Abnormal miRNA functions are widely involved in many diseases recorded in the database of experimentally supported human miRNA-disease associations (HMDD). Some of the associations are complicated: There can be up to five heterogeneous association types of miRNA with the same disease, including genetics type, epigenetics type, circulating miRNAs type, miRNA tissue expression type and miRNA-target interaction type. When one type of association is known for an miRNA-disease pair, it is important to predict any other types of the association for a better understanding of the disease mechanism. It is even more important to reveal associations for currently unassociated miRNAs and diseases. Methods have been recently proposed to make predictions on the association types of miRNA-disease pairs through restricted Boltzman machines, label propagation theories and tensor completion algorithms. None of them has exploited the non-linear characteristics in the miRNA-disease association network to improve the performance. We propose to use attributed multi-layer heterogeneous network embedding to learn the latent representations of miRNAs and diseases from each association type and then to predict the existence of the association type for all the miRNA-disease pairs. The performance of our method is compared with two newest methods via 10-fold cross-validation on the database HMDD v3.2 to demonstrate the superior prediction achieved by our method under different settings. Moreover, our real predictions made beyond the HMDD database can be all validated by NCBI literatures, confirming that our method is capable of accurately predicting new associations of miRNAs with diseases and their association types as well.

Purpose The purpose of the current study was to compare the outcomes of temporary stoma through the specimen extraction site (SSES) and stoma through a new site (SNS) after laparoscopic low anterior resection. Methods The rectal cancer patients who underwent laparoscopic low anterior resection plus temporary ileostomy were recruited in a single clinical database from Jun 2013 to Jun 2020. The SSES group and the SNS group were compared using propensity score matching (PSM) analysis. Results A total of 257 rectal cancer patients were included in this study, there were 162 patients in the SSES group and 95 patients in the SNS group. After 1:1 ratio PSM, there was no difference in baseline information (p > 0.05). The SSES group had smaller intraoperative blood loss (p = 0.016 < 0.05), shorter operation time (p < 0.01) and shorter post-operative hospital stay (p = 0.021 < 0.05) than the SNS group before PSM. However, the SSES group shorter operation time (p = 0.006 < 0.05) than the SNS group after PSM, moreover, there was no significant difference in stoma-related complications (p > 0.05). In the multivariate analysis, longer operation time was an independent factor (p = 0.019 < 0.05, OR = 1.006, 95% CI = 1.001–1.011) for the stoma-related complications. Conclusion Based on the current evidence, the SSES group had smaller intraoperative blood loss, shorter operation time and shorter post-operative hospital stay before PSM, and shorter operation time after PSM. Therefore, SSES might be superior than SNS after laparoscopic low anterior resection for rectal cancer patients.

The number of live births in a litter is an important reproductive trait, and is one of the main indicators which reflect the production level and economic benefit of a pig farm. The ovary is an important reproductive organ of the sow, and it undergoes a series of biological processes during each estrous cycle. A complex transcriptional network containing coding and non-coding RNAs in the ovary closely regulates the reproductive capability of sows. However, the molecular regulation mechanisms affecting sow litter size are still unclear. We investigated the expression profiles of microRNAs (miRNAs) in porcine ovaries from sows with smaller than average litter sizes (SLS) and those with larger litter sizes (LLS). In total, 411 miRNAs were identified, and of these 17 were significantly down-regulated and 16 miRNAs were up-regulated when comparing sows with LLS and SLS, respectively. We further characterized the role of miR-183 which was one of the most up-regulated miRNAs. CCK-8, EdU incorporation and western blotting assays demonstrated that miR-183 promoted the proliferation of granulosa cells (GCs) in pig ovaries. Moreover, miR-183 inhibited the synthesis of estradiol in GCs and promoted the synthesis of progesterone. These results will help in gaining understanding of the role of miRNAs in regulating porcine litter size.

Liver is an important organ for fat metabolism. Excessive intake of a high-fat/energy diet is a major cause of hepatic steatosis and its complications such as non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. Supplementation with lycopene, a natural compound, is effective in lowering triglyceride levels in the liver, although the underlying mechanism at the translational level is unclear. In this study, mice were fed a high-fat diet (HFD) to induce hepatic steatosis and treated with or without lycopene. Translation omics and transcriptome sequencing were performed on the liver to explore the regulatory mechanism of lycopene in liver steatosis induced by HFD, and identify differentially expressed genes (DEGs). We identified 1,358 DEGs at the translational level. Through transcriptomics and translatomics joint analysis, we narrowed the range of functional genes to 112 DEGs and found that lycopene may affect lipid metabolism by regulating the expression of LPIN1 at the transcriptional and translational levels. This study provides a powerful tool for translatome and transcriptome integration and a new strategy for the screening of candidate genes.

More and more research works have indicated that microRNAs (miRNAs) play indispensable roles in exploring the pathogenesis of diseases. Detecting miRNA-disease associations by experimental techniques in biology is expensive and time-consuming. Hence, it is important to propose reliable and accurate computational methods to exploring potential miRNAs related diseases. In our work, we develop a novel method (BRWHNHA) to uncover potential miRNAs associated with diseases based on hybrid recommendation algorithm and unbalanced bi-random walk. We first integrate the Gaussian interaction profile kernel similarity into the miRNA functional similarity network and the disease semantic similarity network. Then we calculate the transition probability matrix of bipartite network by using hybrid recommendation algorithm. Finally, we adopt unbalanced bi-random walk on the heterogeneous network to infer undiscovered miRNA-disease relationships. We tested BRWHNHA on 22 diseases based on five-fold cross-validation and achieves reliable performance with average AUC of 0.857, which an area under the ROC curve ranging from 0.807 to 0.924. As a result, BRWHNHA significantly improves the performance of inferring potential miRNA-disease association compared with previous methods. Moreover, the case studies on lung neoplasms and prostate neoplasms also illustrate that BRWHNHA is superior to previous prediction methods and is more advantageous in exploring potential miRNAs related diseases. All source codes can be downloaded from https://github.com/myl446/BRWHNHA .