Histopathological observation and comparative transcriptome analysis reveal immune response mechanisms to Aeromonas dhakensis infection in Macrobrachium rosenbergii.

The Macrobrachium rosenbergii industry is threatened by various Aeromonas, resulting in high mortality of adult prawns. However, there are few studies on the immune response of M. rosenbergii infected with Aeromonas dhakensis. In this study, we observed the hepatopancreas and gills histopathologically, performed a comparative transcriptome analysis of the hepatopancreas, and analyzed the candidate gene expression of immune-related genes in the hemolymph, hepatopancreas, and gills of M. rosenbergii that had been infected with A. dhakensis. Histopathology revealed the hepatopancreas was successively inflamed, followed by cellular vacuolation, lumen deformation, and finally tissue erosion; partial and severe inflammation of the gills occurred successively, and eventually the gill tissue atrophy and the gill filaments detached from the gill arch. Transcriptome analysis showed that a total of 77,742 unigenes and 8664 differentially expressed genes (DEGs), and the immune-related DEGs were mainly enriched in lysosome and phagosome pathways. In addition, 4 immune-related candidate genes (RhoA, CASP9, PKC, and DSCIGN) based on KEGG and PPI analysis were monitored at 6, 12, and 24h post injection (hpi) in hepatopancreas, hemolymph and gills. Their spatio-temporal expression results indicated that A. dhakensis have activated the immune system of M. rosenbergii. The present study may provide new information on the complex immune mechanism of M. rosenbergii.

The Dynamics of Gene Expression Unraveling the Immune Response of Macrobrachium rosenbergii Infected by Aeromonas veronii.

To further investigate the immune response of Macrobrachium rosenbergii against Aeromonas veronii, comparative transcriptomic analyses of the M. rosenbergii hepatopancreas were conducted on challenge and control groups at 6, 12, and 24 h post-infection (hpi), independently. A total of 51,707 high-quality unigenes were collected from the RNA-seq data, and 8060 differentially expressed genes (DEGs) were discovered through paired comparisons. Among the three comparison groups, a KEGG pathway enrichment analysis showed that 173 immune-related DEGs were considerably clustered into 28 immune-related pathways, including the lysosome, the phagosome, etc. Moreover, the expression levels of the four key immune-related genes (TOLL, PAK1, GSK3ß, and IKKα) were evaluated at various stages following post-infection in the hepatopancreas, hemolymph, and gills. Both PAK1 and GSK3ß genes were highly up-regulated in all three tissues at 6 hpi with A. veronii; TOLL was up-regulated in the hepatopancreas and hemolymph but down-regulated in the gill at 6 hpi, and IKKα was up-regulated in hemolymph and gill, but down-regulated in the hepatopancreas at 6 hpi. These findings lay the groundwork for understanding the immune mechanism of M. rosenbergii after contracting A. veronii.

[Medical nucleus image segmentation network based on convolution and attention mechanism].

Although deep learning plays an important role in cell nucleus segmentation, it still faces problems such as difficulty in extracting subtle features and blurring of nucleus edges in pathological diagnosis. Aiming at the above problems, a nuclear segmentation network combined with attention mechanism is proposed. The network uses UNet network as the basic structure and the depth separable residual (DSRC) module as the feature encoding to avoid losing the boundary information of the cell nucleus. The feature decoding uses the coordinate attention (CA) to enhance the long-range distance in the feature space and highlights the key information of the nuclear position. Finally, the semantics information fusion (SIF) module integrates the feature of deep and shallow layers to improve the segmentation effect. The experiments were performed on the 2018 data science bowl (DSB2018) dataset and the triple negative breast cancer (TNBC) dataset. For the two datasets, the accuracy of the proposed method was 92.01% and 89.80%, the sensitivity was 90.09% and 91.10%, and the mean intersection over union was 89.01% and 89.12%, respectively. The experimental results show that the proposed method can effectively segment the subtle regions of the nucleus, improve the segmentation accuracy, and provide a reliable basis for clinical diagnosis.