SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.

Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA's superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA's potential in discovering new drug-gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations.

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

Designing high thermal conductivity of cross-linked epoxy resin via molecular dynamics simulations.

Coarse-grained (CG) non-equilibrium molecular dynamics simulation was used to study the thermal conductivity of a cross-linked network composed of epoxy resin (E51) and polyether amine (PEA). By probing the mechanism of heat transfer in the cross-linked epoxy resin, we systematically explored the effects of the crosslinking degree, chain length and multi-functional groups of the curing agent on the thermal conductivity behavior. Our results indicate that the thermal conductivity is mainly dependent on the chain length and the functional groups of the curing agent. A shorter chain length and a curing agent with more functional groups contribute to higher thermal conductivity, while the crosslinking degree has a negligible effect. Moreover, it is revealed that the thermal conductivity is manipulated by the non-bonding interaction energy (Epair) and the vibrational density. In general, our work could provide some guidelines for the design and fabrication of a cross-linked epoxy network with high thermal conductivity.

[Differences in intestinal absorption of Gegen Qinlian Decoction between normal rats and rats with large intestinal damp-heat syndrome].

The study aimed to compare the difference in intestinal absorption of the components of Gegen Qinlian Decoction between normal rats and those with large intestinal damp-heat syndrome in the pathological state, in order to explore the rational application of Gegen Qinlian Decoction in the treatment of large intestinal damp-heat syndrome. Puerarin, daidzin, liquiritin, scutellarin, baicalin, wogonoside, coptisine, jatrorrhizine, berberine and palmatine were used as the detection indexes in the in vitro everted gut sacs absorption experiment. The cumulative absorption amount(Q/µg) and the absorption rate(K_a) of each component in each intestine segment were calculated and compared. It was found that the absorption of each component in different intestinal segments were linear absorption, with R~2 greater than 0.9, which conformed to the zero-order absorption rate. There were differences between normal rats and model rats in the absorption of the components in Gegen Qinlian Decoction with the same concentration. Intestinal absorption of most components of Gegen Qinlian Decoction in the model of large intestinal damp-heat syndrome increased to some extent. The components of Gegen Qinlian Decoction with the concentration of 200 g·L~(-1) had the highest absorption in the jejunum of the model rats, and the absorption in the ileum, duodenum and colon successively decreased except daidzin and baicalin. In terms of the absorption rate constant, the absorption in the duodenum and jejunum were significantly increased(P<0.01) compared with normal rats, and the absorption in the ileum was significantly decreased(P<0.01) compared with normal rats. In addition, the absorption of puerarin, daidzin, glycyrrhizin, coptisine and berberine increased selectivity in the colon. Therefore, pathological model animals were recommended in the study of the components relating to absorption effect, in order to really lay a research foundation for the symptomatic treatment of large intestinal damp-heat syndrome.

[Effect of Gegen Qinlian Decoction and it's different compatibility groups on gut microbiota in rats with acute enteritis based on high-throughput sequencing].

This study was designed to investigate the effect of Gegen Qinlian(GGQL) Decoction and its different compatibility groups on gut microbiota in rats with acute enteritis, and to explore the efficacy of GGQL Decoction in improving acute enteritis and gut microbiota. Male SD rats were randomly divided into control group, model group, positive control group(SASP), GGQL decoction group, Glycyrrhizae-free group(QGC), Puerariae-free group(QGG), Qinlian-free group(QQL), and Qinlian group(QL). The pathological sections and detection indexes of the rats were observed before and after modeling and administration. After 7 days of administration, fecal samples from 24 rats were collected and Illumina Miseq platform was used for high-throughput sequencing. From the anti-inflammatory and pharmacodynamic indicators, the effect was the most obvious in GGQL Decoction group, QGC group, QGG group and QL group(P<0.05). The alpha diversity and beta diversity showed that there were significant differences in the composition of intestinal flora in each group. As compared with the model group, the increased abundance and diversity of the flora caused by acute inflammation could be down-regulated in all groups except QQL group(P<0.05). The differential bacteria were explored by using LEfSe analysis, and the results showed that Bifidobacterium and other beneficial bacteria only appeared in the normal group. As compared with the normal group, Lactobacillus was significantly reduced(P<0.01), and Bacteroides, Flavonifractor and Clostridium_sensu_stricto_1 were up-regulated in model group(P<0.05, P<0.01). As compared with the model group, the number of Akkermansia was significantly increased(P<0.05), and the number of Clostridium_sensu_stricto_1 associated with intestinal inflammatory diseases was decreased in the GGQL Decoction group, QGC group and QL group. QGC group and QQL group caused the up-regulation of Ruminococcaceae and induced enrichment of Desulfovibrio which could lead to colon cell toxicity; QGG group caused the up-regulation of Proteobacteria and Burkhonderiales. The study suggests that the GGQL Decoction may play a role in the treatment of acute enteritis partially through improving the intestinal barrier, regulating the immune response and the structure of gut microbiota.

Qualitative and quantitative determination of Atractylodes rhizome using ultra-performance liquid chromatography coupled with linear ion trap-Orbitrap mass spectrometry with data-dependent processing.

A quick and effective workflow based on ultra-performance liquid chromatography coupled with electron spray ionization and LTQ-Orbitrap mass spectrometry (UPLC-LTQ-Orbitrap MS) was established for compositional analysis and screening of the characteristic compounds of three species of Atractylodes rhizome for quality evaluation. This technique was employed to determine the seven main components in Atractylodes rhizome samples. Ultimately, 78 constituents were identified; of these, seven characteristic compounds were selected for species discrimination, comprising atractylodin (63), atractylenolide I (43), atractylenolide II (49), atractylenolide III (53), atractylon (69), methyl-atractylenolide II (54) and (4E,6E,12E)-tetradecadecatriene-8,10-diyne-1,3-diacetate (59). The seven main compounds, including six characteristic compounds, were simultaneously determined in 29 batches of Atractylodes rhizome samples. Thus, the method validation showed acceptable results. Quantitative analysis showed significantly different contents of the seven main components among the three species of Atractylodes rhizome, which indicates possible distinctions in the pharmacological effects. This established method can simultaneously provide qualitative and quantitative results for compositional characterization of Atractylodes rhizomes and for quality control.

[Feasibility study of QAMS for quantitative analysis of multiple structural types of ingredients in Atractylodis Rhizome by GC].

In this study， quantitative analysis of multi-components with single marker(QAMS) was established and validated to simultaneously determine four sesquiterpenoids(ß-eudesmol， atractylon， atractylolideⅠ， atractylolide Ⅱ) in Atractylodis Rhizome based on the gas chromatographic method(GC). Using ß-eudesmol as the contrast， the relative correctionfactors(RCF) of the other three sesquiterpenoids were determined by GC. Within the line arranges，the values of RCF of ß-eudesmol to atractylon， atractylolideⅠand atractylolide Ⅱ were 0.823， 0.690 and 0.766， respectively. The RCF had a good reproducibility in various instruments， chromatographic columns. According to their RCF， we simultaneously determined four sesquiterpenoids in Atractylodis Rhizome only using one marker. The results of QAMS method were validated by comparing with that of internal standard method， and no obvious significant difference was found.