Drug repositioning based on weighted local information augmented graph neural network.

Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.

A weighted bilinear neural collaborative filtering approach for drug repositioning.

Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease associations. Similar to traditional latent factor models, which directly factorize drug-disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug-disease association network, the drug-drug similarity and disease-disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug-disease association, the drug's and disease's neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug-disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug-disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).

Drug repositioning based on the heterogeneous information fusion graph convolutional network.

In silico reuse of old drugs (also known as drug repositioning) to treat common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked drugs, with potentially lower overall development costs and shorter development timelines. Therefore, there is a pressing need for computational drug repurposing methodologies to facilitate drug discovery. In this study, we propose a new method, called DRHGCN (Drug Repositioning based on the Heterogeneous information fusion Graph Convolutional Network), to discover potential drugs for a certain disease. To make full use of different topology information in different domains (i.e. drug-drug similarity, disease-disease similarity and drug-disease association networks), we first design inter- and intra-domain feature extraction modules by applying graph convolution operations to the networks to learn the embedding of drugs and diseases, instead of simply integrating the three networks into a heterogeneous network. Afterwards, we parallelly fuse the inter- and intra-domain embeddings to obtain the more representative embeddings of drug and disease. Lastly, we introduce a layer attention mechanism to combine embeddings from multiple graph convolution layers for further improving the prediction performance. We find that DRHGCN achieves high performance (the average AUROC is 0.934 and the average AUPR is 0.539) in four benchmark datasets, outperforming the current approaches. Importantly, we conducted molecular docking experiments on DRHGCN-predicted candidate drugs, providing several novel approved drugs for Alzheimer's disease (e.g. benzatropine) and Parkinson's disease (e.g. trihexyphenidyl and haloperidol).

Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.

Amid the COVID-19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug-virus association entries from literature by text mining and built a human drug-virus association database. To the best of our knowledge, it is the largest publicly available drug-virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug-virus association network, the drug-drug chemical structure similarity network, and the virus-virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug-virus association is unassociated). A comparison on the curated drug-virus database shows that WRMF performs better than a few state-of-the-art methods. In addition, we selected the other two different public datasets (i.e. Cdataset and HMDD V2.0) to assess WRMF's performance. The case study also demonstrated the accuracy and reliability of WRMF to infer potential drugs for the novel virus. In summary, we offer a useful tool including a novel drug-virus association database and a powerful method WRMF to repurpose potential drugs for new viruses.

Exploring the effect of silicene monolayer on the structure and function of villin headpiece and amyloid fibrils by molecular dynamics simulations.

With the development of various nanomaterial expected to be used in biomedical fields, it is more important to evaluate and understand their potential effects on biological system. In this work, two proteins with different structure, Villin Headpiece (HP35) with α-helix structure and protofibrils Aß1-42 with five ß-strand chains, were selected and their interactions with silicene were studied by means of molecular dynamics (MD) simulation to reveal the potential effect of silicene on the structure and function of biomolecules. The obtained results indicated that silicene could rapidly attract HP35 and Aß1-42 fibrils onto the surface to form a stable binding. The adsorption strength was moderate and no significant structural distortion of HP35 and Aß1-42 fibrils was observed. Moreover, the strength of calculated the H-bonds in neighbor chain of Aß1-42 fibrils indicated that the mild interactions between silicene and fibrils could regularize the structure of Aß1-42 fibrils and stabilize the interactions between five chains of fibrils protein, which might enhance the aggregation of Aß1-42 fibrils. This study provides a new insight for understanding the interaction between nanomaterials and biomolecules and moves forward the development of silicene into biomedical fields.

Adsorption Behaviors of Typical Proteins on BP, GR, and C2N Surfaces.

The biotoxicity of nanomaterials is very important for the application of nanomaterials in biomedical systems. In this study, proteins with varying secondary structures (α-helices, ß-sheets, and mixed α/ß structures) were employed to investigate the biological properties of three representative two-dimensional (2D) nanomaterials; these nanomaterials consisted of black phosphorus (BP), graphene (GR), and nitrogenized graphene (C2N) and were studied using molecular dynamics simulations. The results showed that the α-helix motif underwent a slight structural change on the BP surface and little structural change on the C2N surface. In contrast, the structure of the ß-sheet motif remained fairly intact on both the BP and C2N surfaces. The α-helix and ß-sheet motifs were able to freely migrate on the BP surface, but they were anchored to the C2N surface. In contrast to BP and C2N, GR severely disrupted the structures of the α-helix and ß-sheet motifs. BBA protein with mixed α/ß structures adsorbed on the BP and C2N surfaces and exhibited biological behaviors that were consistent with those of the α-helix and ß-sheet motifs. In summary, C2N may possess better biocompatibility than BP and GR and is expected to have applications in the biomedical field. This study not only comprehensively evaluated the biological characteristics of nanomaterials but also provided a theoretical strategy to explore and distinguish the surface characteristics of nanomaterials.

Primary squamous cell carcinoma of the pancreas with a large pseudocyst of the pancreas as the first manifestation: a rare case report and literature review.

BACKGROUND: Primary squamous cell carcinoma (SCC) of the pancreas with pseudocysts, especially diagnosed by endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA), is extremely rare. CASE PRESENTATION: A 64-year-old man was admitted to our department for abdominal distension. Two months ago, he experienced abdominal pain for 1 day and was diagnosed with acute pancreatitis in another hospital. After admission, laboratory tests showed the following: amylase 400 U/L, lipase 403 U/L, and carbohydrate antigen 19-9 (CA19-9) 347 U/mL. Abdominal computed tomography (CT) revealed pancreatitis with a pseudocyst with a diameter measuring 7 cm. During linear EUS, a large pseudocyst (5.4 × 5.2 cm) was observed in the pancreatic body. EUS-FNA was performed. We obtained specimens for histopathology and placed a plastic stent through the pancreas and stomach to drain the pseudocyst. Puncture fluid examination revealed the following: CA19-9 > 12,000 U/mL carcinoembryonic antigen (CEA) 7097.42 ng/ml, amylase 27,145.3 U/L, and lipase > 6000 U/L. Cytopathology revealed an abnormal cell mass, and cancer was suspected. Furthermore, with the result of immunohistochemistry on cell mass (CK ( +), P40 ( +), p63 ( +), CK7 (-) and Ki-67 (30%)), the patient was examined as squamous cell carcinoma (SCC). However, the patient refused surgery, radiotherapy and chemotherapy. After drainage, the cyst shrank, but the patient died 3 months after diagnosis due to liver metastasis and multiple organ failure. CONCLUSION: For patients with primary pancreatic pseudocysts with elevated serum CEA and CA19-9 levels, we should not rule out pancreatic cancer, which may also be a manifestation of primary pancreatic SCC. EUS-FNA is helpful for obtaining histopathology and cytology and thus improving diagnostic accuracy.

Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study.

The novel coronavirus disease 2019 (COVID-19) pandemic has caused a massive health crisis worldwide and upended the global economy. However, vaccines and traditional drug discovery for COVID-19 cost too much in terms of time, manpower, and money. Drug repurposing becomes one of the promising treatment strategies amid the COVID-19 crisis. At present, there are no publicly existing databases for experimentally supported human drug-virus interactions, and most existing drug repurposing methods require the rich information, which is not always available, especially for a new virus. In this study, on the one hand, we put size-able efforts to collect drug-virus interaction entries from literature and build the Human Drug Virus Database (HDVD). On the other hand, we propose a new approach, called SCPMF (similarity constrained probabilistic matrix factorization), to identify new drug-virus interactions for drug repurposing. SCPMF is implemented on an adjacency matrix of a heterogeneous drug-virus network, which integrates the known drug-virus interactions, drug chemical structures, and virus genomic sequences. SCPMF projects the drug-virus interactions matrix into two latent feature matrices for the drugs and viruses, which reconstruct the drug-virus interactions matrix when multiplied together, and then introduces the weighted similarity interaction matrix as constraints for drugs and viruses. Benchmarking comparisons on two different datasets demonstrate that SCPMF has reliable prediction performance and outperforms several recent approaches. Moreover, SCPMF-predicted drug candidates of COVID-19 also confirm the accuracy and reliability of SCPMF.

IDOL G51S Variant Is Associated With High Blood Cholesterol and Increases Low-Density Lipoprotein Receptor Degradation.

OBJECTIVE: A high level of LDL-C (low-density lipoprotein cholesterol) is a major risk factor for cardiovascular disease. The E3 ubiquitin ligase named IDOL (inducible degrader of the LDLR [LDL receptor]; also known as MYLIP [myosin regulatory light chain interacting protein]) mediates degradation of LDLR through ubiquitinating its C-terminal tail. But the expression profile of IDOL differs greatly in the livers of mice and humans. Whether IDOL is able to regulate LDL-C levels in humans remains to be determined. Approach and Results: By using whole-exome sequencing, we identified a nonsynonymous variant rs149696224 in the IDOL gene that causes a G51S (Gly-to-Ser substitution at the amino acid site 51) from a Chinese Uygur family. Large cohort analysis revealed IDOL G51S carriers (+/G51S) displayed significantly higher LDL-C levels. Mechanistically, the G51S mutation stabilized IDOL protein by inhibiting its dimerization and preventing self-ubiquitination and subsequent proteasomal degradation. IDOL(G51S) exhibited a stronger ability to promote ubiquitination and degradation of LDLR. Adeno-associated virus-mediated expression of IDOL(G51S) in mouse liver decreased hepatic LDLR and increased serum levels of LDL-C, total cholesterol, and triglyceride. CONCLUSIONS: Our study demonstrates that IDOL(G51S) is a gain-of-function variant responsible for high LDL-C in both humans and mice. These results suggest that IDOL is a key player regulating cholesterol level in humans.

Primary isolated asymptomatic gastric tuberculosis of the cardia mimicking gastric stromal tumor: a rare case report and literature review.

BACKGROUND: Primary isolated gastric TB of the cardia presenting as a submucosal tumor is extremely rare. CASE PRESENTATION: A 60-year-old female was admitted to our department; endoscopy revealed a smooth protruding lesion in the gastric cardia. The patient was diagnosed with a gastric cardia stromal tumor and the lesion was seen in muscularis propria by endoscopic ultrasonography (EUS). Endoscopic submucosal dissection (ESD) revealed that the lesion was filled with a milky, white liquid and white granulation tissue. Acid-fast specimen staining was negative. Hematoxylin and eosin staining showed patches of caseating necrosis and granulomatous inflammation. Gene sequencing subsequent to polymerase chain reaction (PCR) analysis of the ESD specimen identified Mycobacterium tuberculosis (M. TB) DNA fragments. The patient was put on ATT for 6 months. CONCLUSION: Primary isolated gastric TB of the cardia should be suspected in patients without clinical symptoms whose manifestations are similar to those associated with submucosal tumors. TB-PCR may be helpful for further diagnosis.

Investigation on Adsorption Mechanism of Peptides with Surface-Modified Super-Macroporous Resins.

Macroporous adsorption resins (MARs) have experienced rapid growth because of their unique properties and applications. Recently, it was discovered that a series of MARs with super-macroporous and diverse functional groups were synthesized to adsorb and enrich peptides; however, the detailed change mechanism of pore diameter and element composition and peptide adsorption mechanism have not yet been established. In this study, MARs and modified MARs were prepared by the surfactant reverse micelles swelling method and Friedel-Crafts reaction, and the pore diameter and element changes of these super-macroporous resin particles were accurately determined to elucidate formation processes of modified MARs. The adsorption mechanism of four peptides on different MARs was investigated. Sieving effect, electrostatic, hydrophobic, and hydrogen bonds interactions were found to play a major role in the adsorption process of peptides. Compared to that of the traditional resins, the adsorption capacity of super-macroporous MARs for peptides enormously increased. Electrostatic interactions have been explained perfectly by determining the isoelectric point. The molecular docking technology proved that the hydrogen-bonding receptor in MARs was a crucial factor for the adsorption capacity by autodock 4.26 and gromacs 5.14. These findings will enable selective adsorption of peptides by MARs, which also provides a theoretical basis for the construction of specific resin to adsorb different peptides.

In Situ Growth of Metal-Organic Frameworks in Three-Dimensional Aligned Lumen Arrays of Wood for Rapid and Highly Efficient Organic Pollutant Removal.

Organic contaminants in water have become one of the most serious environmental problems worldwide. Adsorption is one of the most promising approaches to remove organic pollutants from water. However, the existing adsorbents have relatively low removal efficiency, complex preparation processes, and high cost, which limit their practical applications. Here, we developed three-dimensional (3D) zirconium metal-organic frameworks (MOFs) encapsulated in a natural wood membrane (UiO-66/wood membrane) for highly efficient organic pollutant removal from water. UiO-66 MOFs were in situ grown in the 3D low-tortuosity wood lumens by a facile solvothermal strategy. The resulting UiO-66/wood membrane contains the highly mesoporous UiO-66 MOF structure as well as many elongated and open lumens along the direction of the wood growth. Such a unique structural feature improves the mass transfer of organic pollutants and increases the contact probability of organic contaminants with UiO-66 MOFs as the water flows through the membrane, thereby improving the removal efficiency. Furthermore, the integrated multilayer filter consisting of three pieces of UiO-66/wood membranes exhibits a high removal efficiency (96.0%) for organic pollutants such as rhodamine 6G, propranolol, and bisphenol A at the flux of 1.0 × 103 L·m-2·h-1. The adsorbed capacity of UiO-66/wood for Rh6G (based on the content of UiO-66 MOFs) is calculated to be 690 mg·g-1. We believe that such low-cost and scalable production of the UiO-66/wood membrane has broad applications for wastewater treatment and other related pollutant removal.

Clinical characteristics and literature review of chronic active Epstein-Barr virus-associated enteritis.

Chronic active Epstein-Barr virus (EBV) infection-associated enteritis (CAEAE) in nonimmunodeficient individuals is rare. To report a case of CAEAE, relevant articles were searched through databases. The clinical manifestations, endoscopic findings, strategies of treatment, prognoses, and follow-up results of CAEAE patients were analyzed. Including this report, seven citations in the literature provide descriptions of 27 cases of CAEAE. There were 21 males and six females, with a mean age of 40 years. The main clinical manifestations were fever (25/27), abdominal pain (14/27), diarrhea (16/27), hematochezia or bloody stools (13/27), and decreased hemoglobin and red blood cell counts in routine blood tests (14/27). Elevations in inflammatory markers, white blood cell (WBC) counts, and C-reactive protein (CRP) were common. Coagulation was often abnormal. Histopathology confirmed EBV-encoded small nuclear RNA (EBER) in the affected tissue via in situ hybridization. The average serum EBV DNA load was 6.3 × 10^5 copies/mL. All patients had varying degrees of intestinal ulcers endoscopically, and the ulcers and pathology were uncharacterized and misdiagnosed mostly as inflammatory bowel disease (IBD). The course of the disease was progressive and later complicated by intestinal bleeding, intestinal perforation, septic shock, and a high rate of emergency surgery. However, the conditions of the patients often did not improve after surgery, and some patients soon died due to reperforation or massive hematochezia. Hormone and antiviral treatment had no obvious effect. There was a significant difference in surgical and nonsurgical survival (p < 0.05). The proportion of patients who died within 6 months was as high as 63.6% (7/11). CAEAE belongs to a group of rare, difficult conditions, has an insidious clinical course, has a high case fatality rate, and may later develop into EBV-positive lymphoproliferative disorder (EBV-LPD), which in turn leads to carcinogenesis. Clinicians should raise awareness that in patients with multiple ulcers in the intestine of unknown etiology, attention should be paid to EBV serology, and histology to make the diagnosis as early as possible.

XMECP: Reaching State-of-the-Art MECP Optimization in Multiscale Complex Systems.

The Python-based program, XMECP, is developed for realizing robust, efficient, and state-of-the-art minimum energy crossing point (MECP) optimization in multiscale complex systems. This article introduces the basic capabilities of the XMECP program by theoretically investigating the MECP mechanism of several example systems including (1) the photosensitization mechanism of benzophenone, (2) photoinduced proton-coupled electron transfer in the cytosine-guanine base pair in DNA, (3) the spin-flip process in oxygen activation catalyzed by an iron-containing 2-oxoglutarate-dependent oxygenase (Fe/2OGX), and (4) the photochemical pathway of flavoprotein adjusted by the intensity of an external electric field. MECPs related to multistate reaction and multistate reactivity in large-scale complex biochemical systems can be well-treated by workflows suggested by the XMECP program. The branching plane updating the MECP optimization algorithm is strongly recommended as it provides derivative coupling vector (DCV) with explicit calculation and can equivalently evaluate contributions from non-QM residues to DCV, which can be nonadiabatic coupling or spin-orbit coupling in different cases. In the discussed QM/MM examples, we also found that the influence on the QM region by DCV can occur through noncovalent interactions and decay with distance. In the example of DNA base pairs, the nonadiabatic coupling occurs across the π-π stacking structure formed in the double-helix system. In contrast to general intuition, in the example of Fe/2OGX, the central ferrous and oxygen part contribute little to the spin-orbit coupling; however, a nearby arginine residue, which is treated by molecular mechanics in the QM/MM method, contributes significantly via two hydrogen bonds formed with α-ketoglutarate (α-KG). This indicates that the arginine residue plays a significant role in oxygen activation, driving the initial triplet state toward the productive quintet state, which is more than the previous knowledge that the arginine residue can bind α-KG at the reaction site by hydrogen bonds.

Comparative analysis of the gut bacteria and fungi in migratory demoiselle cranes (Grus virgo) and common cranes (Grus grus) in the Yellow River Wetland, China.

Introduction: Gut microbiota are closely related to the nutrition, immunity, and metabolism of the host and play important roles in maintaining the normal physiological activities of animals. Cranes are important protected avian species in China, and they are sensitive to changes in the ecological environment and are thus good environmental indicators. There have been no reports examining gut fungi or the correlation between bacteria and fungi in wild Demoiselle cranes (Grus virgo) and Common cranes (Grus grus). Related research can provide a foundation for the protection of rare wild animals. Methods: 16S rRNA and ITS high-throughput sequencing techniques were used to analyze the gut bacterial and fungal diversity of Common and Demoiselle cranes migrating to the Yellow River wetland in Inner Mongolia. Results: The results revealed that for gut bacteria α diversity, Chao1 index in Demoiselle cranes was remarkably higher than that in Common cranes (411.07 ± 79.54 vs. 294.92 ± 22.38), while other index had no remarkably differences. There was no remarkable difference in fungal diversity. There were marked differences in the gut microbial composition between the two crane species. At the phylum level, the highest abundance of bacteria in the Common crane and Demoiselle crane samples was Firmicutes, accounting for 87.84% and 74.29%, respectively. The highest abundance of fungi in the guts of the Common and Demoiselle cranes was Ascomycota, accounting for 69.42% and 57.63%, respectively. At the genus level, the most abundant bacterial genus in the Common crane sample was Turicibacter (38.60%), and the most abundant bacterial genus in the Demoiselle crane sample was Catelicoccus (39.18%). The most abundant fungi in the Common crane sample was Penicillium (6.97%), and the most abundant fungi in the Demoiselle crane sample was Saccharomyces (8.59%). Correlation analysis indicated that there was a significant correlation between gut bacteria and fungi. Discussion: This study provided a research basis for the protection of cranes. Indeed, a better understanding of the gut microbiota is very important for the conservation and management of wild birds, as it not only helps us to understand their life history and related mechanisms, but also can hinder the spread of pathogenic microorganisms.

Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.

Involvement and targeted intervention of benzo(a)pyrene-regulated apoptosis related proteome modification and muti-drug resistance in hepatocellular carcinoma.

During the development of hepatocellular carcinoma (HCC), the mutual adaptation and interaction of HCC cells and the microenvironment play an important role. Benzo(a)pyrene (B[a]P) is a common environmental pollutant, which can induce the initiation of various malignant tumors, including HCC. However, the effects of B[a]P exposure on progression of HCC and the potential mechanisms remains largely uninvestigated. Here we found that, after the long-term exposure of HCC cells to low dose of B[a]P, it activated glucose-regulated protein 75 (GRP75), which then induced a modification of apoptosis-related proteome. Among them, we identified the X-linked inhibitor of apoptosis protein (XIAP) as a key downstream factor. XIAP further blocked the caspase cascade activation and promoted the acquisition of the anti-apoptosis abilities, ultimately leading to multi-drug resistance (MDR) in HCC. Furthermore, the abovementioned effects were markedly attenuated when we inhibited GRP75 by using 3,4-dihydroxycinnamic acid (caffeic acid, CaA). Collectively, our present study revealed the effects of B[a]P exposure on the progression of HCC, and identified GRP75 was a meaningful factor involved in.

Enhancing Drug Repositioning through Local Interactive Learning with Bilinear Attention Networks.

Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGBCN, a novel computational method that integrates heterogeneous information through a deep bilinear attention network to infer potential drugs for specific diseases. DRGBCN involves constructing a comprehensive drug-disease network by incorporating multiple similarity networks for drugs and diseases. Firstly, we introduce a layer attention mechanism to effectively learn the embeddings of graph convolutional layers from these networks. Subsequently, a bilinear attention network is constructed to capture pairwise local interactions between drugs and diseases. This combined approach enhances the accuracy and reliability of predictions. Finally, a multi-layer perceptron module is employed to evaluate potential drugs. Through extensive experiments on three publicly available datasets, DRGBCN demonstrates better performance over baseline methods in 10-fold cross-validation, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.9399. Furthermore, case studies on bladder cancer and acute lymphoblastic leukemia confirm the practical application of DRGBCN in real-world drug repositioning scenarios. Importantly, our experimental results from the drug-disease network analysis reveal the successful clustering of similar drugs within the same community, providing valuable insights into drug-disease interactions. In conclusion, DRGBCN holds significant promise for uncovering new therapeutic applications of existing drugs, thereby contributing to the advancement of precision medicine.

Comparative analysis of the gut bacteria of the relict gull (Larus Relictus) and black-necked grebe (Podiceps Nigricollis) in Erdos Relic Gull National Nature Reserve in Inner Mongolia, China.

The gut microbiota promotes host health by maintaining homeostasis and enhancing digestive efficiency. The gut microflora in wild birds affects host physiological characteristics, nutritional status, and stress response. The relict gull (Larus Relictus, a Chinese national first-class protected species) and the black-necked grebe (Podiceps Nigricollis, a secondary protected species) bred in the Ordos Relic Gull National Nature Reserve share similar feeding habits and living environments but are distantly related genetically. To explore the composition and differences in the gut microbiota of these two key protected avian species in Erdos Relic Gull National Nature Reserve and provide a basis for their protection, 16S rRNA gene high-throughput sequencing was performed and the gut microbial diversity and composition of the relict gull (L. Relictus) and black-necked grebe (P. Nigricollis) was characterized. In total, 445 OTUs (operational taxonomic units) were identified and classified into 15 phyla, 22 classes, 64 orders, 126 families, and 249 genera. Alpha diversity analysis indicates that the gut microbial richness of the relict gull is significantly lower than that of the black-necked grebe. Gut microbe composition differs significantly between the two species. The most abundant bacterial phyla in these samples were Proteobacteria, Firmicutes, Fusobacteria, and Bacteroidetes. The prominent phylum in the relict gull was Proteobacteria, whereas the prominent phylum in the black-necked grebe was Firmicutes. The average relative abundance of the 17 genera identified was greater than 1%. The dominant genus in the relict gull was Escherichia-Shigella, whereas Halomonas was dominant in the black-necked grebe. Microbial functional analyses indicate that environmental factors exert a greater impact on relict gulls than on black-necked grebes. Compared with the relict gull, the black-necked grebe was able to use food more efficiently to accumulate its nutrient requirements, and the gut of the relict gull harbored more pathogenic bacteria, which may be one reason for the decline in the relict gull population, rendering it an endangered species. This analysis of the gut microbial composition of these two wild avian species in the same breeding grounds is of great significance, offers important guidance for the protection of these two birds, especially relict gulls, and provides a basis for understanding the propagation of related diseases.

The ratio of monocyte count and high-density lipoprotein cholesterol mediates the association between urinary tungsten and cardiovascular disease: a study from NHANES 2005-2018.

Tungsten (W) is an emerging contaminant that can damage multiple systems in humans. However, studies of its effects on cardiovascular disease (CVD) are limited. The monocyte count to high-density lipoprotein cholesterol ratio (MHR) is a composite inflammatory index of great concern in recent years, derived from lipid and cell inflammation parameters, that is used to indicate the risk of CVD. This study aimed to investigate the association between urinary W and CVD in the general population and compare the mediating effects of lipids, cell inflammatory parameters, and MHR to find a better target for intervention. We analyzed data from 9137 (≥ 20 years) participants in the National Health and Nutrition Examination Survey (NHANES), from 2005 to 2018. Restricted cubic splines (RCS) and survey-weighted generalized linear models (SWGLMs) were used to assess the relationship between W and CVD. Mediated analyses were used to explore lipids, cell inflammatory parameters, and MHR in the possible mediating pathways between W and CVD. In SWGLM, we found that W enhances the risk of CVD, especially congestive heart failure (CHF), coronary heart disease (CHD), and angina pectoris (AP). Women, higher age groups (≥ 55 years), and those with hypertension were vulnerable to W in the subgroup analysis. Mediation analysis showed that monocyte count (MC), white blood cell count (WBC), high-density lipoprotein cholesterol (HDL), and MHR played a mediating role between W and CVD in proportions of 8.49%, 3.70%, 5.18%, and 12.95%, respectively. In conclusion, our study shows that urinary W can increase the risk of CVD, especially for CHF, CHD, and AP. Women, older age groups, and people with hypertension seem to be more vulnerable to W. In addition, MC, WBC, HDL, and MHR mediated the association between W and CVD, especially MHR, which suggests that we should consider it as a priority intervention target in the future.