Predicting post-operative right ventricular failure using video-based deep learning.
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ABSTRACT:
SUBMITTER: Shad R
PROVIDER: S-EPMC8408163 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionRight ventricular failure (RVF) after left ventricular assist device (LVAD) placement is associated with worse outcomes. We hypothesized that decreased right ventricular (RV) ejection fraction (EF) as well as qualitative assessments of RV function and dilation, as assessed by first pass radionuclide angiography (FPRNA), are associated with an increased risk of RVF following LVAD implantation.MethodsWe retrospectively identified 46 patients from 1/2008 to 11/2017 that underwent FPRNA and LVAD implantation. RVF was defined as requiring inotropes for greater than 14 days after LVAD implantation or requiring a right ventricular assist device. FPRNA-derived variables of RV performance and structure were compared between those that did and did not have RVF post implant. Statistical analyses were performed with Mann-Whitney U tests for ordinal and continuous variables. Fisher's exact tests and Pearson's χ2 tests were used for categorical variables.ResultsEight patients developed RVF after device implantation. The average RV EF on FPRNA was 41.45% in those that did not develop RVF and 40.13% in those that did (P = 0.787). RV dilation (P = 0.896) and global RV function (P = 0.827) by FPRNA were not statistically different between the two groups.ConclusionIn patients that required FPRNA for further assessment of RV function prior to LVAD implantation, decreased RV EF, RV dilation and global RV function on FPRNA were not associated with an increased risk of RVF.
| S-EPMC7085956 | biostudies-literature
Project description:Right ventricular failure was induced thourgh pulmonary banding in 11 pigs. Right ventricular failure was defined as a SRVP >50 mmHg during two hours. After right ventricular failure was induced, half the pigs were treatmed with a Glenn-shunt combined with pulmonary banding for one hour, and the other half served as control group with pulmonary banding only. The aim was to study the change in global gene expression during right ventricular failure due to pulmonary banding, and the effect of volume unloading during pulmonary banding. 11 pigs. Samples at the following time periods: 1) Baseline 2) Right ventricular failure 3) Treatment with modified Glenn-shunt/Control. After Right ventricular failure, pigs were divided into two groups a) Treatment with modified Glenn-shunt or b) Control group
2013-06-11 | E-GEOD-47814 | biostudies-arrayexpress
Project description:AimsThe PREPARE-MVR study (PRediction of Early PostoperAtive Right vEntricular failure in Mitral Valve Replacement/Repair patients) sought to investigate the alterations of right ventricular (RV) contraction pattern in patients undergoing mitral valve replacement/repair (MVR) and to explore the associations between pre-operative RV mechanics and early post-operative RV dysfunction (RVD).Methods and resultsWe prospectively enrolled 42 patients (63 ± 11 years, 69% men) undergoing open-heart MVR. Transthoracic three-dimensional (3D) echocardiography was performed pre-operatively, at intensive care unit discharge, and 6 months after surgery. The 3D model of the RV was reconstructed, and RV ejection fraction (RVEF) was calculated. We decomposed the motion of the ventricle to compute longitudinal ejection fraction (LEF) and radial ejection fraction (REF). Pulmonary artery catheterization was performed to monitor RV stroke work index (RVSWi). RVEF was slightly decreased after MVR [52 (50-55) vs. 51 (46-54)%; P = 0.001], whereas RV contraction pattern changed notably. Before MVR, the longitudinal shortening was the main contributor to global systolic RV function [LEF/RVEF vs. REF/RVEF; 0.53 (0.47-0.58) vs. 0.33 (0.22-0.42); P < 0.001]. Post-operatively, the radial motion became dominant [0.33 (0.28-0.43) vs. 0.46 (0.37-0.51); P = 0.004]. However, this shift was temporary as 6 months later the two components contributed equally to global RV function [0.44 (0.38-0.50) vs. 0.41 (0.36-0.49); P = 0.775]. Pre-operative LEF was an independent predictor of post-operative RVD defined as RVSWi < 300 mmHg⋅mL/m2 [OR = 1.33 (95% CI: 1.08-1.77), P < 0.05].ConclusionsMVR induces a significant shift in the RV mechanical pattern. Advanced indices of RV mechanics are associated with invasively measured parameters of RV contractility and may predict post-operative RVD.
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Project description:Right ventricular failure was induced thourgh pulmonary banding in 11 pigs. Right ventricular failure was defined as a SRVP >50 mmHg during two hours. After right ventricular failure was induced, half the pigs were treated with a Glenn-shunt combined with pulmonary banding for one hour, and the other half served as control group with pulmonary banding only. The aim was to study the change in global gene expression during right ventricular failure due to pulmonary banding, and the effect of volume unloading during pulmonary banding.
2013-06-11 | GSE47814 | GEO
Project description:Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here, we describe recombination landscape estimation using recurrent neural networks (ReLERNN), a deep learning method for estimating a genome-wide recombination map that is accurate even with small numbers of pooled or individually sequenced genomes. Rather than use summaries of linkage disequilibrium as its input, ReLERNN takes columns from a genotype alignment, which are then modeled as a sequence across the genome using a recurrent neural network. We demonstrate that ReLERNN improves accuracy and reduces bias relative to existing methods and maintains high accuracy in the face of demographic model misspecification, missing genotype calls, and genome inaccessibility. We apply ReLERNN to natural populations of African Drosophila melanogaster and show that genome-wide recombination landscapes, although largely correlated among populations, exhibit important population-specific differences. Lastly, we connect the inferred patterns of recombination with the frequencies of major inversions segregating in natural Drosophila populations.
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Project description:Both protease- and reactive oxygen species (ROS)-mediated proteolysis are thought to be key effectors of tissue remodeling. We have previously shown that comparison of amino acid composition can predict the differential susceptibilities of proteins to photo-oxidation. However, predicting protein susceptibility to endogenous proteases remains challenging. Here, we aim to develop bioinformatics tools to (i) predict cleavage site locations (and hence putative protein susceptibilities) and (ii) compare the predicted vulnerabilities of skin proteins to protease- and ROS-mediated proteolysis. The first goal of this study was to experimentally evaluate the ability of existing protease cleavage site prediction models (PROSPER and DeepCleave) to identify experimentally determined MMP9 cleavage sites in two purified proteins and in a complex human dermal fibroblast-derived extracellular matrix (ECM) proteome. We subsequently developed deep bidirectional recurrent neural network (BRNN) models to predict cleavage sites for 14 tissue proteases. The predictions of the new models were tested against experimental datasets and combined with amino acid composition analysis (to predict ultraviolet radiation (UVR)/ROS susceptibility) in a new web app: the Manchester proteome susceptibility calculator (MPSC). The BRNN models performed better in predicting cleavage sites in native dermal ECM proteins than existing models (DeepCleave and PROSPER), and application of MPSC to the skin proteome suggests that: compared with the elastic fiber network, fibrillar collagens may be susceptible primarily to protease-mediated proteolysis. We also identify additional putative targets of oxidative damage (dermatopontin, fibulins and defensins) and protease action (laminins and nidogen). MPSC has the potential to identify potential targets of proteolysis in disparate tissues and disease states.
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Project description:Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individual and macro scales. The Korea Center for Disease Control (KCDC) operates a surveillance system to minimize infectious disease contagions. However, in this system, it is difficult to immediately act against infectious disease because of missing and delayed reports. Moreover, infectious disease trends are not known, which means prediction is not easy. This study predicts infectious diseases by optimizing the parameters of deep learning algorithms while considering big data including social media data. The performance of the deep neural network (DNN) and long-short term memory (LSTM) learning models were compared with the autoregressive integrated moving average (ARIMA) when predicting three infectious diseases one week into the future. The results show that the DNN and LSTM models perform better than ARIMA. When predicting chickenpox, the top-10 DNN and LSTM models improved average performance by 24% and 19%, respectively. The DNN model performed stably and the LSTM model was more accurate when infectious disease was spreading. We believe that this study's models can help eliminate reporting delays in existing surveillance systems and, therefore, minimize costs to society.
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Project description:Evidence-based STI (science, technology, and innovation) policy making requires accurate indicators of innovation in order to promote economic growth. However, traditional indicators from patents and questionnaire-based surveys often lack coverage, granularity as well as timeliness and may involve high data collection costs, especially when conducted at a large scale. Consequently, they struggle to provide policy makers and scientists with the full picture of the current state of the innovation system. In this paper, we propose a first approach on generating web-based innovation indicators which may have the potential to overcome some of the shortcomings of traditional indicators. Specifically, we develop a method to identify product innovator firms at a large scale and very low costs. We use traditional firm-level indicators from a questionnaire-based innovation survey (German Community Innovation Survey) to train an artificial neural network classification model on labelled (product innovator/no product innovator) web texts of surveyed firms. Subsequently, we apply this classification model to the web texts of hundreds of thousands of firms in Germany to predict whether they are product innovators or not. We then compare these predictions to firm-level patent statistics, survey extrapolation benchmark data, and regional innovation indicators. The results show that our approach produces reliable predictions and has the potential to be a valuable and highly cost-efficient addition to the existing set of innovation indicators, especially due to its coverage and regional granularity.
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Project description:A porcine microarray study of acute right ventricular failure due to coronary artery ligation of the right ventricular free wall. 1. Baseline sample from the free right ventricular wall. 2. Ligation of the coronary arteries on the right ventricular free wall induced right ventricular heart failure. When the pressure in the right atrium rose to >20 mmHg, heart failure samples were taken from the free right ventricular wall.
2011-08-24 | GSE31619 | GEO
Project description:Right ventricular (RV) failure plays a critical role in any type of heart failure. However, there is no specific therapy developed for RV failure. To understand RV failure, we focused on the RV specific genes. Global gene expression analysis showed that alternative complement pathway-related genes including C3 and Cfd were significantly upregulated in right ventricle in murine heart. We generated the RV failure by right ventricle-specific pressure overload model mice, pulmonary artery constriction (PAC), which induces RV failure around 14 days. In C3 knockout (C3KO) mice, PAC-induced RV dysfunction and fibrosis were significantly attenuated. C3a is produced from C3 by C3 convertase complex including Cfd. Cfd knockout mice also attenuated PAC-induced RV failure. Moreover, C3a receptor (C3aR) antagonist dramatically improved PAC-induced RV dysfunction in wild type mice. Here we revealed the crucial role of C3-Cfd-C3a-C3aR axis in RV failure and highlight the potential therapeutic target for RV failure with no pharmacologic option.
2021-09-07 | GSE183504 | GEO