RESUMEN
Meat consumption is increasing globally. The safety and quality of meat are considered important issues for human health. During evaluations of meat quality and freshness, microbiological parameters are often analyzed. Counts of indicator cells can provide important references for meat quality. In order to eliminate the error of manual operation and improve detection efficiency, this paper proposed a Convolutional Neural Network (CNN) with a backbone called Detect-Cells-Rapidly-Net (DCRNet), which can identify and count stained cells automatically. The DCRNet replaces the single channel of residual blocks with the aggregated residual blocks to learn more features with fewer parameters. The DCRNet combines the deformable convolution network to fit flexible shapes of stained animal cells. The proposed CNN with DCRNet is self-adaptive to different resolutions of images. The experimental results indicate that the proposed CNN with DCRNet achieves an Average Precision of 81.2% and is better than traditional neural networks for this task. The difference between the results of the proposed method and manual counting is less than 0.5% of the total number of cells. The results indicate that DCRNet is a promising solution for cell detection and can be equipped in future meat quality monitoring systems.
RESUMEN
Numerous missense mutations have been reported in autosomal dominant polycystic kidney disease which is one of the most common renal genetic disorders. The underlying mechanism for cystogenesis is still elusive, partly due to the lack of suitable animal models. Currently, we tried to establish a porcine transgenic model overexpressing human PKD2-D511V (hPKD2-D511V), which is a dominant-negative mutation in the vertebrate in vitro models. A total of six cloned pigs were finally obtained using somatic cell nuclear transfer. However, five with functional hPKD2-D511V died shortly after birth, leaving only one with the dysfunctional transgenic event to survive. Compared with the WT pigs, the demised transgenic pigs had elevated levels of hPKD2 expression at the mRNA and protein levels. Additionally, no renal malformation was observed, indicating that hPKD2-D511V did not alter normal kidney development. RNA-seq analysis also revealed that several ADPKD-related pathways were disturbed when overexpressing hPKD2-D511V. Therefore, our study implies that hPKD2-D511V may be lethal due to the dominant-negative effect. Hence, to dissect how PKD2-D511V drives renal cystogenesis, it is better to choose in vitro or invertebrate models.