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To investigate the prevalence and clinical significance of the dense fine speckled (DFS) pattern in a large‐scale cohort of Chinese patients. Data on the antinuclear antibody (ANA) and extractable nuclear antigen (ENA) autoantibody obtained from 165 498 patients who attended Peking Union Medical College Hospital from January 2019 to December 2021 were retrospectively analysed. The prevalence of the DFS pattern was 1.14%, and it mainly appeared in young patients (≤24 years old). A higher positive rate of the DFS pattern was observed in patients with dermatosis (18.12%) and systemic autoimmune rheumatic diseases (SARDs) (13.53%). The DFS pattern titre was mostly low or medium (≤1:160). A higher titre was associated with an increased risk of SARDs (p <.001), dermatosis (p =.015) and pulmonary disease (p =.016). In 37 patients with ENA autoantibody positivity, anti‐SSA antibody was the most common (2.55%). If the low titre (<1:160) or the DFS pattern with negative ENA autoantibody were to be used as exclusion criteria for SARDs, the diagnosis would have been missed in 42 or 77 patients, respectively. The prevalence of the DFS pattern was low in ANA test samples and was more common in patients with dermatosis and SARDs, but the titre was usually higher in patients with SARDs. There was no evidence that the DFS pattern could be used as an exclusion criterion for SARDs diagnosis. The DFS pattern was associated with certain pathological states, which may inform the clinical significance of the DFS pattern. [ABSTRACT FROM AUTHOR]

[Display omitted] • G-CSF enhances the immunosuppressive function of MDSCs in the donor's peripheral blood. • MDSCs in grafts undergoing transplantation in hematologic patients affect patient prognosis. • The equilibrium range of absolute values of total MDSCs in the patient's graft was (1.7–4.3) × 107/kg. • The transfer of grafts with high levels of MDSCs to the patient's body is beneficial for the development speed of MDSCs. To discuss the effects of mobilization of healthy donors with granulocyte colony-stimulating factor (G-CSF) on the absolute values and functions of myeloid-derived suppressor cells (MDSCs) and subpopulations of M-MDSCs and P-MDSCs in their peripheral blood. In addition, this study also aims to investigate the impacts of the adoptively transferred MDSCs from the grafts to the patients on their prognosis and immune reconstitution. The selection of 72 donors and 72 patients were conducted for allogeneic hematopoietic stem cell transplantation (allo-HSCT) from August 2022 to December 2022 at Lu Daopei Hospital in Beijing, China. Statistical calculations were performed by Wilcoxon signed-rank test, Kruskal Wallis test, χ2 test, Kaplan Meier test, and log-rank test to analyze the data. G-CSF induced significant amplification of MDSCs in the peripheral blood of donors in percentage and absolute values. Whether the level of P-MDSCs in patients conducted for the adoptive transfer of P - MDSCs is higher than 3.7× 107/kg or lower than 1.4× 107/kg leads to a poor prognosis of the patients. Ensuring a balanced state of MDSCs is crucial for effective immunotherapy. Transferring a high level of MDSCs from the graft to the patient's body is advantageous for the development of MDSCs while simultaneously inhibiting the proliferation of lymphocyte subgroups. [ABSTRACT FROM AUTHOR]

PM 2.5 is the main component of most haze, and the presence of high concentrations of PM 2.5 in the air for an extended time can cause serious effects on human health, so there is an urgent need for research work related to PM 2.5. Traditional PM 2.5 monitoring uses ground-based monitoring stations with low spatial resolution. Other studies have retrieved the Moderate Resolution Imaging Spectroradiometer aerosol optical depth product by the dark-target algorithm. However, the estimated PM 2.5 concentration on the ground will produce missing values, which will lead to the reduction of spatial and temporal resolution. Based on this, this study proposes a machine learning algorithm to estimate PM 2.5 by using the fifth generation reanalysis (ERA5) data set published by the European Center for Medium-range Weather Forecasts (ECMWF). In this study, two different methods of back propagation neural network (BPNN) and random forest (RF) were used to develop the models. Firstly, the meteorological parameters (precipitable water vapor, water vapor pressure and relative humidity, etc.) and pollution parameters (O 3 , CO, NO 2 , SO 2 , PM 10 , and PM 2.5) were used to establish PM 2.5 model in 2021. The results showed that the R2 and RMSE for BPNN and RF were 0.94/0.96 and 10.37/8.77 µg/m3, respectively. Then, due to the lack and the low spatial resolution of the pollution parameters, using only the ERA5 meteorological data with the high spatiotemporal resolution to develop the PM 2.5 model in winter, the R2 of the RF model (0.93) was 0.05 higher and the RMSE (12.50 µg/m3) was 4.19 µg/m3 lower than that of the BPNN model, which indicates that it is feasible to develop the PM 2.5 model using only meteorological parameters. Finally, using the RF model of the second stage and ERA5 meteorological data with a spatial resolution of 0.05° (obtained by cubic spline interpolation) to generate the hourly PM 2.5 map of Beijing and compare it with China High Air Pollutants dataset, the R2 and RMSE of Beijing were 0.78 µg/m3 and 14.85 µg/m3, respectively. On this basis, it is found that the areas with high PM 2.5 concentration are close to the areas with serious pollution in Hebei Province by analyzing the PM 2.5 map of Beijing, and area transport and human activities are important sources of air pollution. [ABSTRACT FROM AUTHOR]