1
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Avci CB, Bagca BG, Shademan B, Takanlou LS, Takanlou MS, Nourazarian A. Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy. Funct Integr Genomics 2024; 24:182. [PMID: 39365298 DOI: 10.1007/s10142-024-01462-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2024] [Revised: 09/23/2024] [Accepted: 09/24/2024] [Indexed: 10/05/2024]
Abstract
This review analyzes the application of machine learning (ML) in oncological pharmacogenomics, focusing on customizing chemotherapy treatments. It explores how ML can analyze extensive genomic, proteomic, and other omics datasets to identify genetic patterns associated with drug responses. This, in turn, facilitates personalized therapies that are more effective and have fewer side effects. Recent studies have emphasized ML's revolutionary role of ML in personalized oncology treatment by identifying genetic variability and understanding cancer pharmacodynamics. Integrating ML with electronic health records and clinical data shows promise in refining chemotherapy recommendations by considering the complex influencing factors. Although standard chemotherapy depends on population-based doses and treatment regimens, customized techniques use genetic information to tailor treatments for specific patients, potentially enhancing efficacy and reducing adverse effects.However, challenges, such as model interpretability, data quality, transparency, ethical issues related to data privacy, and health disparities, remain. Machine learning has been used to transform oncological pharmacogenomics by enabling personalized chemotherapy treatments. This review highlights ML's potential of ML to enhance treatment effectiveness and minimize side effects through detailed genetic analysis. It also addresses ongoing challenges including improved model interpretability, data quality, and ethical considerations. The review concludes by emphasizing the importance of rigorous clinical trials and interdisciplinary collaboration in the ethical implementation of ML-driven personalized medicine, paving the way for improved outcomes in cancer patients and marking a new frontier in cancer treatment.
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Affiliation(s)
- Cigir Biray Avci
- Department of Medical Biology, Faculty of Medicine, Ege University, Izmir, Turkey
| | - Bakiye Goker Bagca
- Department of Medical Biology, Faculty of Medicine, Adnan Menderes University, Aydın, Turkey
| | - Behrouz Shademan
- Stem Cell Research Centre, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | | | - Alireza Nourazarian
- Department of Basic Medical Sciences, Khoy University of Medical Sciences, Khoy, Iran.
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2
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Sel K, Osman D, Zare F, Masoumi Shahrbabak S, Brattain L, Hahn JO, Inan OT, Mukkamala R, Palmer J, Paydarfar D, Pettigrew RI, Quyyumi AA, Telfer B, Jafari R. Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact. J Am Heart Assoc 2024; 13:e031981. [PMID: 39087582 DOI: 10.1161/jaha.123.031981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.
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Affiliation(s)
- Kaan Sel
- Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology Cambridge MA USA
| | - Deen Osman
- Department of Electrical and Computer Engineering Texas A&M University College Station TX USA
| | - Fatemeh Zare
- Department of Electrical and Computer Engineering Texas A&M University College Station TX USA
| | | | - Laura Brattain
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
| | - Jin-Oh Hahn
- Department of Mechanical Engineering University of Maryland College Park MD USA
| | - Omer T Inan
- School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Anesthesiology and Perioperative Medicine University of Pittsburgh Pittsburgh PA USA
| | - Jeffrey Palmer
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
| | - David Paydarfar
- Department of Neurology The University of Texas at Austin Dell Medical School Austin TX USA
| | | | - Arshed A Quyyumi
- Emory Clinical Cardiovascular Research Institute, Division of Cardiology, Department of Medicine Emory University School of Medicine Atlanta GA USA
| | - Brian Telfer
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
| | - Roozbeh Jafari
- Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology Cambridge MA USA
- Department of Electrical and Computer Engineering Texas A&M University College Station TX USA
- Lincoln Laboratory Massachusetts Institute of Technology Lexington MA USA
- School of Engineering Medicine Texas A&M University Houston TX USA
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Inam M, Sheikh S, Khoja A, Abubakar A, Shah R, Samad Z, Ngugi A, Alarakhiya F, Waljee A, Virani SS. Health Data Sciences and Cardiovascular Disease in Africa: Needs and the Way Forward. Curr Atheroscler Rep 2024:10.1007/s11883-024-01235-1. [PMID: 39240493 DOI: 10.1007/s11883-024-01235-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/24/2024] [Indexed: 09/07/2024]
Abstract
PURPOSE OF REVIEW The rising burden of cardiovascular disease (CVD) in Africa is of great concern. Health data sciences is a rapidly developing field which has the potential to improve health outcomes, especially in low-middle income countries with burdened healthcare systems. We aim to explore the current CVD landscape in Africa, highlighting the importance of health data sciences in the region and identifying potential opportunities for application and growth by leveraging health data sciences to improve CVD outcomes. RECENT FINDINGS While there have been a number of initiatives aimed at developing health data sciences in Africa over the recent decades, the progress and growth are still in their early stages. Its maximum potential can be leveraged through adequate funding, advanced training programs, focused resource allocation, encouraging bidirectional international partnerships, instituting best ethical practices, and prioritizing data science health research in the region. The findings of this review explore the current landscape of CVD and highlight the potential benefits and utility of health data sciences to address CVD challenges in Africa. By understanding and overcoming the barriers associated with health data sciences training, research, and application in the region, focused initiatives can be developed to promote research and development. These efforts will allow policymakers to form informed, evidence-based frameworks for the prevention and management of CVDs, and ultimately result in improved CVD outcomes in the region.
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Affiliation(s)
- Maha Inam
- Office of the Vice Provost, Research, Aga Khan University, Karachi, Pakistan
- Department of Medicine, Temple University Hospital, Philadelphia, PA, 19140, USA
| | - Sana Sheikh
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Adeel Khoja
- Department of Medicine, Aga Khan University, Karachi, Pakistan
- Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, 5000, Australia
| | - Amina Abubakar
- Institute for Human Development, Aga Khan University, Nairobi, Kenya
| | - Reena Shah
- Department of Medicine, Aga Khan University, Nairobi, Kenya
| | - Zainab Samad
- Department of Medicine, Aga Khan University, Karachi, Pakistan
- Section of Cardiology, Department of Medicine, Aga Khan University Hospital, Karachi, Pakistan
| | - Anthony Ngugi
- Department of Population Health, Aga Khan University, Nairobi, Kenya
- Centre of Excellence in Women and Child Health, Aga Khan University, Nairobi, Kenya
| | | | - Akbar Waljee
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, USA
- Department of Internal Medicine, Division of Gastroenterology, University of Michigan, Ann Arbor, MI, USA
- Center for Global Health and Equity, University of Michigan, Ann Arbor, USA
| | - Salim S Virani
- Office of the Vice Provost, Research, Aga Khan University, Karachi, Pakistan.
- Department of Medicine, Aga Khan University, Karachi, Pakistan.
- Section of Cardiology, Department of Medicine, Aga Khan University Hospital, Karachi, Pakistan.
- The Texas Heart Institute, Houston, TX, USA.
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4
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Shen Q, Ge L, Lu W, Wu H, Zhang L, Xu J, Tang O, Muhammad I, Zheng J, Wu Y, Wang SW, Zeng XX, Xue J, Cheng K. Transplanting network pharmacology technology into food science research: A comprehensive review on uncovering food-sourced functional factors and their health benefits. Compr Rev Food Sci Food Saf 2024; 23:e13429. [PMID: 39217524 DOI: 10.1111/1541-4337.13429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 07/21/2024] [Accepted: 07/24/2024] [Indexed: 09/04/2024]
Abstract
Network pharmacology is an emerging interdisciplinary research method. The application of network pharmacology to reveal the nutritional effects and mechanisms of active ingredients in food is of great significance in promoting the development of functional food, facilitating personalized nutrition, and exploring the mechanisms of food health effects. This article systematically reviews the application of network pharmacology in the field of food science using a literature review method. The application progress of network pharmacology in food science is discussed, and the mechanisms of functional factors in food on the basis of network pharmacology are explored. Additionally, the limitations and challenges of network pharmacology are discussed, and future directions and application prospects are proposed. Network pharmacology serves as an important tool to reveal the mechanisms of action and health benefits of functional factors in food. It helps to conduct in-depth research on the biological activities of individual ingredients, composite foods, and compounds in food, and assessment of the potential health effects of food components. Moreover, it can help to control and enhance their functionality through relevant information during the production and processing of samples to guarantee food safety. The application of network pharmacology in exploring the mechanisms of functional factors in food is further analyzed and summarized. Combining machine learning, artificial intelligence, clinical experiments, and in vitro validation, the achievement transformation of functional factor in food driven by network pharmacology is of great significance for the future development of network pharmacology research.
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Affiliation(s)
- Qing Shen
- Laboratory of Food Nutrition and Clinical Research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China
- Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Lijun Ge
- Laboratory of Food Nutrition and Clinical Research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China
| | - Weibo Lu
- Laboratory of Food Nutrition and Clinical Research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China
| | - Huixiang Wu
- Laboratory of Food Nutrition and Clinical Research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China
| | - Li Zhang
- Quzhou Hospital of Traditional Chinese Medicine, Quzhou, Zhejiang, China
| | - Jun Xu
- Ningbo Hospital of Traditional Chinese Medicine, Affiliated Hospital of Zhejiang Chinese Medical University, Ningbo, Zhejiang, China
| | - Oushan Tang
- Shaoxing Second Hospital, Shaoxing, Zhejiang, China
| | - Imran Muhammad
- Laboratory of Food Nutrition and Clinical Research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China
| | - Jing Zheng
- Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Yeshun Wu
- Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Si-Wei Wang
- Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Xi-Xi Zeng
- Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
| | - Jing Xue
- Laboratory of Food Nutrition and Clinical Research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China
| | - Keyun Cheng
- Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China
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Theodorakis N, Feretzakis G, Tzelves L, Paxinou E, Hitas C, Vamvakou G, Verykios VS, Nikolaou M. Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine. J Pers Med 2024; 14:931. [PMID: 39338186 PMCID: PMC11433587 DOI: 10.3390/jpm14090931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Revised: 08/27/2024] [Accepted: 08/30/2024] [Indexed: 09/30/2024] Open
Abstract
Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level is crucial for developing interventions that could delay or reverse its effects. This review explores the integration of machine learning (ML) with multi-omics technologies-including genomics, transcriptomics, epigenomics, proteomics, and metabolomics-in studying the molecular hallmarks of aging to develop personalized medicine interventions. These hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Using ML to analyze big and complex datasets helps uncover detailed molecular interactions and pathways that play a role in aging. The advances of ML can facilitate the discovery of biomarkers and therapeutic targets, offering insights into personalized anti-aging strategies. With these developments, the future points toward a better understanding of the aging process, aiming ultimately to promote healthy aging and extend life expectancy.
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Affiliation(s)
- Nikolaos Theodorakis
- Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece
- School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias, 11527 Athens, Greece
| | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece
| | - Lazaros Tzelves
- 2nd Department of Urology, Sismanoglio General Hospital, Sismanogliou 37, National and Kapodistrian University of Athens, 15126 Athens, Greece
| | - Evgenia Paxinou
- School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece
| | - Christos Hitas
- Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece
| | - Georgia Vamvakou
- Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece
| | - Vassilios S Verykios
- School of Science and Technology, Hellenic Open University, 18 Aristotelous Str., 26335 Patras, Greece
| | - Maria Nikolaou
- Department of Cardiology & 65+ Clinic, Amalia Fleming General Hospital, 14, 25th Martiou Str., 15127 Melissia, Greece
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6
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Klementi T, Piho G, Ross P. A reference architecture for personal health data spaces using decentralized content-addressable storage networks. Front Med (Lausanne) 2024; 11:1411013. [PMID: 39081693 PMCID: PMC11286498 DOI: 10.3389/fmed.2024.1411013] [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: 04/02/2024] [Accepted: 06/19/2024] [Indexed: 08/02/2024] Open
Abstract
Introduction This paper addresses the dilemmas of accessibility, comprehensiveness, and ownership related to health data. To resolve these dilemmas, we propose and justify a novel, globally scalable reference architecture for a Personal Health Data Space (PHDS). This architecture leverages decentralized content-addressable storage (DCAS) networks, ensuring that the data subject retains complete control and ownership of their personal health data. In today's globalized world, where people are increasingly mobile for work and leisure, healthcare is transitioning from episodic symptom-based treatment toward continuity of care. The main aims of this are patient engagement, illness prevention, and active and healthy longevity. This shift, along with the secondary use of health data for societal benefit, has intensified the challenges associated with health data accessibility, comprehensiveness, and ownership. Method The study is structured around four health data use case scenarios from the Estonian National Health Information System (EHIS): primary medical use, medical emergency use, secondary use, and personal use. We analyze these use cases from the perspectives of accessibility, comprehensiveness, and ownership. Additionally, we examine the security, privacy, and interoperability aspects of health data. Results The proposed architectural solution allows individuals to consolidate all their health data into a unified Personal Health Record (PHR). This data can come from various healthcare institutions, mobile applications, medical devices for home use, and personal health notes. Discussions The comprehensive PHR can then be shared with healthcare providers in a semantically interoperable manner, regardless of their location or the information systems they use. Furthermore, individuals maintain the autonomy to share, sell, or donate their anonymous or pseudonymous health data for secondary use with different systems worldwide. The proposed reference architecture aligns with the principles of the European Health Data Space (EHDS) initiative, enhancing health data management by providing a secure, cost-effective, and sustainable solution.
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Affiliation(s)
- Toomas Klementi
- Department of Software Science, Tallinn University of Technology (TalTech), Tallinn, Estonia
| | - Gunnar Piho
- Department of Software Science, Tallinn University of Technology (TalTech), Tallinn, Estonia
| | - Peeter Ross
- Department of Health Technologies, TalTech, Tallinn, Estonia
- Research Department, East Tallinn Central Hospital, Tallinn, Estonia
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Ji H, Kim S, Sunwoo L, Jang S, Lee HY, Yoo S. Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension. JMIR Med Inform 2024; 12:e59187. [PMID: 38996330 PMCID: PMC11282389 DOI: 10.2196/59187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/10/2024] [Accepted: 06/08/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. OBJECTIVE This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. METHODS Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. RESULTS This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. CONCLUSIONS These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.
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Affiliation(s)
- Hyerim Ji
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Health Science and Technology, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Seok Kim
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Sowon Jang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Ho-Young Lee
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
| | - Sooyoung Yoo
- Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea
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8
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Cunha-Oliveira T, Ioannidis JPA, Oliveira PJ. Best practices for data management and sharing in experimental biomedical research. Physiol Rev 2024; 104:1387-1408. [PMID: 38451234 PMCID: PMC11380994 DOI: 10.1152/physrev.00043.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/07/2024] [Accepted: 02/29/2024] [Indexed: 03/08/2024] Open
Abstract
Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers.
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Affiliation(s)
- Teresa Cunha-Oliveira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - John P A Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford, California, United States
- Department of Statistics, Stanford University, Stanford, California, United States
| | - Paulo J Oliveira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
- Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
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9
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Thakur GK, Thakur A, Kulkarni S, Khan N, Khan S. Deep Learning Approaches for Medical Image Analysis and Diagnosis. Cureus 2024; 16:e59507. [PMID: 38826977 PMCID: PMC11144045 DOI: 10.7759/cureus.59507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 05/01/2024] [Indexed: 06/04/2024] Open
Abstract
In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential to streamline workflows, reduce interpretation time, and ultimately improve patient outcomes. The scalability and adaptability of deep learning algorithms enable their deployment across diverse clinical settings, ranging from radiology departments to point-of-care facilities. Furthermore, ongoing research efforts focus on addressing the challenges of data heterogeneity, model interpretability, and regulatory compliance, paving the way for seamless integration of deep learning solutions into routine clinical practice. As the field continues to evolve, collaborations between clinicians, data scientists, and industry stakeholders will be paramount in harnessing the full potential of deep learning for advancing medical image analysis and diagnosis. Furthermore, the integration of deep learning algorithms with other technologies, including natural language processing and computer vision, may foster multimodal medical data analysis and clinical decision support systems to improve patient care. The future of deep learning in medical image analysis and diagnosis is promising. With each success and advancement, this technology is getting closer to being leveraged for medical purposes. Beyond medical image analysis, patient care pathways like multimodal imaging, imaging genomics, and intelligent operating rooms or intensive care units can benefit from deep learning models.
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Affiliation(s)
- Gopal Kumar Thakur
- Department of Data Sciences, Harrisburg University of Science and Technology, Harrisburg, USA
| | - Abhishek Thakur
- Department of Data Sciences, Harrisburg University of Science and Technology, Harrisburg, USA
| | - Shridhar Kulkarni
- Department of Data Sciences, Harrisburg University of Science and Technology, Harrisburg, USA
| | - Naseebia Khan
- Department of Data Sciences, Harrisburg University of Science and Technology, Harrisburg, USA
| | - Shahnawaz Khan
- Department of Computer Application, Bundelkhand University, Jhansi, IND
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10
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Muniappan S, Jeyaraman M, Yadav S, Jeyaraman N, Muthu S, Ramasubramanian S, Patro BP. Applications of Blockchain-Based Technology for Healthcare Devices Post-market Surveillance. Cureus 2024; 16:e57881. [PMID: 38725738 PMCID: PMC11079575 DOI: 10.7759/cureus.57881] [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] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
The volume of data analysis for medical device post-market surveillance (PMS) has increased dramatically in recent years. It is the more stringent and intricate regulatory criteria of the health authorities that are meant to improve the medical device safety review. As regulators scrutinize device safety more closely, proactive approaches to PMS processes are becoming crucial. To solve some of the issues brought on by this shifting regulatory landscape, new technologies have been investigated. This study envisages the technical features of blockchain technology (BCT) and its role in enhancing the PMS for medical devices. To address the aforementioned challenges, our model involves the establishment of a secure, permissioned blockchain for PMS data management, utilizing a proof-of-authority consensus mechanism. This blockchain framework will exclusively permit a carefully vetted and designated set of participants to validate transactions and record them in the PMS data ledger. The utilization of BCT holds the potential to introduce enhanced efficiency and provide several advantages to the various stakeholders involved in the PMS procedure, including its potential to support emerging regulatory efforts.
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Affiliation(s)
- Swarna Muniappan
- Electronics and Communication Engineering, Dr MGR Educational and Research Institute, Chennai, IND
| | - Madhan Jeyaraman
- Clinical Research, Viriginia Tech India, Dr MGR Educational and Research Institute, Chennai, IND
- Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai, IND
- Department of Orthopaedics, Orthopaedic Research Group, Coimbatore, IND
| | - Sankalp Yadav
- Medicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND
| | - Naveen Jeyaraman
- Orthopaedics, ACS Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai, IND
| | - Sathish Muthu
- Department of Orthopaedics, Government Karur Medical College, Karur, IND
- Department of Orthopaedics, Orthopaedic Research Group, Coimbatore, IND
- Department of Biotechnology, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, IND
| | | | - Bishnu P Patro
- Orthopaedics, All India Institute of Medical Sciences, Bhubaneswar, IND
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11
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Ayalew BD, Rodoshi ZN, Patel VK, Alresheq A, Babu HM, Aurangzeb RF, Aurangzeb RI, Mdivnishvili M, Rehman A, Shehryar A, Hassan A. Nuclear Cardiology in the Era of Precision Medicine: Tailoring Treatment to the Individual Patient. Cureus 2024; 16:e58960. [PMID: 38800181 PMCID: PMC11127713 DOI: 10.7759/cureus.58960] [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] [Accepted: 04/23/2024] [Indexed: 05/29/2024] Open
Abstract
Nuclear cardiology, employing advanced imaging technologies like positron emission tomography (PET) and single photon emission computed tomography (SPECT), is instrumental in diagnosing, risk stratifying, and managing heart diseases. Concurrently, precision medicine advocates for treatments tailored to each patient's genetic, environmental, and lifestyle specificities, promising a revolution in personalized cardiovascular care. This review explores the synergy between nuclear cardiology and precision medicine, highlighting advancements, potential enhancements in patient outcomes, and the challenges and opportunities of this integration. We examined the evolution of nuclear cardiology technologies, including PET and SPECT, and their role in cardiovascular diagnostics. We also delved into the principles of precision medicine, focusing on genetic and molecular profiling, data analytics, and individualized treatment strategies. The integration of these domains aims to optimize diagnostic accuracy, therapeutic interventions, and prognostic evaluations in cardiovascular care. Advancements in molecular imaging and the application of artificial intelligence in nuclear cardiology have significantly improved the precision of diagnostics and treatment plans. The adoption of precision medicine principles in nuclear cardiology enables the customization of patient care, leveraging genetic information and biomarkers for enhanced therapeutic outcomes. However, challenges such as data integration, accessibility, cost, and the need for specialized expertise persist. The confluence of nuclear cardiology and precision medicine offers a promising pathway toward revolutionizing cardiovascular healthcare, providing more accurate, effective, and personalized patient care. Addressing existing challenges and fostering interdisciplinary collaboration is crucial for realizing the full potential of this integration in improving patient outcomes.
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Affiliation(s)
- Biruk D Ayalew
- Internal Medicine, Saint Paul's Hospital Millennium Medical College, Addis Ababa, ETH
| | | | | | - Alaa Alresheq
- Primary Care, United Nations for Relief and Works Agency, Ramallah, PSE
| | - Hisham M Babu
- Internal Medicine, Jagadguru Sri Shivarathreeshwara (JSS) Medical College and Hospital, JSS Academy of Higher Education and Research (JSSAHER), Mysore, IND
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12
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Bottomly D, McWeeney S. Just how transformative will AI/ML be for immuno-oncology? J Immunother Cancer 2024; 12:e007841. [PMID: 38531545 DOI: 10.1136/jitc-2023-007841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/15/2024] [Indexed: 03/28/2024] Open
Abstract
Immuno-oncology involves the study of approaches which harness the patient's immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ML applications in the clinic. We highlight four clinical/biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations.
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Affiliation(s)
- Daniel Bottomly
- Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Shannon McWeeney
- Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon, USA
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13
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Soltani A, Edward Harrison J, Ryder C, Flavel J, Watson A. Police and hospital data linkage for traffic injury surveillance: A systematic review. ACCIDENT; ANALYSIS AND PREVENTION 2024; 197:107426. [PMID: 38183692 DOI: 10.1016/j.aap.2023.107426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 01/08/2024]
Abstract
This systematic review examines studies of traffic injury that involved linkage of police crash data and hospital data and were published from 1994 to 2023 worldwide in English. Inclusion and exclusion criteria were the basis for selecting papers from PubMed, Web of Science, and Scopus, and for identifying additional relevant papers using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and supplementary snowballing (n = 60). The selected papers were reviewed in terms of research objectives, data items and sample size included, temporal and spatial coverage, linkage methods and software tools, as well as linkage rates and most significant findings. Many studies found that the number of clinically significant road injury cases was much higher according to hospital data than crash data. Under-estimation of cases in crash data differs by road user type, pedestrian cases commonly being highly under-counted. A limited number of the papers were from low- and middle-income countries. The papers reviewed lack consistency in what was reported and how, which limited comparability.
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Affiliation(s)
- Ali Soltani
- Injury Studies, FHMRI, Bedford Park, Flinders University, SA 5042, Australia; Urban Planning Department, Shiraz University, Shiraz, Iran.
| | | | - Courtney Ryder
- Injury Studies, FHMRI, Bedford Park, Flinders University, SA 5042, Australia; George Institute for Global Health, Newtown, NSW 2042, Australia; School of Population Health, UNSW, Kensington, NSW 2052, Australia.
| | - Joanne Flavel
- Injury Studies, FHMRI, Bedford Park, Flinders University, SA 5042, Australia; Stretton Institute, University of Adelaide, SA 5005, Australia.
| | - Angela Watson
- The Australian Centre for Health Services Innovation (AusHSI), Queensland University of Technology, Qld 4000, Australia; School of Public Health & Social Work, Queensland University of Technology, Qld 4000, Australia.
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14
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Molfino NA, Turcatel G, Riskin D. Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review. Adv Ther 2024; 41:534-552. [PMID: 38110652 PMCID: PMC10838858 DOI: 10.1007/s12325-023-02743-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 11/15/2023] [Indexed: 12/20/2023]
Abstract
The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advances in the treatment and management of asthma, a significant proportion of patients continue to suffer acute exacerbations, irrespective of disease severity and therapeutic regimen. The situation is further complicated by the constellation of factors that influence disease activity in a patient with asthma, such as medical history, biomarker phenotype, pulmonary function, level of healthcare access, treatment compliance, comorbidities, personal habits, and environmental conditions. A growing body of work has demonstrated the potential for AI and ML to accurately predict asthma exacerbations while also capturing the entirety of the patient experience. However, application in the clinical setting remains mostly unexplored, and important questions on the strengths and limitations of this technology remain. This review presents an overview of the rapidly evolving landscape of AI and ML integration into asthma management by providing a snapshot of the existing scientific evidence and proposing potential avenues for future applications.
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Affiliation(s)
- Nestor A Molfino
- Global Development, Amgen Inc., One Amgen Center Dr, Thousand Oaks, CA, 91320, USA.
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15
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Velikic G, Maric DM, Maric DL, Supic G, Puletic M, Dulic O, Vojvodic D. Harnessing the Stem Cell Niche in Regenerative Medicine: Innovative Avenue to Combat Neurodegenerative Diseases. Int J Mol Sci 2024; 25:993. [PMID: 38256066 PMCID: PMC10816024 DOI: 10.3390/ijms25020993] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 11/30/2023] [Accepted: 12/06/2023] [Indexed: 01/24/2024] Open
Abstract
Regenerative medicine harnesses the body's innate capacity for self-repair to restore malfunctioning tissues and organs. Stem cell therapies represent a key regenerative strategy, but to effectively harness their potential necessitates a nuanced understanding of the stem cell niche. This specialized microenvironment regulates critical stem cell behaviors including quiescence, activation, differentiation, and homing. Emerging research reveals that dysfunction within endogenous neural stem cell niches contributes to neurodegenerative pathologies and impedes regeneration. Strategies such as modifying signaling pathways, or epigenetic interventions to restore niche homeostasis and signaling, hold promise for revitalizing neurogenesis and neural repair in diseases like Alzheimer's and Parkinson's. Comparative studies of highly regenerative species provide evolutionary clues into niche-mediated renewal mechanisms. Leveraging endogenous bioelectric cues and crosstalk between gut, brain, and vascular niches further illuminates promising therapeutic opportunities. Emerging techniques like single-cell transcriptomics, organoids, microfluidics, artificial intelligence, in silico modeling, and transdifferentiation will continue to unravel niche complexity. By providing a comprehensive synthesis integrating diverse views on niche components, developmental transitions, and dynamics, this review unveils new layers of complexity integral to niche behavior and function, which unveil novel prospects to modulate niche function and provide revolutionary treatments for neurodegenerative diseases.
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Affiliation(s)
- Gordana Velikic
- Department for Research and Development, Clinic Orto MD-Parks Dr. Dragi Hospital, 21000 Novi Sad, Serbia
- Hajim School of Engineering, University of Rochester, Rochester, NY 14627, USA
| | - Dusan M. Maric
- Department for Research and Development, Clinic Orto MD-Parks Dr. Dragi Hospital, 21000 Novi Sad, Serbia
- Faculty of Stomatology Pancevo, University Business Academy, 26000 Pancevo, Serbia;
| | - Dusica L. Maric
- Department of Anatomy, Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
| | - Gordana Supic
- Institute for Medical Research, Military Medical Academy, 11000 Belgrade, Serbia; (G.S.); (D.V.)
- Medical Faculty of Military Medical Academy, University of Defense, 11000 Belgrade, Serbia
| | - Miljan Puletic
- Faculty of Stomatology Pancevo, University Business Academy, 26000 Pancevo, Serbia;
| | - Oliver Dulic
- Department of Surgery, Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia;
| | - Danilo Vojvodic
- Institute for Medical Research, Military Medical Academy, 11000 Belgrade, Serbia; (G.S.); (D.V.)
- Medical Faculty of Military Medical Academy, University of Defense, 11000 Belgrade, Serbia
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16
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Adebamowo CA, Callier S, Akintola S, Maduka O, Jegede A, Arima C, Ogundiran T, Adebamowo SN. The promise of data science for health research in Africa. Nat Commun 2023; 14:6084. [PMID: 37770478 PMCID: PMC10539491 DOI: 10.1038/s41467-023-41809-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 09/15/2023] [Indexed: 09/30/2023] Open
Abstract
Data science health research promises tremendous benefits for African populations, but its implementation is fraught with substantial ethical governance risks that could thwart the delivery of these anticipated benefits. We discuss emerging efforts to build ethical governance frameworks for data science health research in Africa and the opportunities to advance these through investments by African governments and institutions, international funding organizations and collaborations for research and capacity development.
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Affiliation(s)
- Clement A Adebamowo
- Department of Epidemiology and Public Health, and Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA.
- Department of Research, Center for Bioethics and Research, Ibadan, Nigeria.
| | - Shawneequa Callier
- Department of Clinical Research and Leadership, School of Medicine and Health Sciences, The George Washington University, Washington DC, USA
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Simisola Akintola
- Department of Research, Center for Bioethics and Research, Ibadan, Nigeria
- Department of Business Law, Faculty of Law, University of Ibadan, Ibadan, Nigeria
- Department of Bioethics and Medical Humanities, Faculty of Multidisciplinary Studies, University of Ibadan, Ibadan, Nigeria
| | - Oluchi Maduka
- Department of Research, Center for Bioethics and Research, Ibadan, Nigeria
| | - Ayodele Jegede
- Department of Research, Center for Bioethics and Research, Ibadan, Nigeria
- Department of Bioethics and Medical Humanities, Faculty of Multidisciplinary Studies, University of Ibadan, Ibadan, Nigeria
- Department of Sociology, University of Ibadan, Ibadan, Nigeria
| | | | - Temidayo Ogundiran
- Department of Research, Center for Bioethics and Research, Ibadan, Nigeria
- Department of Bioethics and Medical Humanities, Faculty of Multidisciplinary Studies, University of Ibadan, Ibadan, Nigeria
- Department of Surgery, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Sally N Adebamowo
- Department of Epidemiology and Public Health, and Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Research, Center for Bioethics and Research, Ibadan, Nigeria
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Gaudio HA, Padmanabhan V, Landis WP, Silva LEV, Slovis J, Starr J, Weeks MK, Widmann NJ, Forti RM, Laurent GH, Ranieri NR, Mi F, Degani RE, Hallowell T, Delso N, Calkins H, Dobrzynski C, Haddad S, Kao SH, Hwang M, Shi L, Baker WB, Tsui F, Morgan RW, Kilbaugh TJ, Ko TS. A Template for Translational Bioinformatics: Facilitating Multimodal Data Analyses in Preclinical Models of Neurological Injury. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.17.547582. [PMID: 37503137 PMCID: PMC10370067 DOI: 10.1101/2023.07.17.547582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background Pediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for the treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal data sets. Methods Here, we present a generalizable data framework that was implemented for large animal research at the Children's Hospital of Philadelphia to address this technological gap. The presented framework culminates in an interactive dashboard for exploratory analysis and filtered data set download. Results Compared with existing clinical and preclinical data management solutions, the presented framework accommodates heterogeneous data types (single measure, repeated measures, time series, and imaging), integrates data sets across various experimental models, and facilitates dynamic visualization of integrated data sets. We present a use case of this framework for predictive model development for intra-arrest prediction of cardiopulmonary resuscitation outcome. Conclusions The described preclinical data framework may serve as a template to aid in data management efforts in other translational research labs that generate heterogeneous data sets and require a dynamic platform that can easily evolve alongside their research.
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18
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Wang L, Niu W. More Explorations Needed to Assess Matrix Metalloproteinase 7 Expression When Predicting the Pathologic Response to Neoadjuvant Therapy for Pancreatic Ductal Adenocarcinoma. JAMA Surg 2023; 158:102-103. [PMID: 36223123 DOI: 10.1001/jamasurg.2022.4975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Lin Wang
- Department of Child Health Care, Children's Hospital, Capital Institute of Pediatrics, Beijing, China
| | - Wenquan Niu
- Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China
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19
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Improving child health through Big Data and data science. Pediatr Res 2023; 93:342-349. [PMID: 35974162 PMCID: PMC9380977 DOI: 10.1038/s41390-022-02264-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/10/2022] [Accepted: 06/28/2022] [Indexed: 12/04/2022]
Abstract
Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.
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20
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Nelson AE, Arbeeva L. Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions. J Rheumatol 2022; 49:1191-1200. [PMID: 35840150 PMCID: PMC9633365 DOI: 10.3899/jrheum.220326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/22/2022]
Abstract
There has been rapid growth in the use of artificial intelligence (AI) analytics in medicine in recent years, including in rheumatic and musculoskeletal diseases (RMDs). Such methods represent a challenge to clinicians, patients, and researchers, given the "black box" nature of most algorithms, the unfamiliarity of the terms, and the lack of awareness of potential issues around these analyses. Therefore, this review aims to introduce this subject area in a way that is relevant and meaningful to clinicians and researchers. We hope to provide some insights into relevant strengths and limitations, reporting guidelines, as well as recent examples of such analyses in key areas, with a focus on lessons learned and future directions in diagnosis, phenotyping, prognosis, and precision medicine in RMDs.
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Affiliation(s)
- Amanda E Nelson
- A.E. Nelson, MD, MSCR, Department of Medicine, Division of Rheumatology, Allergy, and Immunology, University of North Carolina at Chapel Hill;
| | - Liubov Arbeeva
- L. Arbeeva, MS, Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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21
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Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring. BIG DATA AND COGNITIVE COMPUTING 2022. [DOI: 10.3390/bdcc6030075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
The main aims of urban air pollution monitoring are to optimize the interaction between humanity and nature, to combine and integrate environmental databases, and to develop sustainable approaches to the production and the organization of the urban environment. One of the main applications of urban air pollution monitoring is for exposure assessment and public health studies. Artificial intelligence (AI) and machine learning (ML) approaches can be used to build air pollution models to predict pollutant concentrations and assess environmental and health risks. Air pollution data can be uploaded into AI/ML models to estimate different exposure levels within different communities. The correlation between exposure estimates and public health surveys is important for assessing health risks. These aspects are critical when it concerns environmental injustice. Computational approaches should efficiently manage, visualize, and integrate large datasets. Effective data integration and management are a key to the successful application of computational intelligence approaches in ecology. In this paper, we consider some of these constraints and discuss possible ways to overcome current problems and environmental injustice. The most successful global approach is the development of the smart city; however, such an approach can only increase environmental injustice as not all the regions have access to AI/ML technologies. It is challenging to develop successful regional projects for the analysis of environmental data in the current complicated operating conditions, as well as taking into account the time, computing power, and constraints in the context of environmental injustice.
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