BackgroundPyroptosis is a new programmed cell death discovered in recent years. Pyroptosis plays an important role in various diseases. Nevertheless, there are few bibliometric analysis systematically studies this field. We aimed to visualize the research hotspots and trends of pyroptosis using a bibliometric analysis to help understand the future development of basic and clinical research.MethodsThe articles and reviews regarding pyroptosis were culled from Web of Science Core Collection. Countries, institutions, authors, references and keywords in this field were visually analyzed by using CtieSpace and VOSviewer software.ResultsA total of 2845 articles and reviews were included. The number of articles regarding pyroptosis significantly increased yearly. These publications mainly come from 70 countries led by China and the USA and 418 institutions. We identified 605 authors, among which Thirumaladevi Kanneganti had the most significant number of articles, and Shi JJ was co-cited most often. Frontiers in immunology was the journal with the most studies, and Nature was the most commonly cited journal. After analysis, the most common keywords are nod like receptor family pyrin domain containing 3 inflammasome, apoptosis, cell death, gasdermin D, mechanism, caspase-1, and others are current and developing areas of study.ConclusionResearch on the pyroptosis is flourishing. Cooperation and exchanges between countries and institutions must be strengthened in the future. The related pathway mechanism of pyroptosis, the relationship between pyroptosis and other types of programmed cell deaths as well as the role of pyroptosis in various diseases have been the focus of current research and developmental trends in the future research.

Background: Exosomes in cardiovascular diseases (CVDs) have become an active research field with substantial value and potential. Nevertheless, there are few bibliometric studies in this field. We aimed to visualize the research hotspots and trends of exosomes in CVDs using a bibliometric analysis to help understand the future development of basic and clinical research.Methods: The articles and reviews regarding exosomes in the CVDs were culled from the Web of Science Core Collection, and knowledge maps were generated using CiteSpace and VOSviewer software.Results: A total of 1,039 articles were included. The number of exosome articles in the CVDs increased yearly. These publications came from 60 countries/regions, led by the US and China. The primary research institutions were Shanghai Jiao Tong University and Nanjing Medical University. Circulation Research was the journal and co-cited journal with the most studies. We identified 473 authors among which Lucio Barile had the most significant number of articles and Thery C was co-cited most often. After analysis, the most common keywords are myocardium infarction, microRNA and mesenchymal stem cells. Ischemic heart disease, pathogenesis, regeneration, stem cells, targeted therapy, biomarkers, cardiac protection, and others are current and developing areas of study.Conclusion: We identified the research hotspots and trends of exosomes in CVDs using bibliometric and visual methods. Research on exosomes is flourishing in the cardiovascular medicine. Regenerative medicine, exosome engineering, delivery vehicles, and biomarkers will likely become the focus of future research.

Background Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid. Methods A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI, was generated. The proposed approach simultaneously fused the topology of complex networks and diverse information from heterogeneous data sources, and coped with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validated the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo. Results The experimental results showed that the DLDTI model achieved promising performance under fivefold cross-validations with AUC values of 0.9172, which was higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models. Conclusions DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.

No abstract

Background/ObjectiveEndothelial dysfunction is associated with the long-term outcomes in patients with coronary artery disease (CAD). Recent evidence suggests that ticagrelor, a potent antiplatelet agent, improves endothelial function. However, several studies demonstrated contrasting results. The objective of this meta-analysis was to determine the efficacy of ticagrelor treatment on endothelial function.Materials and MethodsA systematic literature study was conducted on databases including PubMed, Web of Science, EMBASE, Scopus, and the Cochrane Library. A historical search was performed for a reference list of the selected studies as of August 2021. The randomized controlled trials (RCTs) were assessed using the Cochrane tool. The weighted mean difference (WMD) 95% CI was treated as the overall effect size, and data were pooled using the fixed-effect model or random-effect model according to the heterogeneity. Subgroup and sensitivity analyses were performed to measure the effects of potential confounders.ResultsA total of 21 studies were included. The meta-analysis indicated that ticagrelor resulted in a significant increase of flow-mediated dilation (FMD) (WMD: 1.48; 95% CI: 0.36, 2.60), reactive hyperemia index (RHI) (WMD: 0.06; 95% CI: 0.00, 0.13), and circulating progenitor endothelial cells (CEPCs) (WMD: 13.84; 95% CI: 5.70, 21.98), and a reduction in the index of microvascular resistance (IMR) (WMD: −15.39; 95% CI: −25.11, −5.68).ConclusionTicagrelor has a significant effect on some markers of endothelial function in patients with CAD. However, the results should be interpreted with caution due to the heterogeneity and limited studies.

Prethrombotic status (PTS) in patients with stable coronary artery disease (SCAD) increases the risk of coronary thrombosis. Accumulating evidences have indicated that micro-ribonucleic acids (miRNAs) may serve as promising biomarkers for SCAD patients with PTS. The present study aimed to identify the miRNA signature in SCAD patients with PTS and evaluated their diagnostic significance. In the screening phase, 32 differently expressed miRNAs (DEMs) in peripheral blood mononuclear cells (PBMCs) were detected in 35 SCAD patients compared with 5 healthy controls by microarray. MiRNA-gene network analysis was then performed, and 4 DEMs were selected for validation with reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) test in an independent cohort comprising 79 SCAD patients and 19 healthy controls. Compared with healthy controls, RT-qPCR test verified the upregulations of miR-34a-5p, miR-432–5p, and miR-370–3p detected by microarray; while the upregulation of miR-495–3p measured by RT-qPCR was not consistent with its low expression detected by microarray. Only miR-34a-5p and miR-495–3p were significantly upregulated in the PTS group compared with the non-PTS group (p < 0.01, p < 0.05). Receiver-operating characteristic (ROC) analysis showed that PBMCs-derived miR-34a-5p and miR-495–3p may discriminate PTS with the areas under the ROC curve (AUC) of 0.780 (confidence interval [CI]95% = 0.673–0.866) and 0.712 (CI95% = 0.599–0.808), respectively. The combination of miR-34a-5p and fibrinogen (FIB, a traditional biomarker for PTS) improved AUC to 0.885 (CI95% = 0.793–0.946) and showed added predictive ability compared with FIB, with an integrated discrimination improvement of 0.201 (p < 0.01). Therefore, the combination of miR-34a-5p and FIB may serve as an efficient tool for distinguishing PTS in SCAD patients.

No abstract

No abstract