Pulmonary arterial hypertension (PAH) is a fatal cardiovascular disease that could eventually result in right ventricular failure. Recently, the roles of microRNAs (miRNAs) in PAH have been highlighted. The present study aims to investigate the effects of miRNA (miR)-340-5p on PAH induced by acute pulmonary embolism (APE) and the underlying mechanisms. miR-340-5p was lowly expressed, whereas interleukin 1β (IL-1β) and IL-6 were highly expressed in plasma of APE-PAH patients as compared to normal human plasma. Subsequently, IL-1β and IL-6 were confirmed to be two target genes of miR-340-5p using a dual-luciferase reporter gene assay. By conducting overexpression and rescue experiments, overexpression of miR-340-5p was evidenced to inhibit proliferation and migration of pulmonary artery smooth muscle cells (PASMCs) and inflammation via reducing IL-1β and IL-6 levels. Meanwhile, miR-340-5p led to the blocked nuclear factor κB (NF-κB) pathway with reduced NF-κB p65, matrix metalloproteinase 2 (MMP2), and MMP9 expression in PASMCs. Finally, the ameliorative effect of miR-340-5p on pathological lesions was further verified in rat models of APE-PAH. Altogether, overexpressed miR-340-5p inhibited the inflammatory response, proliferation, and migration of PASMCs by downregulating IL-1β and IL-6, thereby suppressing the progression of APE-PAH. miR-340-5p therefore holds promise as an anti-inflammatory therapeutic target.

The coffee beverage is the second most consumed drink worldwide after water. In coffee beans, cell wall storage polysaccharides (CWSPs) represent around 50 per cent of the seed dry mass, mainly consisting of galactomannans and arabinogalactans. These highly abundant structural components largely influence the organoleptic properties of the coffee beverage, mainly due to the complex changes they undergo during the roasting process. From a nutritional point of view, coffee CWSPs are soluble dietary fibers shown to provide numerous health benefits in reducing the risk of human diseases. Due to their influence on coffee quality and their health-promoting benefits, CWSPs have been attracting significant research attention. The importance of cell walls to the coffee industry is not restricted to beans used for beverage production, as several coffee by-products also present high concentrations of cell wall components. These by-products include cherry husks, cherry pulps, parchment skin, silver skin, and spent coffee grounds, which are currently used or have the potential to be utilized either as food ingredients or additives, or for the generation of downstream products such as enzymes, pharmaceuticals, and bioethanol. In addition to their functions during plant development, cell walls also play a role in the plant’s resistance to stresses. Here, we review several aspects of coffee cell walls, including chemical composition, biosynthesis, their function in coffee’s responses to stresses, and their influence on coffee quality. We also propose some potential cell wall–related biotechnological strategies envisaged for coffee improvements.

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Rainfall-triggered flood and landslide hazards pose significant threats to human lives and infrastructure worldwide. This study aims to evaluate the applicability of three satellite rainfall data sets—namely, CMORPH, GPM, and TRMM—for the prediction of flood and landslide hazards using a coupled hydrological-slope stability model. The spatial distribution of annual rainfall from the three satellite data sets was similar to that of gauge rainfall, with an increasing trend from the north to the south of Shaanxi Province. The average annual rainfall of CMORPH was the lowest, while that of TRMM was the highest. The modeled discharges forcing by satellite rainfall generally matched the observed discharges at four hydrological stations for the period 2010–2012, with average correlation coefficients of 0.51, 0.61, and 0.57 for the CMORPH, GPM, and TRMM rainfall, respectively. The exceedance probabilities of modeled discharges for the three satellite rainfall data sets were close to those of the observations, particularly when the discharges were low. Moreover, the landslide prediction results demonstrated that the three satellite rainfall data sets could simulate the spatial distribution of landslide events well; these simulations were consistent with the information in the landslide inventory map. Furthermore, when compared to the classical Intensity-Duration (ID) rainfall threshold method, the physically based slope stability model presented higher global accuracy under all three satellite rainfall data sets. The global accuracy of GPM rainfall was the highest among the three data sets (0.973 for GPM vs. 0.951 for CMORPH and 0.965 for TRMM), indicating that GPM rainfall provides the highest quality compared to CMORPH and TRMM rainfall. These findings provide a crucial basis for the application of satellite rainfall data in the context of flood and landslide prediction.