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Although substantial progress has been made in cancer biology and treatment, clinical outcomes of bladder carcinoma (BC) patients are still not satisfactory. The tumor microenvironment (TME) is a potential target. Here, by single-cell RNA sequencing on 8 BC tumor samples and 3 para tumor samples, we identify 19 different cell types in the BC microenvironment, indicating high intra-tumoral heterogeneity. We find that tumor cells down regulated MHC-II molecules, suggesting that the downregulated immunogenicity of cancer cells may contribute to the formation of an immunosuppressive microenvironment. We also find that monocytes undergo M2 polarization in the tumor region and differentiate. Furthermore, the LAMP3 + DC subgroup may be able to recruit regulatory T cells, potentially taking part in the formation of an immunosuppressive TME. Through correlation analysis using public datasets containing over 3000 BC samples, we identify a role for inflammatory cancer-associated fibroblasts (iCAFs) in tumor progression, which is significantly related to poor prognosis. Additionally, we characterize a regulatory network depending on iCAFs. These results could help elucidate the protumor mechanisms of iCAFs. Our results provide deep insight into cancer immunology and provide an essential resource for drug discovery in the future. Bladder urothelial carcinoma is one of the most prevalent urogenital cancer types with limited therapeutic options. Here, the authors characterize the tumor immune microenvironment of bladder cancer using single cell RNA sequencing and suggest a role for inflammatory cancer-associated fibroblasts in tumor progression.

Background Immune checkpoint inhibitor-related cardiotoxicity is one of the most lethal adverse effects, and thus, the identification of underlying mechanisms for developing strategies to overcome it has clinical importance. This study aimed to investigate whether microbiota-host interactions contribute to PD-1/PD-L1 inhibitor-related cardiotoxicity. Methods A mouse model of immune checkpoint inhibitor-related cardiotoxicity was constructed by PD-1/PD-L1 inhibitor BMS-1 (5 and 10 mg/kg), and cardiomyocyte apoptosis and cardiotoxicity were determined by hematoxylin and eosin, Masson’s trichome and TUNEL assays. 16S rRNA sequencing was used to define the gut microbiota composition. Gut microbiota metabolites short-chain fatty acids (SCFAs) were determined by HPLC. The serum levels of myocardial enzymes (creatine kinase, aspartate transaminase, creatine kinase-MB and lactate dehydrogenase) and the production of M1 factors (TNF-α and IL-1β) were measured by ELISA. The colonic macrophage phenotype was measured by mmunofluorescence and qPCR. The expression of Claudin-1, Occludin, ZO-1 and p-p65 was measured by western blot. The gene expression of peroxisome proliferator-activated receptor α (PPARα) and cytochrome P450 (CYP) 4X1 was determined using qPCR. Statistical analyses were performed using Student’s t-test for two-group comparisons, and one-way ANOVA followed by Student–Newman–Keul test for multiple-group comparisons. Results We observed intestinal barrier injury and gut microbiota dysbiosis characterized by Prevotellaceae and Rikenellaceae genus depletion and Escherichia-Shigella and Ruminococcaceae genus enrichment, accompanied by low butyrate production and M1-like polarization of colonic macrophages in BMS-1 (5 and 10 mg/kg)-induced cardiotoxicity. Fecal microbiota transplantation mirrored the effect of BMS-1 on cardiomyocyte apoptosis and cardiotoxicity, while macrophage depletion and neutralization of TNF-α and IL-1β greatly attenuated BMS-1-induced cardiotoxicity. Importantly, Prevotella loescheii recolonization and butyrate supplementation alleviated PD-1/PD-L1 inhibitor-related cardiotoxicity. Mechanistically, gut microbiota dysbiosis promoted M1-like polarization of colonic macrophages and the production of proinflammatory factors TNF-α and IL-1β through downregulation of PPARα-CYP4X1 axis. Conclusions Intestinal barrier dysfunction amplifies PD-1/PD-L1 inhibitor-related cardiotoxicity by upregulating proinflammatory factors TNF-α and IL-1β in colonic macrophages via downregulation of butyrate-PPARα-CYP4X1 axis. Thus, targeting gut microbiota to polarize colonic macrophages away from the M1-like phenotype could provide a potential therapeutic strategy for PD-1/PD-L1 inhibitor-related cardiotoxicity. Graphical abstract

Single-cell sequencing technologies have noteworthily improved our understanding of the genetic map and molecular characteristics of bladder cancer (BC). Here we identify CD39 as a potential therapeutic target for BC via single-cell transcriptome analysis. In a subcutaneous tumor model and orthotopic bladder cancer model, inhibition of CD39 (CD39i) by sodium polyoxotungstate is able to limit the growth of BC and improve the overall survival of tumor-bearing mice. Via single cell RNA sequencing, we find that CD39i increase the intratumor NK cells, conventional type 1 dendritic cells (cDC1) and CD8 + T cells and decrease the Treg abundance. The antitumor effect and reprogramming of the tumor microenvironment are blockaded in both the NK cells depletion model and the cDC1-deficient Batf3 −/− model. In addition, a significant synergistic effect is observed between CD39i and cisplatin, but the CD39i + anti-PD-L1 (or anti-PD1) strategy does not show any synergistic effects in the BC model. Our results confirm that CD39 is a potential target for the immune therapy of BC. The molecular mechanisms underlying tumour heterogeneity in bladder cancer remain to be explored. Here, the authors perform single cell RNA sequencing and identify CD39 as a potential target for immunotherapy, which they validate in vivo.

Cyclone Global Navigation Satellite System (CYGNSS) has emerged as an effective technique for inland water body detection due to its high sensitivity to inland waters. However, existing methods for inland water body detection using CYGNSS are limited by the difficulty in balancing high spatiotemporal resolution with strong generalization capability. Moreover, the limited spatial redundancy in short-term CYGNSS data restricts its capacity for high-precision inland water detection on its own. To address these issues, this study proposed a novel dual-branch model, termed STRUE. The model integrated a Swin Transformer and ResNet within a U-Net-enhanced student-teacher framework. This framework was developed through the fusion of multi-source data, including CYGNSS, SMAP, FABDEM, MODIS, and GSWE. The results showed that, for inland water body detection, the model attained a spatial resolution of 0.01° and a temporal resolution of 7 days. In terms of performance, it achieved an F1-score (F1) of 0.914, a mean Intersection over Union (mIoU) of 0.880, a Matthews Correlation Coefficient (MCC) of 0.873, and a Recall (R) of 0.963. Additionally, compared with traditional methods and models, the proposed model demonstrated a better performance in spatial continuity, structural integrity, and detail recovery, while mitigating common limitations such as cloud obscuration, spatial incoherence, and overestimation artifacts. These results further enhance the capacity of spaceborne GNSS-R for inland water body detection.

Reanalysis products have been applied to calculate the tropospheric delay for Global Navigation Satellite System (GNSS) positioning purposes widely. It is necessary to obtain high-precision tropospheric delay information from GNSS users with a high-precision tropospheric vertical stratification model because the height of the grids of the atmospheric reanalysis data is inconsistent with that of GNSS users, especially in regions with high terrains. In addition, the variation of the tropospheric delay in the vertical direction is much higher than that in the horizontal direction. The zenith total delay (ZTD) vertical stratification model is also key to the development of real-time and high-precision ZTD models. A new approach, the sliding window algorithm, is proposed to develop a ZTD vertical stratification model. In this work, a ZTD vertical stratification model considering spatiotemporal factors is developed based on the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data, which is named the GZTD-H model. Radiosonde and International GNSS Service (IGS) data are treated as reference values to evaluate the performance of the GZTD-H model, which is compared to the model GPT2w. The results show that the GZTD-H model realizes the highest performance in ZTD layered vertical interpolation against ZTD layered profiles obtained at radiosonde sites, which achieves an improvement of 10% over the model GPT2w. Compared to model GPT2w, the GZTD-H model attains a spatial interpolation improvement of 8% for the Global Geodetic Observing System (GGOS) Atmosphere gridded ZTD over the surface ZTD calculated from radiosonde profiles. Furthermore, compared to model GPT2w, the model GZTD-H also attains improvements of 11% over the precise ZTD products acquired at IGS sites. In terms of model parameters, the GZTD-H model is greatly reduced and optimized over model GPT2w. Hence, the applicability of this model is enhanced in terms of GNSS atmospheric sounding and precise GNSS positioning.

Background Asthma is a common chronic respiratory disease in children. In addition to medications, physical therapy is considered as a treatment strategy for asthma. We conducted this study to investigate the effects of physical therapy on lung function in children with asthma. Methods Three databases were searched. We conducted the meta-analysis for the forced expiratory volume in the first second in percent predicted values [FEV1(%pred)], the forced vital capacity in percent predicted values [FVC(%pred)], and the peak expiratory flow in percent predicted values [PEF(%pred)] by using a random effect model. Results Of the 6474 identified studies, 18 studies (16 in physical training, 2 in breathing exercise or inspiratory muscle training) were included in the systematic review and 11 studies (all in physical training) were included in the meta-analysis. The meta-analysis showed a significantly improved FVC(%pred) in the experimental group. Conclusions Physical training improved FVC(%pred) significantly in children with asthma. Further study is needed, especially on the effects of breathing exercise and inspiratory muscle training in children with asthma. Impact Our study reviewed the physical therapies for children with asthma and clarified whether and how these therapies affect them. Our study found that physical training improved the forced vital capacity in percent predicted values [FVC(%pred)] significantly in asthmatic children. Our study provided evidence that physical training could improve lung function in children with asthma, which is not identical to the Global Initiative for Asthma (GINA) guidelines.

In this study, a new vegetation observable derived from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) was developed. Firstly, a linear relationship between the Cyclone Global Navigation Satellite System (CYGNSS) reflectivity and soil moisture was derived based on the tau-omega (τ−w) model. The intercept and slope of this linear function were associated with the vegetation properties. Moreover, the intercept is not affected by soil moisture and depends only on vegetation properties. Secondly, to validate the new observable, the intercept demonstrated a significant correlation with vegetation water content (VWC), with the highest correlation coefficient of 0.742. Based on the intercept and slope, a linear model and an artificial neural network (ANN) model were established to retrieve VWC by combining geographical location and land cover information. The correlation coefficient and root-mean-square error (RMSE) of VWC retrieval based on the linear model were 0.795 and 2.155 kg/m2, respectively. The correlation coefficient and RMSE for the ANN model were 0.940 and 1.392 kg/m2, respectively. Compared with the linear model, the ANN model greatly improves the global VWC retrieval in accuracy, especially in areas with poor linear model retrieval results. Therefore, compared with conventional remote sensing techniques, the spaceborne GNSS-R can provide a new and effective approach to global VWC monitoring.

Hyperspectral image classification is a critical task in remote sensing, but existing methods often employ fixed feature fusion strategies, making it difficult to adapt to the data characteristics of different scenarios. Additionally, there is a lack of effective synergy between multi-scale feature extraction and attention mechanisms. To address this issue, this paper proposes a dynamic gated fusion network with hierarchical multi-scale attention (DGFNet). This method comprises three core modules: the multi-scale feature aggregator (MSFA), which uses a pyramid expansion convolution structure to concurrently extract spatial features with different receptive fields, achieving comprehensive scale coverage from local texture to global context; the enhanced channel-spatial attention (ECSA) module, which employs multi-pooling strategies and a cascaded structure to achieve deep interaction between channel and spatial attention, thereby adaptively enhancing discriminative features; and the dynamic gated fusion module, which learns input-related fusion weights to adaptively adjust the contribution ratios of multi-scale features and attention features based on data characteristics. Experimental results on four benchmark datasets—Pavia University, Houston, Indian Pines, and WHU-HongHu—show that DGFNet achieves overall accuracy rates of 96.91, 97.12, 94.05, and 94.46%, respectively, representing significant improvements over existing state-of-the-art methods. Ablation experiments thoroughly validate the effectiveness and necessity of each module. Additionally, this paper systematically compares five different fusion strategies (cross-attention, hierarchical fusion, parallel fusion, recurrent fusion, and sequential fusion). Experimental results demonstrate that dynamic gated fusion outperforms other fusion methods in terms of classification accuracy, computational efficiency, and model stability. This method provides an efficient, accurate, and robust solution for hyperspectral image classification. The code will be published on https://github.com/willianbilledu-alt/DGFNet .

Background Lipid metabolic reprogramming in colon cancer shows a potential impact on tumor immune microenvironment and is associated with response to immunotherapy. Therefore, this study aimed to develop a lipid metabolism-related prognostic risk score (LMrisk) to provide new biomarkers and combination therapy strategies for colon cancer immunotherapy. Methods Differentially expressed lipid metabolism-related genes (LMGs) including cytochrome P450 (CYP) 19A1 were screened to construct LMrisk in TCGA colon cancer cohort. The LMrisk was then validated in three GEO datasets. The differences of immune cell infiltration and immunotherapy response between LMrisk subgroups were investigated via bioinformatic analysis. These results were comfirmed by in vitro coculture of colon cancer cells with peripheral blood mononuclear cells, human colon cancer tissue microarray analysis, multiplex immunofluorescence staining and mouse xenograft models of colon cancer. Results Six LMGs including CYP19A1, ALOXE3, FABP4, LRP2, SLCO1A2 and PPARGC1A were selected to establish the LMrisk. The LMrisk was positively correlated with the abundance of macrophages, carcinoma-associated fibroblasts (CAFs), endothelial cells and the levels of biomarkers for immunotherapeutic response including programmed cell death ligand 1 (PD-L1) expression, tumor mutation burden and microsatellite instability, but negatively correlated with CD8 + T cell infiltration levels. CYP19A1 protein expression was an independent prognostic factor, and positively correlated with PD-L1 expression in human colon cancer tissues. Multiplex immunofluorescence analyses revealed that CYP19A1 protein expression was negatively correlated with CD8 + T cell infiltration, but positively correlated with the levels of tumor-associated macrophages, CAFs and endothelial cells. Importantly, CYP19A1 inhibition downregulated PD-L1, IL-6 and TGF-β levels through GPR30-AKT signaling, thereby enhancing CD8 + T cell-mediated antitumor immune response in vitro co-culture studies. CYP19A1 inhibition by letrozole or siRNA strengthened the anti-tumor immune response of CD8 + T cells, induced normalization of tumor blood vessels, and enhanced the efficacy of anti-PD-1 therapy in orthotopic and subcutaneous mouse colon cancer models. Conclusion A risk model based on lipid metabolism-related genes may predict prognosis and immunotherapeutic response in colon cancer. CYP19A1-catalyzed estrogen biosynthesis promotes vascular abnormality and inhibits CD8 + T cell function through the upregulation of PD-L1, IL-6 and TGF-β via GPR30-AKT signaling. CYP19A1 inhibition combined with PD-1 blockade represents a promising therapeutic strategy for colon cancer immunotherapy.

Precipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature ( T m ) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new T m model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.