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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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Xu M, Yang F, Shen B, Wang J, Niu W, Chen H, Li N, Chen W, Wang Q, HE Z, Ding R. A bibliometric analysis of acute myocardial infarction in women from 2000 to 2022. Front Cardiovasc Med 2023; 10:1090220. [PMID: 37576112 PMCID: PMC10416645 DOI: 10.3389/fcvm.2023.1090220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 06/01/2023] [Indexed: 08/15/2023] Open
Abstract
Background Plenty of publications had been written in the last several decades on acute myocardial infarction (AMI) in women. However, there are few bibliometric analyses in such field. In order to solve this problem, we attempted to examine the knowledge structure and development of research about AMI in women based on analysis of related publications. Method The Web of Science Core Collection was used to extract all publications regarding AMI in women, ranging from January 2000 to August 2022. Bibliometric analysis was performed using VOSviewer, Cite Space, and an online bibliometric analysis platform. Results A total of 14,853 publications related to AMI in women were identified from 2000 to 2022. Over the past 20 years, the United States had published the most articles in international research and participated in international cooperation the most frequently. The primary research institutions were Harvard University and University of Toronto. Circulation was the most cited journal and had an incontrovertible academic impact. 67,848 authors were identified, among which Harlan M Krumholz had the most significant number of articles and Thygesen K was co-cited most often. And the most common keywords included risk factors, disease, prognosis, mortality, criteria and algorithm. Conclusion The research hotspots and trends of AMI in women were identified and explored using bibliometric and visual methods. Researches about AMI in women are flourishing. Criteria and algorithms might be the focus of research in the near future, which deserved great attentions.
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Affiliation(s)
- Ming Xu
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
- Department of Cardiology, Shanghai Navy Feature Medical Center, Naval Medical University, Shanghai, China
| | - Fupeng Yang
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Bin Shen
- Department of Cardiology, Shanghai Navy Feature Medical Center, Naval Medical University, Shanghai, China
| | - Jiamei Wang
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Wenhao Niu
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Hui Chen
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Na Li
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Wei Chen
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Qinqin Wang
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Zhiqing HE
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Ru Ding
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
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Javeed A, Khan SU, Ali L, Ali S, Imrana Y, Rahman A. Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9288452. [PMID: 35154361 PMCID: PMC8831075 DOI: 10.1155/2022/9288452] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/15/2022] [Indexed: 12/13/2022]
Abstract
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Sweden
| | - Shafqat Ullah Khan
- Department of Electrical Engineering, University of Science and Technology Bannu, Pakistan
| | - Liaqat Ali
- Department of Electronics, University of Buner, Buner, Pakistan
| | - Sardar Ali
- School of Engineering and Applied Sciences, Isra University Islamabad Campus, Pakistan
| | - Yakubu Imrana
- School of Engineering, University of Development Studies, Tamale, Ghana
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Atiqur Rahman
- Department of Computer Science, University of Science and Technology Bannu, Pakistan
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Staffini A, Svensson T, Chung UI, Svensson AK. Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning. SENSORS (BASEL, SWITZERLAND) 2021; 22:s22010034. [PMID: 35009581 PMCID: PMC8747593 DOI: 10.3390/s22010034] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 05/04/2023]
Abstract
Physiological time series are affected by many factors, making them highly nonlinear and nonstationary. As a consequence, heart rate time series are often considered difficult to predict and handle. However, heart rate behavior can indicate underlying cardiovascular and respiratory diseases as well as mood disorders. Given the importance of accurate modeling and reliable predictions of heart rate fluctuations for the prevention and control of certain diseases, it is paramount to identify models with the best performance in such tasks. The objectives of this study were to compare the results of three different forecasting models (Autoregressive Model, Long Short-Term Memory Network, and Convolutional Long Short-Term Memory Network) trained and tested on heart rate beats per minute data obtained from twelve heterogeneous participants and to identify the architecture with the best performance in terms of modeling and forecasting heart rate behavior. Heart rate beats per minute data were collected using a wearable device over a period of 10 days from twelve different participants who were heterogeneous in age, sex, medical history, and lifestyle behaviors. The goodness of the results produced by the models was measured using both the mean absolute error and the root mean square error as error metrics. Despite the three models showing similar performance, the Autoregressive Model gave the best results in all settings examined. For example, considering one of the participants, the Autoregressive Model gave a mean absolute error of 2.069 (compared to 2.173 of the Long Short-Term Memory Network and 2.138 of the Convolutional Long Short-Term Memory Network), achieving an improvement of 5.027% and 3.335%, respectively. Similar results can be observed for the other participants. The findings of the study suggest that regardless of an individual's age, sex, and lifestyle behaviors, their heart rate largely depends on the pattern observed in the previous few minutes, suggesting that heart rate can be reasonably regarded as an autoregressive process. The findings also suggest that minute-by-minute heart rate prediction can be accurately performed using a linear model, at least in individuals without pathologies that cause heartbeat irregularities. The findings also suggest many possible applications for the Autoregressive Model, in principle in any context where minute-by-minute heart rate prediction is required (arrhythmia detection and analysis of the response to training, among others).
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Affiliation(s)
- Alessio Staffini
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Department of Economics and Finance, Catholic University of Milan, 20123 Milan, Italy
- Business Promotion Division, ALBERT Inc., Tokyo 169-0074, Japan
| | - Thomas Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- School of Health Innovation, Kanagawa University of Human Services Graduate School, Yokosuka 210-0821, Japan
- Department of Clinical Sciences, Lund University, Skåne University Hospital, 221 84 Malmo, Sweden
- Correspondence:
| | - Ung-il Chung
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- School of Health Innovation, Kanagawa University of Human Services Graduate School, Yokosuka 210-0821, Japan
- Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
| | - Akiko Kishi Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Department of Clinical Sciences, Lund University, Skåne University Hospital, 221 84 Malmo, Sweden
- Department of Diabetes and Metabolic Diseases, The University of Tokyo, Tokyo 113-8655, Japan
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Schmid W, Fan Y, Chi T, Golanov E, Regnier-Golanov AS, Austerman RJ, Podell K, Cherukuri P, Bentley T, Steele CT, Schodrof S, Aazhang B, Britz GW. Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries. J Neural Eng 2021; 18. [PMID: 34330120 DOI: 10.1088/1741-2552/ac1982] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 07/30/2021] [Indexed: 12/16/2022]
Abstract
Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making 'go/no-go' decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute andearly-stagemTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Yingying Fan
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Taiyun Chi
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Eugene Golanov
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | | | - Ryan J Austerman
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | - Kenneth Podell
- Department of Neurology, Houston Methodist Hospital, Houston, TX 77030, United States of America
| | - Paul Cherukuri
- Institute of Biosciences and Bioengineering (IBB), Rice University, Houston, TX 77005, United States of America
| | - Timothy Bentley
- Office of Naval Research, Arlington, VA 22203, United States of America
| | - Christopher T Steele
- Military Operational Medicine Research Program, US Army Medical Research and Development Command, Fort Detrick, MD 21702, United States of America
| | - Sarah Schodrof
- Department of Athletics-Sports Medicine, Rice University, Houston, TX 77005, United States of America
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering and Neuroengineering Initiative (NEI), Rice University, Houston, TX 77005, United States of America
| | - Gavin W Britz
- Department of Neurosurgery, Houston Methodist Hospital, Houston, TX 77030, United States of America
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Mohd Faizal AS, Thevarajah TM, Khor SM, Chang SW. A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106190. [PMID: 34077865 DOI: 10.1016/j.cmpb.2021.106190] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 05/10/2021] [Indexed: 06/12/2023]
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide and is a global health issue. Traditionally, statistical models are used commonly in the risk prediction and assessment of CVD. However, the adoption of artificial intelligent (AI) approach is rapidly taking hold in the current era of technology to evaluate patient risks and predict the outcome of CVD. In this review, we outline various conventional risk scores and prediction models and do a comparison with the AI approach. The strengths and limitations of both conventional and AI approaches are discussed. Besides that, biomarker discovery related to CVD are also elucidated as the biomarkers can be used in the risk stratification as well as early detection of the disease. Moreover, problems and challenges involved in current CVD studies are explored. Lastly, future prospects of CVD risk prediction and assessment in the multi-modality of big data integrative approaches are proposed.
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Affiliation(s)
- Aizatul Shafiqah Mohd Faizal
- Bioinformatics Programme, Institute of Biological Science, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - T Malathi Thevarajah
- Department of Pathology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Sook Mei Khor
- Department of Chemistry, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Siow-Wee Chang
- Bioinformatics Programme, Institute of Biological Science, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia.
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Joshi A, Rienks M, Theofilatos K, Mayr M. Systems biology in cardiovascular disease: a multiomics approach. Nat Rev Cardiol 2021; 18:313-330. [PMID: 33340009 DOI: 10.1038/s41569-020-00477-1] [Citation(s) in RCA: 108] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/02/2020] [Indexed: 12/13/2022]
Abstract
Omics techniques generate large, multidimensional data that are amenable to analysis by new informatics approaches alongside conventional statistical methods. Systems theories, including network analysis and machine learning, are well placed for analysing these data but must be applied with an understanding of the relevant biological and computational theories. Through applying these techniques to omics data, systems biology addresses the problems posed by the complex organization of biological processes. In this Review, we describe the techniques and sources of omics data, outline network theory, and highlight exemplars of novel approaches that combine gene regulatory and co-expression networks, proteomics, metabolomics, lipidomics and phenomics with informatics techniques to provide new insights into cardiovascular disease. The use of systems approaches will become necessary to integrate data from more than one omic technique. Although understanding the interactions between different omics data requires increasingly complex concepts and methods, we argue that hypothesis-driven investigations and independent validation must still accompany these novel systems biology approaches to realize their full potential.
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Affiliation(s)
- Abhishek Joshi
- King's British Heart Foundation Centre, King's College London, London, UK
- Bart's Heart Centre, St. Bartholomew's Hospital, London, UK
| | - Marieke Rienks
- King's British Heart Foundation Centre, King's College London, London, UK
| | | | - Manuel Mayr
- King's British Heart Foundation Centre, King's College London, London, UK.
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Borracci RA, Higa CC, Ciambrone G, Gambarte J. Treatment of individual predictors with neural network algorithms improves Global Registry of Acute Coronary Events score discrimination. ARCHIVOS DE CARDIOLOGIA DE MEXICO 2021; 91:58-65. [PMID: 33661883 PMCID: PMC8258905 DOI: 10.24875/acm.20000011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Objective: The aim of this study was to develop, train, and test different neural network (NN) algorithm-based models to improve the Global Registry of Acute Coronary Events (GRACE) score performance to predict in-hospital mortality after an acute coronary syndrome. Methods: We analyzed a prospective database, including 40 admission variables of 1255 patients admitted with the acute coronary syndrome in a community hospital. Individual predictors included in GRACE score were used to train and test three NN algorithm-based models (guided models), namely: one- and two-hidden layer multilayer perceptron and a radial basis function network. Three extra NNs were built using the 40 admission variables of the entire database (unguided models). Expected mortality according to GRACE score was calculated using the logistic regression equation. Results: In terms of receiver operating characteristic area and negative predictive value (NPV), almost all NN algorithms outperformed logistic regression. Only radial basis function models obtained a better accuracy level based on NPV improvement, at the expense of positive predictive value (PPV) reduction. The independent normalized importance of variables for the best unguided NN was: creatinine 100%, Killip class 61%, ejection fraction 52%, age 44%, maximum creatine-kinase level 41%, glycemia 40%, left bundle branch block 35%, and weight 33%, among the top 8 predictors. Conclusions: Treatment of individual predictors of GRACE score with NN algorithms improved accuracy and discrimination power in all models with respect to the traditional logistic regression approach; nevertheless, PPV was only marginally enhanced. Unguided variable selection would be able to achieve better results in PPV terms.
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Affiliation(s)
| | - Claudio C Higa
- Department of Cardiology, Herzzentrum, Hospital Alemán. Buenos Aires, Argentina
| | - Graciana Ciambrone
- Department of Cardiology, Herzzentrum, Hospital Alemán. Buenos Aires, Argentina
| | - Jimena Gambarte
- Department of Cardiology, Herzzentrum, Hospital Alemán. Buenos Aires, Argentina
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Mittal H, Khanuja M. Hydrothermal in-situ synthesis of MoSe2-polypyrrole nanocomposite for efficient photocatalytic degradation of dyes under dark and visible light irradiation. Sep Purif Technol 2021. [DOI: 10.1016/j.seppur.2020.117508] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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10
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Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome. Sci Rep 2019; 9:14631. [PMID: 31601916 PMCID: PMC6787006 DOI: 10.1038/s41598-019-50933-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 09/18/2019] [Indexed: 12/20/2022] Open
Abstract
Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We first used Bootstrap Lasso Regression (BLR) – a Machine Learning method for selecting important variables – to identify a prognostic set of features that identify patients at high risk of death 6-months after presenting with an Acute Coronary Syndrome. Using data derived from the Global Registry of Acute Coronary Events (GRACE) we trained a logistic regression model using these features and evaluated its performance on a development set (N = 43,063) containing patients who have values for all features, and a separate dataset (N = 6,363) that contains patients who have missing feature values. The final model, Ridge Logistic Regression with Variable Inputs (RLRVI), uses imputation to estimate values for missing features. BLR identified 19 features, 8 of which appear in the GRACE score. RLRVI had modest, yet statistically significant, improvement over the standard GRACE score on both datasets. Moreover, for patients who are relatively low-risk (GRACE≤87), RLRVI had an AUC and Hazard Ratio of 0.754 and 6.27, respectively, vs. 0.688 and 2.46 for GRACE, (p < 0.007). RLRVI has improved discriminatory performance on patients who have values for the 8 GRACE features plus any subset of the 11 non-GRACE features. Our results demonstrate that BLR and data imputation can be used to obtain improved risk stratification metrics, particularly for patients who are classified as low risk using traditional methods.
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Kopanitsa G, Dudchenko A, Ganzinger M. Machine Learning Algorithms in Cardiology Domain: A Systematic Review (Preprint). JMIR Med Inform 2019. [DOI: 10.2196/14784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Naqvi SA, Thompson GC, Joffe AR, Blackwood J, Martin DA, Brindle M, Barkema HW, Jenne CN. Cytokines and Chemokines in Pediatric Appendicitis: A Multiplex Analysis of Inflammatory Protein Mediators. Mediators Inflamm 2019; 2019:2359681. [PMID: 30918467 PMCID: PMC6409077 DOI: 10.1155/2019/2359681] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 01/15/2019] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVES We aimed to demonstrate the potential of precision medicine to describe the inflammatory landscape present in children with suspected appendicitis. Our primary objective was to determine levels of seven inflammatory protein mediators previously associated with intra-abdominal inflammation (C-reactive protein-CRP, procalcitonin-PCT, interleukin-6 (IL), IL-8, IL-10, monocyte chemoattractant protein-1-MCP-1, and serum amyloid A-SAA) in a cohort of children with suspected appendicitis. Subsequently, using a multiplex proteomics approach, we examined an expansive array of novel candidate cytokine and chemokines within this population. METHODS We performed a secondary analysis of targeted proteomics data from Alberta Sepsis Network studies. Plasma mediator levels, analyzed by Luminex multiplex assays, were evaluated in children aged 5-17 years with nonappendicitis abdominal pain (NAAP), acute appendicitis (AA), and nonappendicitis sepsis (NAS). We used multivariate regression analysis to evaluate the seven target proteins, followed by decision tree and heat mapping analyses for all proteins evaluated. RESULTS 185 children were included: 83 with NAAP, 79 AA, and 23 NAS. Plasma levels of IL-6, CRP, MCP-1, PCT, and SAA were significantly different in children with AA compared to those with NAAP (p < 0.001). Expansive proteomic analysis demonstrated 6 patterns in inflammatory mediator profiles based on severity of illness. A decision tree incorporating the proteins CRP, ferritin, SAA, regulated on activation normal T-cell expressed and secreted (RANTES), monokine induced by gamma interferon (MIG), and PCT demonstrated excellent specificity (0.920) and negative predictive value (0.882) for children with appendicitis. CONCLUSIONS Multiplex proteomic analyses described the inflammatory landscape of children presenting to the ED with suspected appendicitis. We have demonstrated the feasibility of this approach to identify potential novel candidate cytokines/chemokine patterns associated with a specific illness (appendicitis) amongst those with a broad ED presentation (abdominal pain). This approach can be modelled for future research initiatives in pediatric emergency medicine.
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Affiliation(s)
- S. Ali Naqvi
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary AB, Canada
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary AB, Canada
| | - Graham C. Thompson
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary AB, Canada
- Department of Emergency Medicine, Cumming School of Medicine, University of Calgary, Calgary AB, Canada
| | - Ari R. Joffe
- Department of Pediatrics, Division of Critical Care, University of Alberta, Edmonton AB, Canada
| | - Jaime Blackwood
- Department of Pediatrics, Division of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary AB, Canada
| | - Dori-Ann Martin
- Department of Pediatrics, Division of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary AB, Canada
| | - Mary Brindle
- Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary AB, Canada
| | - Herman W. Barkema
- Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, Calgary AB, Canada
| | - Craig N. Jenne
- Department of Microbiology, Immunology and Infectious Diseases, Cumming School of Medicine, University of Calgary, Calgary AB, Canada
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary AB, Canada
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Henglin M, Stein G, Hushcha PV, Snoek J, Wiltschko AB, Cheng S. Machine Learning Approaches in Cardiovascular Imaging. Circ Cardiovasc Imaging 2017; 10:CIRCIMAGING.117.005614. [PMID: 28956772 DOI: 10.1161/circimaging.117.005614] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging.
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Affiliation(s)
- Mir Henglin
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.).
| | - Gillian Stein
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Pavel V Hushcha
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Jasper Snoek
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Alexander B Wiltschko
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
| | - Susan Cheng
- From the Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (M.H., G.S., P.V.H., S.C.); Google Brain, Google Inc, Cambridge, MA (J.S., A.W.); and Framingham Heart Study, MA (S.C.)
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