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A 56-year-old man with a 5-year history of paroxysmal palpitations, which have worsened over the past year, was diagnosed with atrial fibrillation. During evaluation, transesophageal echocardiography revealed a left atrium (LA) tumoral mass attached to the atrial septal fossa ovale, with intra-tumoral blood flow and blood stream draining from the mass. Both coronary computed tomography angiography and coronary angiography demonstrated a coro-cameral fistula connection between the left circumflex artery (LCX) branch and the LA. In addition, they showed feeding arteries of the mass arising from the LCX. The patient underwent surgical resection of the LA mass and repair of the coronary artery fistula. Intraoperative exploration revealed a 1.7 cm × 1.0 cm jelly-like, brittle LA mass and confirmed a rupture of the supplying artery, leading to a coronary artery–left atrial fistula. Surgical ligation was executed to ensure complete sealing of the supplying coronary branch within the atrial septum. Histopathological examination confirmed the diagnosis of left atrial myxoma. The 6-month follow-up indicated no recurrence of the myxoma and restoration of sinus rhythm after radiofrequency ablation. In the literature, cases of a left circumflex artery branch–left atrial fistula due to rupture of the artery supplying a left atrial myxoma are rare.

Abstract 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.

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.

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.

Abstract 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.

Owing to early diagnosis and rapid development of treatments for cancers, the five-year survival rate of all cancer types has markedly improved worldwide. Over time, however, there has been an increase in the number of cancer patients who develop coronary artery disease (CAD) due to different causes. First, many risk factors are shared between cancer and CAD. Second, inflammation and oxidative stress are common underlying pathogeneses in both disorders. Lastly, cancer therapy can result in endothelial injury, coronary artery spasm, and coagulation, thereby increasing the risk of CAD. As more cancer patients are being diagnosed with CAD, specialized cardiac care should be established to minimize the cardiovascular mortality of cancer survivors. Keywords: Cancer, Coronary artery disease, Cardio-oncology

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.

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. [ABSTRACT FROM AUTHOR]

Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the “omic” diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a p-nearest neighbor graph to sparsify drug similarity matrix and cell line similarity matrix, respectively. Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines. Keywords: drug response, cell line, graph regularization, matrix factorization, response prediction

Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations. Keywords: microRNA, small molecule, association prediction, HeteSim algorithm, heterogeneous network

Abstract Background l-2-aminobutyric acid (l-ABA) is an unnatural amino acid that is a key intermediate for the synthesis of several important pharmaceuticals. To make the biosynthesis of l-ABA environmental friendly and more suitable for the industrial-scale production. We expand the nature metabolic network of Escherichia coli using metabolic engineering approach for the production of l-ABA. Results In this study, Escherichia coli THR strain with a modified pathway for threonine-hyperproduction was engineered via deletion of the rhtA gene from the chromosome. To redirect carbon flux from 2-ketobutyrate (2-KB) to l-ABA, the ilvIH gene was deleted to block the l-isoleucine pathway. Furthermore, the ilvA gene from Escherichia coli W3110 and the leuDH gene from Thermoactinomyces intermedius were amplified and co-overexpressed. The promoter was altered to regulate the expression strength of ilvA* and leuDH. The final engineered strain E. coli THR ΔrhtAΔilvIH/Gap-ilvA*-Pbs-leuDH was able to produce 9.33 g/L of l-ABA with a yield of 0.19 g/L/h by fed-batch fermentation in a 5 L bioreactor. Conclusions This novel metabolically tailored strain offers a promising approach to fulfill industrial requirements for production of l-ABA.

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.

With increasingly fierce competition for jobs, the pressures on people have risen in recent years, leading to lifestyle and diet disorders that result in significantly higher risks of cardiovascular disease. Hypertension is one of the common chronic cardiovascular diseases; however, mainstream blood pressure measurement devices are relatively heavy. When multiple measurements are required, the user experience and the measurement results may be unsatisfactory. In this paper, we describe the design of a signal collection module that collects pulse waves and electrocardiograph (ECG) signals. The collected signals are input into a signal processing module to filter the noise and amplify the useful physiological signals. Then, we use a wavelet transform to eliminate baseline drift noise and detect the feature points of the pulse waves and ECG signals. We propose the concept of detecting the wave shape associated with an instance, an approach that minimizes the impact of atypical pulse waves on blood pressure measurements. Finally, we propose an improved method for measuring blood pressure based on pulse wave velocity that improves the accuracy of blood pressure measurements by 58%. Moreover, the results meet the american medical instrument promotion association standards, which demonstrate the feasibility of our measurement system.

The human-associated microbiota is diverse and complex. It takes an essential role in human health and behavior and is closely related to the occurrence and development of disease. Although the diversity and distribution of microbial communities have been widely studied, little is known about the function and dynamics of microbes in the human body or the complex mechanisms of interaction between them and drugs, which are important for drug discovery and design. A high-quality comprehensive microbe and drug association database will be extremely beneficial to explore the relationship between them. In this article, we developed the Microbe-Drug Association Database (MDAD), a collection of clinically or experimentally supported associations between microbes and drugs, collecting 5,055 entries that include 1,388 drugs and 180 microbes from multiple drug databases and related publications. Moreover, we provided detailed annotations for each record, including the molecular form of drugs or hyperlinks from DrugBank, microbe target information from Uniprot and the original reference links. We hope MDAD will be a useful resource for deeper understanding of microbe and drug interactions and will also be beneficial to drug design, disease therapy and human health.

Background: Previous studies revealed that culprit vessels of ST-segment elevation myocardial infarction (STEMI) were often related to mild or moderate stenosis. However, recent studies suggested that severe stenosis was primarily found in culprit lesions. The objective of this study was to analyze the stenosis severity of culprit lesions in STEMI patients and to clarify the paradoxical results. Methods: A total of 489 consecutive STEMI patients who underwent primary percutaneous coronary intervention were retrospectively studied from January 2012 to December 2014. The patients were divided into three groups based on stenosis severity using quantitative coronary analysis: Group A, 314 cases, stenosis ≥70%; Group B, 127 cases, stenosis 50–70%; and Group C, 48 cases, stenosis ≤50%. The clinical, demographic, and angiographic data of all groups were analyzed. Results: Patients in Group A exhibited a significantly higher prevalence of history of angina pectoris (95.9% vs. 62.5%, P< 0.001), multivessel disease (73.2% vs. 54.2%, P = 0.007), and lower cardiac ejection fraction (53.3 ± 8.6 vs. 56.8 ± 8.4, P= 0.009) than those in Group C. Multivariable analysis revealed that history of angina pectoris (odds ratio [OR]: 13.89, 95% confidence interval [CI]: 6.21–31.11) and multivessel disease (OR: 2.32, 95% CI: 1.25–4.31) were correlated with severe stenosis of the culprit lesion in Group A. Conclusions: Most culprit lesions in STEMI patients were severe stenosis. These patients exhibited a higher prevalence of angina history and multivessel diseases.