A lncRNA-disease association prediction model based on the two-step PU learning and fully connected neural networks

Long non-coding RNAs (lncRNAs) have been shown to play a regulatory role in various processes of human diseases. However, lncRNA experiments are inefficient, time-consuming and highly subjective, so that the number of experimentally verified associations between lncRNA and diseases is limited. In the era of big data, numerous machine learning methods have been proposed to predict the potential association between lncRNA and diseases, but the characteristics of the associated data were seldom explored. In these methods, negative samples are randomly selected for model training and the model is prone to learn the potential positive association error, thus affecting the prediction accuracy. In this paper, we proposed a cyclic optimization model of predicting lncRNA-disease associations (COPTLDA in short). In COPTLDA, the two-step training strategy is adopted to search for the samples with the greater probability of being negative examples from unlabeled samples and the determined samples are treated as negative samples, which are combined together with known positive samples to train the model. The searching and training steps are repeated until the best model is obtained as the final prediction model. In order to evaluate the performance of the model, 30% of the known positive samples are used to calculate the model accuracy and 10% of positive samples are used to calculate the recall rate of the model. The sampling strategy used in this paper can improve the accuracy and the AUC value reaches 0.9348. The results of case studies showed that the model could predict the potential associations between lncRNA and malignant tumors such as colorectal cancer, gastric cancer, and breast cancer. The predicted top 20 associated lncRNAs included 10 colorectal cancer lncRNAs, 2 gastric cancer lncRNAs, and 8 breast cancer lncRNAs

Dynamic Slicing and Reconstruction Algorithm for Precise Canopy Volume Estimation in 3D Citrus Tree Point Clouds

Crop phenotyping data collection is the basis for precision agriculture and smart decision-making applications. Accurately obtaining the canopy volume of citrus trees is crucial for yield prediction, precise fertilization and cultivation management. To this end, we developed a dynamic slicing and reconstruction (DR) algorithm based on 3D point clouds. The algorithm dynamically slices nearby slices based on their proportional area change and density difference; for each slice point cloud, the average distance of each point from others is taken as the initial α value for the AS algorithm. This value is iteratively summed until it reconstructs the complete shape, allowing the volume of each slice shape to be determined. Compared with six point cloud-based reconstruction algorithms, the DR approach achieved the best results in removing perforations and lacunae (0.84) and exhibited volumetric consistency (1.53) that closely aligned with the growth pattern of citrus trees. The DR algorithm effectively addresses the challenges of adapting the thickness and number of canopy point cloud slices to the shape and size of the canopy in the ASBS and CHBS algorithms, as well as overcoming inaccuracies and incompleteness in reconstructed canopy models caused by limitations in capturing detailed features using the PCH algorithm. It offers improved adaptive ability, finer volume computations, better noise reduction, and anomaly removal

Salvianolic Acid A Protects Against Diabetic Nephropathy through Ameliorating Glomerular Endothelial Dysfunction via Inhibiting AGE-RAGE Signaling

Background/Aims: Glomerular endothelium dysfunction leads to the progression of renal architectonic and functional abnormalities in early-stage diabetic nephropathy (DN). Advanced glycation end products (AGEs) and receptor for AGEs (RAGE) are proved to play important roles in diabetic nephropathy. This study investigated the role of Salvianolic acid A (SalA) on early-stage DN and its possible underlying mechanism. Methods: In vitro AGEs formation and breaking rate were measured to illustrate the effect of SalA on AGEs. Type 2 diabetic nephropathy rats were induced by high-fat diet and low-dose streptozocin (STZ). After eight-week treatment with SalA 1 mg/kg/day, 24h-urine protein, creatinine clearance was tested and renal structural injury was assessed by PAS and PASM staining. Primary glomerular endothelial cell permeability was evaluated after exposed to AGEs. AGEs-induced RhoA/ROCK and subsequently activated disarrange of cytoskeleton were assessed by western blot and immunofluorescence. Results: Biochemical assay and histological examination demonstrated that SalA markedly reduced endothelium loss and glomerular hyperfiltration, suppressed glomerular hypertrophy and mesangial matrix expansion, eventually reduced urinary albumin and ameliorated renal function. Further investigation suggested that SalA exerted its renoprotective effects through inhibiting AGE-RAGE signaling. It not only inhibited formation of AGEs and increased its breaking in vitro, but also reduced AGEs accumulation in vivo and downregulated RAGE expression. SalA restored glomerular endothelial permeability through suppressing AGEs-induced rearrangement of actin cytoskeleton via AGE-RAGE-RhoA/ ROCK pathway. Moreover, SalA attenuated oxidative stress induced by AGEs, subsequently alleviated inflammation and restored the disturbed autophagy in glomerular endothelial cell and diabetic rats via AGE-RAGE-Nox4 axis. Conclusion: Our study indicated that SalA restored glomerular endothelial function and alleviated renal structural deterioration through inhibiting AGE-RAGE, thus effectively ameliorated early-stage diabetic nephropathy. SalA might be a promising therapeutic agent for the treatment of diabetic nephropathy

Puerarin Mitigates Diabetic Hepatic Steatosis and Fibrosis by Inhibiting TGF-β Signaling Pathway Activation in Type 2 Diabetic Rats

Lipid metabolism disorder and inflammation are essential promoters in pathogenesis of liver injury in type 2 diabetes. Puerarin (PUR) has been reported to exert beneficial effects on many diabetic cardiovascular diseases and chemical-induced liver injuries, but its effects on diabetic liver injury and its mechanism are still unclear. The current study was designed to explore the therapeutic effect and mechanism of PUR on liver injury in a type 2 diabetic rat model induced by a high-fat diet combined with low-dose streptozotocin. The diabetic rats were treated with or without PUR (100 mg/kg/day) by gavaging for 8 weeks, and biochemical and histological changes in liver were examined. Results showed that treatment with PUR significantly attenuated hepatic steatosis by regulating blood glucose and ameliorating lipid metabolism disorder. Liver fibrosis was relieved by PUR treatment. PUR inhibited oxidative stress and inflammation which was associated with inactivation of NF-κB signaling, thereby blocking the upregulation of proinflammatory cytokines (IL-1β, TNF-α) and chemokine (MCP-1). This protection of PUR on diabetic liver injury is possibly related with inhibition on TGF-β/Smad signaling. In conclusion, the present study provides evidence that PUR attenuated type 2 diabetic liver injury by inhibiting NF-κB-driven liver inflammation and the TGF-β/Smad signaling pathway

Neuroprotective Effect of Salvianolic Acid A against Diabetic Peripheral Neuropathy through Modulation of Nrf2

Oxidative stress has been recognized as the contributor to diabetic peripheral neuropathy (DPN). Antioxidant strategies have been most widely explored; nevertheless, whether antioxidants alone prevent DPN still remains inconclusive. In the present study, we established an in vitro DPN cell model for drug screening using Schwann RSC96 cells under high glucose (HG) stimulation, and we found that salvianolic acid A (SalA) mitigated HG-induced injury evidenced by cell viability and myelination. Mechanistically, SalA exhibited strong antioxidative effects by inhibiting 1,1-diphenyl-2-picrylhydrazyl (DPPH) and reducing reactive oxygen species (ROS), malondialdehyde (MDA), and oxidized glutathione (GSSG) content, as well as upregulating antioxidative enzyme mRNA expression. In addition, SalA significantly extenuated neuroinflammation with downregulated inflammatory factor mRNA expression. Furthermore, SalA improved the mitochondrial function of HG-injured Schwann cells by scavenging mitochondrial ROS, decreasing mitochondrial membrane potential (MMP), and enhancing ATP production, as well as upregulating oxidative phosphorylation gene expression. More importantly, we identified nuclear factor-E2-related factor 2 (Nrf2) as the upstream regulator which mediated protective effects of SalA on DPN. SalA directly bound to the Kelch domain of Kelch-like ECH-associated protein 1 (Keap1) and thus disrupted the interaction of Nrf2 and Keap1 predicted by LibDock of Discovery Studio. Additionally, SalA significantly inhibited Nrf2 promoter activity and downregulated Nrf2 mRNA expression but without affecting Nrf2 protein expression. Interestingly, SalA upregulated the nuclear Nrf2 expression and promoted Nrf2 nuclear translocation by high content screening assay, which was confirmed to be involved in its antiglucotoxicity effect by the knockdown of Nrf2 in RSC96 cells. In KK-Ay mice, we demonstrated that SalA could effectively improve the abnormal glucose and lipid metabolism and significantly protect against DPN by increasing the mechanical withdrawal threshold and sciatic nerve conduction velocity and restoring the ultrastructural impairment of the injured sciatic nerve induced by diabetes. Hence, SalA protected against DPN by antioxidative stress, attenuating neuroinflammation, and improving mitochondrial function via Nrf2. SalA may be prospective therapeutics for treating DPN

Genome-Wide Identification and Expression Analyses of Aquaporin Gene Family during Development and Abiotic Stress in Banana

Aquaporins (AQPs) function to selectively control the flow of water and other small molecules through biological membranes, playing crucial roles in various biological processes. However, little information is available on the AQP gene family in bananas. In this study, we identified 47 banana AQP genes based on the banana genome sequence. Evolutionary analysis of AQPs from banana, Arabidopsis, poplar, and rice indicated that banana AQPs (MaAQPs) were clustered into four subfamilies. Conserved motif analysis showed that all banana AQPs contained the typical AQP-like or major intrinsic protein (MIP) domain. Gene structure analysis suggested the majority of MaAQPs had two to four introns with a highly specific number and length for each subfamily. Expression analysis of MaAQP genes during fruit development and postharvest ripening showed that some MaAQP genes exhibited high expression levels during these stages, indicating the involvement of MaAQP genes in banana fruit development and ripening. Additionally, some MaAQP genes showed strong induction after stress treatment and therefore, may represent potential candidates for improving banana resistance to abiotic stress. Taken together, this study identified some excellent tissue-specific, fruit development- and ripening-dependent, and abiotic stress-responsive candidate MaAQP genes, which could lay a solid foundation for genetic improvement of banana cultivars