Resveratrol Treatment Attenuates the Wound-Induced Inflammation in Zebrafish Larvae through the Suppression of Myeloperoxidase Expression

[[abstract]]Resveratrol, a polyphenolic phytoalexin found in many plants, was reported to exhibit anti-inflammatory effects, but its molecular mechanism is not fully understood. This study was aimed to investigate the anti-inflammatory effects of resveratrol in vivo using a zebrafish model of wound-induced inflammation. Caudal fin-wounded zebrafish larvae were treated with resveratrol for 8 h. Neutrophil recruitment was visualized in transgenic line “Tg (mpx：GFP)” expressing GFP-tagged neutrophil-specific myeloperoxidase (mpx). The enzymatic activity of Mpx was evaluated by histochemical staining. Relative mRNA levels of mpx and cyclooxegenase-2 (cox2) were quantified by qRT-PCR, and the protein expression levels of Mpx and Cox2 were detected by immunostaining. Results showed that wound-induced neutrophil recruitment in zebrafish was not affected by resveratrol, but Mpx enzymatic activity in zebrafish was significantly suppressed by resveratrol in a dose-dependent manner. Moreover, both mRNA and protein expression levels of Mpx and Cox2 were significantly down-regulated by resveratrol. Taken together, our results provide in vivo evidence that the anti-inflammatory effects of resveratrol on wound-induced inflammation are significantly mediated through the suppression of Mpx and Cox2 at both transcriptional and protein levels, rather than the down-regulation of neutrophil recruitment. In conclusion, this in vivo zebrafish model can be readily applied to screen other potential anti-inflammatory compounds at a whole-organism level.[[incitationindex]]SCI[[booktype]]紙

Discovery of gamma-ray emission from a strongly lobe-dominated quasar 3C 275.1

We systematically analyze the 6-year {\it Fermi}/LAT data of the lobe-dominated quasars (LDQs) in the complete LDQ sample from 3CRR survey and report the discovery of high-energy $\gamma$-ray emission from 3C 275.1. The $\gamma$-ray emission of 3C 207 is confirmed and significant variability of the lightcurve is identified. We do not find statistically significant $\gamma$-ray emission from other LDQs. 3C 275.1 is the known $\gamma$-ray quasar with the lowest core dominance parameter (i.e., $R=0.11$). We also show that both the northern radio hotspot and parsec jet models can reasonably reproduce the $\gamma$-ray data. The parsec jet model, however, is favored by the potential $\gamma$-ray variability at the timescale of months. We suggest that some dimmer $\gamma$-ray LDQs will be detected in the future and LDQs could contribute non-negligibly to the extragalactic $\gamma$-ray background.Comment: 26 pages, 10 figures, 3 tables; ApJ in pres

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

Cervical abnormal cell detection is a challenging task as the morphological discrepancies between abnormal and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references to identify its abnormality. To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection. Specifically, both contextual relationships between cells and cell-to-global images are exploited to enhance features of each region of interest (RoI) proposals. Accordingly, two modules, dubbed as RoI-relationship attention module (RRAM) and global RoI attention module (GRAM), are developed and their combination strategies are also investigated. We establish a strong baseline by using Double-Head Faster R-CNN with feature pyramid network (FPN) and integrate our RRAM and GRAM into it to validate the effectiveness of the proposed modules. Experiments conducted on a large cervical cell detection dataset reveal that the introduction of RRAM and GRAM both achieves better average precision (AP) than the baseline methods. Moreover, when cascading RRAM and GRAM, our method outperforms the state-of-the-art (SOTA) methods. Furthermore, we also show the proposed feature enhancing scheme can facilitate both image-level and smear-level classification. The code and trained models are publicly available at https://github.com/CVIU-CSU/CR4CACD.Comment: 10 pages, 14 tables, and 3 figure