We evaluated whether the inhibitory effects of vascular endothelial growth factor (VEGF)-targeted drugs on the proliferation of cancer cells differed according to VEGF receptor (VEGFR) genes, Flt1 and KDR, promoter methylation status. Five hyper-VEGFR-methylation and six no-VEGFR-methylation cancer cells were used for the present study, together with human umbilical endothelial cells (HUVECs) as a control. No-VEGFR-methylation cancer cells showed higher expression of Flt1 and KDR than hyper-VEGFR-methylation cancer cells. Hyper-VEGFR-methylation cancer cells only showed increased expression and protein levels of Flt1 and KDR after treatment with the demethylase 5-aza-2'-deoxycytidine. Two drugs (a VEGF-specific-antibody, bevacizumab, and a KDR-specific-antibody) targeting extracellular VEGF-VEGFR signaling and two VEGF-specific-tyrosine kinase inhibitors (PTK/ZK and sunitinib) targeting intracellular VEGFR signaling were used in the cell proliferation assay. HUVECs showed dose- and time-dependent proliferation decrease with all tested drugs over a 72 h incubation period. No- or hyper-VEGFR-methylation cancer cells showed no significant proliferation differences after treatment with VEGF-specific-antibody or VEGFR2-specific-antibody. After PTK/ZK or sunitinib treatment, no-VEGFR-methylation cancer cells showed dose- or time-dependent decreases in proliferation. Hyper-VEGFR-methylation cancer cells also showed proliferation inhibition by VEGF-specific-tyrosine kinase inhibitors after demethylation of Flt1 and KDR. Proliferation inhibition synergistically increased after combination of demethylation with PTK/ZK in hyper-VEGF-methylation cancer cells. We observed that intracellular targeting of VEGF-VEGFR signaling could be more effective than extracellular targeting of the pathway in the suppression of proliferation of some cancer cells. In particular, the efficacy of intracellular targeting of VEGF-specific-tyrosine kinase inhibitors might be influenced by the epigenetic alteration of VEGFRs.
Objectives Biosignal data captured by patient monitoring systems could provide key evidence for detecting or predicting critical clinical events; however, noise in these data hinders their use. Because deep learning algorithms can extract features without human annotation, this study hypothesized that they could be used to screen unacceptable electrocardiograms (ECGs) that include noise. To test that, a deep learning-based model for unacceptable ECG screening was developed, and its screening results were compared with the interpretations of a medical expert. Methods To develop and apply the screening model, we used a biosignal database comprising 165,142,920 ECG II (10-second lead II electrocardiogram) data gathered between August 31, 2016 and September 30, 2018 from a trauma intensive-care unit. Then, 2,700 and 300 ECGs (ratio of 9:1) were reviewed by a medical expert and used for 9-fold cross-validation (training and validation) and test datasets. A convolutional neural network-based model for unacceptable ECG screening was developed based on the training and validation datasets. The model exhibiting the lowest cross-validation loss was subsequently selected as the final model. Its performance was evaluated through comparison with a test dataset. Results When the screening results of the proposed model were compared to the test dataset, the area under the receiver operating characteristic curve and the F1-score of the model were 0.93 and 0.80 (sensitivity = 0.88, specificity = 0.89, positive predictive value = 0.74, and negative predictive value = 0.96). Conclusions The deep learning-based model developed in this study is capable of detecting and screening unacceptable ECGs efficiently.
The Electrocardiogram Vigilance with Electronic data Warehouse II (ECG-ViEW II) is a large, single-center database comprising numeric parameter data of the surface electrocardiograms of all patients who underwent testing from 1 June 1994 to 31 July 2013. The electrocardiographic data include the test date, clinical department, RR interval, PR interval, QRS duration, QT interval, QTc interval, P axis, QRS axis, and T axis. These data are connected with patient age, sex, ethnicity, comorbidities, age-adjusted Charlson comorbidity index, prescribed drugs, and electrolyte levels. This longitudinal observational database contains 979,273 electrocardiograms from 461,178 patients over a 19-year study period. This database can provide an opportunity to study electrocardiographic changes caused by medications, disease, or other demographic variables. ECG-ViEW II is freely available at http://www.ecgview.org.
Synergy of specific microRNAs (miRNAs) with cardiovascular risk factors to estimate atherosclerosis presence in ischemic stroke patients has not been investigated. The present study aimed to identify atherosclerosis-related circulating miRNAs and to evaluate interaction with other cardiovascular markers to improve the estimation of atherosclerosis presence. We performed a miRNA profiling study using serum of 15 patients with acute ischemic stroke who were classified by the presence of no (n = 8) or severe (n = 7) stenosis on intracranial and extracranial vessels, which identified miR-212, -372, -454, and -744 as miRNAs related with atherosclerosis presence. Of the 4 miRNAs, only miR-212 showed a significant increase in expression in atherosclerosis patients in a validation study (atherosclerotic patients, n = 32, non-atherosclerotic patients, n = 33). Hemoglobin A1c, a high-density lipoprotein cholesterol, and lipoprotein(a), both established risk markers, were independently related with atherosclerosis presence in the validation population. miR-212 enhanced the accuracy of atherosclerosis presence by the three existing markers (three markers, 78.5%; three markers+miR-212, 84.6%, P<0.05) and significantly added to the area under the receiver operating characteristic curve (three markers, 0.8258; three markers+miR-212, 0.8646, P<0.05). The inclusion of miR-212 increased the reclassification index calculated using net reclassification improvement (0.4527, P<0.05) and integrated discrimination improvement (0.0737, P<0.05). We identified circulating miR-212 as a novel marker of atherosclerosis. miR-212 enhanced the estimation of atherosclerosis presence in combination with hemoglobin A1c, high-density lipoprotein cholesterol, and lipoprotein(a). Thus, miR-212 is expected to improve the estimation of atherosclerosis using peripheral blood of patients.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.