Conquer coronary artery perforation with magic hands.

Coronary artery perforation (CAP) poses a significant challenge for interventional cardiologists. Management of CAP depends on the location and severity of the perforation. The conventional method for addressing the perforation of large vessels involves the placement of a covered stent, while the perforation of distal and collateral vessels is typically managed using coils, autologous skin, subcutaneous fat, microspheres, gelatin sponge, thrombin or other substances. However, the above techniques have certain limitations and are not applicable in all scenarios. Our team has developed a range of innovative strategies for effectively managing CAP. This article provides an insightful review of the various tips and tricks for the treatment of CAP.

Prokaryotic virus host prediction with graph contrastive augmentaion.

Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task. However, most existing prediction models can be constrained due to the substantial number of unknown interactions in comparison to the constrained diversity of available training data. To solve the problem, we introduce a model for prokaryotic virus host prediction with graph contrastive augmentation (PHPGCA). Specifically, we construct a comprehensive heterogeneous graph by integrating virus-virus protein similarity and virus-host DNA sequence similarity information. As the backbone encoder for learning node representations in the virus-prokaryote graph, we employ LGCN, a state-of-the-art graph embedding technique. Additionally, we apply graph contrastive learning to augment the node representations without the need for additional labels. We further conducted two case studies aimed at predicting the host range of multi-species phages, helping to understand the phage ecology and evolution.

scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data.

Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data. However, existing methods lack good generalization and robustness in cell annotation tasks, partially due to difficulties in dealing with technical differences between datasets, as well as not considering the heterogeneous associations of genes in regulatory mechanism levels. Here, we propose the scPML model, which utilizes various gene signaling pathway data to partition the genetic features of cells, thus characterizing different interaction maps between cells. Extensive experiments demonstrate that scPML performs better in cell type annotation and detection of unknown cell types from different species, platforms, and tissues.

Effects of parasternal intercostal block on surgical site wound infection and pain in patients undergoing cardiac surgery: A meta-analysis.

This study aimed to assess the effect of parasternal intercostal block on postoperative wound infection, pain, and length of hospital stay in patients undergoing cardiac surgery. PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, VIP, and Wanfang databases were extensively queried using a computer, and randomised controlled studies (RCTs) from the inception of each database to July 2023 were sought using keywords in English and Chinese language. Literature quality was assessed using Cochrane-recommended tools, and the included data were collated and analysed using Stata 17.0 software for meta-analysis. Ultimately, eight RCTs were included. Meta-analysis revealed that utilising parasternal intercostal block during cardiac surgery significantly reduced postoperative wound pain (standardised mean difference [SMD] = -1.01, 95% confidence intervals [CI]: -1.70 to -0.31, p = 0.005) and significantly shortened hospital stay (SMD = -0.40, 95% CI: -0.77 to -0.04, p = 0.029), though it may increase the risk of wound infection (OR = 5.03, 95% CI:0.58-44.02, p = 0.144); however, the difference was not statistically significant. The application of parasternal intercostal block during cardiac surgery can significantly reduce postoperative pain and shorten hospital stay. This approach is worth considering for clinical implementation. Decisions regarding its adoption should be made in conjunction with the relevant clinical indices and surgeon's experience.

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.

GraphCPIs: A novel graph-based computational model for potential compound-protein interactions.

Identifying proteins that interact with drug compounds has been recognized as an important part in the process of drug discovery. Despite extensive efforts that have been invested in predicting compound-protein interactions (CPIs), existing traditional methods still face several challenges. The computer-aided methods can identify high-quality CPI candidates instantaneously. In this research, a novel model is named GraphCPIs, proposed to improve the CPI prediction accuracy. First, we establish the adjacent matrix of entities connected to both drugs and proteins from the collected dataset. Then, the feature representation of nodes could be obtained by using the graph convolutional network and Grarep embedding model. Finally, an extreme gradient boosting (XGBoost) classifier is exploited to identify potential CPIs based on the stacked two kinds of features. The results demonstrate that GraphCPIs achieves the best performance, whose average predictive accuracy rate reaches 90.09%, average area under the receiver operating characteristic curve is 0.9572, and the average area under the precision and recall curve is 0.9621. Moreover, comparative experiments reveal that our method surpasses the state-of-the-art approaches in the field of accuracy and other indicators with the same experimental environment. We believe that the GraphCPIs model will provide valuable insight to discover novel candidate drug-related proteins.

MGRCDA: Metagraph Recommendation Method for Predicting CircRNA-Disease Association.

Clinical evidence began to accumulate, suggesting that circRNAs can be novel therapeutic targets for various diseases and play a critical role in human health. However, limited by the complex mechanism of circRNA, it is difficult to quickly and large-scale explore the relationship between disease and circRNA in the wet-lab experiment. In this work, we design a new computational model MGRCDA on account of the metagraph recommendation theory to predict the potential circRNA-disease associations. Specifically, we first regard the circRNA-disease association prediction problem as the system recommendation problem, and design a series of metagraphs according to the heterogeneous biological networks; then extract the semantic information of the disease and the Gaussian interaction profile kernel (GIPK) similarity of circRNA and disease as network attributes; finally, the iterative search of the metagraph recommendation algorithm is used to calculate the scores of the circRNA-disease pair. On the gold standard dataset circR2Disease, MGRCDA achieved a prediction accuracy of 92.49% with an area under the ROC curve of 0.9298, which is significantly higher than other state-of-the-art models. Furthermore, among the top 30 disease-related circRNAs recommended by the model, 25 have been verified by the latest published literature. The experimental results prove that MGRCDA is feasible and efficient, and it can recommend reliable candidates to further wet-lab experiment and reduce the scope of the experiment.

Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images.

Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1) Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at "pollen localization problem") and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at "pollen classification problem"). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method.

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Oil and its pollutants, which enter environment through natural oil seepage and many human activities, have considerable impacts on birds. We summarized the research advances in how oil pollutants influence birds and the cleaning technology of polluted birds and their habitats. The toxicity and destruction to feather structure are the major impacts of oil pollution on birds. Oil pollution can lead to birds' death, and also produce many chronic harms, including causing hemolytic anemia, reducing their immunity, disrupting thermal insulation and waterproo-fing performance of feather. It is an important way to reduce the impacts of oil pollution on birds by timely cleaning up the oil in bird habitats as well as carrying out the clean and repair work to the polluted birds. As a big oil-consuming country, China has been left behind by foreign countries in the studies of the effects of oil pollution on birds. More attention should be paid on the short-term and long-term impacts of oil pollution on birds and the cleaning and remediation technologies of the polluted birds and their habitats.

GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions.

MOTIVATION: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network. RESULTS: In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale. AVAILABILITY: Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI.

Profiles of Cytokinins Metabolic Genes and Endogenous Cytokinins Dynamics during Shoot Multiplication In Vitro of Phalaenopsis.

Shoot multiplication induced by exogenous cytokinins (CKs) has been commonly used in Phalaenopsis micropropagation for commercial production. Despite this, mechanisms of CKs action on shoot multiplication remain unclear in Phalaenopsis. In this study, we first identified key CKs metabolic genes, including six isopentenyltransferase (PaIPTs), six cytokinin riboside 5' monophosphate phosphoribohydrolase (PaLOGs), and six cytokinin dehydrogenase (PaCKXs), from the Phalaenopsis genome. Then, we investigated expression profiles of these CKs metabolic genes and endogenous CKs dynamics in shoot proliferation by thidiazuron (TDZ) treatments (an artificial plant growth regulator with strong cytokinin-like activity). Our data showed that these CKs metabolic genes have organ-specific expression patterns. The shoot proliferation in vitro was effectively promoted with increased TDZ concentrations. Following TDZ treatments, the highly expressed CKs metabolic genes in micropropagated shoots were PaIPT1, PaLOG2, and PaCKX4. By 30 days of culture, TDZ treatments significantly induced CK-ribosides levels in micropropagated shoots, such as tZR and iPR (2000-fold and 200-fold, respectively) as compared to the controls, whereas cZR showed only a 10-fold increase. Overexpression of PaIPT1 and PaLOG2 by agroinfiltration assays resulted in increased CK-ribosides levels in tobacco leaves, while overexpression of PaCKX4 resulted in decreased CK-ribosides levels. These findings suggest de novo biosynthesis of CKs induced by TDZ, primarily in elevation of tZR and iPR levels. Our results provide a better understanding of CKs metabolism in Phalaenopsis micropropagation.

GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences.

BACKGROUND: Recent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological "haystack" merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale. RESULTS: Based on the known microbe-disease associations derived from the Human Microbe-Disease Association Database (HMDAD), the proposed model shows reliable performance with high values of the area under ROC curve (AUC) of 0.9456 and 0.8866 in leave-one-out cross validations and five-fold cross validations, respectively. In case studies of colorectal carcinoma, 80% out of the top-20 predicted microbes have been experimentally confirmed via published literatures. CONCLUSION: Based on the assumption that functionally similar microbes tend to share the similar interaction patterns with human diseases, we here propose a group based computational model of Bayesian disease-oriented ranking to prioritize the most potential microbes associating with various human diseases. Based on the sequence information of genes, two computational approaches (BLAST+ and MEGA 7) are leveraged to measure the microbe-microbe similarity from different perspectives. The disease-disease similarity is calculated by capturing the hierarchy information from the Medical Subject Headings (MeSH) data. The experimental results illustrate the accuracy and effectiveness of the proposed model. This work is expected to facilitate the characterization and identification of promising microbial biomarkers.

Heterogeneous graph embedding model for predicting interactions between TF and target gene.

MOTIVATION: Identifying the target genes of transcription factors (TFs) is of great significance for biomedical researches. However, using biological experiments to identify TF-target gene interactions is still time consuming, expensive and limited to small scale. Existing computational methods for predicting underlying genes for TF to target is mainly proposed for their binding sites rather than the direct interaction. To bridge this gap, we in this work proposed a deep learning prediction model, named HGETGI, to identify the new TF-target gene interaction. Specifically, the proposed HGETGI model learns the patterns of the known interaction between TF and target gene complemented with their involvement in different human disease mechanisms. It performs prediction based on random walk for meta-path sampling and node embedding in a skip-gram manner. RESULTS: We evaluated the prediction performance of the proposed method on a real dataset and the experimental results show that it can achieve the average area under the curve of 0.8519 ± 0.0731 in fivefold cross validation. Besides, we conducted case studies on the prediction of two important kinds of TF, NFKB1 and TP53. As a result, 33 and 32 in the top-40 ranking lists of NFKB1 and TP53 were successfully confirmed by looking up another public database (hTftarget). It is envisioned that the proposed HGETGI method is feasible and effective for predicting TF-target gene interactions on a large scale. AVAILABILITY AND IMPLEMENTATION: The source code and dataset are available at https://github.com/PGTSING/HGETGI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Programmed cell death protein-1 inhibitor combined with chimeric antigen receptor T cells in the treatment of relapsed refractory non-Hodgkin lymphoma: A case report.

BACKGROUND: Chimeric antigen receptor T cell (CART) therapy has benefited many refractory lymphoma patients, but some patients experience poor effects. Previous studies have shown that programmed cell death protein-1 (PD-1) inhibitors can improve and prolong the therapeutic effect of CAR-T cell treatment. CASE SUMMARY: A 61-year-old male presented with 15-d history of diarrhea and lower-limb edema. A large mass was detected in the pelvis, and pathology indicated non-Hodgkin diffuse large B-cell lymphoma. After three cycles of the R-CHOP chemotherapeutic regimen, the patient showed three subcutaneous nodules under the left armpit and both sides of the cervical spine. Pathological examination of the nodules indicated DLBCL again. The patient was diagnosed with relapsed and refractory diffuse large B-cell lymphoma. We recommended CAR-T cell treatment. Before treatment, the patient's T cell function and expression of immune detection points were tested. Expression of PD-1 was obviously increased (52.7%) on cluster of differentiation (CD)3+ T cells. The PD-1 inhibitor (3 mg/kg) was infused prior to lymphodepleting chemotherapy with fludarabine and cyclophosphamide. CAR-CD19 T cells of 3 × 106/kg and CAR-CD22 T cells 1 × 106/kg were infused, respectively. The therapeutic effect was significant, and the deoxyribonucleic acid copy numbers of CAR-CD19 T cells and CAR-CD22 T cells were stable. Presently, the patient has been disease-free for more than 12 mo. CONCLUSION: This case suggests that the combination of PD-1 inhibitors and CAR-T cells improved therapeutic efficacy in B-cell lymphoma.

IMS-CDA: Prediction of CircRNA-Disease Associations From the Integration of Multisource Similarity Information With Deep Stacked Autoencoder Model.

Emerging evidence indicates that circular RNA (circRNA) has been an indispensable role in the pathogenesis of human complex diseases and many critical biological processes. Using circRNA as a molecular marker or therapeutic target opens up a new avenue for our treatment and detection of human complex diseases. The traditional biological experiments, however, are usually limited to small scale and are time consuming, so the development of an effective and feasible computational-based approach for predicting circRNA-disease associations is increasingly favored. In this study, we propose a new computational-based method, called IMS-CDA, to predict potential circRNA-disease associations based on multisource biological information. More specifically, IMS-CDA combines the information from the disease semantic similarity, the Jaccard and Gaussian interaction profile kernel similarity of disease and circRNA, and extracts the hidden features using the stacked autoencoder (SAE) algorithm of deep learning. After training in the rotation forest (RF) classifier, IMS-CDA achieves 88.08% area under the ROC curve with 88.36% accuracy at the sensitivity of 91.38% on the CIRCR2Disease dataset. Compared with the state-of-the-art support vector machine and K -nearest neighbor models and different descriptor models, IMS-CDA achieves the best overall performance. In the case studies, eight of the top 15 circRNA-disease associations with the highest prediction score were confirmed by recent literature. These results indicated that IMS-CDA has an outstanding ability to predict new circRNA-disease associations and can provide reliable candidates for biological experiments.

The RNA-binding protein RBM47 inhibits non-small cell lung carcinoma metastasis through modulation of AXIN1 mRNA stability and Wnt/ß-catentin signaling.

BACKGROUND: Non-small-cell lung cancer (NSCLC) remains a highly prevalent and deadly form of cancer, with efforts to better understand the molecular basis of the progression of this disease being essential to its effective treatment. Several recent studies have highlighted the ability of RNA-binding proteins (RBPs) to regulate a wide range of cellular processes in both healthy and pathogenic contexts. Among these RBPs, RNA binding motif protein 47 (RBM47) has recently been identified as a tumor suppressor in both breast and colon cancers, whereas its role in NSCLC is poorly understood. METHODS: RBM47 expression in NSCLC samples was evaluated by RT-PCR, western blotting and immunohistochemistry analysis. Molecular and cellular techniques including lentiviral vector-mediated knockdown were used to elucidate the functions and mechanisms of RBM47. RESULTS: This study sought to analyze the expression and role of RBM47 in NSCLC. In the present study, we observed reduced levels of RBM47 expression in NSCLC, with these reductions corresponding to a poorer prognosis and more advanced disease including a higher TNM stage (p = 0.022), a higher likelihood of tumor thrombus (p = 0.001), and pleural invasion (p = 0.033). Through functional analyses in vitro and in vivo, we further demonstrated that these RBP was able to disrupt the proliferation, migration, and invasion of NSCLC cells. At a molecular level, we determined that RBM47 was able to bind the AXIN1 mRNA, stabilizing it and thereby enhancing the consequent suppression of Wnt/ß-catentin signaling. CONCLUSION: Together our findings reveal that RBM47 targets AXIN1 in order to disrupt Wnt/ß-catenin signaling in NSCLC and thereby disrupting tumor progression. These results thus offer new insights into the molecular biology of NSCLC, and suggest that RBM47 may also have value as a prognostic biomarker and/or therapeutic target in NSCLC patients.

Dual specific CD19/CD22-targeted chimeric antigen receptor T-cell therapy for refractory diffuse large B-cell lymphoma: A case report.

Clinical trials of chimeric antigen receptors (CARs) targeting CD19 have produced impressive results in hematological malignancies, including diffuse large B-cell lymphoma (DLBCL). However, a notable number of patients with DLBCL fail to achieve remission after CD19 CAR T-cell therapy and may therefore require a dual targeted CAR T-cell therapy. A 31-year-old man with refractory DLBCL was assessed in the present case report. The patient was treated with sequential infusion of single CD19 CAR T cells followed by dual CD19/CD22-targeted CAR T cells. The outcome was that the patient achieved partial remission after the first single CD19 CAR T-cell infusion and complete remission after the dual CD19/CD22-targeted CAR T-cell infusion. Grade 1 cytokine release syndrome (CRS) was observed after the single CD19 CAR T-cell infusion, while grade 3 CRS and hemophagocytic syndrome were observed after the dual targeted CAR T-cell infusion, but these adverse effects alleviated after the treatments. To the best of our knowledge, the present case report is the first to describe the successful application of dual CD19/CD22-targeted CAR T-cell therapy for the treatment of refractory DLBCL. The report suggests that dual CD19/CD22-targeted CAR T-cell therapy may represent a promising option for the treatment of refractory DLBCL; however, caution should be taken due to potential CRS development.

iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation.

Found in recent research, tumor cell invasion, proliferation, or other biological processes are controlled by circular RNA. Understanding the association between circRNAs and diseases is an important way to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Most methods, such as k-mer and PSSM, based on the analysis of high-throughput expression data have the tendency to think functionally similar nucleic acid lack direct linear homology regardless of positional information and only quantify nonlinear sequence relationships. However, in many complex diseases, the sequence nonlinear relationship between the pathogenic nucleic acid and ordinary nucleic acid is not much different. Therefore, the analysis of positional information expression can help to predict the complex associations between circRNA and disease. To fill up this gap, we propose a new method, named iCDA-CGR, to predict the circRNA-disease associations. In particular, we introduce circRNA sequence information and quantifies the sequence nonlinear relationship of circRNA by Chaos Game Representation (CGR) technology based on the biological sequence position information for the first time in the circRNA-disease prediction model. In the cross-validation experiment, our method achieved 0.8533 AUC, which was significantly higher than other existing methods. In the validation of independent data sets including circ2Disease, circRNADisease and CRDD, the prediction accuracy of iCDA-CGR reached 95.18%, 90.64% and 95.89%. Moreover, in the case studies, 19 of the top 30 circRNA-disease associations predicted by iCDA-CGR on circRDisease dataset were confirmed by newly published literature. These results demonstrated that iCDA-CGR has outstanding robustness and stability, and can provide highly credible candidates for biological experiments.

MicroRNA-small molecule association identification: from experimental results to computational models.

Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules. In this review, we summarized the concept and functions of small molecule and miRNAs. Especially, five aspects of miRNA functions were exhibited in detail with individual examples. In addition, four disease states that have been linked to miRNA alterations were summed up. Then, small molecules related to four important miRNAs miR-21, 122, 4644 and 27 were selected for introduction. Some important publicly accessible databases and web servers of the experimentally validated or potential small molecule-miRNA associations were discussed. Identifying small molecule targeting miRNAs has become an important goal of biomedical research. Thus, several experimental and computational models have been developed and implemented to identify novel small molecule-miRNA associations. Here, we reviewed four experimental techniques used in the past few years to search for small-molecule inhibitors of miRNAs, as well as three types of models of predicting small molecule-miRNA associations from different perspectives. Finally, we summarized the limitations of existing methods and discussed the future directions for further development of computational models.