Background Ozone (O₃) is a major air pollutant. The existing monitoring system has uneven distribution of sites, insufficient coverage in underdeveloped areas, and low temporal resolution, making it difficult to obtain hourly data. This limits the dynamic identification of pollution and the formulation of prevention and control strategies. Objective To construct an hourly O₃ concentration estimation model based on ensemble machine learning, aiming to improve the accuracy of pollution exposure assessment and explore O₃ health impacts. Methods This study integrated land use regression modeling with modern machine learning techniques, employing random forest and XGBoost algorithms to construct base models, and stacking integration using non-negative least squares. The ensemble model was trained and validated across China using high-resolution, multi-source geographic data (e.g., meteorologicaldata, population density, land cover types, and aerosol optical thickness). It was tested in Taiyuan City, combined with a distributed lag non-linear model to analyze the association between O₃ and emergency admissions. Results The constructed ensemble model performed well in predicting O₃ concentration, with a higher coefficient of determination (R²) and a lower root-mean-square deviation (RMSE) compared to the single models. The R² improved from 0.90 to 0.92, and the RMSE decreased from 11.41 to 10.62, enhancing both prediction accuracy and generalization ability. In the application to Taiyuan City, the model successfully imputed the hourly-level data for the entire year. The distributed lag non-linear model analysis revealed that the relative risk (RR) values for the 6th to 8th days following O₃ exposure were 1.14 (95%CI: 1.01, 1.29), 1.16 (95%CI: 1.02, 1.31), and 1.14 (95%CI: 1.01, 1.29), respectively, which were significantly higher than 1, indicating a significant lagged association (lagged 6-8 d) between O₃ and the number of emergency room visits. Conclusion A high-precision, hourly-level O₃ concentration estimation model is successfully constructed by combining the land use regression model with an ensemble machine learning approach to provide a scientific basis for environmental policy formulation and public health intervention. The application of the model verifies its generalization ability and practical application value, which can provide a new technical framework for subsequent environmental health research.

Background and Objectives: Psoriasis is a chronic inflammatory skin disease, which the mechanisms behind its initiation and development are related to many factors. DMSCs (dermal mesenchymal stem cells) represent an important member of the skin microenvironment and play an important role in the surrounding environment and in neighbouring cells, but they are also affected by the microenvironment. We studied the glucose metabolism of DMSCs in psoriasis patients and a control group to reveal the relationship among glucose metabolism, cell proliferation activity，and VEC (vascular endothelial cell) differentiation in vitro, we demonstrated the biological activity and molecular mechanisms of DMSCs in psoriasis. Methods: and Results: We found that the OCR of DMSCs in psoriatic lesions was higher than that in the control group, and mRNA of GLUT1 and HK2 were up-regulated compared with the control group. The proliferative activity of DMSCs in psoriasis was reduced at an early stage, and mRNA involved in proliferation, JUNB and FOS were expressed at lower levels than those in the control group. The number of blood vessels in psoriatic lesions was significantly higher than that in the control group (p＜0.05), which the mRNA of VEC differentiation, CXCL12, CXCR7, HEYL and RGS5 tended to be increased in psoriatic lesions compared to the control group, in addition to Notch3. Conclusions We speculated that DMSCs affected local psoriatic blood vessels through glucose metabolism, and the differentiation of VECs, which resulted in the pathophysiological process of psoriasis.

Objective To evaluate the clinical application effect of transparent film dressing in fistula nursing of patients with percutanueous nephrostomy (PCN).Methods By using before-after self control study method,40 patients with pelvic tumor that caused by postrenal renal failure,and needed long-term fistula remained after PCN surgery were selected as the research object.Conventional sterile gauze and tape were used to nurse the fistula before intervention and transparent film dressing was used after intervention.Results Patents always worried about trachea cannula exodus and infection before intervention.After intervention,the comfort degree of their daily life (like showering,sleeping and daily activities) was obviously higher than that before intervention (P＜0.01).The incidence of renal fistula local adverse effect after intervention is relatively low.And the difference was statistically significant (P＜ 0.01).Before intervention,the tape was loosing in (5.47 ± 1.48) days,there were three cases of accidental trachea cannula exodus;and after intervention,transparent film dressing hemmed in (14.22±1.99) days,and no accidental trachea cannula exodus occurred (P＜0.01);and no hyper-sensitive cases happened with the transparent film dressing.Conclusions The transparent film dressing has the advantages of stable viscosity,waterproofing,fungi-proofing,breathability and hypoallergenic,etc.It can well protect and fix the fistula,and it is suitable for nursing care of patients with long-term renal fistula.

In order to investigate biological functions of the 14-3-3 genes and their response to abiotic stress, two cDNAs (designated as Ta14R1 and Ta14R2) encoding putative 14-3-3 proteins were isolated from wheat by PCR and rapid amplification of cDNA end (RACE) technique. The cDNA of Ta14R1 is 999bp and encodes a protein of 262 amino acids, while the cDNA of Ta14R2 is 897bp in length and encodes a protein of 261 amino acids. Transient expression assays using Ta14R1/Ta14R2-GFP fusion constructs indicated that Ta14R1 and Ta14R2 were located in cytoplasm and cell membrane but not in chloroplasts. Real-time quantitative (RT-PCR) analysis revealed that Ta14R1 and Ta14R2 were differentially expressed in wheat tissues and significantly up-regulated in roots and shoots 1d after germination, indicating they may play a role in process of seed germination. The expression of the two genes in roots and leaves were significantly induced by plant hormone ABA, as well as heat, cold and drought treatments, suggesting that the two 14-3-3 genes in wheat may be involved in ABA dependent stress-responding pathway and response to heat, cold and drought stress.
14-3-3 Proteins ; genetics ; Abscisic Acid ; pharmacology ; DNA, Complementary ; Droughts ; Gene Expression Regulation, Plant ; Genes, Plant ; Germination ; Plant Leaves ; genetics ; physiology ; Plant Roots ; genetics ; physiology ; Stress, Physiological ; Temperature ; Triticum ; genetics ; physiology