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Mateus P, Moonen J, Beran M, Jaarsma E, van der Landen SM, Heuvelink J, Birhanu M, Harms AGJ, Bron E, Wolters FJ, Cats D, Mei H, Oomens J, Jansen W, Schram MT, Dekker A, Bermejo I. Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model: A Netherlands consortium of dementia cohorts case study. J Biomed Inform 2024; 155:104661. [PMID: 38806105 DOI: 10.1016/j.jbi.2024.104661] [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: 01/29/2024] [Revised: 05/22/2024] [Accepted: 05/23/2024] [Indexed: 05/30/2024]
Abstract
BACKGROUND Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner. METHODS In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms. RESULTS We successfully applied our ETL tool and observed a complete coverage of the cohorts' data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data. CONCLUSION In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.
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Affiliation(s)
- Pedro Mateus
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands.
| | - Justine Moonen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands; Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Magdalena Beran
- Department of Internal Medicine, School for Cardiovascular Diseases (CARIM), Maastricht University, Maastricht, Netherlands; Department of Epidemiology and Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Eva Jaarsma
- Center for Nutrition, Prevention, and Health Services, National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands; Amsterdam UMC location Vrije Universiteit Amsterdam, Epidemiology and Data Science, Amsterdam, Netherlands
| | - Sophie M van der Landen
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands; Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Joost Heuvelink
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands
| | - Mahlet Birhanu
- Biomedical Imaging Group Rotterdam, Dept. Radiology & Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Alexander G J Harms
- Biomedical Imaging Group Rotterdam, Dept. Radiology & Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Esther Bron
- Biomedical Imaging Group Rotterdam, Dept. Radiology & Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Frank J Wolters
- Erasmus MC - University Medical Centre Rotterdam, Departments of Epidemiology and Radiology & Nuclear Medicine, Netherlands
| | - Davy Cats
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, Netherlands
| | - Hailiang Mei
- Sequencing Analysis Support Core, Department of Biomedical Data Sciences, Leiden University Medical Center, Netherlands
| | - Julie Oomens
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Netherlands
| | - Willemijn Jansen
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Netherlands
| | - Miranda T Schram
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, Netherlands; Department of Internal Medicine, Maastricht University Medical Centre, Maastricht, Netherlands; MHeNS School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands; Heart and Vascular Center, Maastricht University Medical Center+, Maastricht, Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Inigo Bermejo
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
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Walshe J, Elphinstone B, Nicol D, Taylor M. A systematic literature review of the 'commercialisation effect' on public attitudes towards biobank and genomic data repositories. PUBLIC UNDERSTANDING OF SCIENCE (BRISTOL, ENGLAND) 2024; 33:548-567. [PMID: 38389329 PMCID: PMC11264570 DOI: 10.1177/09636625241230864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Initiatives that collect and share genomic data to advance health research are widespread and accelerating. Commercial interests in these efforts, while vital, may erode public trust and willingness to provide personal genomic data, upon which these initiatives depend. Understanding public attitudes towards providing genomic data for health research in the context of commercial involvement is critical. A PRISMA-guided search of six online academic databases identified 113 quantitative and qualitative studies using primary data pertaining to public attitudes towards commercial actors in the management, collection, access, and use of biobank and genomic data. The presence of commercial interests yields interrelated public concerns around consent, privacy and data security, trust in science and scientists, benefit sharing, and the ownership and control of health data. Carefully considered regulatory and data governance and access policies are therefore required to maintain public trust and support for genomic health initiatives.
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Carraro C, Montgomery JV, Klimmt J, Paquet D, Schultze JL, Beyer MD. Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs. Front Mol Neurosci 2024; 17:1414886. [PMID: 38952421 PMCID: PMC11215216 DOI: 10.3389/fnmol.2024.1414886] [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: 04/09/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.
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Affiliation(s)
- Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Jessica V. Montgomery
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| | - Julien Klimmt
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Dominik Paquet
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
| | - Marc D. Beyer
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
- Immunogenomics & Neurodegeneration, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
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Guglielmi V, Dalle Grave R, Leonetti F, Solini A. Female obesity: clinical and psychological assessment toward the best treatment. Front Endocrinol (Lausanne) 2024; 15:1349794. [PMID: 38765954 PMCID: PMC11099266 DOI: 10.3389/fendo.2024.1349794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 04/19/2024] [Indexed: 05/22/2024] Open
Abstract
Obesity is a heterogeneous condition which results from complex interactions among sex/gender, sociocultural, environmental, and biological factors. Obesity is more prevalent in women in most developed countries, and several clinical and psychological obesity complications show sex-specific patterns. Females differ regarding fat distribution, with males tending to store more visceral fat, which is highly correlated to increased cardiovascular risk. Although women are more likely to be diagnosed with obesity and appear more motivated to lose weight, as confirmed by their greater representation in clinical trials, males show better outcomes in terms of body weight and intra-abdominal fat loss and improvements in the metabolic risk profile. However, only a few relatively recent studies have investigated gender differences in obesity, and sex/gender is rarely considered in the assessment and management of the disease. This review summarizes the evidence of gender differences in obesity prevalence, contributing factors, clinical complications, and psychological challenges. In addition, we explored gender differences in response to obesity treatments in the specific context of new anti-obesity drugs.
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Affiliation(s)
- Valeria Guglielmi
- Unit of Internal Medicine and Obesity Center, Department of Systems Medicine, Policlinico Tor Vergata, University of Rome Tor Vergata, Rome, Italy
| | - Riccardo Dalle Grave
- Department of Eating and Weight Disorders, Villa Garda Hospital, Garda, VR, Italy
| | - Frida Leonetti
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Rome, Italy
| | - Anna Solini
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy
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Wang HE, Triebkorn P, Breyton M, Dollomaja B, Lemarechal JD, Petkoski S, Sorrentino P, Depannemaecker D, Hashemi M, Jirsa VK. Virtual brain twins: from basic neuroscience to clinical use. Natl Sci Rev 2024; 11:nwae079. [PMID: 38698901 PMCID: PMC11065363 DOI: 10.1093/nsr/nwae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 05/05/2024] Open
Abstract
Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.
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Affiliation(s)
- Huifang E Wang
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Paul Triebkorn
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Martin Breyton
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
- Service de Pharmacologie Clinique et Pharmacosurveillance, AP–HM, Marseille, 13005, France
| | - Borana Dollomaja
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Spase Petkoski
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Pierpaolo Sorrentino
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Damien Depannemaecker
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Meysam Hashemi
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Viktor K Jirsa
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
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Badr Y, Abdul Kader L, Shamayleh A. The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment. J Pers Med 2024; 14:383. [PMID: 38673011 PMCID: PMC11051308 DOI: 10.3390/jpm14040383] [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: 01/28/2024] [Revised: 03/18/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.
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Affiliation(s)
- Yara Badr
- Department of Biomedical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (Y.B.); (L.A.K.)
| | - Lamis Abdul Kader
- Department of Biomedical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (Y.B.); (L.A.K.)
| | - Abdulrahim Shamayleh
- Department of Industrial Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates
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Löscher W. On hidden factors and design-associated errors that may lead to data misinterpretation: An example from preclinical research on the potential seasonality of neonatal seizures. Epilepsia 2024; 65:287-292. [PMID: 38037258 DOI: 10.1111/epi.17840] [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: 10/26/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/02/2023]
Abstract
Unintentional misinterpretation of research in published biomedical reports that is not based on statistical flaws is often underrecognized, despite its possible impact on science, clinical practice, and public health. Important causes of such misinterpretation of scientific data, resulting in either false positive or false negative conclusions, include design-associated errors and hidden (or latent) variables that are not easily recognized during data analysis. Furthermore, cognitive biases, such as the inclination to seek patterns in data whether they exist or not, may lead to misinterpretation of data. Here, we give an example of these problems from hypothesis-driven research on the potential seasonality of neonatal seizures in a rat model of birth asphyxia. This commentary aims to raise awareness among the general scientific audience about the issues related to the presence of unintentional misinterpretation in published reports.
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Affiliation(s)
- Wolfgang Löscher
- Translational Neuropharmacology Lab, NIFE, Department of Experimental Otology of the ENT Clinics, Hannover Medical School, Hannover, Germany
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Lejeune S, Kaushik A, Parsons ES, Chinthrajah S, Snyder M, Desai M, Manohar M, Prunicki M, Contrepois K, Gosset P, Deschildre A, Nadeau K. Untargeted metabolomic profiling in children identifies novel pathways in asthma and atopy. J Allergy Clin Immunol 2024; 153:418-434. [PMID: 38344970 DOI: 10.1016/j.jaci.2023.09.040] [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: 05/06/2023] [Revised: 09/19/2023] [Accepted: 09/25/2023] [Indexed: 02/15/2024]
Abstract
BACKGROUND Asthma and other atopic disorders can present with varying clinical phenotypes marked by differential metabolomic manifestations and enriched biological pathways. OBJECTIVE We sought to identify these unique metabolomic profiles in atopy and asthma. METHODS We analyzed baseline nonfasted plasma samples from a large multisite pediatric population of 470 children aged <13 years from 3 different sites in the United States and France. Atopy positivity (At+) was defined as skin prick test result of ≥3 mm and/or specific IgE ≥ 0.35 IU/mL and/or total IgE ≥ 173 IU/mL. Asthma positivity (As+) was based on physician diagnosis. The cohort was divided into 4 groups of varying combinations of asthma and atopy, and 6 pairwise analyses were conducted to best assess the differential metabolomic profiles between groups. RESULTS Two hundred ten children were classified as At-As-, 42 as At+As-, 74 as At-As+, and 144 as At+As+. Untargeted global metabolomic profiles were generated through ultra-high-performance liquid chromatography-tandem mass spectroscopy. We applied 2 independent machine learning classifiers and short-listed 362 metabolites as discriminant features. Our analysis showed the most diverse metabolomic profile in the At+As+/At-As- comparison, followed by the At-As+/At-As- comparison, indicating that asthma is the most discriminant condition associated with metabolomic changes. At+As+ metabolomic profiles were characterized by higher levels of bile acids, sphingolipids, and phospholipids, and lower levels of polyamine, tryptophan, and gamma-glutamyl amino acids. CONCLUSION The At+As+ phenotype displays a distinct metabolomic profile suggesting underlying mechanisms such as modulation of host-pathogen and gut microbiota interactions, epigenetic changes in T-cell differentiation, and lower antioxidant properties of the airway epithelium.
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Affiliation(s)
- Stéphanie Lejeune
- Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; University of Lille, Pediatric Pulmonology and Allergy Department, Hôpital Jeanne de Flandre, CHU Lille, Lille, France; University of Lille, INSERM Unit 1019, CNRS UMR 9017, CHU Lille, Institut Pasteur de Lille, Center for Infection and Immunity of Lille, Lille, France.
| | - Abhinav Kaushik
- Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Department of Environmental Health, T. H. Chan School of Public Health, Harvard University, Boston, Mass
| | - Ella S Parsons
- Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif
| | - Sharon Chinthrajah
- Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif
| | - Michael Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, Calif
| | - Manisha Desai
- Quantitative Science Unit, Department of Medicine, Stanford University School of Medicine, Stanford, Calif
| | - Monali Manohar
- Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif
| | - Mary Prunicki
- Department of Medicine, Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, Calif; Department of Environmental Health, T. H. Chan School of Public Health, Harvard University, Boston, Mass
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, Calif
| | - Philippe Gosset
- University of Lille, INSERM Unit 1019, CNRS UMR 9017, CHU Lille, Institut Pasteur de Lille, Center for Infection and Immunity of Lille, Lille, France
| | - Antoine Deschildre
- University of Lille, Pediatric Pulmonology and Allergy Department, Hôpital Jeanne de Flandre, CHU Lille, Lille, France; University of Lille, INSERM Unit 1019, CNRS UMR 9017, CHU Lille, Institut Pasteur de Lille, Center for Infection and Immunity of Lille, Lille, France
| | - Kari Nadeau
- Department of Environmental Health, T. H. Chan School of Public Health, Harvard University, Boston, Mass
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Thi HV, Hoang TN, Le NQK, Chu DT. Application of data science and bioinformatics in RNA therapeutics. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 203:83-97. [PMID: 38360007 DOI: 10.1016/bs.pmbts.2023.12.019] [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: 02/17/2024]
Abstract
Nowadays, information technology (IT) has been holding a significant role in daily life worldwide. The trajectory of data science and bioinformatics promises pioneering personalized therapies, reshaping medical landscapes and patient care. For RNA therapy to reach more patients, a comprehensive understanding of the application of data science and bioinformatics to this therapy is essential. Thus, this chapter has summarized the application of data science and bioinformatics in RNA therapeutics. Data science applications in RNA therapy, such as data integration and analytics, machine learning, and drug development, have been discussed. In addition, aspects of bioinformatics such as RNA design and evaluation, drug delivery system simulation, and databases for personalized medicine have also been covered in this chapter. These insights have shed light on existing evidence and opened potential future directions. From there, scientists can elevate RNA-based therapeutics into an era of tailored treatments and revolutionary healthcare.
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Affiliation(s)
- Hue Vu Thi
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam; Faculty of Applied Sciences, International School, Vietnam National University, Hanoi, Vietnam
| | - Thanh-Nhat Hoang
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan
| | - Dinh-Toi Chu
- Center for Biomedicine and Community Health, International School, Vietnam National University, Hanoi, Vietnam; Faculty of Applied Sciences, International School, Vietnam National University, Hanoi, Vietnam.
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Yayan J, Franke KJ, Berger M, Windisch W, Rasche K. Adhesion, metastasis, and inhibition of cancer cells: a comprehensive review. Mol Biol Rep 2024; 51:165. [PMID: 38252369 PMCID: PMC10803487 DOI: 10.1007/s11033-023-08920-5] [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: 08/25/2023] [Accepted: 10/23/2023] [Indexed: 01/23/2024]
Abstract
This comprehensive review delves into cancer's complexity, focusing on adhesion, metastasis, and inhibition. It explores the pivotal role of these factors in disease progression and therapeutic strategies. This review covers cancer cell migration, invasion, and colonization of distant organs, emphasizing the significance of cell adhesion and the intricate metastasis process. Inhibition approaches targeting adhesion molecules, such as integrins and cadherins, are discussed. Overall, this review contributes significantly to advancing cancer research and developing targeted therapies, holding promise for improving patient outcomes worldwide. Exploring different inhibition strategies revealed promising therapeutic targets to alleviate adhesion and metastasis of cancer cells. The effectiveness of integrin-blocking antibodies, small molecule inhibitors targeting Focal adhesion kinase (FAK) and the Transforming Growth Factor β (TGF-β) pathway, and combination therapies underscores their potential to disrupt focal adhesions and control epithelial-mesenchymal transition processes. The identification of as FAK, Src, β-catenin and SMAD4 offers valuable starting points for further research and the development of targeted therapies. The complex interrelationships between adhesion and metastatic signaling networks will be relevant to the development of new treatment approaches.
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Affiliation(s)
- Josef Yayan
- Department of Internal Medicine, Division of Pulmonary, Allergy, and Sleep Medicine, Witten/Herdecke University, HELIOS Clinic Wuppertal, Heusnerstr. 40, 42283, Wuppertal, Germany.
| | - Karl-Josef Franke
- Department of Internal Medicine, Pulmonary Division, Internal Intensive Care Medicine, Infectiology, and Sleep Medicine, Märkische Clinics Health Holding Ltd, Clinic Lüdenscheid, Witten/Herdecke University, Lüdenscheid, Germany
| | - Melanie Berger
- Department of Pneumology, Cologne Merheim Hospital, Witten/Herdecke University, Cologne, Germany
| | - Wolfram Windisch
- Department of Pneumology, Cologne Merheim Hospital, Witten/Herdecke University, Cologne, Germany
| | - Kurt Rasche
- Department of Internal Medicine, Division of Pulmonary, Allergy, and Sleep Medicine, Witten/Herdecke University, HELIOS Clinic Wuppertal, Heusnerstr. 40, 42283, Wuppertal, Germany
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Kumar R, Fu J, Ortiz BL, Cao X, Shedden K, Choi SW. Dyadic and Individual Variation in 24-Hour Heart Rates of Cancer Patients and Their Caregivers. Bioengineering (Basel) 2024; 11:95. [PMID: 38247972 PMCID: PMC10813060 DOI: 10.3390/bioengineering11010095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Twenty-four-hour heart rate (HR) integrates multiple physiological and psychological systems related to health and well-being, and can be continuously monitored in high temporal resolution over several days with wearable HR monitors. Using HR data from two independent datasets of cancer patients and their caregivers, we aimed to identify dyadic and individual patterns of 24 h HR variation and assess their relationship to demographic, environmental, psychological, and clinical variables of interest. METHODS a novel regularized approach to high-dimensional canonical correlation analysis (CCA) was used to identify factors reflecting dyadic and individual variation in the 24 h (circadian) HR trajectories of 430 people in 215 dyads, then regression analysis was used to relate these patterns to explanatory variables. RESULTS Four distinct factors of dyadic covariation in circadian HR were found, contributing approximately 7% to overall circadian HR variation. These factors, along with non-dyadic factors reflecting individual variation exhibited diverse and statistically robust patterns of association with explanatory variables of interest. CONCLUSIONS Both dyadic and individual anomalies are present in the 24 h HR patterns of cancer patients and their caregivers. These patterns are largely synchronous, and their presence robustly associates with multiple explanatory variables. One notable finding is that higher mood scores in cancer patients correspond to an earlier HR nadir in the morning and higher HR during the afternoon.
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Affiliation(s)
- Rajnish Kumar
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, USA; (R.K.); (B.L.O.); (X.C.)
| | - Junhan Fu
- Department of Statistics, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA; (J.F.); (K.S.)
| | - Bengie L. Ortiz
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, USA; (R.K.); (B.L.O.); (X.C.)
| | - Xiao Cao
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, USA; (R.K.); (B.L.O.); (X.C.)
| | - Kerby Shedden
- Department of Statistics, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA; (J.F.); (K.S.)
| | - Sung Won Choi
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI 48109, USA; (R.K.); (B.L.O.); (X.C.)
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Muharremi G, Meçani R, Muka T. The Buzz Surrounding Precision Medicine: The Imperative of Incorporating It into Evidence-Based Medical Practice. J Pers Med 2023; 14:53. [PMID: 38248754 PMCID: PMC10820165 DOI: 10.3390/jpm14010053] [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: 10/24/2023] [Revised: 12/17/2023] [Accepted: 12/28/2023] [Indexed: 01/23/2024] Open
Abstract
Precision medicine (PM), through the integration of omics and environmental data, aims to provide a more precise prevention, diagnosis, and treatment of disease. Currently, PM is one of the emerging approaches in modern healthcare and public health, with wide implications for health care delivery, public health policy making formulation, and entrepreneurial endeavors. In spite of its growing popularity and the buzz surrounding it, PM is still in its nascent phase, facing considerable challenges that need to be addressed and resolved for it to attain the acclaim for which it strives. In this article, we discuss some of the current methodological pitfalls of PM, including the use of big data, and provide a perspective on how these challenges can be overcome by bringing PM closer to evidence-based medicine (EBM). Furthermore, to maximize the potential of PM, we present real-world illustrations of how EBM principles can be integrated into a PM approach.
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Affiliation(s)
| | - Renald Meçani
- Epistudia, 3008 Bern, Switzerland; (G.M.); (R.M.)
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8010 Graz, Austria
| | - Taulant Muka
- Epistudia, 3008 Bern, Switzerland; (G.M.); (R.M.)
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YOUSEF M, ALLMER J. Deep learning in bioinformatics. Turk J Biol 2023; 47:366-382. [PMID: 38681776 PMCID: PMC11045206 DOI: 10.55730/1300-0152.2671] [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: 09/20/2023] [Revised: 12/28/2023] [Accepted: 12/18/2023] [Indexed: 05/01/2024] Open
Abstract
Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic concepts of deep learning and then survey the recent advances and challenges of applying deep learning to various bioinformatics problems, such as genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis. We also discuss future directions and opportunities for deep learning in bioinformatics. We aim to provide an overview of deep learning so that bioinformaticians applying deep learning models can consider all critical technical and ethical aspects. Thus, our target audience is biomedical informatics researchers who use deep learning models for inference. This review will inspire more bioinformatics researchers to adopt deep-learning methods for their research questions while considering fairness, potential biases, explainability, and accountability.
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Affiliation(s)
- Malik YOUSEF
- Department of Information Systems, Zefat Academic College, Zefat,
Israel
| | - Jens ALLMER
- Medical Informatics and Bioinformatics, Institute for Measurement Engineering and Sensor Technology, Hochschule Ruhr West, University of Applied Sciences, Mülheim an der Ruhr,
Germany
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Yuan J, Mi L, Wang S, Cheng Y, Hou X. Comparing the influence of big data resources on medical knowledge recall for staff with and without medical collaboration platform. BMC MEDICAL EDUCATION 2023; 23:956. [PMID: 38093304 PMCID: PMC10720120 DOI: 10.1186/s12909-023-04926-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND This study aims to examine how big data resources affect the recall of prior medical knowledge by healthcare professionals, and how this differs in environments with and without remote consultation platforms. METHOD This study investigated two distinct categories of medical institutions, namely 132 medical institutions with platforms, and 176 medical institutions without the platforms. Big data resources are categorized into two levels-medical institutional level and public level-and three types, namely data, technology, and services. The data are analyzed using SmartPLS2. RESULTS (1) In both scenarios, shared big data resources at the public level have a significant direct impact on the recall of prior medical knowledge. However, there is a significant difference in the direct impact of big data resources at the institutional level in both scenarios. (2) In institutions with platforms, for the three big data resources (the medical big data assets and big data deployment technical capacity at the medical institutional level, and policies of medical big data at the public level) without direct impacts, there exist three indirect pathways. (3) In institutions without platforms, for the two big data resources (the service capability and big data technical capacity at the medical institutional level) without direct impacts, there exist three indirect pathways. CONCLUSIONS The different interactions between big data, technology, and services, as well as between different levels of big data resources, affect the way clinical doctors recall relevant medical knowledge. These interaction patterns vary between institutions with and without platforms. This study provides a reference for governments and institutions to design big data environments for improving clinical capabilities.
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Affiliation(s)
- JunYi Yuan
- Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, 241 West Huaihai Road, Shanghai, China
| | - Linhui Mi
- Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, 241 West Huaihai Road, Shanghai, China
| | - SuFen Wang
- Glorious Sun School of Business and Management, Donghua University, 1882 West Yanan Road, Shanghai, China
| | - Yuejia Cheng
- Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, 241 West Huaihai Road, Shanghai, China
| | - Xumin Hou
- Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, 241 West Huaihai Road, Shanghai, China.
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Chen P, Wang B, Zhao L, Ma S, Wang Y, Zhu Y, Zeng X, Bai Z, Shi B. Machine learning for predicting intrahospital mortality in ST-elevation myocardial infarction patients with type 2 diabetes mellitus. BMC Cardiovasc Disord 2023; 23:585. [PMID: 38012550 PMCID: PMC10683359 DOI: 10.1186/s12872-023-03626-9] [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: 08/11/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
In an era of increasing need for precision medicine, machine learning has shown promise in making accurate acute myocardial infarction outcome predictions. The accurate assessment of high-risk patients is a crucial component of clinical practice. Type 2 diabetes mellitus (T2DM) complicates ST-segment elevation myocardial infarction (STEMI), and currently, there is no practical method for predicting or monitoring patient prognosis. The objective of the study was to compare the ability of machine learning models to predict in-hospital mortality among STEMI patients with T2DM. We compared six machine learning models, including random forest (RF), CatBoost classifier (CatBoost), naive Bayes (NB), extreme gradient boosting (XGBoost), gradient boosting classifier (GBC), and logistic regression (LR), with the Global Registry of Acute Coronary Events (GRACE) risk score. From January 2016 to January 2020, we enrolled patients aged > 18 years with STEMI and T2DM at the Affiliated Hospital of Zunyi Medical University. Overall, 438 patients were enrolled in the study [median age, 62 years; male, 312 (71%); death, 42 (9.5%]). All patients underwent emergency percutaneous coronary intervention (PCI), and 306 patients with STEMI who underwent PCI were enrolled as the training cohort. Six machine learning algorithms were used to establish the best-fit risk model. An additional 132 patients were recruited as a test cohort to validate the model. The ability of the GRACE score and six algorithm models to predict in-hospital mortality was evaluated. Seven models, including the GRACE risk model, showed an area under the curve (AUC) between 0.73 and 0.91. Among all models, with an accuracy of 0.93, AUC of 0.92, precision of 0.79, and F1 value of 0.57, the CatBoost model demonstrated the best predictive performance. A machine learning algorithm, such as the CatBoost model, may prove clinically beneficial and assist clinicians in tailoring precise management of STEMI patients and predicting in-hospital mortality complicated by T2DM.
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Affiliation(s)
- Panke Chen
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Department of the Clinical Institute, Zunyi Medical University, Zunyi, China
| | - Bine Wang
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Li Zhao
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Shuai Ma
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China
- Department of the Clinical Institute, Zunyi Medical University, Zunyi, China
| | - Yanping Wang
- Department of the Clinical Institute, Zunyi Medical University, Zunyi, China
| | - Yunyue Zhu
- Department of the Clinical Institute, Zunyi Medical University, Zunyi, China
| | - Xin Zeng
- Department of the Clinical Institute, Zunyi Medical University, Zunyi, China
| | - Zhixun Bai
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
- Department of the Clinical Institute, Zunyi Medical University, Zunyi, China.
- Organ Transplant Center, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
| | - Bei Shi
- Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
- Department of the Clinical Institute, Zunyi Medical University, Zunyi, China.
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Sharma D, Radha, Kumar M, Andrade-Cetto A, Puri S, Kumar A, Thakur M, Chandran D, Pundir A, Prakash S, Pandiselvam R, Sandhu S, Khosla A, Kumar S, Lorenzo JM. Chemical Diversity and Medicinal Potential of Vitex negundo L.: From Traditional Knowledge to Modern Clinical Trials. Chem Biodivers 2023; 20:e202301086. [PMID: 37851484 DOI: 10.1002/cbdv.202301086] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 09/27/2023] [Accepted: 10/01/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUND In Vedic context, Nirgundi (V. negundo) has been utilized for its anti-inflammatory, analgesic, and wound-healing properties. It has been employed to alleviate pain, treat skin conditions, and address various ailments. The plant's leaves, roots, and seeds have all found applications in traditional remedies. The knowledge of Nirgundi's medicinal benefits has been passed down through generations, and it continues to be a part of Ayurvedic and traditional medicine practices in India.
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Affiliation(s)
- Diksha Sharma
- School of Biological and Environmental Sciences, Shoolini University of Biotechnology and Management Sciences, Solan, 173229, India
| | - Radha
- School of Biological and Environmental Sciences, Shoolini University of Biotechnology and Management Sciences, Solan, 173229, India
| | - Manoj Kumar
- Chemical and Biochemical Processing Division, ICAR- Central Institute for Research on Cotton Technology, Mumbai, 400019, India
- Department of Biology, East Carolina University, Greenville, 27858, USA
| | - Adolfo Andrade-Cetto
- Laboratorio de Etnofarmacología, Departamento de BiologíaCelular, Facultad de Ciencias, Universidad Nacional Autónoma de México, Av. Universidad 3000, Circuito Exterior S/N, Coyoacán, C.U., Mexico City, 04510, Mexico
| | - Sunil Puri
- School of Biological and Environmental Sciences, Shoolini University of Biotechnology and Management Sciences, Solan, 173229, India
| | - Amit Kumar
- GLA University, Mathura, Uttar Pradesh, 281 406, India
| | - Mamta Thakur
- School of Biological and Environmental Sciences, Shoolini University of Biotechnology and Management Sciences, Solan, 173229, India
| | - Deepak Chandran
- Department of Animal Husbandry, Government of Kerala, Palakkad, 679335, Kerala, India
| | - Ashok Pundir
- School of Mechanical and Civil Engineering, Shoolini University of Biotechnology and Management Sciences, Solan, 173229, India
| | - Suraj Prakash
- School of Biological and Environmental Sciences, Shoolini University of Biotechnology and Management Sciences, Solan, 173229, India
| | - Ravi Pandiselvam
- Division of Physiology, Biochemistry and Post-Harvest Technology, ICAR - Central Plantation Crops Research Institute (CPCRI), Kasaragod, 671 124, Kerala, India
| | - Surinder Sandhu
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004, India
| | - Ananya Khosla
- Stanford University, 450 Serra Mall, Stanford, California, USA, 94305
| | - Sunil Kumar
- Indian Institute of Farming Systems Research, Modipuram, 250110, India
| | - Jose M Lorenzo
- CentroTecnológico de la Carne de Galicia, rúa Galicia n○ 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
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17
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Kang CC, Lee TY, Lim WF, Yeo WWY. Opportunities and challenges of 5G network technology toward precision medicine. Clin Transl Sci 2023; 16:2078-2094. [PMID: 37702288 PMCID: PMC10651640 DOI: 10.1111/cts.13640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/14/2023] Open
Abstract
Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.
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Affiliation(s)
- Chia Chao Kang
- School of Electrical Engineering and Artificial IntelligenceXiamen University MalaysiaSepangSelangorMalaysia
| | - Tze Yan Lee
- School of Liberal Arts, Science and Technology (PUScLST)Perdana UniversityKuala LumpurMalaysia
| | - Wai Feng Lim
- Sunway Medical CentreSubang JayaSelangor Darul EhsanMalaysia
| | - Wendy Wai Yeng Yeo
- School of PharmacyMonash University MalaysiaBandar SunwaySelangor Darul EhsanMalaysia
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18
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Li MP, Liu WC, Wu JB, Luo K, Liu Y, Zhang Y, Xiao SN, Liu ZL, Huang SH, Liu JM. Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:3825-3835. [PMID: 37195363 DOI: 10.1007/s00586-023-07772-8] [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: 02/25/2023] [Revised: 04/20/2023] [Accepted: 05/05/2023] [Indexed: 05/18/2023]
Abstract
PURPOSE The purpose of this study was to establish the best prediction model for postoperative nosocomial pulmonary infection through machine learning (ML) and assist physicians to make accurate diagnosis and treatment decisions. METHODS Patients with spinal cord injury (SCI) who admitted to a general hospital between July 2014 and April 2022 were included in this study. The data were segmented according to the ratio of seven to three, 70% were randomly selected to train the model, and the other 30% were used for testing. We used LASSO regression to screen the variables, and the selected variables were used in the construction of six different ML models. Shapley additive explanations and permutation importance were used to explain the output of the ML models. Finally, sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC) were used as the evaluation index of the model. RESULTS A total of 870 patients were enrolled in this study, of whom 98 (11.26%) developed pulmonary infection. Seven variables were used for ML model construction and multivariate logistic regression analysis. Among these variables, age, ASIA scale and tracheotomy were found to be the independent risk factors for postoperative nosocomial pulmonary infection in SCI patients. Meanwhile, the prediction model based on RF algorithm performed best in the training and test sets. (AUC = 0.721, accuracy = 0.664, sensitivity = 0.694, specificity = 0.656). CONCLUSION Age, ASIA scale and tracheotomy were the independent risk factors of postoperative nosocomial pulmonary infection in SCI. The prediction model based on RF algorithm had the best performance.
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Affiliation(s)
- Meng-Pan Li
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- The First Clinical Medical College of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Wen-Cai Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- The First Clinical Medical College of Nanchang University, Nanchang, 330006, People's Republic of China
- Department of Orthopaedics, Shanghai Jiao Tong University Affifiliated Sixth People's Hospital, Shanghai, China
| | - Jia-Bao Wu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Kun Luo
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Yu Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Yu Zhang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Shi-Ning Xiao
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Zhi-Li Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China
| | - Shan-Hu Huang
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
| | - Jia-Ming Liu
- Department of Orthopedic Surgery, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
- Institute of Spine and Spinal Cord, Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang, 330006, Jiangxi Province, People's Republic of China.
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Jiang S, Wang T, Zhang KH. Data-driven decision-making for precision diagnosis of digestive diseases. Biomed Eng Online 2023; 22:87. [PMID: 37658345 PMCID: PMC10472739 DOI: 10.1186/s12938-023-01148-1] [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: 01/14/2023] [Accepted: 08/15/2023] [Indexed: 09/03/2023] Open
Abstract
Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.
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Affiliation(s)
- Song Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Ting Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
| | - Kun-He Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006 China
- Jiangxi Institute of Gastroenterology and Hepatology, Nanchang, 330006 China
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Alomair L, Abolfotouh MA. Awareness and Predictors of the Use of Bioinformatics in Genome Research in Saudi Arabia. Int J Gen Med 2023; 16:3413-3425. [PMID: 37587979 PMCID: PMC10426440 DOI: 10.2147/ijgm.s421815] [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] [Received: 06/01/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023] Open
Abstract
Background With the advances in genomics research, many countries still need more bioinformatics skills. This study aimed to assess the levels of awareness of bioinformatics and predictors of its use in genomics research among scientists in Saudi Arabia. Methods In a cross-sectional survey, 309 scientists of different biological and biomedical specialties were subjected to a previously validated e-questionnaire to collect data on (1) Knowledge about bioinformatics programming languages and tools, (2) Attitude toward acceptance of bioinformatics resources in genome-related research, and (3) The pattern of information-seeking to online bioinformatics resources. Logistic regression analysis was applied to identify the predictors of using bioinformatics in research. Significance was set at p<0.05. Results More than one-half (248, 56.4%) of all scientists reported a lack of bioinformatics knowledge. Most participants had a neutral attitude toward bioinformatics (295, 95.4%). The barriers facing acceptance of bioinformatics tools reported were; lack of training (210, 67.9%), insufficient support (180, 58.2%), and complexity of software (138, 44.6%). The limited experience was reported in; having one or more bioinformatics tools (98, 31.7%), using a supercomputer in their research inside (44, 14.2%) and outside Saudi Arabia (55, 17.8%), the need for developing a program to solve a biological problem (129, 41.7%), working in one or more fields of bioinformatics (93, 30.1%), using web applications (112, 36.2%), and using programming languages (102, 33.0%). Significant predictors of conducting genomics research were; younger scientists (p=0.039), Ph.D. education (p=0.003), more than five years of experience (p<0.05), previous training (p<0.001), and higher bioinformatics knowledge scores (p<0.001). Conclusion The study revealed a short knowledge, a neutral attitude, a lack of resources, and limited use of bioinformatics resources in genomics research. Education and training during each education level and during the job is recommended. Cloud-based resources may help scientists do research using publicly available Omics data. Further studies are necessary to evaluate collaboration among bioinformatics software developers and biologists.
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Affiliation(s)
- Lamya Alomair
- AI and Bioinformatics Department, King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
- King Saud Bin-Abdulaziz University for Health Sciences (KSAU-HS), Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
| | - Mostafa A Abolfotouh
- King Saud Bin-Abdulaziz University for Health Sciences (KSAU-HS), Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
- Research Training and Development Section, King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
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Mayo KR, Basford MA, Carroll RJ, Dillon M, Fullen H, Leung J, Master H, Rura S, Sulieman L, Kennedy N, Banks E, Bernick D, Gauchan A, Lichtenstein L, Mapes BM, Marginean K, Nyemba SL, Ramirez A, Rotundo C, Wolfe K, Xia W, Azuine RE, Cronin RM, Denny JC, Kho A, Lunt C, Malin B, Natarajan K, Wilkins CH, Xu H, Hripcsak G, Roden DM, Philippakis AA, Glazer D, Harris PA. The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research. Annu Rev Biomed Data Sci 2023; 6:443-464. [PMID: 37561600 PMCID: PMC11157478 DOI: 10.1146/annurev-biodatasci-122120-104825] [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] [Indexed: 08/12/2023]
Abstract
The All of Us Research Program's Data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.
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Affiliation(s)
- Kelsey R Mayo
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Melissa A Basford
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robert J Carroll
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Moira Dillon
- Verily Life Sciences, South San Francisco, California, USA
| | - Heather Fullen
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jesse Leung
- Verily Life Sciences, South San Francisco, California, USA
| | - Hiral Master
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Shimon Rura
- Verily Life Sciences, South San Francisco, California, USA
| | - Lina Sulieman
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Nan Kennedy
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Eric Banks
- Data Sciences Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - David Bernick
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Asmita Gauchan
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lee Lichtenstein
- Data Sciences Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Brandy M Mapes
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kayla Marginean
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Steve L Nyemba
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Andrea Ramirez
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Charissa Rotundo
- Vanderbilt University Medical Center Enterprise Cybersecurity, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Keri Wolfe
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Weiyi Xia
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Romuladus E Azuine
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Robert M Cronin
- Department of Internal Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Joshua C Denny
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Abel Kho
- Department of Medicine and Institute for Augmented Intelligence in Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Christopher Lunt
- The All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Bradley Malin
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Consuelo H Wilkins
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Hua Xu
- Section of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, Connecticut, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Dan M Roden
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - David Glazer
- Verily Life Sciences, South San Francisco, California, USA
| | - Paul A Harris
- Deparment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
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22
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Restrepo JC, Dueñas D, Corredor Z, Liscano Y. Advances in Genomic Data and Biomarkers: Revolutionizing NSCLC Diagnosis and Treatment. Cancers (Basel) 2023; 15:3474. [PMID: 37444584 DOI: 10.3390/cancers15133474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/23/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023] Open
Abstract
Non-small cell lung cancer (NSCLC) is a significant public health concern with high mortality rates. Recent advancements in genomic data, bioinformatics tools, and the utilization of biomarkers have improved the possibilities for early diagnosis, effective treatment, and follow-up in NSCLC. Biomarkers play a crucial role in precision medicine by providing measurable indicators of disease characteristics, enabling tailored treatment strategies. The integration of big data and artificial intelligence (AI) further enhances the potential for personalized medicine through advanced biomarker analysis. However, challenges remain in the impact of new biomarkers on mortality and treatment efficacy due to limited evidence. Data analysis, interpretation, and the adoption of precision medicine approaches in clinical practice pose additional challenges and emphasize the integration of biomarkers with advanced technologies such as genomic data analysis and artificial intelligence (AI), which enhance the potential of precision medicine in NSCLC. Despite these obstacles, the integration of biomarkers into precision medicine has shown promising results in NSCLC, improving patient outcomes and enabling targeted therapies. Continued research and advancements in biomarker discovery, utilization, and evidence generation are necessary to overcome these challenges and further enhance the efficacy of precision medicine. Addressing these obstacles will contribute to the continued improvement of patient outcomes in non-small cell lung cancer.
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Affiliation(s)
- Juan Carlos Restrepo
- Grupo de Investigación en Salud Integral (GISI), Departamento Facultad de Salud, Universidad Santiago de Cali, Cali 760035, Colombia
| | - Diana Dueñas
- Grupo de Investigación en Salud Integral (GISI), Departamento Facultad de Salud, Universidad Santiago de Cali, Cali 760035, Colombia
| | - Zuray Corredor
- Grupo de Investigaciones en Odontología (GIOD), Facultad de Odontología, Universidad Cooperativa de Colombia, Pasto 520002, Colombia
- Facultad de Salud, Departamento de Ciencias Básicas, Universidad Libre, Cali 760026, Colombia
| | - Yamil Liscano
- Grupo de Investigación en Salud Integral (GISI), Departamento Facultad de Salud, Universidad Santiago de Cali, Cali 760035, Colombia
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23
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Trottet C, Vogels T, Keitel K, Kulinkina AV, Tan R, Cobuccio L, Jaggi M, Hartley MA. Modular Clinical Decision Support Networks (MoDN)-Updatable, interpretable, and portable predictions for evolving clinical environments. PLOS DIGITAL HEALTH 2023; 2:e0000108. [PMID: 37459285 PMCID: PMC10351690 DOI: 10.1371/journal.pdig.0000108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 06/12/2023] [Indexed: 07/20/2023]
Abstract
Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance in evolving clinical environments. Data-driven models could resolve this issue by updating predictions according to the data collected. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often imperfectly interoperable (IIO) or unshareable. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, as well as being robust to the systematic missingness common to CDSS-derived data, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules that can be combined in any number or combination to make any number or combination of diagnostic predictions, updatable at each step of a consultation. The model is validated on a real-world CDSS-derived dataset, comprising 3,192 paediatric outpatients in Tanzania. MoDN significantly outperforms 'monolithic' baseline models (which take all features at once at the end of a consultation) with a mean macro F1 score across all diagnoses of 0.749 vs 0.651 for logistic regression and 0.620 for multilayer perceptron (p < 0.001). To test collaborative learning between IIO datasets, we create subsets with various percentages of feature overlap and port a MoDN model trained on one subset to another. Even with only 60% common features, fine-tuning a MoDN model on the new dataset or just making a composite model with MoDN modules matched the ideal scenario of sharing data in a perfectly interoperable setting. MoDN integrates into consultation logic by providing interpretable continuous feedback on the predictive potential of each question in a CDSS questionnaire. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data.
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Affiliation(s)
- Cécile Trottet
- Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Thijs Vogels
- Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Kristina Keitel
- Division of Pediatric Emergency Medicine, Department of Pediatrics, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Alexandra V. Kulinkina
- Digital Health Unit, Swiss Center for International Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Rainer Tan
- Clinical Research Unit, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- Ifakara Health Institute, Ifakara, Tanzania
- Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Ludovico Cobuccio
- Clinical Research Unit, Swiss Tropical and Public Health Institute, Allschwil, Switzerland
| | - Martin Jaggi
- Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Mary-Anne Hartley
- Intelligent Global Health Research Group, Machine Learning and Optimization Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
- Laboratory of Intelligent Global Health Technologies, Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
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24
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Nissen M, Flaucher M, Jaeger KM, Huebner H, Danzberger N, Titzmann A, Pontones CA, Fasching PA, Eskofier BM, Leutheuser H. WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082860 DOI: 10.1109/embc40787.2023.10340204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made available to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser's insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical practice.Clinical relevance- WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation.
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25
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Baird A, Westphalen C, Blum S, Nafria B, Knott T, Sargeant I, Harnik H, Brooke N, Wicki N, Wong‐Rieger D. How can we deliver on the promise of precision medicine in oncology and beyond? A practical roadmap for action. Health Sci Rep 2023; 6:e1349. [PMID: 37359405 PMCID: PMC10286856 DOI: 10.1002/hsr2.1349] [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] [Received: 03/02/2023] [Revised: 05/24/2023] [Accepted: 06/05/2023] [Indexed: 06/28/2023] Open
Abstract
Background Precision medicine (PM) is a form of personalized medicine that recognizes that individuals with the same condition may have different underlying factors and uses molecular information to provide tailored treatments. This approach can improve treatment outcomes and transform lives through favorable risk/benefit ratios, avoidance of ineffective interventions, and possible cost savings, as evidenced in the field of lung cancer and other oncology/therapeutic settings, including cardiac disease, diabetes, and rare diseases. However, the potential benefits of PM have yet to be fully realized. Discussion There are many barriers to the implementation of PM in clinical practice, including fragmentation of the PM landscape, siloed approaches to address shared challenges, unwarranted variation in availability and access to PM, lack of standardization, and limited understanding of patients' experience and needs throughout the PM pathway. We believe that a diverse, intersectoral multistakeholder collaboration, with three main pillars of activity: generation of data to demonstrate the benefit of PM, education to support informed decision-making, and addressing barriers across the patient pathway, is necessary to reach the shared goal of making PM an accessible and sustainable reality. Besides healthcare providers, researchers, policymakers/regulators/payers, and industry representatives, patients in particular must be equal partners and should be central to the PM approach-from early research through to clinical trials and approval of new treatments-to ensure it represents their entire experience and identifies barriers, solutions, and opportunities at the point of delivery. Conclusion We propose a practical and iterative roadmap to advance PM and call for all stakeholders across the healthcare system to employ a collaborative, cocreated, patient-centered methodology to close gaps and fully realize the potential of PM.
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Affiliation(s)
- Anne‐Marie Baird
- Lung Cancer Europe (LuCE)BernSwitzerland
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
| | - C. Benedikt Westphalen
- Comprehensive Cancer Center Munich and Department of Medicine IIIUniversity Hospital, LMU MunichMunichGermany
| | - Sandra Blum
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- RocheBaselSwitzerland
| | - Begonya Nafria
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- Institut de Recerca Sant Joan de DéuBarcelonaSpain
- Innovation and Research Department, Hospital Sant Joan de Déu PgBarcelonaSpain
| | - Tanya Knott
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- Sarah Jennifer Knott (SJK) FoundationDublinRepublic of Ireland
| | | | - Helena Harnik
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- The SynergistBrusselsBelgium
| | - Nicholas Brooke
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- The SynergistBrusselsBelgium
| | - Nicole Wicki
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- The SynergistBrusselsBelgium
| | - Durhane Wong‐Rieger
- From Testing to Targeted Treatments (FT3) Program Team, The SynergistBrusselsBelgium
- Canadian Organization for Rare DisordersTorontoOntarioCanada
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26
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Bettio V, Mazzucco E, Aleni C, Cracas S, Rinaldi C, Antona A, Varalda M, Venetucci J, Ferrante D, Rimedio A, Capello D. UPO Biobank: The Challenge of Integrating Biobanking into the Academic Environment to Support Translational Research. J Pers Med 2023; 13:911. [PMID: 37373900 DOI: 10.3390/jpm13060911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/18/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
Biobanks are driving motors of precision and personalized medicine by providing high-quality biological material/data through the standardization and harmonization of their collection, preservation, and distribution. UPO Biobank was established in 2020 as an institutional, disease, and population biobank within the University of Piemonte Orientale (UPO) for the promotion and support of high-quality, multidisciplinary studies. UPO Biobank collaborates with UPO researchers, sustaining academic translational research, and supports the Novara Cohort Study, a longitudinal cohort study involving the population in the Novara area that will collect data and biological specimens that will be available for epidemiological, public health, and biological studies on aging. UPO Biobank has been developed by implementing the quality standards for the field and the ethical and legal issues and normative about privacy protection, data collection, and sharing. As a member of the "Biobanking and Biomolecular Resources Research Infrastructure" (BBMRI) network, UPO Biobank aims to expand its activity worldwide and launch cooperation with new national and international partners and researchers. The objective of this manuscript is to report an institutional and operational experience through the description of the technical and procedural solutions and ethical and scientific implications associated with the establishment of this university research biobank.
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Affiliation(s)
- Valentina Bettio
- UPO Biobank, University of Piemonte Orientale, 28100 Novara, Italy
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Eleonora Mazzucco
- UPO Biobank, University of Piemonte Orientale, 28100 Novara, Italy
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Chiara Aleni
- Department of Sustainable Development and Ecological Transition, University of Piemonte Orientale, 13100 Vercelli, Italy
| | - Silvia Cracas
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Carmela Rinaldi
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
- Learning and Research Area, A.O.U. Maggiore della Carità, 28100 Novara, Italy
| | - Annamaria Antona
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Marco Varalda
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Jacopo Venetucci
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Daniela Ferrante
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
| | - Antonio Rimedio
- Ethics Committee of the University "Hospital Major of Charity" in Novara, Local Health Authorities Biella, 28100 Novara, Italy
| | - Daniela Capello
- UPO Biobank, University of Piemonte Orientale, 28100 Novara, Italy
- Department of Translational Medicine, Center of Excellence in Aging Sciences, University of Piemonte Orientale, 28100 Novara, Italy
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27
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Callahan TJ, Stefanski AL, Wyrwa JM, Zeng C, Ostropolets A, Banda JM, Baumgartner WA, Boyce RD, Casiraghi E, Coleman BD, Collins JH, Deakyne Davies SJ, Feinstein JA, Lin AY, Martin B, Matentzoglu NA, Meeker D, Reese J, Sinclair J, Taneja SB, Trinkley KE, Vasilevsky NA, Williams AE, Zhang XA, Denny JC, Ryan PB, Hripcsak G, Bennett TD, Haendel MA, Robinson PN, Hunter LE, Kahn MG. Ontologizing health systems data at scale: making translational discovery a reality. NPJ Digit Med 2023; 6:89. [PMID: 37208468 PMCID: PMC10196319 DOI: 10.1038/s41746-023-00830-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 04/28/2023] [Indexed: 05/21/2023] Open
Abstract
Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.
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Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Adrianne L Stefanski
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jordan M Wyrwa
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Chenjie Zeng
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA
| | - William A Baumgartner
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260, USA
| | - Elena Casiraghi
- Computer Science, Università degli Studi di Milano, Milan, Italy
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Ben D Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Janine H Collins
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Sara J Deakyne Davies
- Department of Research Informatics & Data Science, Analytics Resource Center, Children's Hospital Colorado, Aurora, CO, 80045, USA
| | - James A Feinstein
- Adult and Child Center for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Asiyah Y Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Blake Martin
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | | | | | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Katy E Trinkley
- Department of Family Medicine, University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Nicole A Vasilevsky
- Translational and Integrative Sciences Lab, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Andrew E Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Tufts University, Boston, MA, 02155, USA
| | - Xingmin A Zhang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patrick B Ryan
- Janssen Research and Development, Raritan, NJ, 08869, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Tellen D Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
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28
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Shah AA, Alturise F, Alkhalifah T, Faisal A, Khan YD. EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma. Genes (Basel) 2023; 14:genes14051104. [PMID: 37239464 DOI: 10.3390/genes14051104] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/11/2023] [Accepted: 05/14/2023] [Indexed: 05/28/2023] Open
Abstract
The most common cause of mortality and disability globally right now is cholangiocarcinoma, one of the worst forms of cancer that may affect people. When cholangiocarcinoma develops, the DNA of the bile duct cells is altered. Cholangiocarcinoma claims the lives of about 7000 individuals annually. Women pass away less often than men. Asians have the greatest fatality rate. Following Whites (20%) and Asians (22%), African Americans (45%) saw the greatest increase in cholangiocarcinoma mortality between 2021 and 2022. For instance, 60-70% of cholangiocarcinoma patients have local infiltration or distant metastases, which makes them unable to receive a curative surgical procedure. Across the board, the median survival time is less than a year. Many researchers work hard to detect cholangiocarcinoma, but this is after the appearance of symptoms, which is late detection. If cholangiocarcinoma progression is detected at an earlier stage, then it will help doctors and patients in treatment. Therefore, an ensemble deep learning model (EDLM), which consists of three deep learning algorithms-long short-term model (LSTM), gated recurrent units (GRUs), and bi-directional LSTM (BLSTM)-is developed for the early identification of cholangiocarcinoma. Several tests are presented, such as a 10-fold cross-validation test (10-FCVT), an independent set test (IST), and a self-consistency test (SCT). Several statistical techniques are used to evaluate the proposed model, such as accuracy (Acc), sensitivity (Sn), specificity (Sp), and Matthew's correlation coefficient (MCC). There are 672 mutations in 45 distinct cholangiocarcinoma genes among the 516 human samples included in the proposed study. The IST has the highest Acc at 98%, outperforming all other validation approaches.
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Affiliation(s)
- Asghar Ali Shah
- Department of Computer Science, Bahria University, Islamabad 04408, Pakistan
| | - Fahad Alturise
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Qassim, Saudi Arabia
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Qassim, Saudi Arabia
| | - Amna Faisal
- Department of Computer Science, Bahria University, Lahore 54782, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, University of Management and Technology, Lahore 54782, Pakistan
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Landers C, Ormond KE, Blasimme A, Brall C, Vayena E. Talking Ethics Early in Health Data Public Private Partnerships. JOURNAL OF BUSINESS ETHICS : JBE 2023; 190:649-659. [PMID: 38487176 PMCID: PMC10933190 DOI: 10.1007/s10551-023-05425-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 04/25/2023] [Indexed: 03/17/2024]
Abstract
Data access and data sharing are vital to advance medicine. A growing number of public private partnerships are set up to facilitate data access and sharing, as private and public actors possess highly complementary health data sets and treatment development resources. However, the priorities and incentives of public and private organizations are frequently in conflict. This has complicated partnerships and sparked public concerns around ethical issues such as trust, justice or privacy-in turn raising an important problem in business and data ethics: how can ethical theory inform the practice of public and private partners to mitigate misaligned incentives, and ensure that they can deliver societally beneficial innovation? In this paper, we report on the development of the Swiss Personalized Health Network's ethical guidelines for health data sharing in public private partnerships. We describe the process of identifying ethical issues and engaging core stakeholders to incorporate their practical reality on these issues. Our report highlights core ethical issues in health data public private partnerships and provides strategies for how to overcome these in the Swiss health data context. By agreeing on and formalizing ethical principles and practices at the beginning of a partnership, partners and society can benefit from a relationship built around a mutual commitment to ethical principles. We present this summary in the hope that it will contribute to the global data sharing dialogue.
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Affiliation(s)
- Constantin Landers
- Health Ethics and Policy Lab, ETH Zurich, Hottingerstrasse 10, 8032 Zurich, Switzerland
| | - Kelly E. Ormond
- Health Ethics and Policy Lab, ETH Zurich, Hottingerstrasse 10, 8032 Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, ETH Zurich, Hottingerstrasse 10, 8032 Zurich, Switzerland
| | - Caroline Brall
- Ethics and Policy Lab, Multidisciplinary Center for Infectious Diseases, University of Bern, Länggassstrasse 49a, 3012 Bern, Switzerland
- Institute of Philosophy, University of Bern, Länggassstrasse 49a, 3012 Bern, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, ETH Zurich, Hottingerstrasse 10, 8032 Zurich, Switzerland
- ELSI Advisory Group, Swiss Personalized Health Network, Laupenstrasse 7, 3001 Bern, Switzerland
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Lee YH, Jang YJ, Lee SK. Obstacles to Health Big Data Utilization Based on the Perceptions and Demands of Health Care Workers in South Korea: Web-Based Survey Study. JMIR Form Res 2023; 7:e45913. [PMID: 37052992 PMCID: PMC10141300 DOI: 10.2196/45913] [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: 01/21/2023] [Revised: 03/08/2023] [Accepted: 03/23/2023] [Indexed: 04/14/2023] Open
Abstract
BACKGROUND This study focuses on the potential of health big data in the South Korean context. Despite huge data reserves and pan-government efforts to increase data use, the utilization is limited to public interest research centered in public institutions that have data. To increase the use of health big data, it is necessary to identify and develop measures to meet the various demands for such data from individuals, private companies, and research institutes. OBJECTIVE The aim of this study was to identify the perceptions of and demands for health big data analysis and use among workers in health care-related occupations and to clarify the obstacles to the use of health big data. METHODS From May 8 to May 18, 2022, we conducted a web-based survey among 390 health care-related workers in South Korea. We used Fisher exact test and analysis of variance to estimate the differences among occupations. We expressed the analysis results by item in frequency and percentage and expressed the difficulties in analyzing health big data by mean and standard deviation. RESULTS The respondents who revealed the need to use health big data in health care work-related fields accounted for 86.4% (337/390); 65.6% (256/390) of the respondents had never used health big data. The lack of awareness about the source of the desired data was the most cited reason for nonuse by 39.6% (153/386) of the respondents. The most cited obstacle to using health big data by the respondents was the difficulty in data integration and expression unit matching, followed by missing value processing and noise removal. Thus, the respondents experienced the greatest difficulty in the data preprocessing stage during the health big data analysis process, regardless of occupation. Approximately 91.8% (358/390) of the participants responded that they were willing to use the system if a system supporting big data analysis was developed. As suggestions for the specific necessary support system, the reporting and provision of appropriate data and expert advice on questions arising during the overall process of big data analysis were mentioned. CONCLUSIONS Our findings indicate respondents' high awareness of and demand for health big data. Our findings also reveal the low utilization of health big data and the need to support health care workers in their analysis and use of such data. Hence, we recommend the development of a customized support system that meets the specific requirements of big data analysis by users such as individuals, nongovernmental agencies, and academia. Our study is significant because it identified important but overlooked failure factors. Thus, it is necessary to prepare practical measures to increase the utilization of health big data in the future.
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Affiliation(s)
- Yoon Heui Lee
- Department of Nursing, Graduate School, Keimyung University, Daegu, Republic of Korea
| | - Yu-Jin Jang
- Dongsan Hospital, Keimyung University, Daegu, Republic of Korea
| | - Soo-Kyoung Lee
- College of Nursing, Keimyung University, Daegu, Republic of Korea
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Hulsen T, Friedecký D, Renz H, Melis E, Vermeersch P, Fernandez-Calle P. From big data to better patient outcomes. Clin Chem Lab Med 2023; 61:580-586. [PMID: 36539928 DOI: 10.1515/cclm-2022-1096] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022]
Abstract
Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.
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Affiliation(s)
- Tim Hulsen
- Department of Hospital Services & Informatics, Philips Research, Eindhoven, The Netherlands
| | - David Friedecký
- Department of Clinical Biochemistry, Laboratory for Inherited Metabolic Disorders, University Hospital Olomouc and Faculty of Medicine and Dentistry, Palacký University in Olomouc, Olomouc, Czech Republic
| | - Harald Renz
- Institute of Laboratory Medicine, member of the German Center for Lung Research (DZL), and the Universities of Giessen and Marburg Lung Center (UGMLC), Philipps University Marburg, Marburg, Germany
- Department of Clinical Immunology and Allergy, Laboratory of Immunopathology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Els Melis
- Ortho Clinical Diagnostics, Zaventem, Belgium
| | - Pieter Vermeersch
- Clinical Department of Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
- European Federation of Clinical Chemistry and Laboratory Medicine (EFLM), Milan, Italy
| | - Pilar Fernandez-Calle
- European Federation of Clinical Chemistry and Laboratory Medicine (EFLM), Milan, Italy
- Department of Laboratory Medicine, Hospital Universitario La Paz, Madrid, Spain
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Quan X, Cai W, Xi C, Wang C, Yan L. AIMedGraph: a comprehensive multi-relational knowledge graph for precision medicine. Database (Oxford) 2023; 2023:7059703. [PMID: 36856726 PMCID: PMC9976745 DOI: 10.1093/database/baad006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/01/2023] [Accepted: 02/10/2023] [Indexed: 03/02/2023]
Abstract
The development of high-throughput molecular testing techniques has enabled the large-scale exploration of the underlying molecular causes of diseases and the development of targeted treatment for specific genetic alterations. However, knowledge to interpret the impact of genetic variants on disease or treatment is distributed in different databases, scientific literature studies and clinical guidelines. AIMedGraph was designed to comprehensively collect and interrogate standardized information about genes, genetic alterations and their therapeutic and diagnostic relevance and build a multi-relational, evidence-based knowledge graph. Graph database Neo4j was used to represent precision medicine knowledge as nodes and edges in AIMedGraph. Entities in the current release include 30 340 diseases/phenotypes, 26 140 genes, 187 541 genetic variants, 2821 drugs, 15 125 clinical trials and 797 911 supporting literature studies. Edges in this release cover 621 731 drug interactions, 9279 drug susceptibility impacts, 6330 pharmacogenomics effects, 30 339 variant pathogenicity and 1485 drug adverse reactions. The knowledge graph technique enables hidden knowledge inference and provides insight into potential disease or drug molecular mechanisms. Database URL: http://aimedgraph.tongshugene.net:8201.
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Affiliation(s)
- Xueping Quan
- Correspondence may also be addressed to Xueping Quan. Tel: +8621-58886662;
| | - Weijing Cai
- Department of Innovative Technology, Shanghai Tongshu Biotechnology Research Institute, No26 and 28, 377 Lane of Shanlian Road, Baoshan District, Shanghai 200444, China
| | - Chenghang Xi
- Department of Artificial Intelligence, Shanghai Tongshu Biotechnology Research Institute, No26 and 28, 377 Lane of Shanlian Road, Baoshan District, Shanghai 200444, China
| | - Chunxiao Wang
- Department of Innovative Technology, Shanghai Tongshu Biotechnology Research Institute, No26 and 28, 377 Lane of Shanlian Road, Baoshan District, Shanghai 200444, China
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Aguilera-Cobos L, García-Sanz P, Rosario-Lozano MP, Claros MG, Blasco-Amaro JA. An innovative framework to determine the implementation level of personalized medicine: A systematic review. Front Public Health 2023; 11:1039688. [PMID: 36817923 PMCID: PMC9936069 DOI: 10.3389/fpubh.2023.1039688] [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: 09/08/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
Background Personalized medicine (PM) is now the new frontier in patient care. The application of this new paradigm extends to various pathologies and different patient care phases, such as diagnosis and treatment. Translating biotechnological advances to clinical routine means adapting health services at all levels is necessary. Purpose This article aims to identify the elements for devising a framework that will allow the level of PM implementation in the country under study to be quantitatively and qualitatively assessed and that can be used as a guideline for future implementation plans. Methods A systematic review was conducted per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The research question was: What are the domains for determining the level of implementation of PM at the national level? The domains for assessing the degree of PM implementation, which would form the framework, were established. Results 19 full-text studies that met the inclusion criteria were peer-selected in the systematic review. From all the studies that were included, 37 elements-encompassed in 11 domains-were extracted for determining the degree of PM implementation. These domains and their constituent elements comprise the qualitative and quantitative assessment framework presented herein. Each of the elements can be assessed individually. On the other hand, the domains were standardized to all have the same weight in an overall assessment. Conclusions A framework has been developed that takes a multi-factorial approach to determine the degree of implementation of PM at the national level. This framework could also be used to rank countries and their implementation strategies according to the score they receive in the application of the latter. It could also be used as a guide for developing future national PM implementation strategies. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022338611, Identifier: CRD42022338611.
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Affiliation(s)
- Lorena Aguilera-Cobos
- Health Technology Assessment Area-AETSA, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain,Department of Molecular Biology and Biochemistry, Universidad de Málaga, Málaga, Spain,*Correspondence: Lorena Aguilera-Cobos ✉
| | - Patricia García-Sanz
- Health Technology Assessment Area-AETSA, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain,Patricia García-Sanz ✉
| | - María Piedad Rosario-Lozano
- Health Technology Assessment Area-AETSA, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
| | - M. Gonzalo Claros
- Department of Molecular Biology and Biochemistry, Universidad de Málaga, Málaga, Spain,Institute of Biomedical Research in Málaga (IBIMA), Málaga, Spain,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Málaga, Spain,Institute for Mediterranean and Subtropical Horticulture “La Mayora”, Universidad de Málaga-Consejo Superior de Investigaciones Científicas (IHSM-UMA-CSIC), Málaga, Spain
| | - Juan Antonio Blasco-Amaro
- Health Technology Assessment Area-AETSA, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
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Abstract
Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized repositories, and evolving ontologies describing various scales of biological organization between genotypes and clinical phenotypes. Here, we present PrimeKG, a multimodal knowledge graph for precision medicine analyses. PrimeKG integrates 20 high-quality resources to describe 17,080 diseases with 4,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scales, and the entire range of approved drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs. PrimeKG contains an abundance of 'indications', 'contradictions', and 'off-label use' drug-disease edges that lack in other knowledge graphs and can support AI analyses of how drugs affect disease-associated networks. We supplement PrimeKG's graph structure with language descriptions of clinical guidelines to enable multimodal analyses and provide instructions for continual updates of PrimeKG as new data become available.
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35
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Ting DSJ, Deshmukh R, Ting DSW, Ang M. Big data in corneal diseases and cataract: Current applications and future directions. Front Big Data 2023; 6:1017420. [PMID: 36818823 PMCID: PMC9929069 DOI: 10.3389/fdata.2023.1017420] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of "5 Vs"-variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.
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Affiliation(s)
- Darren S. J. Ting
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom,Birmingham and Midland Eye Centre, Birmingham, United Kingdom,Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom,*Correspondence: Darren S. J. Ting ✉
| | - Rashmi Deshmukh
- Department of Cornea and Refractive Surgery, LV Prasad Eye Institute, Hyderabad, India
| | - Daniel S. W. Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
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Kataoka H, Nitta K, Hoshino J. Visceral fat and attribute-based medicine in chronic kidney disease. Front Endocrinol (Lausanne) 2023; 14:1097596. [PMID: 36843595 PMCID: PMC9947142 DOI: 10.3389/fendo.2023.1097596] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/13/2023] [Indexed: 02/11/2023] Open
Abstract
Visceral adipose tissue plays a central role in obesity and metabolic syndrome and is an independent risk factor for both cardiovascular and metabolic disorders. Increased visceral adipose tissue promotes adipokine dysregulation and insulin resistance, leading to several health issues, including systemic inflammation, oxidative stress, and activation of the renin-angiotensin-aldosterone system. Moreover, an increase in adipose tissue directly and indirectly affects the kidneys by increasing renal sodium reabsorption, causing glomerular hyperfiltration and hypertrophy, which leads to increased proteinuria and kidney fibrosis/dysfunction. Although the interest in the adverse effects of obesity on renal diseases has grown exponentially in recent years, the relationship between obesity and renal prognosis remains controversial. This may be attributed to the long clinical course of obesity, numerous obesity-related metabolic complications, and patients' attributes. Multiple individual attributes influencing the pathophysiology of fat accumulation make it difficult to understand obesity. In such cases, it may be effective to elucidate the pathophysiology by conducting research tailored to individual attributes from the perspective of attribute-based medicine/personalized medicine. We consider the appropriate use of clinical indicators necessary, according to attributes such as chronic kidney disease stage, level of visceral adipose tissue accumulation, age, and sex. Selecting treatments and clinical indicators based on individual attributes will allow for advancements in the clinical management of patients with obesity and chronic kidney disease. In the clinical setting of obesity-related nephropathy, it is first necessary to accumulate attribute-based studies resulting from the accurate evaluation of visceral fat accumulation to establish evidence for promoting personalized medicine.
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Bright K, Mills A, Bradford JP, Stewart DJ. RAPID framework for improved access to precision oncology for lethal disease: Results from a modified multi-round delphi study. FRONTIERS IN HEALTH SERVICES 2023; 3:1015621. [PMID: 36926496 PMCID: PMC10012713 DOI: 10.3389/frhs.2023.1015621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 02/10/2023] [Indexed: 03/03/2023]
Abstract
Introduction Predictive oncology, germline technologies, and adaptive seamless trials are promising advances in the treatment of lethal cancers. Yet, access to these therapies is stymied by costly research, regulatory barriers, and structural inequalities worsened by the COVID-19 pandemic. Methods To address the need for a comprehensive strategy for rapid and more equitable access to breakthrough therapies for lethal cancers, we conducted a modified multi-round Delphi study with 70 experts in oncology, clinical trials, legal and regulatory processes, patient advocacy, ethics, drug development, and health policy in Canada, Europe, and the US. Semi-structured ethnographic interviews (n = 33) were used to identify issues and solutions that participants subsequently evaluated in a survey (n = 47). Survey and interview data were co-analyzed to refine topics for an in-person roundtable where recommendations for system change were deliberated and drafted by 26 participants. Results Participants emphasized major issues in patient access to novel therapeutics including burdens of time, cost, and transportation required to complete eligibility requirements or to participate in trials. Only 12% of respondents reported satisfaction with current research systems, with "patient access to trials" and "delays in study approval" the topmost concerns. Conclusion Experts agree that an equity-centered precision oncology communication model should be developed to improve access to adaptive seamless trials, eligibility reforms, and just-in-time trial activation. International advocacy groups are a key mobilizer of patient trust and should be involved at every stage of research and therapy approval. Our results also show that governments can promote better and faster access to life-saving therapeutics by engaging researchers and payors in an ecosystem approach that responds to the unique clinical, structural, temporal, and risk-benefit situations that patients with life-threatening cancers confront.
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Affiliation(s)
- Kristin Bright
- Department of Anthropology, Middlebury College, Middlebury, VT, United States.,Department of Anthropology, University of Toronto, Toronto, ON, Canada
| | - Anneliese Mills
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
| | | | - David J Stewart
- Department of Medicine, University of Ottawa, Ottawa, ON, Canada
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Ray SK, Mukherjee S. Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy. Curr Mol Med 2023; 23:111-126. [PMID: 34939542 DOI: 10.2174/1566524022666211221152947] [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: 03/19/2021] [Revised: 10/18/2021] [Accepted: 10/26/2021] [Indexed: 12/16/2022]
Abstract
Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.
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Affiliation(s)
| | - Sukhes Mukherjee
- Department of Biochemistry. All India Institute of Medical Sciences. Bhopal, Madhya Pradesh-462020. India
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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: 6] [Impact Index Per Article: 6.0] [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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Horgan D, Hamdi Y, Lal JA, Nyawira T, Meyer S, Kondji D, Francisco NM, De Guzman R, Paul A, Nallamalla KR, Park WY, Triapthi V, Tripathi R, Johns A, Singh MP, Phipps ME, Dube F, Abu Rasheed HM, Kozaric M, Pinto JA, Stefani SD, Aponte Rueda ME, Alarcon RF, Barrera-Saldana HA. Empowering quality data - the gordian knot of bringing real innovation into healthcare system. Diagnosis (Berl) 2022; 10:140-157. [PMID: 36548810 DOI: 10.1515/dx-2022-0115] [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: 10/19/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES The introduction of Personalised Medicine (PM) into healthcare systems could benefit from a clearer understanding of the distinct national and regional frameworks around the world. Recent engagement by international regulators on maximising the use of real-world evidence (RWE) has highlighted the scope for improving the exploitation of the treasure-trove of health data that is currently largely neglected in many countries. The European Alliance for Personalised Medicine (EAPM) led an international study aimed at identifying the current status of conditions. METHODS A literature review examined how far such frameworks exist, with a view to identifying conducive factors - and crucial gaps. This extensive review of key factors across 22 countries and 5 regions revealed a wide variety of attitudes, approaches, provisions and conditions, and permitted the construction of a comprehensive overview of the current status of PM. Based on seven key pillars identified from the literature review and expert panels, the data was quantified, and on the basis of further analysis, an index was developed to allow comparison country by country and region by region. RESULTS The results show that United States of America is leading according to overall outcome whereas Kenya scored the least in the overall outcome. CONCLUSIONS Still, common approaches exist that could help accelerate take-up of opportunities even in the less prosperous parts of the world.
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Affiliation(s)
- Denis Horgan
- European Alliance for Personalised Medicine, Brussels, Belgium
- Department of Molecular and Cellular Engineering, Jacob Institute of Biotechnology and Bioengineering Sam Higginbottom University of Agriculture, Technology and Sciences Prayagraj, India
| | - Yosr Hamdi
- Laboratory of Biomedical Genomics and Oncogenetics, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Laboratory of Human and Experimental Pathology, Institut Pasteur de Tunis, Tunis, Tunisia
| | - Jonathan A Lal
- Department of Molecular and Cellular Engineering, Jacob Institute of Biotechnology and Bioengineering Sam Higginbottom University of Agriculture, Technology and Sciences Prayagraj, India
- Department of Genetics and Cell Biology, GROW School of Oncology and Developmental Biology, Faculty of Health, Medicine and Life Sciences, Institute for Public Health Genomics, Maastricht University, Maastricht, Netherlands
| | - Teresia Nyawira
- National Commission for Science, Technology and Innovation in Kenya (NACOSTI), Nairobi Kenya, Kenya
| | | | - Dominique Kondji
- Health & Development Communication, Building Capacity for Better Health in Africa Building Capacities for Better Health in AFRICA, Yaounde, Cameroun
| | - Ngiambudulu M Francisco
- Grupo de Investigação Microbiana e Imunológica, Instituto Nacional de Investigação em Saúde (National Institute for Health Research), Luanda, Angola
| | - Roselle De Guzman
- Oncology and Pain Management Section, Manila Central University-Filemon D. Tanchoco Medical Foundation Hospital, Caloocan City, Philippines
| | - Anupriya Paul
- Department of Mathematics and Statistics, Faculty of Science, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, India
| | | | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University, Seoul, Korea
| | - Vijay Triapthi
- Department of Molecular and Cellular Engineering, Jacob Institute of Biotechnology and Bioengineering Sam Higginbottom University of Agriculture, Technology and Sciences Prayagraj, India
| | - Ravikant Tripathi
- Department Health Govt of India, Ministry of labor, New Delhi, India
| | - Amber Johns
- Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Cancer Division, Sydney, Australia
| | - Mohan P Singh
- Center of Biotechnology, University of Allahabad, Allahabad, India
| | - Maude E Phipps
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor, Malaysia
| | - France Dube
- Astra Zeneca, Concord Pike, Wilmington, DE, USA
| | | | - Marta Kozaric
- European Alliance for Personalised Medicine, Brussels, Belgium
| | - Joseph A Pinto
- Center for Basic and Translational Research, Auna Ideas, Lima, Peru
| | | | | | - Ricardo Fujita Alarcon
- Centro de Genética y Biología Molecular, Universidad de San Martín de Porres, Lima, Perú
| | - Hugo A Barrera-Saldana
- Innbiogem SC/Vitagenesis SA at National Laboratory for Services of Research, Development, and Innovation for the Pharma and Biotech Industries (LANSEIDI) of CONACyT Vitaxentrum Group, Monterrey, Mexico
- Schools of Medicine and Biology, Autonomous University of Nuevo Leon, Monterrey, Mexico
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Mosch L, Fürstenau D, Brandt J, Wagnitz J, Klopfenstein SAI, Poncette AS, Balzer F. The medical profession transformed by artificial intelligence: Qualitative study. Digit Health 2022; 8:20552076221143903. [PMID: 36532112 PMCID: PMC9756357 DOI: 10.1177/20552076221143903] [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] [Received: 07/20/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear. OBJECTIVE We analyzed physicians' and AI experts' stances towards AI-induced changes. This concerned (1) physicians' tasks, (2) job replacement risk, and (3) implications for the ways of working, including human-AI interaction, changes in job profiles, and hierarchical and cross-professional collaboration patterns. METHODS We adopted an exploratory, qualitative research approach, using semi-structured interviews with 24 experts in the fields of AI and medicine, medical informatics, digital medicine, and medical education and training. Thematic analysis of the interview transcripts was performed. RESULTS Specialized tasks currently performed by physicians in all areas of medicine would likely be taken over by AI, including bureaucratic tasks, clinical decision support, and research. However, the concern that physicians will be replaced by an AI system is unfounded, according to experts; AI systems today would be designed only for a specific use case and could not replace the human factor in the patient-physician relationship. Nevertheless, the job profile and professional role of physicians would be transformed as a result of new forms of human-AI collaboration and shifts to higher-value activities. AI could spur novel, more interprofessional teams in medical practice and research and, eventually, democratization and de-hierarchization. CONCLUSIONS The study highlights changes in job profiles of physicians and outlines demands for new categories of medical professionals considering AI-induced changes of work. Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. There is a need for the development of scenarios and concepts for future job profiles in the health professions as well as their education and training.
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Affiliation(s)
- Lina Mosch
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Department of Anesthesiology and Intensive Care Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,Lina Mosch, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany
| | - Daniel Fürstenau
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Department of Business IT, IT University of Copenhagen, København, Denmark
| | - Jenny Brandt
- Universitätsmedizin Mainz, corporate member of Johannes Gutenberg University, Mainz, Germany
| | - Jasper Wagnitz
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany
| | - Sophie AI Klopfenstein
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Akira-Sebastian Poncette
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Department of Anesthesiology and Intensive Care Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Felix Balzer
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany
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Moris D, Henao R, Hensman H, Stempora L, Chasse S, Schobel S, Dente CJ, Kirk AD, Elster E. Multidimensional machine learning models predicting outcomes after trauma. Surgery 2022; 172:1851-1859. [PMID: 36116976 DOI: 10.1016/j.surg.2022.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients. METHODS This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation. RESULTS A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data. CONCLUSION Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.
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Affiliation(s)
| | | | - Hannah Hensman
- DecisionQ, Arlington, VA; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Linda Stempora
- Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Scott Chasse
- Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Seth Schobel
- Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD
| | | | - Allan D Kirk
- Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Eric Elster
- Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD; Walter Reed National Military Medical Center, Bethesda, MD
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Hulsen T. Literature analysis of artificial intelligence in biomedicine. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1284. [PMID: 36618779 PMCID: PMC9816850 DOI: 10.21037/atm-2022-50] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 10/19/2022] [Indexed: 11/20/2022]
Abstract
Artificial intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning (ML), deep learning (DL) and neural networks (NNs). AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of 'big data' and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-3 of diseases studied using AI; China, the United States, South Korea, the United Kingdom and Canada are publishing the most articles in AI research; Stanford University is the world's leading university in AI research; and convolutional NNs are by far the most popular DL algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.
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Comparison of artificial intelligence and human-based prediction and stratification of the risk of long-term kidney allograft failure. COMMUNICATIONS MEDICINE 2022; 2:150. [PMID: 36418380 PMCID: PMC9684574 DOI: 10.1038/s43856-022-00201-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 10/14/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Clinical decisions are mainly driven by the ability of physicians to apply risk stratification to patients. However, this task is difficult as it requires complex integration of numerous parameters and is impacted by patient heterogeneity. We sought to evaluate the ability of transplant physicians to predict the risk of long-term allograft failure and compare them to a validated artificial intelligence (AI) prediction algorithm. METHODS We randomly selected 400 kidney transplant recipients from a qualified dataset of 4000 patients. For each patient, 44 features routinely collected during the first-year post-transplant were compiled in an electronic health record (EHR). We enrolled 9 transplant physicians at various career stages. At 1-year post-transplant, they blindly predicted the long-term graft survival with probabilities for each patient. Their predictions were compared with those of a validated prediction system (iBox). We assessed the determinants of each physician's prediction using a random forest survival model. RESULTS Among the 400 patients included, 84 graft failures occurred at 7 years post-evaluation. The iBox system demonstrates the best predictive performance with a discrimination of 0.79 and a median calibration error of 5.79%, while physicians tend to overestimate the risk of graft failure. Physicians' risk predictions show wide heterogeneity with a moderate intraclass correlation of 0.58. The determinants of physicians' prediction are disparate, with poor agreement regardless of their clinical experience. CONCLUSIONS This study shows the overall limited performance and consistency of physicians to predict the risk of long-term graft failure, demonstrated by the superior performances of the iBox. This study supports the use of a companion tool to help physicians in their prognostic judgement and decision-making in clinical care.
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Moreira FC, Sarquis DP, de Souza JES, Avelar DDS, Araújo TMT, Khayat AS, dos Santos SEB, de Assumpção PP. Treasures from trash in cancer research. Oncotarget 2022; 13:1246-1257. [PMID: 36395362 PMCID: PMC9671455 DOI: 10.18632/oncotarget.28308] [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] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION Cancer research has significantly improved in recent years, primarily due to next-generation sequencing (NGS) technology. Consequently, an enormous amount of genomic and transcriptomic data has been generated. In most cases, the data needed for research goals are used, and unwanted reads are discarded. However, these eliminated data contain relevant information. Aiming to test this hypothesis, genomic and transcriptomic data were acquired from public datasets. MATERIALS AND METHODS Metagenomic tools were used to explore genomic cancer data; additional annotations were used to explore differentially expressed ncRNAs from miRNA experiments, and variants in adjacent to tumor samples from RNA-seq experiments were also investigated. RESULTS In all analyses, new data were obtained: from DNA-seq data, microbiome taxonomies were characterized with a similar performance of dedicated metagenomic research; from miRNA-seq data, additional differentially expressed sncRNAs were found; and in tumor and adjacent to tumor tissue data, somatic variants were found. CONCLUSIONS These findings indicate that unexplored data from NGS experiments could help elucidate carcinogenesis and discover putative biomarkers with clinical applications. Further investigations should be considered for experimental design, providing opportunities to optimize data, saving time and resources while granting access to multiple genomic perspectives from the same sample and experimental run.
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Affiliation(s)
- Fabiano Cordeiro Moreira
- 1Núcleo de Pesquisas em Oncologia/Universidade Federal do Pará, Belém, Pará, Brazil,*Co-first authors
| | - Dionison Pereira Sarquis
- 1Núcleo de Pesquisas em Oncologia/Universidade Federal do Pará, Belém, Pará, Brazil,*Co-first authors
| | | | | | | | - André Salim Khayat
- 1Núcleo de Pesquisas em Oncologia/Universidade Federal do Pará, Belém, Pará, Brazil
| | - Sidney Emanuel Batista dos Santos
- 1Núcleo de Pesquisas em Oncologia/Universidade Federal do Pará, Belém, Pará, Brazil,3Instituto de Ciências Biológicas/Universidade Federal do Pará, Belém, Pará, Brazil
| | - Paulo Pimentel de Assumpção
- 1Núcleo de Pesquisas em Oncologia/Universidade Federal do Pará, Belém, Pará, Brazil,Correspondence to:Paulo Pimentel de Assumpção, email:
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Abdalvand N, Sadeghi M, Mahdavi SR, Abdollahi H, Qasempour Y, Mohammadian F, Birgani MJT, Hosseini K. Brachytherapy outcome modeling in cervical cancer patients: A predictive machine learning study on patient-specific clinical, physical and dosimetric parameters. Brachytherapy 2022; 21:769-782. [PMID: 35933272 DOI: 10.1016/j.brachy.2022.06.007] [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: 03/12/2022] [Revised: 06/09/2022] [Accepted: 06/26/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE To predict clinical response in locally advanced cervical cancer (LACC) patients by a combination of measures, including clinical and brachytherapy parameters and several machine learning (ML) approaches. METHODS Brachytherapy features such as insertion approaches, source metrics, dosimetric, and clinical measures were used for modeling. Four different ML approaches, including LASSO, Ridge, support vector machine (SVM), and Random Forest (RF), were applied to extracted measures for model development alone or in combination. Model performance was evaluated using the area under the curve (AUC) of receiver operating characteristics curve, sensitivity, specificity, and accuracy. Our results were compared with a reference model developed by simple logistic regression applied to three distinct clinical features identified by previous papers. RESULTS One hundred eleven LACC patients were included. Nine data sets were obtained based on the features, and 36 predictive models were built. In terms of AUC, the model developed using RF applied to dosimetric, physical, and total BT sessions features were found as the most predictive [AUC; 0.82 (0.95 confidence interval (CI); 0.79 -0.93), sensitivity; 0.79, specificity; 0.76, and accuracy; 0.77]. The AUC (0.95 CI), sensitivity, specificity, and accuracy for the reference model were found as 0.56 (0.52 ...0.68), 0.51, 0.51, and 0.48, respectively. Most RF models had significantly better performance than the reference model (Bonferroni corrected p-value < 0.0014). CONCLUSION Brachytherapy response can be predicted using dosimetric and physical parameters extracted from treatment parameters. Machine learning algorithms, including Random Forest, could play a critical role in such predictive modeling.
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Affiliation(s)
- Neda Abdalvand
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mahdi Sadeghi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Seied Rabi Mahdavi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Technology, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Younes Qasempour
- Student Research Committee, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran
| | - Fatemeh Mohammadian
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
| | | | - Khadijeh Hosseini
- Department of Radiation Oncology, Golestan Hospital, Ahvaz Jundishapour University of Medical Sciences, Ahvaz, Iran
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Montisci A, Palmieri V, Vietri MT, Sala S, Maiello C, Donatelli F, Napoli C. Big Data in cardiac surgery: real world and perspectives. J Cardiothorac Surg 2022; 17:277. [PMID: 36309702 PMCID: PMC9617748 DOI: 10.1186/s13019-022-02025-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 10/14/2022] [Indexed: 11/10/2022] Open
Abstract
Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.
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Bönisch C, Kesztyüs D, Kesztyüs T. Harvesting metadata in clinical care: a crosswalk between FHIR, OMOP, CDISC and openEHR metadata. Sci Data 2022; 9:659. [PMID: 36307424 PMCID: PMC9616884 DOI: 10.1038/s41597-022-01792-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 10/20/2022] [Indexed: 11/29/2022] Open
Abstract
Metadata describe information about data source, type of creation, structure, status and semantics and are prerequisite for preservation and reuse of medical data. To overcome the hurdle of disparate data sources and repositories with heterogeneous data formats a metadata crosswalk was initiated, based on existing standards. FAIR Principles were included, as well as data format specifications. The metadata crosswalk is the foundation of data provision between a Medical Data Integration Center (MeDIC) and researchers, providing a selection of metadata information for research design and requests. Based on the crosswalk, metadata items were prioritized and categorized to demonstrate that not one single predefined standard meets all requirements of a MeDIC and only a maximum data set of metadata is suitable for use. The development of a convergence format including the maximum data set is the anticipated solution for an automated transformation of metadata in a MeDIC.
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Affiliation(s)
- Caroline Bönisch
- Medical Data Integration Center, Department of Medical Informatics, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Germany.
| | - Dorothea Kesztyüs
- Medical Data Integration Center, Department of Medical Informatics, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Germany
| | - Tibor Kesztyüs
- Medical Data Integration Center, Department of Medical Informatics, University Medical Center Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Germany
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Jeong JC, Hands I, Kolesar JM, Rao M, Davis B, Dobyns Y, Hurt-Mueller J, Levens J, Gregory J, Williams J, Witt L, Kim EM, Burton C, Elbiheary AA, Chang M, Durbin EB. Local data commons: the sleeping beauty in the community of data commons. BMC Bioinformatics 2022; 23:386. [PMID: 36151511 PMCID: PMC9502580 DOI: 10.1186/s12859-022-04922-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 09/12/2022] [Indexed: 12/03/2022] Open
Abstract
Background Public Data Commons (PDC) have been highlighted in the scientific literature for their capacity to collect and harmonize big data. On the other hand, local data commons (LDC), located within an institution or organization, have been underrepresented in the scientific literature, even though they are a critical part of research infrastructure. Being closest to the sources of data, LDCs provide the ability to collect and maintain the most up-to-date, high-quality data within an organization, closest to the sources of the data. As a data provider, LDCs have many challenges in both collecting and standardizing data, moreover, as a consumer of PDC, they face problems of data harmonization stemming from the monolithic harmonization pipeline designs commonly adapted by many PDCs. Unfortunately, existing guidelines and resources for building and maintaining data commons exclusively focus on PDC and provide very little information on LDC. Results This article focuses on four important observations. First, there are three different types of LDC service models that are defined based on their roles and requirements. These can be used as guidelines for building new LDC or enhancing the services of existing LDC. Second, the seven core services of LDC are discussed, including cohort identification and facilitation of genomic sequencing, the management of molecular reports and associated infrastructure, quality control, data harmonization, data integration, data sharing, and data access control. Third, instead of commonly developed monolithic systems, we propose a new data sharing method for data harmonization that combines both divide-and-conquer and bottom-up approaches. Finally, an end-to-end LDC implementation is introduced with real-world examples. Conclusions Although LDCs are an optimal place to identify and address data quality issues, they have traditionally been relegated to the role of passive data provider for much larger PDC. Indeed, many LDCs limit their functions to only conducting routine data storage and transmission tasks due to a lack of information on how to design, develop, and improve their services using limited resources. We hope that this work will be the first small step in raising awareness among the LDCs of their expanded utility and to publicize to a wider audience the importance of LDC.
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Affiliation(s)
- Jong Cheol Jeong
- Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, KY, USA. .,Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA.
| | - Isaac Hands
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA.,Kentucky Cancer Registry, Lexington, KY, USA
| | - Jill M Kolesar
- Department of Pharmacy Practice and Science, College of Pharmacy, University of Kentucky, Lexington, KY, USA
| | - Mahadev Rao
- Department of Pharmacy Practice, Center for Translational Research, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bront Davis
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA.,Kentucky Cancer Registry, Lexington, KY, USA
| | - York Dobyns
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA.,Kentucky Cancer Registry, Lexington, KY, USA
| | - Joseph Hurt-Mueller
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA.,Kentucky Cancer Registry, Lexington, KY, USA
| | - Justin Levens
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA.,Kentucky Cancer Registry, Lexington, KY, USA
| | - Jenny Gregory
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA.,Kentucky Cancer Registry, Lexington, KY, USA
| | - John Williams
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA.,Kentucky Cancer Registry, Lexington, KY, USA
| | - Lisa Witt
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA.,Kentucky Cancer Registry, Lexington, KY, USA
| | - Eun Mi Kim
- Department of Computer Science, Eastern Kentucky University, Richmond, KY, USA
| | - Carlee Burton
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA
| | - Amir A Elbiheary
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA
| | - Mingguang Chang
- Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA
| | - Eric B Durbin
- Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, KY, USA. .,Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, Lexington, KY, USA. .,Kentucky Cancer Registry, Lexington, KY, USA.
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Liu C, Yu Y, Wang G, Liu J, Liu R, Liu L, Yang X, Li H, Gao C, Lu Y, Zhuang J. From tumor mutational burden to characteristic targets analysis: Identifying the predictive biomarkers and natural product interventions in cancer management. Front Nutr 2022; 9:989989. [PMID: 36204371 PMCID: PMC9530334 DOI: 10.3389/fnut.2022.989989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 08/19/2022] [Indexed: 12/24/2022] Open
Abstract
High-throughput next-generation sequencing (NGS) provides insights into genome-wide mutations and can be used to identify biomarkers for the prediction of immune and targeted responses. A deeper understanding of the molecular biological significance of genetic variation and effective interventions is required and ultimately needs to be associated with clinical benefits. We conducted a retrospective observational study of patients in two cancer cohorts who underwent NGS in a “real-world” setting. The association between differences in tumor mutational burden (TMB) and clinical presentation was evaluated. We aimed to identify several key mutation targets and describe their biological characteristics and potential clinical value. A pan-cancer dataset was downloaded as a verification set for further analysis and summary. Natural product screening for the targeted intervention of key markers was also achieved. The majority of tumor patients were younger adult males with advanced cancer. The gene identified with the highest mutation rate was TP53, followed by PIK3CA, EGFR, and LRP1B. The association of TMB (0–103.7 muts/Mb) with various clinical subgroups was determined. More frequent mutations, such as in LRP1B, as well as higher levels of ferritin and neuron-specific enolase, led to higher TMB levels. Further analysis of the key targets, LRP1B and APC, was performed, and mutations in LRP1B led to better immune benefits compared to APC. APC, one of the most frequently mutated genes in gastrointestinal tumors, was further investigated, and the potential interventions by cochinchinone B and rottlerin were clarified. In summary, based on the analysis of the characteristics of gene mutations in the “real world,” we obtained the potential association indicators of TMB, found the key signatures LRP1B and APC, and further described their biological significance and potential interventions.
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Affiliation(s)
- Cun Liu
- College of Traditional Chinese Medicine, Weifang Medical University, Weifang, China
| | - Yang Yu
- College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ge Wang
- Clinical Medical Colleges, Weifang Medical University, Weifang, China
| | - Jingyang Liu
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ruijuan Liu
- Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China
| | - Lijuan Liu
- Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China
- Department of Special Medicine, School of Basic Medicine, Qingdao University, Qingdao, China
| | - Xiaoxu Yang
- School of Life Sciences and Technology, Weifang Medical University, Weifang, China
| | - Huayao Li
- College of Traditional Chinese Medicine, Weifang Medical University, Weifang, China
| | - Chundi Gao
- First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yi Lu
- Department of Clinical Nutrition, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Traditional Chinese Medicine Oncology, Zhejiang Cancer Hospital, Hangzhou, China
- *Correspondence: Yi Lu,
| | - Jing Zhuang
- Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China
- Jing Zhuang,
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