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.

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.

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.

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.

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.

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.

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.

Endoscopic transpapillary drainage for walled-off pancreatic necrosis with complete main pancreatic duct disruption by metallic stent placement: A retrospective study.

Background: Walled-off pancreatic necrosis (WOPN) is a serious complication of acute necrotizing pancreatitis (ANP) and may lead to disruption of the main pancreatic duct (MPD). Endoscopic passive transpapillary drainage (PTD) is an effective method for treating MPD disruptions. However, WOPN with complete MPD disruption is usually accompanied by disconnected pancreatic duct syndrome (DPDS), especially with infected necrosis. Endoscopic PTD with a fully covered self-expanding metallic stent (FCSEMS) and a plastic stent placement may have the potential for future application in treating complete MPD disruption in patients with WOPN. Methods: Patients with WOPN caused by ANP were classified according to the 2012 Atlanta classification and definition. In all patients, ERCP was performed 2 times. First, 3 patients were diagnosed with complete MPD disruption by ERCP. At the time of diagnosis, a plastic pancreatic stent (7Fr) was placed. Second, they underwent endoscopic PTD for WOPN with complete MPD disruption in which an FCSEMS and plastic stent placement were the only access routes to the necrotic cavity. Results: The etiology of pancreatitis in these patients was of biliary, lipogenic, and alcoholic origin. The WOPN lesion size ranged from 6.5 to 10.2 cm in this study, and the type of WOPN was mixed in two cases and central in one case. The type of MPD disruption was complete in all three patients. The locations of disruption included the pancreatic body and head. The time from occurrence to the first ERCP was 18, 23, and 26 days, respectively. The main symptoms were abdominal pain, abdominal distention, fever, gastrointestinal obstruction, and/or weight loss. The three patients with symptomatic WOPN and MPD disruption underwent endoscopic PTD with FCSEMS and plastic pancreatic stent placement. Technical and therapeutic successes were achieved in 3/3 of patients. The mean time of stenting was 28-93 days. The clinical symptoms connected with WOPN and collection disappeared postoperatively in all three patients. During the follow-up period of 4-18 months, no patient developed collection recurrence or other complications, such as gastrointestinal bleeding or reinfection. All patients recovered uneventfully. Conclusion: In patients with WOPN with complete MPD disruption, endoscopic PTD with FCSEMSs and plastic stent placement may be an effective and safe method of treatment.

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.

Evaluating the performance of dropout imputation and clustering methods for single-cell RNA sequencing data.

Recent advances in single-cell RNA sequencing (scRNA-seq) provide exciting opportunities for transcriptome analysis at single-cell resolution. Clustering individual cells is a key step to reveal cell subtypes and infer cell lineage in scRNA-seq analysis. Although many dedicated algorithms have been proposed, clustering quality remains a computational challenge for scRNA-seq data, which is exacerbated by inflated zero counts due to various technical noise. To address this challenge, we assess the combinations of nine popular dropout imputation methods and eight clustering methods on a collection of 10 well-annotated scRNA-seq datasets with different sample sizes. Our results show that (i) imputation algorithms do typically improve the performance of clustering methods, and the quality of data visualization using t-Distributed Stochastic Neighbor Embedding; and (ii) the performance of a particular combination of imputation and clustering methods varies with dataset size. For example, the combination of single-cell analysis via expression recovery and Sparse Subspace Clustering (SSC) methods usually works well on smaller datasets, while the combination of adaptively-thresholded low-rank approximation and single-cell interpretation via multikernel learning (SIMLR) usually achieves the best performance on larger datasets.

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).

Graph neural network and multi-data heterogeneous networks for microbe-disease prediction.

The research on microbe association networks is greatly significant for understanding the pathogenic mechanism of microbes and promoting the application of microbes in precision medicine. In this paper, we studied the prediction of microbe-disease associations based on multi-data biological network and graph neural network algorithm. The HMDAD database provided a dataset that included 39 diseases, 292 microbes, and 450 known microbe-disease associations. We proposed a Microbe-Disease Heterogeneous Network according to the microbe similarity network, disease similarity network, and known microbe-disease associations. Furthermore, we integrated the network into the graph convolutional neural network algorithm and developed the GCNN4Micro-Dis model to predict microbe-disease associations. Finally, the performance of the GCNN4Micro-Dis model was evaluated via 5-fold cross-validation. We randomly divided all known microbe-disease association data into five groups. The results showed that the average AUC value and standard deviation were 0.8954 ± 0.0030. Our model had good predictive power and can help identify new microbe-disease associations. In addition, we compared GCNN4Micro-Dis with three advanced methods to predict microbe-disease associations, KATZHMDA, BiRWHMDA, and LRLSHMDA. The results showed that our method had better prediction performance than the other three methods. Furthermore, we selected breast cancer as a case study and found the top 12 microbes related to breast cancer from the intestinal flora of patients, which further verified the model's accuracy.

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).

HFS-SLPEE: A Novel Hierarchical Feature Selection and Second Learning Probability Error Ensemble Model for Precision Cancer Diagnosis.

The emergence of high-throughput RNA-seq data has offered unprecedented opportunities for cancer diagnosis. However, capturing biological data with highly nonlinear and complex associations by most existing approaches for cancer diagnosis has been challenging. In this study, we propose a novel hierarchical feature selection and second learning probability error ensemble model (named HFS-SLPEE) for precision cancer diagnosis. Specifically, we first integrated protein-coding gene expression profiles, non-coding RNA expression profiles, and DNA methylation data to provide rich information; afterward, we designed a novel hierarchical feature selection method, which takes the CpG-gene biological associations into account and can select a compact set of superior features; next, we used four individual classifiers with significant differences and apparent complementary to build the heterogeneous classifiers; lastly, we developed a second learning probability error ensemble model called SLPEE to thoroughly learn the new data consisting of classifiers-predicted class probability values and the actual label, further realizing the self-correction of the diagnosis errors. Benchmarking comparisons on TCGA showed that HFS-SLPEE performs better than the state-of-the-art approaches. Moreover, we analyzed in-depth 10 groups of selected features and found several novel HFS-SLPEE-predicted epigenomics and epigenetics biomarkers for breast invasive carcinoma (BRCA) (e.g., TSLP and ADAMTS9-AS2), lung adenocarcinoma (LUAD) (e.g., HBA1 and CTB-43E15.1), and kidney renal clear cell carcinoma (KIRC) (e.g., IRX2 and BMPR1B-AS1).

Study on novel PtNP-sorafenib and its interaction with VEGFR2.

With the developments of nanodrugs, some drugs have combined with nanoparticles (NPs) to reduce their side-effects and increase their therapeutic activities. Here, a novel nanodrug platinum nanoparticle-sorafenib (PtNP-SOR) was proposed for the first time. By means of molecular dynamics simulation, the stability and biocompatibility of PtNP-SOR were investigated. Then, the interaction mechanism between PtNP-SOR and vascular endothelial growth factor receptor 2 (VEGFR2) was explored and compared with that of the peptide 2a coated PtNPs. The results showed that PtNP-SOR could bind to VEGFR2 more stably, which was driven by the Coulombic and strong dispersion interaction between PtNP-SOR and VEGFR2. According to their contributions obtained from the decomposition of binding free energies, the key residues in VEGFR2 were identified to form the specific space, which increased the affinity with PtNP-SOR. This study provided useful insights to the design of PtNP-drugs as well as important theoretical proofs to the interaction between PtNP-SOR and VEGFR2 at a molecular level, which can be of large help during the development and optimization of novel nanodrugs.

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.

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.

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.