai Suburban Adult Cohort and Biobank for the study with screening questionnaires and spirometry. Questionnaires included the COPD screening questionnaire （COPD-SQ）， the COPD population screener （COPD-PS） and the revised COPD diagnostic questionnaire （revised-CDQ）. Evaluation of the efficacy of these questionnaires was based on the area under the receiver operating characteristic curve （AUC） of the subjects. DeLong test was used to compare the accuracy of different questionnaires； Z test was used to compare the accuracy of different cut-off values for the same questionnaire. ResultsAmong 3 184 community residents， a total of 259 （8.1%） COPD patients were screened by spirometry. AUC values of these 3 screening questionnaires were >0.7 indicating that they were reliable COPD screening tools. The sensitivity and specificity of the questionnaires at the recommended cut-off values were COPD-SQ （63.7% and 72.2%）， COPD-PS （12.0% and 96.1%）， and revised CDQ （78.8% and 52.7%）， with the COPD-SQ having the highest screening accuracy （AUC=0.754）. The optimal and recommended cut-off values for the three questionnaires differed in this population， but the difference in accuracy was statistically significant only for COPD-PS. The optimal cut-off values for the three questionnaires differed between male and female， and the sensitivity and accuracy of COPD-SQ and COPD-PS improved when lower cut-off values were used for women. The AUC was greater when two questionnaires were utilized simultaneously for screening， but the differences were not statistically significant. ConclusionThe COPD-SQ is recommended for primary COPD screening； a lower cut-off value for women should be considered. The COPD screening questionnaire needs to be further improved for the early diagnosis and treatment of COPD patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Objective:To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.Methods:The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas(TCGA)database.Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells.The classification of breast cancer patients was achieved by unsupervised clustering,and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed.The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis(WGCNA),and the key genes in the modules were identified.A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.Results:B cells,mast cells,neutrophils,T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer.The patients were clustered into two groups(Cluster 1′ and Custer 2)based on immune cell infiltration scores.Compared with patients with Cluster 1′,patients with Cluster 2 were more likely to benefit from immunotherapy(P<0.05),and patients with Cluster 2 were more sensitive to Enbeaten,Docetaxel,Cyclopamine,and Akadixin(P<0.05).35 genes related to key immune cells were screened out by WGCNA and 4 genes(GPR171,HOXB3,HOXB5 and HOXB6)related to the prognosis of bladder cancer were further screened by LASSO Cox regression.The areas under the ROC curve(AUC)of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-,3-and 5-year survival of patients were 0.735,0.765 and 0.799,respectively.The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-,3-,and 5-year overall survival of bladder cancer patients.Conclusions:According to the immune cell infiltration score,bladder cancer patients can be classified.Furthermore,bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.