Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.

Background: Mesenchymal cells, including hepatic stellate cells (HSCs), fibroblasts (FBs), myofibroblasts (MFBs), and vascular smooth muscle cells (VSMCs), are the main cells that affect liver fibrosis and play crucial roles in maintaining tissue homeostasis. The dynamic evolution of mesenchymal cells is very important but remains to be explored for researching the reversible mechanism of hepatic fibrosis and its evolution mechanism of hepatic fibrosis to cirrhosis. Methods: Here, we analysed the transcriptomes of more than 50,000 human single cells from three cirrhotic and three healthy liver tissue samples and the mouse hepatic mesenchymal cells of two healthy and two fibrotic livers to reconstruct the evolutionary trajectory of hepatic mesenchymal cells from a healthy to a cirrhotic state, and a subsequent integrative analysis of bulk RNA sequencing (RNA-seq) data of HSCs from quiescent to active (using transforming growth factor β1 (TGF-β1) to stimulate LX-2) to inactive states. Results: We identified core genes and transcription factors (TFs) involved in mesenchymal cell differentiation. In healthy human and mouse livers, the expression of NR1H4 and members of the ZEB families (ZEB1 and ZEB2) changed significantly with the differentiation of FB into HSC and VSMC. In cirrhotic human livers, VSMCs transformed into HSCs with downregulation of MYH11, ACTA2, and JUNB and upregulation of PDGFRB, RGS5, IGFBP5, CD36, A2M, SOX5, and MEF2C. Following HSCs differentiation into MFBs with the upregulation of COL1A1, TIMP1, and NR1H4, a small number of MFBs reverted to inactivated HSCs (iHSCs). The differentiation trajectory of mouse hepatic mesenchymal cells was similar to that in humans; however, the evolution trajectory and proportion of cell subpopulations that reverted from MFBs to iHSCs suggest that the mouse model may not accurately reflect disease progression and outcome in humans. Conclusions: Our analysis elucidates primary genes and TFs involved in mesenchymal cell differentiation during liver fibrosis using scRNA-seq data, and demonstrated the core genes and TFs in process of HSC activation to MFB and MFB reversal to iHSC using bulk RNA-seq data of human fibrosis induced by TGF-β1. Furthermore, our findings suggest promising targets for the treatment of liver fibrosis and provide valuable insights into the molecular mechanisms underlying its onset and progression.

Hepatocellular carcinoma (HCC) is one of the most fatal solid malignancies worldwide. Evidence suggests that thrombin stimulates tumor progression via fibrin formation and platelet activation. Meanwhile, we also found a correlation between thrombin and HCC through bioinformatics analysis. Dabigatran is a selective, direct thrombin inhibitor that reversibly binds to thrombin. Dabigatran was used as the lead agent in this study, and 19 dabigatran derivatives were designed and synthesized based on docking mode. The thrombin-inhibitory activity of the derivative AX-2 was slightly better than that of dabigatran. BX-2, a prodrug of AX-2, showed a fairly strong inhibitory effect on thrombin-induced platelet aggregation, and effectively antagonized proliferation of HCC tumor cells induced by thrombin at the cellular level. Furthermore, BX-2 reduced tumor volume, weight, lung metastasis, and secondary tumor occurrence in nude mouse models. BX-2 combined with sorafenib increased sorafenib efficacy. This study lays the foundation for discovering new anti-HCC mechanism based on thrombin. BX-2 can be used as an anti-HCC drug lead for further research.

Both chronic hepatitis B (CHB) and stroke contribute to a high burden of disease in the majority of low- and middle-income countries. Epidemiological studies yield conflicting results on the association between CHB and stroke, and the causal relationship remains inconclusive. This study aimed to assess the causal effects of CHB on stroke and its subtypes in East Asians by Mendelian randomization (MR) analysis. Variants associated with CHB were obtained from a genome-wide association study (GWAS) of Chinese samples as instrumental variables. The summary statistics for stroke in East Asians were derived from the largest published GWAS to date. Two-sample MR analyses were implemented to evaluate the causal effects of CHB on stroke and its subtypes by using the canonical inverse variance weighting method and other supplementary approaches. We observed an association between genetic predisposition to CHB and a decreased risk of large-artery atherosclerotic stroke (odds ratio = 0.872, 95% confidence interval = 0.786–0.967, p = 0.010). The causal effects of CHB on other stroke outcomes were not statistically significant. Evidence for heterogeneity and horizontal pleiotropy were not found in our analyses. This study provides genetic evidence for a negative association between CHB and stroke in East Asians, which helps improve our understanding of the etiology of stroke.

Introduction: Stroke, of which ischemic stroke (IS) is the major type, is the second leading cause of disability and death worldwide. Circular RNAs (circRNAs) are reported to play important role in the physiology and pathology of IS. CircRNAs often act as competing endogenous RNA (ceRNA) to regulate gene expression by acting as miRNA sponges. However, whole transcriptome-wide screenings of circRNA-mediated ceRNA networks associated with IS are still lacking. In the present study, we constructed a circRNA-miRNA-mRNA ceRNA network by whole transcriptome-wide analysis.Methods: CircRNAs, miRNAs and mRNAs expression profiles were downloaded from the Gene Expression Omnibus (GEO) datasets. We identified differentially expressed (DE) circRNAs, miRNAs, and mRNAs in IS patients. StarBase and CircBank databases were used to predict the miRNA targets of DEcircRNAs, and mirDIP database was used to predict the mRNA targets of DEmiRNAs. CircRNA-miRNA pairs and miRNA-mRNA pairs were established. Then, we identified hub genes via protein-protein interaction analysis and constructed a core ceRNA sub-network.Results: In total, 276 DEcircRNAs, 43 DEmiRNAs, and 1926 DEmRNAs were explored. The ceRNA network included 69 circRNAs, 24 miRNAs, and 92 mRNAs. The core ceRNA subnetwork included hsa_circ_0011474, hsa_circ_0023110, CDKN1A, FHL2, RPS2, CDK19, KAT6A, CBX1, BRD4, and ZFHX3.Discussion: In conclusion, we established a novel hsa_circ_0011474 - hsa-miR-20a-5p/hsa-miR-17-5p - CDKN1A ceRNA regulatory axis associated with IS. Our findings provide new insights into the pathogenesis of IS and offer promising diagnostic and predictive biomarkers.

Pancreatic cancer is a highly malignant cancer with a poor prognosis. Owing to the strong drug resistance of pancreatic cancer, adjuvant chemotherapy has failed to achieve good results in clinical practice. The expression profile data of circular RNA (circRNA) (GSE110580), microRNA (miRNA) (GSE79234), and messenger RNA (mRNA) (GSE140077, GES35141) were obtained from the gene expression omnibus database. The Cancer-Specific circRNA Database identified the structural pattern of circRNA, and the starBase and circBank databases together predicted the miRNA of circRNA. The mirDIP database predicts the target mRNAs of miRNAs and identifies the ceRNA network of circRNA-miRNA-mRNA via negative regulatory mechanisms. The final validation was performed using clinical data from the cancer treatment response gene signature database of patients treated with gemcitabine for pancreatic cancer of the cancer genome atlas. By differential expression analysis, 22 differential circRNAs (8 upregulated and 14 downregulated), 70 differential microRNAs (37 upregulated and 33 downregulated), and 256 differential messenger RNA (DEmRNA) (161 upregulated and 95 downregulated) were obtained. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses showed that DEmRNAs were associated with drug response, exogenous cellular stimulation, and the tumor necrosis factor signaling pathway. The screened downregulated differential circular RNA (hsa_circ_0007401), upregulated differential microRNA (hsa-miR-6509-3p), and downregulated DEmRNA (FLI1) were consistent with the negative regulation mechanism of the ceRNA network, and FLI1 was significantly downregulated in the data of gemcitabine-resistant pancreatic cancer patients in the cancer genome atlas (n = 26).