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Lindovsky J, Nichtova Z, Dragano NRV, Pajuelo Reguera D, Prochazka J, Fuchs H, Marschall S, Gailus-Durner V, Sedlacek R, Hrabě de Angelis M, Rozman J, Spielmann N. A review of standardized high-throughput cardiovascular phenotyping with a link to metabolism in mice. Mamm Genome 2023; 34:107-122. [PMID: 37326672 PMCID: PMC10290615 DOI: 10.1007/s00335-023-09997-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 05/03/2023] [Indexed: 06/17/2023]
Abstract
Cardiovascular diseases cause a high mortality rate worldwide and represent a major burden for health care systems. Experimental rodent models play a central role in cardiovascular disease research by effectively simulating human cardiovascular diseases. Using mice, the International Mouse Phenotyping Consortium (IMPC) aims to target each protein-coding gene and phenotype multiple organ systems in single-gene knockout models by a global network of mouse clinics. In this review, we summarize the current advances of the IMPC in cardiac research and describe in detail the diagnostic requirements of high-throughput electrocardiography and transthoracic echocardiography capable of detecting cardiac arrhythmias and cardiomyopathies in mice. Beyond that, we are linking metabolism to the heart and describing phenotypes that emerge in a set of known genes, when knocked out in mice, such as the leptin receptor (Lepr), leptin (Lep), and Bardet-Biedl syndrome 5 (Bbs5). Furthermore, we are presenting not yet associated loss-of-function genes affecting both, metabolism and the cardiovascular system, such as the RING finger protein 10 (Rfn10), F-box protein 38 (Fbxo38), and Dipeptidyl peptidase 8 (Dpp8). These extensive high-throughput data from IMPC mice provide a promising opportunity to explore genetics causing metabolic heart disease with an important translational approach.
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Affiliation(s)
- Jiri Lindovsky
- Czech Centre for Phenogenomics, Institute of Molecular Genetics, Czech Academy of Sciences, Prumyslova 595, 252 50 Vestec, Czech Republic
| | - Zuzana Nichtova
- Czech Centre for Phenogenomics, Institute of Molecular Genetics, Czech Academy of Sciences, Prumyslova 595, 252 50 Vestec, Czech Republic
| | - Nathalia R. V. Dragano
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Center Munich, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - David Pajuelo Reguera
- Czech Centre for Phenogenomics, Institute of Molecular Genetics, Czech Academy of Sciences, Prumyslova 595, 252 50 Vestec, Czech Republic
| | - Jan Prochazka
- Czech Centre for Phenogenomics, Institute of Molecular Genetics, Czech Academy of Sciences, Prumyslova 595, 252 50 Vestec, Czech Republic
| | - Helmut Fuchs
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Center Munich, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Susan Marschall
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Center Munich, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Valerie Gailus-Durner
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Center Munich, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Radislav Sedlacek
- Czech Centre for Phenogenomics, Institute of Molecular Genetics, Czech Academy of Sciences, Prumyslova 595, 252 50 Vestec, Czech Republic
| | - Martin Hrabě de Angelis
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Center Munich, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Jan Rozman
- Czech Centre for Phenogenomics, Institute of Molecular Genetics, Czech Academy of Sciences, Prumyslova 595, 252 50 Vestec, Czech Republic
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Nadine Spielmann
- Institute of Experimental Genetics, German Mouse Clinic, Helmholtz Center Munich, German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
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Sehrawat O, Kashou AH, Noseworthy PA. Artificial Intelligence and Atrial Fibrillation. J Cardiovasc Electrophysiol 2022; 33:1932-1943. [PMID: 35258136 PMCID: PMC9717694 DOI: 10.1111/jce.15440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/03/2022] [Accepted: 03/01/2022] [Indexed: 11/30/2022]
Abstract
In the context of atrial fibrillation (AF), traditional clinical practices have thus far fallen short in several domains such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. In this review we focus on the roles of artificial intelligence-enabled ECG pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Further, we overview some of the challenges facing AF screening and the harms and benefits of screening. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm and is discussed in detail with its potential uses. Knowledge gaps also remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. Role of DL models assessing AF burden from long duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, role of adequate external validation of the models and clinical trials to study true performance is discussed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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Cao GY, Li JC, Wang WJ, Wu HB. The Relationship Between the Neutrophil to Lymphocyte Ratio, The Platelet to Lymphocyte Ratio, and Cardiac Syndrome X. Healthc Policy 2022; 15:427-433. [PMID: 35308194 PMCID: PMC8924931 DOI: 10.2147/rmhp.s359733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 12/10/2021] [Indexed: 11/23/2022] Open
Affiliation(s)
- Guang-Yun Cao
- Department of Cardiology, Hebei General Hospital, Shijiazhuang, Hebei Province, 050051, People’s Republic of China
| | - Jian-Chao Li
- Department of Cardiology, Hebei General Hospital, Shijiazhuang, Hebei Province, 050051, People’s Republic of China
- HeBei North University, Zhangjiakou, Hebei Province, 050051, People’s Republic of China
| | - Wen-Jing Wang
- Department of Cardiology, Hebei General Hospital, Shijiazhuang, Hebei Province, 050051, People’s Republic of China
| | - Hai-Bo Wu
- Department of Cardiology, Hebei General Hospital, Shijiazhuang, Hebei Province, 050051, People’s Republic of China
- Correspondence: Hai-Bo Wu, Department of Cardiology, Hebei General Hospital, 348 Heping West Road, Shijiazhuang, Hebei Province, 050051, People’s Republic of China, Tel +86 15128138630, Email
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Li Y, He Y, Meng Y, Fu B, Xue S, Kang M, Duan Z, Chen Y, Wang Y, Tian H. Development and validation of a prediction model to estimate risk of acute pulmonary embolism in deep vein thrombosis patients. Sci Rep 2022; 12:649. [PMID: 35027609 PMCID: PMC8758720 DOI: 10.1038/s41598-021-04657-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 12/28/2021] [Indexed: 12/24/2022] Open
Abstract
Venous thromboembolism (VTE), clinically presenting as deep vein thrombosis (DVT) or pulmonary embolism (PE). Not all DVT patients carry the same risk of developing acute pulmonary embolism (APE). To develop and validate a prediction model to estimate risk of APE in DVT patients combined with past medical history, clinical symptoms, physical signs, and the sign of the electrocardiogram. We analyzed data from a retrospective cohort of patients who were diagnosed as symptomatic VTE from 2013 to 2018 (n = 1582). Among them, 122 patients were excluded. All enrolled patients confirmed by pulmonary angiography or computed tomography pulmonary angiography (CTPA) and compression venous ultrasonography. Using the LASSO and logistics regression, we derived a predictive model with 16 candidate variables to predict the risk of APE and completed internal validation. Overall, 52.9% patients had DVT + APE (773 vs 1460), 47.1% patients only had DVT (687 vs 1460). The APE risk prediction model included one pre-existing disease or condition (respiratory failure), one risk factors (infection), three symptoms (dyspnea, hemoptysis and syncope), five signs (skin cold clammy, tachycardia, diminished respiration, pulmonary rales and accentuation/splitting of P2), and six ECG indicators (SIQIIITIII, right axis deviation, left axis deviation, S1S2S3, T wave inversion and Q/q wave), of which all were positively associated with APE. The ROC curves of the model showed AUC of 0.79 (95% CI, 0.77–0.82) and 0.80 (95% CI, 0.76–0.84) in the training set and testing set. The model showed good predictive accuracy (calibration slope, 0.83 and Brier score, 0.18). Based on a retrospective single-center population study, we developed a novel prediction model to identify patients with different risks for APE in DVT patients, which may be useful for quickly estimating the probability of APE before obtaining definitive test results and speeding up emergency management processes.
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Affiliation(s)
- You Li
- Department of Peripheral Vascular Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China
| | - Yuncong He
- School of Mathematics, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Yan Meng
- Department of Peripheral Vascular Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China
| | - Bowen Fu
- Department of Peripheral Vascular Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China
| | - Shuanglong Xue
- Department of Peripheral Vascular Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China
| | - Mengyang Kang
- Department of Peripheral Vascular Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China
| | - Zhenzhen Duan
- Department of Peripheral Vascular Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China
| | - Yan Chen
- Department of Peripheral Vascular Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China
| | - Yifan Wang
- Department of Peripheral Vascular Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China
| | - Hongyan Tian
- Department of Peripheral Vascular Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China.
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Idrobo-Ávila E, Loaiza-Correa H, Muñoz-Bolaños F, van Noorden L, Vargas-Cañas R. Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart. Heliyon 2021; 7:e07565. [PMID: 34345739 PMCID: PMC8319012 DOI: 10.1016/j.heliyon.2021.e07565] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/01/2021] [Accepted: 07/09/2021] [Indexed: 12/22/2022] Open
Abstract
The intention of the experiment is to investigate whether different sounds have influence on heart signal features in the situation the observer is judging the different sounds as positive or negative. As the heart is under (para)sympathetic control of the nervous system this experiment could give information about the processing of sound stimuli beyond the conscious processing of the subject. As the nature of the influence on the heart signal is not known these signals are to be analysed with AI/machine learning techniques. Heart rate variability (HRV) is a variable derived from the R-R interval peaks of electrocardiogram which exposes the interplay between the sympathetic and parasympathetic nervous system. In addition to its uses as a diagnostic tool and an active part in the clinic and research domain, the HRV has been used to study the effects of sound and music on the heart response; among others, it was observed that heart rate is higher in response to exciting music compared with tranquilizing music while heart rate variability and its low-frequency and high-frequency power are reduced. Nevertheless, it is still unclear which musical element is related to the observed changes. Thus, this study assesses the effects of harmonic intervals and noise stimuli on the heart response by using machine learning. The results show that noises and harmonic intervals change heart activity in a distinct way; e.g., the ratio between the axis of the ellipse fitted in the Poincaré plot increased between harmonic intervals and noise exposition. Moreover, the frequency content of the stimuli produces different heart responses, both with noise and harmonic intervals. In the case of harmonic intervals, it is also interesting to note how the effect of consonance quality could be found in the heart response.
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Affiliation(s)
- Ennio Idrobo-Ávila
- PSI – Intelligent Systems and Perception, Universidad del Valle, Cali, Colombia
- Corresponding author.
| | | | - Flavio Muñoz-Bolaños
- CIFIEX – Experimental Physiological Sciences, Universidad del Cauca, Popayán, Colombia
| | - Leon van Noorden
- IPEM – Institute for Systematic Musicology, Ghent University, Ghent, Belgium
| | - Rubiel Vargas-Cañas
- SIDICO – Dynamic Systems Instrumentation and Control, Universidad del Cauca, Popayán, Colombia
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Saini SK, Gupta R. Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-09999-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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