Inflammation and apoptosis play important roles in the initiation and progression of acute lung injury (ALI). Our previous study has shown that progranulin (PGRN) exerts lung protective effects during LPS‐induced ALI. Here, we have investigated the potential roles of PGRN‐targeting microRNAs (miRNAs) in regulating inflammation and apoptosis in ALI and have highlighted the important role of PGRN. LPS‐induced lung injury and the protective roles of PGRN in ALI were first confirmed. The function of miR‐34b‐5p in ALI was determined by transfection of a miR‐34b‐5p mimic or inhibitor in intro and in vivo. The PGRN level gradually increased and subsequently significantly decreased, reaching its lowest value by 24 hr; PGRN was still elevated compared to the control. The change was accompanied by a release of inflammatory mediators and accumulation of inflammatory cells in the lungs. Using bioinformatics analysis and RT‐PCR, we demonstrated that, among 12 putative miRNAs, the kinetics of the miR‐34b‐5p levels were closely associated with PGRN expression in the lung homogenates. The gain‐ and loss‐of‐function analysis, dual‐luciferase reporter assays, and rescue experiments confirmed that PGRN was the functional target of miR‐34b‐5p. Intravenous injection of miR‐34b‐5p antagomir in vivo significantly inhibited miR‐34b‐5p up‐regulation, reduced inflammatory cytokine release, decreased alveolar epithelial cell apoptosis, attenuated lung inflammation, and improved survival by targeting PGRN during ALI. miR‐34b‐5p knockdown attenuates lung inflammation and apoptosis in an LPS‐induced ALI mouse model by targeting PGRN. This study shows that miR‐34b‐5p and PGRN may be potential targets for ALI treatments.

BackgroundFoxM1 has been reported to be important in initiation and progression of various tumors. However, whether FoxM1 has any indication for prognosis in non-small cell lung cancer patients remains unclear.Methodology/Principal FindingsIn this study, FoxM1 expression in tumor cells was examined first by immunohistochemistry in 175 NSCLC specimens, the result of which showed that FoxM1 overexpression was significantly associated with positive smoking status (P = 0.001), poorer tissue differentiation (P = 0.0052), higher TNM stage (P<0.0001), lymph node metastasis (P<0.0001), advanced tumor stage (P<0.0001), and poorer prognosis (P<0.0001). Multivariable analysis showed that FoxM1 expression increased the hazard of death (hazard ratio, 1.899; 95% CI, 1.016–3.551). Furthermore, by various in vitro and in vivo experiments, we showed that targeted knockdown of FoxM1 expression could inhibit the migratory and invasive abilities of NSCLC cells, whereas enforced expression of FoxM1 could increased the invasion and migration of NSCLC cells. Finally, we found that one of the cellular mechanisms by which FoxM1 promotes tumor metastasis is through inducing epithelial-mesenchymal transition (EMT) program.ConclusionsThese results suggested that FoxM1 overexpression in tumor tissues is significantly associated with the poor prognosis of NSCLC patients through promoting tumor metastasis.

Moiré artifacts are generally caused by the interference between the overlap of the sensor's sampling grid and high-frequency (nearly) periodic textures, and heavily affect the image quality. However, it is difficult to effectively remove moiré artifacts from textured images as the structure of moiré patterns is similar to that of textures in some sense. In this paper, we propose a novel textured image demoiréing method by signal decomposition and guided filtering. Given a textured image with moiré artifacts, we first remove moiré artifacts in the green (G) channel using the proposed low-rank and sparse matrix decomposition model. This model regularizes the texture layer by the low-rank prior in spatial domain and the moiré layer by sparse representation in frequency domain. An alternating direction method under the augmented Lagrangian multiplier framework is used to solve the matrix decomposition model. Then, since the red (R) and blue (B) channels are more heavily polluted by moiré artifacts than the G channel, we propose to remove moiré artifacts in its R and B channels via guided filtering by the obtained texture layer of the G channel. Experimental results demonstrate that our method outperforms the state-of-the-art methods for both synthetic and real images.