1
|
Cheng F, Wang F, Tang J, Zhou Y, Fu Z, Zhang P, Haines JL, Leverenz JB, Gan L, Hu J, Rosen-Zvi M, Pieper AA, Cummings J. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cell Rep Med 2024; 5:101379. [PMID: 38382465 PMCID: PMC10897520 DOI: 10.1016/j.xcrm.2023.101379] [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: 11/23/2022] [Revised: 08/15/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.
Collapse
Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA.
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Jian Tang
- Mila-Quebec Institute for Learning Algorithms and CIFAR AI Research Chair, HEC Montreal, Montréal, QC H3T 2A7, Canada
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zhimin Fu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; College of Pharmacy, Northeast Ohio Medical University, Rootstown, OH 44272, USA
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46037, USA
| | - Jonathan L Haines
- Cleveland Institute for Computational Biology, and Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Li Gan
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Jianying Hu
- IBM Research, Yorktown Heights, New York, NY 10598, USA
| | - Michal Rosen-Zvi
- AI for Accelerated Healthcare and Life Sciences Discovery, IBM Research Labs, Haifa 3498825, Israel; Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9190500, Israel
| | - Andrew A Pieper
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland OH 44106, USA; Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH, 44106, USA; Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV 89154, USA
| |
Collapse
|
2
|
Chadwick W, Maudsley S, Hull W, Havolli E, Boshoff E, Hill MDW, Goetghebeur PJD, Harrison DC, Nizami S, Bedford DC, Coope G, Real K, Thiemermann C, Maycox P, Carlton M, Cole SL. The oDGal Mouse: A Novel, Physiologically Relevant Rodent Model of Sporadic Alzheimer's Disease. Int J Mol Sci 2023; 24:ijms24086953. [PMID: 37108119 PMCID: PMC10138655 DOI: 10.3390/ijms24086953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/17/2023] [Accepted: 03/30/2023] [Indexed: 04/29/2023] Open
Abstract
Sporadic Alzheimer's disease (sAD) represents a serious and growing worldwide economic and healthcare burden. Almost 95% of current AD patients are associated with sAD as opposed to patients presenting with well-characterized genetic mutations that lead to AD predisposition, i.e., familial AD (fAD). Presently, the use of transgenic (Tg) animals overexpressing human versions of these causative fAD genes represents the dominant research model for AD therapeutic development. As significant differences in etiology exist between sAD and fAD, it is perhaps more appropriate to develop novel, more sAD-reminiscent experimental models that would expedite the discovery of effective therapies for the majority of AD patients. Here we present the oDGal mouse model, a novel model of sAD that displays a range of AD-like pathologies as well as multiple cognitive deficits reminiscent of AD symptomology. Hippocampal cognitive impairment and pathology were delayed with N-acetyl-cysteine (NaC) treatment, which strongly suggests that reactive oxygen species (ROS) are the drivers of downstream pathologies such as elevated amyloid beta and hyperphosphorylated tau. These features demonstrate a desired pathophenotype that distinguishes our model from current transgenic rodent AD models. A preclinical model that presents a phenotype of non-genetic AD-like pathologies and cognitive deficits would benefit the sAD field, particularly when translating therapeutics from the preclinical to the clinical phase.
Collapse
Affiliation(s)
- Wayne Chadwick
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Stuart Maudsley
- Receptor Biology Lab, University of Antwerp, 2000 Antwerp, Belgium
| | - William Hull
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Centre for Translational Medicine and Therapeutics, Queen Mary University of London, London E1 4NS, UK
| | - Enes Havolli
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Eugene Boshoff
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Mark D W Hill
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | | | - David C Harrison
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Sohaib Nizami
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - David C Bedford
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Gareth Coope
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Katia Real
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Christoph Thiemermann
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Centre for Translational Medicine and Therapeutics, Queen Mary University of London, London E1 4NS, UK
| | - Peter Maycox
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Mark Carlton
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Sarah L Cole
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| |
Collapse
|
3
|
Gopinath N. Artificial intelligence and neuroscience: An update on fascinating relationships. Process Biochem 2023. [DOI: 10.1016/j.procbio.2022.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
4
|
Zhu J, Liang Q, He S, Wang C, Lin X, Wu D, Lin G, Wang Z. Research trends and hotspots of neurodegenerative diseases employing network pharmacology: A bibliometric analysis. Front Pharmacol 2023; 13:1109400. [PMID: 36712694 PMCID: PMC9878685 DOI: 10.3389/fphar.2022.1109400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Background: Employing network pharmacology in neurodegenerative diseases (NDs) has been extensively studied recently. However, no comprehensive study has conducted on this subject employing bibliometrics so far. The purpose of this study was to find out the developmental trends and hotspots, and to predict potential research directions in this filed. Methods: Relevant research were collected from the Web of Science Core Collection Bibliometrics and visual analysis were executed using CiteSpace, VOSviewer, Histcite and R-bibliometrix. Results: A total of 420 English articles on network pharmacology in NDs published in 2008-2022 were obtained from the WOSCC database. From 2008 to 2022, annual publications showed a steady growing trend, especially in 2014-2022. China, Beijing Univ Chinese Med, Frontiers in Pharmacology, and Geerts H are the most prolific country, institution, journal, and author, respectively. China, Nucleic Acids Research, and Hopkins AL are the most highly cited country, journal, and author, respectively. Moreover, network pharmacology and Alzheimer's disease are the focal areas of current researches according to analysis of co-cited references and keywords. Finally, in the detection of burst keywords, systems pharmacology and database are new approaches to disease and drug research, while traditional Chinese medicine (TCM) and Alzheimer's disease are hot research directions. The above keywords are speculated to be the research frontiers. Conclusion: Network pharmacology and Alzheimers' disease are the main topics of researches on network pharmacology in NDs. Network pharmacology and the TCM treatment of Alzheimer's disease have been the recent research hotspots. To sum up, the potential for exploring TCM treatment of AD with network pharmacology is huge.
Collapse
Affiliation(s)
- Jie Zhu
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Qingchun Liang
- Department of Anesthesiology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China
| | - Siyi He
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Chen Wang
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Xiafei Lin
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Duozhi Wu
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China
| | - Guanwen Lin
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China,*Correspondence: Guanwen Lin, ; Zhihua Wang,
| | - Zhihua Wang
- Department of Anesthesiology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China,*Correspondence: Guanwen Lin, ; Zhihua Wang,
| |
Collapse
|
5
|
Hajjo R, Sabbah DA, Abusara OH, Al Bawab AQ. A Review of the Recent Advances in Alzheimer's Disease Research and the Utilization of Network Biology Approaches for Prioritizing Diagnostics and Therapeutics. Diagnostics (Basel) 2022; 12:diagnostics12122975. [PMID: 36552984 PMCID: PMC9777434 DOI: 10.3390/diagnostics12122975] [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: 10/16/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 11/29/2022] Open
Abstract
Alzheimer's disease (AD) is a polygenic multifactorial neurodegenerative disease that, after decades of research and development, is still without a cure. There are some symptomatic treatments to manage the psychological symptoms but none of these drugs can halt disease progression. Additionally, over the last few years, many anti-AD drugs failed in late stages of clinical trials and many hypotheses surfaced to explain these failures, including the lack of clear understanding of disease pathways and processes. Recently, different epigenetic factors have been implicated in AD pathogenesis; thus, they could serve as promising AD diagnostic biomarkers. Additionally, network biology approaches have been suggested as effective tools to study AD on the systems level and discover multi-target-directed ligands as novel treatments for AD. Herein, we provide a comprehensive review on Alzheimer's disease pathophysiology to provide a better understanding of disease pathogenesis hypotheses and decipher the role of genetic and epigenetic factors in disease development and progression. We also provide an overview of disease biomarkers and drug targets and suggest network biology approaches as new tools for identifying novel biomarkers and drugs. We also posit that the application of machine learning and artificial intelligence to mining Alzheimer's disease multi-omics data will facilitate drug and biomarker discovery efforts and lead to effective individualized anti-Alzheimer treatments.
Collapse
Affiliation(s)
- Rima Hajjo
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, The University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA
- National Center for Epidemics and Communicable Disease Control, Amman 11118, Jordan
- Correspondence:
| | - Dima A. Sabbah
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Osama H. Abusara
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Abdel Qader Al Bawab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| |
Collapse
|
6
|
Breitner J, Dodge HH, Khachaturian ZS, Khachaturian AS. "Exceptions that prove the rule"-Why have clinical trials failed to show efficacy of risk factor interventions suggested by observational studies of the dementia-Alzheimer's disease syndrome? Alzheimers Dement 2022; 18:389-392. [PMID: 35245406 PMCID: PMC8940699 DOI: 10.1002/alz.12633] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Indexed: 12/28/2022]
Affiliation(s)
- John Breitner
- Douglas Hospital Research Center and McGill University, Quebec, Canada
| | - Hiroko H. Dodge
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | | | | |
Collapse
|
7
|
Leysen H, Walter D, Christiaenssen B, Vandoren R, Harputluoğlu İ, Van Loon N, Maudsley S. GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease. Int J Mol Sci 2021; 22:ijms222413387. [PMID: 34948182 PMCID: PMC8708147 DOI: 10.3390/ijms222413387] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 02/06/2023] Open
Abstract
GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.
Collapse
Affiliation(s)
- Hanne Leysen
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Deborah Walter
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Bregje Christiaenssen
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Romi Vandoren
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - İrem Harputluoğlu
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
- Department of Chemistry, Middle East Technical University, Çankaya, Ankara 06800, Turkey
| | - Nore Van Loon
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
| | - Stuart Maudsley
- Receptor Biology Lab, University of Antwerp, 2610 Wilrijk, Belgium; (H.L.); (D.W.); (B.C.); (R.V.); (İ.H.); (N.V.L.)
- Correspondence:
| |
Collapse
|
8
|
Wassan JT, Zheng H, Wang H. Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review. Cells 2021; 10:cells10112924. [PMID: 34831148 PMCID: PMC8616301 DOI: 10.3390/cells10112924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 11/16/2022] Open
Abstract
Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been used to predict aging-related diseases and issues, aiding clinical providers in decision-making based on patient’s medical records. Deep learning (DL), as one of the most recent generations of AI technologies, has embraced rapid progress in the early prediction and classification of aging-related issues. In this paper, a scoping review of publications using DL approaches to predict common aging-related diseases (such as age-related macular degeneration, cardiovascular and respiratory diseases, arthritis, Alzheimer’s and lifestyle patterns related to disease progression), was performed. Google Scholar, IEEE and PubMed are used to search DL papers on common aging-related issues published between January 2017 and August 2021. These papers were reviewed, evaluated, and the findings were summarized. Overall, 34 studies met the inclusion criteria. These studies indicate that DL could help clinicians in diagnosing disease at its early stages by mapping diagnostic predictions into observable clinical presentations; and achieving high predictive performance (e.g., more than 90% accurate predictions of diseases in aging).
Collapse
Affiliation(s)
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast BT15 1ED, UK;
- Correspondence:
| | - Haiying Wang
- School of Computing, Ulster University, Belfast BT15 1ED, UK;
| |
Collapse
|
9
|
Yang YC, Islam SU, Noor A, Khan S, Afsar W, Nazir S. Influential Usage of Big Data and Artificial Intelligence in Healthcare. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5812499. [PMID: 34527076 PMCID: PMC8437645 DOI: 10.1155/2021/5812499] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 08/09/2021] [Indexed: 01/07/2023]
Abstract
Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.
Collapse
Affiliation(s)
- Yan Cheng Yang
- Foreign Language Department, Luoyang Institute of Science and Technology, Luoyang, Henan, China
- Foreign Language Department/Language and Cognition Center, Hunan University, Changsha, Hunan, China
| | - Saad Ul Islam
- Department of Computer Science, University of Swabi, Swabi, Pakistan
| | - Asra Noor
- Department of Computer Science, University of Swabi, Swabi, Pakistan
| | - Sadia Khan
- Department of Computer Science, University of Swabi, Swabi, Pakistan
| | - Waseem Afsar
- Department of Computer Science, University of Swabi, Swabi, Pakistan
| | - Shah Nazir
- Department of Computer Science, University of Swabi, Swabi, Pakistan
| |
Collapse
|
10
|
Fabrizio C, Termine A, Caltagirone C, Sancesario G. Artificial Intelligence for Alzheimer's Disease: Promise or Challenge? Diagnostics (Basel) 2021; 11:1473. [PMID: 34441407 PMCID: PMC8391160 DOI: 10.3390/diagnostics11081473] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/12/2021] [Accepted: 08/12/2021] [Indexed: 01/23/2023] Open
Abstract
Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.
Collapse
Affiliation(s)
- Carlo Fabrizio
- Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (C.F.); (A.T.)
| | - Andrea Termine
- Laboratory of Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation, 00143 Rome, Italy; (C.F.); (A.T.)
| | - Carlo Caltagirone
- Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy;
| | - Giulia Sancesario
- Biobank, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
- European Center for Brain Research, Experimental Neuroscience, 00143 Rome, Italy
| |
Collapse
|
11
|
Češková E, Šilhán P. From Personalized Medicine to Precision Psychiatry? Neuropsychiatr Dis Treat 2021; 17:3663-3668. [PMID: 34934319 PMCID: PMC8684413 DOI: 10.2147/ndt.s337814] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/16/2021] [Indexed: 12/28/2022] Open
Abstract
Personalised medicine aims to find an individualized approach for each particular patient. Most factors used in current psychiatry, however, depend on the assessment made by the individual clinician and lack a higher degree of reliability. Precision medicine bases decisions on quantifiable indicators available thanks to the tremendous progress in science and technology facilitating the acquisition, processing and analysis of huge amounts of data. So far, psychiatry has not been benefiting enough from the advanced diagnostic technologies; nevertheless, we are witnessing the dawn of the era of precision psychiatry, starting with the gathering of sufficient amounts of data and its analysis by the means of artificial intelligence and machine learning. First results of this approach in psychiatry are available, which facilitate diagnosis assessment, course prediction, and appropriate treatment choice. These processes are often so complex and difficult to understand that they may resemble a "black box", which can slow down the acceptance of the results of this approach in clinical practice. Still, bringing precision medicine including psychiatry to standard clinical practice is a big challenge that can result in a completely new and transformative concept of health care. Such extensive changes naturally have both their supporters and opponents. This paper aims to familiarize clinically oriented physicians with precision psychiatry and to attract their attention to its recent developments. We cover the theoretical basis of precision medicine, its specifics in psychiatry, and provide examples of its use in the field of diagnostic assessment, course prediction, and appropriate treatment planning.
Collapse
Affiliation(s)
- Eva Češková
- Department of Psychiatry, University Hospital Ostrava, Ostrava, Czech Republic.,Department of Clinical Neurosciences, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic.,Department of Psychiatry, University Hospital Brno, Brno, Czech Republic.,Department of Psychiatry, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Petr Šilhán
- Department of Psychiatry, University Hospital Ostrava, Ostrava, Czech Republic.,Department of Clinical Neurosciences, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
| |
Collapse
|
12
|
Moseholm KF, Tybjerg K, Jensen MK, Westendorp RGJ. Too narrow and too broad: Differentiating late-onset dementia from its historical entanglement with Alzheimer's disease. AGING BRAIN 2021; 1:100010. [PMID: 36911504 PMCID: PMC9997125 DOI: 10.1016/j.nbas.2021.100010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Affiliation(s)
- Kristine F Moseholm
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Denmark
| | - Karin Tybjerg
- Medical Museion, Department of Public Health, University of Copenhagen, Denmark.,Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Denmark
| | - Majken K Jensen
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Denmark.,Center for Healthy Aging, University of Copenhagen, Denmark
| | - Rudi G J Westendorp
- Section for Epidemiology, Department of Public Health, University of Copenhagen, Denmark.,Center for Healthy Aging, University of Copenhagen, Denmark
| |
Collapse
|
13
|
Wong-Lin K, McClean PL, McCombe N, Kaur D, Sanchez-Bornot JM, Gillespie P, Todd S, Finn DP, Joshi A, Kane J, McGuinness B. Shaping a data-driven era in dementia care pathway through computational neurology approaches. BMC Med 2020; 18:398. [PMID: 33323116 PMCID: PMC7738245 DOI: 10.1186/s12916-020-01841-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 11/03/2020] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making. MAIN BODY Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations. CONCLUSION The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.
Collapse
Affiliation(s)
- KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Londonderry, Northern Ireland, UK.
| | - Paula L McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Magee Campus, Londonderry, Northern Ireland, UK
| | - Niamh McCombe
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Londonderry, Northern Ireland, UK
| | - Daman Kaur
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Magee Campus, Londonderry, Northern Ireland, UK
| | - Jose M Sanchez-Bornot
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Londonderry, Northern Ireland, UK
| | - Paddy Gillespie
- Health Economics and Policy Analysis Centre, Discipline of Economics, National University of Ireland, Galway, Ireland
| | - Stephen Todd
- Altnagelvin Area Hospital, Western Health and Social Care Trust, Londonderry, Northern Ireland, UK
| | - David P Finn
- Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre, National University of Ireland, Galway, Ireland
| | - Alok Joshi
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Londonderry, Northern Ireland, UK
| | - Joseph Kane
- School of Medicine, Dentistry and Biomedical Sciences, Institute for Health Sciences, Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - Bernadette McGuinness
- School of Medicine, Dentistry and Biomedical Sciences, Institute for Health Sciences, Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland, UK
| |
Collapse
|
14
|
van Gastel J, Leysen H, Boddaert J, Vangenechten L, Luttrell LM, Martin B, Maudsley S. Aging-related modifications to G protein-coupled receptor signaling diversity. Pharmacol Ther 2020; 223:107793. [PMID: 33316288 DOI: 10.1016/j.pharmthera.2020.107793] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 11/26/2020] [Indexed: 02/06/2023]
Abstract
Aging is a highly complex molecular process, affecting nearly all tissue systems in humans and is the highest risk factor in developing neurodegenerative disorders such as Alzheimer's and Parkinson's disease, cardiovascular disease and Type 2 diabetes mellitus. The intense complexity of the aging process creates an incentive to develop more specific drugs that attenuate or even reverse some of the features of premature aging. As our current pharmacopeia is dominated by therapeutics that target members of the G protein-coupled receptor (GPCR) superfamily it may be prudent to search for effective anti-aging therapeutics in this fertile domain. Since the first demonstration of GPCR-based β-arrestin signaling, it has become clear that an enhanced appreciation of GPCR signaling diversity may facilitate the creation of therapeutics with selective signaling activities. Such 'biased' ligand signaling profiles can be effectively investigated using both standard molecular biological techniques as well as high-dimensionality data analyses. Through a more nuanced appreciation of the quantitative nature across the multiple dimensions of signaling bias that drugs possess, researchers may be able to further refine the efficacy of GPCR modulators to impact the complex aberrations that constitute the aging process. Identifying novel effector profiles could expand the effective pharmacopeia and assist in the design of precision medicines. This review discusses potential non-G protein effectors, and specifically their potential therapeutic suitability in aging and age-related disorders.
Collapse
Affiliation(s)
- Jaana van Gastel
- Receptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium; Faculty of Pharmacy, Biomedical and Veterinary Science, University of Antwerp, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium; Faculty of Pharmacy, Biomedical and Veterinary Science, University of Antwerp, Antwerp, Belgium
| | - Jan Boddaert
- Molecular Pathology Group, Faculty of Medicine and Health Sciences, Laboratory of Cell Biology and Histology, Antwerp, Belgium
| | - Laura Vangenechten
- Receptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Louis M Luttrell
- Division of Endocrinology, Diabetes & Medical Genetics, Medical University of South Carolina, USA
| | - Bronwen Martin
- Faculty of Pharmacy, Biomedical and Veterinary Science, University of Antwerp, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Lab, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium; Faculty of Pharmacy, Biomedical and Veterinary Science, University of Antwerp, Antwerp, Belgium.
| |
Collapse
|
15
|
Kolagar TA, Farzaneh M, Nikkar N, Khoshnam SE. Human Pluripotent Stem Cells in Neurodegenerative Diseases: Potentials, Advances and Limitations. Curr Stem Cell Res Ther 2020; 15:102-110. [PMID: 31441732 DOI: 10.2174/1574888x14666190823142911] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Revised: 06/15/2019] [Accepted: 07/17/2019] [Indexed: 12/14/2022]
Abstract
Neurodegenerative diseases are progressive and uncontrolled gradual loss of motor neurons function or death of neuron cells in the central nervous system (CNS) and the mechanisms underlying their progressive nature remain elusive. There is urgent need to investigate therapeutic strategies and novel treatments for neural regeneration in disorders like Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS). Currently, the development and identification of pluripotent stem cells enabling the acquisition of a large number of neural cells in order to improve cell recovery after neurodegenerative disorders. Pluripotent stem cells which consist of embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) are characterized by their ability to indefinitely self-renew and the capacity to differentiate into different types of cells. The first human ESC lines were established from donated human embryos; while, because of a limited supply of donor embryos, human ESCs derivation remains ethically and politically controversial. Hence, hiPSCs-based therapies have been shown as an effective replacement for human ESCs without embryo destruction. Compared to the invasive methods for derivation of human ESCs, human iPSCs has opened possible to reprogram patient-specific cells by defined factors and with minimally invasive procedures. Human pluripotent stem cells are a good source for cell-based research, cell replacement therapies and disease modeling. To date, hundreds of human ESC and human iPSC lines have been generated with the aim of treating various neurodegenerative diseases. In this review, we have highlighted the recent potentials, advances, and limitations of human pluripotent stem cells for the treatment of neurodegenerative disorders.
Collapse
Affiliation(s)
- Tannaz Akbari Kolagar
- Faculty of Biological Sciences, Tehran North Branch, Islamic Azad University, Tehran, Iran
| | - Maryam Farzaneh
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Negin Nikkar
- Department of Biology, Faculty of Sciences, Alzahra University, Tehran, Iran
| | - Seyed Esmaeil Khoshnam
- Physiology Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| |
Collapse
|
16
|
Tarawneh R. Biomarkers: Our Path Towards a Cure for Alzheimer Disease. Biomark Insights 2020; 15:1177271920976367. [PMID: 33293784 PMCID: PMC7705771 DOI: 10.1177/1177271920976367] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/13/2020] [Indexed: 12/12/2022] Open
Abstract
Over the last decade, biomarkers have significantly improved our understanding of
the pathophysiology of Alzheimer disease (AD) and provided valuable tools to
examine different disease mechanisms and their progression over time. While
several markers of amyloid, tau, neuronal, synaptic, and axonal injury,
inflammation, and immune dysregulation in AD have been identified, there is a
relative paucity of biomarkers which reflect other disease mechanisms such as
oxidative stress, mitochondrial injury, vascular or endothelial injury, and
calcium-mediated excitotoxicity. Importantly, there is an urgent need to
standardize methods for biomarker assessments across different centers, and to
identify dynamic biomarkers which can monitor disease progression over time
and/or response to potential disease-modifying treatments. The updated research
framework for AD, proposed by the National Institute of Aging- Alzheimer’s
Association (NIA-AA) Work Group, emphasizes the importance of incorporating
biomarkers in AD research and defines AD as a biological construct consisting of
amyloid, tau, and neurodegeneration which spans pre-symptomatic and symptomatic
stages. As results of clinical trials of AD therapeutics have been
disappointing, it has become increasingly clear that the success of future AD
trials will require the incorporation of biomarkers in participant selection,
prognostication, monitoring disease progression, and assessing response to
treatments. We here review the current state of fluid AD biomarkers, and discuss
the advantages and limitations of the updated NIA-AA research framework.
Importantly, the integration of biomarker data with clinical, cognitive, and
imaging domains through a systems biology approach will be essential to
adequately capture the molecular, genetic, and pathological heterogeneity of AD
and its spatiotemporal evolution over time.
Collapse
Affiliation(s)
- Rawan Tarawneh
- Department of Neurology, The Ohio State University, Columbus, OH, USA
| |
Collapse
|
17
|
Fluid Candidate Biomarkers for Alzheimer's Disease: A Precision Medicine Approach. J Pers Med 2020; 10:jpm10040221. [PMID: 33187336 PMCID: PMC7712586 DOI: 10.3390/jpm10040221] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 11/04/2020] [Accepted: 11/05/2020] [Indexed: 12/11/2022] Open
Abstract
A plethora of dynamic pathophysiological mechanisms underpins highly heterogeneous phenotypes in the field of dementia, particularly in Alzheimer's disease (AD). In such a faceted scenario, a biomarker-guided approach, through the implementation of specific fluid biomarkers individually reflecting distinct molecular pathways in the brain, may help establish a proper clinical diagnosis, even in its preclinical stages. Recently, ultrasensitive assays may detect different neurodegenerative mechanisms in blood earlier. ß-amyloid (Aß) peptides, phosphorylated-tau (p-tau), and neurofilament light chain (NFL) measured in blood are gaining momentum as candidate biomarkers for AD. P-tau is currently the more convincing plasma biomarker for the diagnostic workup of AD. The clinical role of plasma Aβ peptides should be better elucidated with further studies that also compare the accuracy of the different ultrasensitive techniques. Blood NFL is promising as a proxy of neurodegeneration process tout court. Protein misfolding amplification assays can accurately detect α-synuclein in cerebrospinal fluid (CSF), thus representing advancement in the pathologic stratification of AD. In CSF, neurogranin and YKL-40 are further candidate biomarkers tracking synaptic disruption and neuroinflammation, which are additional key pathophysiological pathways related to AD genesis. Advanced statistical analysis using clinical scores and biomarker data to bring together individuals with AD from large heterogeneous cohorts into consistent clusters may promote the discovery of pathophysiological causes and detection of tailored treatments.
Collapse
|
18
|
Hendrickx JO, van Gastel J, Leysen H, Martin B, Maudsley S. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacol Rev 2020; 72:191-217. [PMID: 31843941 DOI: 10.1124/pr.119.017921] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.
Collapse
Affiliation(s)
- Jhana O Hendrickx
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Jaana van Gastel
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Bronwen Martin
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| |
Collapse
|
19
|
Spengler H, Lang C, Mahapatra T, Gatz I, Kuhn KA, Prasser F. Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation. JMIR Med Inform 2020; 8:e15918. [PMID: 32706673 PMCID: PMC7404007 DOI: 10.2196/15918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 02/16/2020] [Accepted: 05/06/2020] [Indexed: 01/16/2023] Open
Abstract
Background Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. Objective Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. Methods We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. Results The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. Conclusions Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.
Collapse
Affiliation(s)
- Helmut Spengler
- Institute of Medical Informatics, Statistics and Epidemiology, University Medical Center rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Claudia Lang
- Institute of Medical Informatics, Statistics and Epidemiology, University Medical Center rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Tanmaya Mahapatra
- Institute of Medical Informatics, Statistics and Epidemiology, University Medical Center rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Ingrid Gatz
- Institute of Medical Informatics, Statistics and Epidemiology, University Medical Center rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Klaus A Kuhn
- Institute of Medical Informatics, Statistics and Epidemiology, University Medical Center rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Fabian Prasser
- Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
| |
Collapse
|
20
|
Albertini C, Salerno A, Sena Murteira Pinheiro P, Bolognesi ML. From combinations to multitarget‐directed ligands: A continuum in Alzheimer's disease polypharmacology. Med Res Rev 2020; 41:2606-2633. [DOI: 10.1002/med.21699] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 06/01/2020] [Indexed: 12/12/2022]
Affiliation(s)
- Claudia Albertini
- Department of Pharmacy and Biotechnology Alma Mater Studiorum–University of Bologna Bologna Italy
| | - Alessandra Salerno
- Department of Pharmacy and Biotechnology Alma Mater Studiorum–University of Bologna Bologna Italy
| | - Pedro Sena Murteira Pinheiro
- Department of Pharmacy and Biotechnology Alma Mater Studiorum–University of Bologna Bologna Italy
- Programa de Pós‐Graduação em Farmacologia e Química Medicinal, Instituto de Ciências Biomédicas Universidade Federal do Rio de Janeiro Rio de Janeiro Rio de Janeiro Brazil
| | - Maria L. Bolognesi
- Department of Pharmacy and Biotechnology Alma Mater Studiorum–University of Bologna Bologna Italy
| |
Collapse
|
21
|
Baldacci F, Mazzucchi S, Della Vecchia A, Giampietri L, Giannini N, Koronyo-Hamaoui M, Ceravolo R, Siciliano G, Bonuccelli U, Elahi FM, Vergallo A, Lista S, Giorgi FS. The path to biomarker-based diagnostic criteria for the spectrum of neurodegenerative diseases. Expert Rev Mol Diagn 2020; 20:421-441. [PMID: 32066283 PMCID: PMC7445079 DOI: 10.1080/14737159.2020.1731306] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 02/14/2020] [Indexed: 12/21/2022]
Abstract
Introduction: The postmortem examination still represents the reference standard for detecting the pathological nature of chronic neurodegenerative diseases (NDD). This approach displays intrinsic conceptual limitations since NDD represent a dynamic spectrum of partially overlapping phenotypes, shared pathomechanistic alterations that often give rise to mixed pathologies.Areas covered: We scrutinized the international clinical diagnostic criteria of NDD and the literature to provide a roadmap toward a biomarker-based classification of the NDD spectrum. A few pathophysiological biomarkers have been established for NDD. These are time-consuming, invasive, and not suitable for preclinical detection. Candidate screening biomarkers are gaining momentum. Blood neurofilament light-chain represents a robust first-line tool to detect neurodegeneration tout court and serum progranulin helps detect genetic frontotemporal dementia. Ultrasensitive assays and retinal scans may identify Aβ pathology early, in blood and the eye, respectively. Ultrasound also represents a minimally invasive option to investigate the substantia nigra. Protein misfolding amplification assays may accurately detect α-synuclein in biofluids.Expert opinion: Data-driven strategies using quantitative rather than categorical variables may be more reliable for quantification of contributions from pathophysiological mechanisms and their spatial-temporal evolution. A systems biology approach is suitable to untangle the dynamics triggering loss of proteostasis, driving neurodegeneration and clinical evolution.
Collapse
Affiliation(s)
- Filippo Baldacci
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l’hôpital, Paris, France
| | - Sonia Mazzucchi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | | - Linda Giampietri
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Nicola Giannini
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Roberto Ceravolo
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Gabriele Siciliano
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Ubaldo Bonuccelli
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Fanny M. Elahi
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Andrea Vergallo
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l’hôpital, Paris, France
- Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, Paris, France
- Department of Neurology, Institute of Memory and Alzheimer’s Disease (IM2A), Pitié-Salpêtrière Hospital, Paris, France
| | - Simone Lista
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l’hôpital, Paris, France
- Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, Paris, France
- Department of Neurology, Institute of Memory and Alzheimer’s Disease (IM2A), Pitié-Salpêtrière Hospital, Paris, France
| | - Filippo Sean Giorgi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | |
Collapse
|
22
|
Martín-Galiano AJ, McConnell MJ. Using Omics Technologies and Systems Biology to Identify Epitope Targets for the Development of Monoclonal Antibodies Against Antibiotic-Resistant Bacteria. Front Immunol 2019; 10:2841. [PMID: 31921119 PMCID: PMC6914692 DOI: 10.3389/fimmu.2019.02841] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/19/2019] [Indexed: 12/12/2022] Open
Abstract
Over the past few decades, antimicrobial resistance has emerged as an important threat to public health due to the global dissemination of multidrug-resistant strains from several bacterial species. This worrisome trend, in addition to the paucity of new antibiotics with novel mechanisms of action in the development pipeline, warrants the development of non-antimicrobial approaches to combating infection caused by these isolates. Monoclonal antibodies (mAbs) have emerged as highly effective molecules for the treatment of multiple diseases. However, in spite of the fact that antibodies play an important role in protective immunity against bacteria, only three mAb therapies have been approved for clinical use in the treatment of bacterial infections. In the present review, we briefly outline the therapeutic potential of mAbs in the treatment of bacterial diseases and discuss how their development can be facilitated when assisted by “omics” technologies and interpreted under a systems biology paradigm. Specifically, methods employing large genomic, transcriptomic, structural, and proteomic datasets allow for the rational identification of epitopes. Ideally, these include those that are present in the majority of circulating isolates, highly conserved at the amino acid level, surface-exposed, located on antigens essential for virulence, and expressed during critical stages of infection. Therefore, these knowledge-based approaches can contribute to the identification of high-value epitopes for the development of effective mAbs against challenging bacterial clones.
Collapse
Affiliation(s)
- Antonio J Martín-Galiano
- Intrahospital Infections Laboratory, National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda, Spain
| | - Michael J McConnell
- Intrahospital Infections Laboratory, National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda, Spain
| |
Collapse
|
23
|
Chen S, He Z, Han X, He X, Li R, Zhu H, Zhao D, Dai C, Zhang Y, Lu Z, Chi X, Niu B. How Big Data and High-performance Computing Drive Brain Science. GENOMICS PROTEOMICS & BIOINFORMATICS 2019; 17:381-392. [PMID: 31805369 PMCID: PMC6943776 DOI: 10.1016/j.gpb.2019.09.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 09/12/2019] [Accepted: 09/29/2019] [Indexed: 12/17/2022]
Abstract
Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.
Collapse
Affiliation(s)
- Shanyu Chen
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Zhipeng He
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xinyin Han
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoyu He
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Ruilin Li
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Haidong Zhu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Dan Zhao
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Chuangchuang Dai
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yu Zhang
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China
| | - Zhonghua Lu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
| | - Xuebin Chi
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China; Center of Scientific Computing Applications & Research, Chinese Academy of Sciences, Beijing 100190, China
| | - Beifang Niu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100190, China; Guizhou University School of Medicine, Guiyang 550025, China.
| |
Collapse
|
24
|
Geerts H, Barrett JE. Neuronal Circuit-Based Computer Modeling as a Phenotypic Strategy for CNS R&D. Front Neurosci 2019; 13:723. [PMID: 31379482 PMCID: PMC6646593 DOI: 10.3389/fnins.2019.00723] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 06/28/2019] [Indexed: 12/13/2022] Open
Abstract
With the success rate of drugs for CNS indications at an all-time low, new approaches are needed to turn the tide of failed clinical trials. This paper reviews the history of CNS drug Discovery over the last 60 years and proposes a new paradigm based on the lessons learned. The initial wave of successful therapeutics discovered using careful clinical observations was followed by an emphasis on a phenotypic target-agnostic approach, often leading to successful drugs with a rich pharmacology. The subsequent introduction of molecular biology and the focus on a target-driven strategy has largely dominated drug discovery efforts over the last 30 years, but has not increased the probability of success, because these highly selective molecules are unlikely to address the complex pathological phenotypes of most CNS disorders. In many cases, reliance on preclinical animal models has lacked robust translational power. We argue that Quantitative Systems Pharmacology (QSP), a mechanism-based computer model of biological processes informed by preclinical knowledge and enhanced by neuroimaging and clinical data could be a new powerful knowledge generator engine and paradigm for rational polypharmacy. Progress in the academic discipline of computational neurosciences, allows one to model the effect of pathology and therapeutic interventions on neuronal circuit firing activity that can relate to clinical phenotypes, driven by complex properties of specific brain region activation states. The model is validated by optimizing the correlation between relevant emergent properties of these neuronal circuits and historical clinical and imaging datasets. A rationally designed polypharmacy target profile will be discovered using reverse engineering and sensitivity analysis. Small molecules will be identified using a combination of Artificial Intelligence methods and computational modeling, tested subsequently in heterologous cellular systems with human targets. Animal models will be used to establish target engagement and for ADME-Tox, with the QSP approach complemented by in vivo preclinical models that can be further refined to increase predictive validity. The QSP platform can also mitigate the variability in clinical trials with the concept of virtual patients. Because the QSP platform integrates knowledge from a wide variety of sources in an actionable simulation, it offers the possibility of substantially improving the success rate of CNS R&D programs while, at the same time, reducing both cost and the number of animals.
Collapse
Affiliation(s)
- Hugo Geerts
- In Silico Biosciences, Inc., Berwyn, IL, United States
| | - James E Barrett
- Center for Substance Abuse Research, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States
| |
Collapse
|
25
|
Godfrey A, Brodie M, van Schooten KS, Nouredanesh M, Stuart S, Robinson L. Inertial wearables as pragmatic tools in dementia. Maturitas 2019; 127:12-17. [PMID: 31351515 DOI: 10.1016/j.maturitas.2019.05.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/22/2019] [Accepted: 05/23/2019] [Indexed: 01/02/2023]
Abstract
Dementia is a critically important issue due to its wide impact on health services as well as its personal and societal costs. Limitations exist for current dementia protocols, and there are calls to introduce modern technology that facilitates the addition of digital biomarkers to routine clinical practice. Wearable technology (wearables) are nearly ubiquitous in everyday life, gathering discrete and continuous digital data on habitual activities, but their utility in modern medicine remains low. Due to advances in data analytics, wearables are now commonly discussed as pragmatic tools to aid the diagnosis and treatment of a range of neurological disorders. Inertial sensor-based wearables are one such technology; they offer a low-cost approach to quantify routine movements that are fundamental to normal activities of daily living, most notably postural control and gait. Here, we provide a narrative review of how wearables are providing useful postural control and gait data to facilitate the capture of digital markers to aid dementia research. We outline the history of wearables, from their humble beginnings to their current use beyond the clinic, and explore their integration into modern systems, as well as the ongoing standardisation and regulatory efforts to integrate their use in clinical trials.
Collapse
Affiliation(s)
- A Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle, UK.
| | - M Brodie
- Falls Balance & Injury Research Centre, Neuroscience Research Australia, NSW, Australia; Graduate School of Biomedical Engineering, University of New South Wales, NSW, Australia
| | - K S van Schooten
- Neuroscience Research Australia, University of New South Wales, Sydney, Australia; School of Public Health and Community Medicine, University of New South Wales, NSW, Australia
| | - M Nouredanesh
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada
| | - S Stuart
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - L Robinson
- Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK
| |
Collapse
|
26
|
Hachinski V. Dementia: Paradigm shifting into high gear. Alzheimers Dement 2019; 15:985-994. [PMID: 30979540 DOI: 10.1016/j.jalz.2019.01.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 01/08/2019] [Accepted: 01/09/2019] [Indexed: 12/30/2022]
Abstract
Redressing the rising threat of dementia demands not only an increase, but a diversification of efforts. We need new approaches, trials, and partners. We cannot afford to continue to only round up the usual suspects, β amyloid, and tau and try to stop them with a single drug "silver bullet". Dementia of late onset is not a disease, but an amalgam of interactive pathologies on the shifting background of aging, requiring multimodal targeting. Cerebrovascular diseases coexist and coact with all major neurodegenerative pathologies, increasing two-fold the likelihood that they will manifest clinically. Cerebrovascular diseases need to be controlled, to give antidegenerative drugs a chance to succeed. This calls for new types of trials and designs. Stroke doubles the chances of developing dementia and decreases in stroke incidence correlate with decreases in dementia. Ninety percent of strokes are potentially preventable and so are a proportion of dementias. The stroke and dementia communities need to partner and complement the search for silver bullets with the golden opportunity of doing something now.
Collapse
Affiliation(s)
- Vladimir Hachinski
- Schulich School of Medicine & Dentistry, Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada.
| |
Collapse
|
27
|
Hampel H, Vergallo A, Perry G, Lista S. The Alzheimer Precision Medicine Initiative. J Alzheimers Dis 2019; 68:1-24. [DOI: 10.3233/jad-181121] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Harald Hampel
- AXA Research Fund & Sorbonne University Chair, Paris, France
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l’hôpital, Paris, France
- Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Paris, France
- Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l’hôpital, Paris, France
| | - Andrea Vergallo
- AXA Research Fund & Sorbonne University Chair, Paris, France
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l’hôpital, Paris, France
- Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Paris, France
- Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l’hôpital, Paris, France
| | - George Perry
- College of Sciences, One UTSA Circle, The University of Texas at San Antonio, San Antonio, TX, USA
| | - Simone Lista
- AXA Research Fund & Sorbonne University Chair, Paris, France
- Sorbonne University, GRC n° 21, Alzheimer Precision Medicine (APM), AP-HP, Pitié-Salpêtrière Hospital, Boulevard de l’hôpital, Paris, France
- Brain & Spine Institute (ICM), INSERM U 1127, CNRS UMR 7225, Paris, France
- Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, AP-HP, Boulevard de l’hôpital, Paris, France
| | | |
Collapse
|
28
|
Theory and Theorizing in Nursing Science: Commentary from the Nursing Research Special Issue Editorial Team. Nurs Res 2019; 67:188-195. [PMID: 29489638 DOI: 10.1097/nnr.0000000000000273] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Articles from three landmark symposia on theory for nursing-published in Nursing Research in 1968-1969-served as a key underpinning for the development of nursing as an academic discipline. The current special issue on Theory and Theorizing in Nursing Science celebrates the 50th anniversary of publication of these seminal works in nursing theory. OBJECTIVE The purpose of this commentary is to consider the future of nursing theory development in light of articles published in the anniversary issue. APPROACH The Editorial Team for the special issue identified core questions about continued nursing theory development, as related to the nursing metaparadigm, practice theory, big data, and doctoral education. Using a dialogue format, the editors discussed these core questions. DISCUSSION The classic nursing metaparadigm (health, person, environment, nursing) was viewed as a continuing unifying element for the discipline but is in need of revision in today's scientific and practice climates. Practice theory and precision healthcare jointly arise from an emphasis on individualization. Big data and the methods of e-science are challenging the assumptions on which nursing theory development was originally based. Doctoral education for nursing scholarship requires changes to ensure that tomorrow's scholars are prepared to steward the discipline by advancing (not reifying) past approaches to nursing theory. CONCLUSION Ongoing reexamination of theory is needed to clarify the domain of nursing, guide nursing science and practice, and direct and communicate the unique and essential contributions of nursing science to the broader health research effort and of nursing to healthcare.
Collapse
|
29
|
Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer's Disease. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:6216530. [PMID: 30863455 PMCID: PMC6378032 DOI: 10.1155/2019/6216530] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 12/17/2018] [Indexed: 11/18/2022]
Abstract
Background Alzheimer's disease (AD) is a major public health concern, and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients through validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials. Methods We implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers, and cognitive impairment as nonlinear first-order ordinary differential equations (ODEs) to include amyloid-dependent and nondependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of antiamyloid therapy in late-onset AD. Results The computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration and the onset of cognitive decline based on cognitive reserve, as predicted by the prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or nonamyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by the prior literature. Forward simulation of antiamyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients. Conclusions We have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early- and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies and may in the future play a key role in personalizing approaches to treatment.
Collapse
|
30
|
Satpathy A, Datta P, Wu Y, Ayan B, Bayram E, Ozbolat IT. Developments with 3D bioprinting for novel drug discovery. Expert Opin Drug Discov 2018; 13:1115-1129. [PMID: 30384781 PMCID: PMC6494715 DOI: 10.1080/17460441.2018.1542427] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 10/26/2018] [Indexed: 02/06/2023]
Abstract
Introduction: Although there have been significant contributions from the pharmaceutical industry to clinical practice, several diseases remain unconquered, with the discovery of new drugs remaining a paramount objective. The actual process of drug discovery involves many steps including pre-clinical and clinical testing, which are highly time- and resource-consuming, driving researchers to improve the process efficiency. The shift of modelling technology from two-dimensions (2D) to three-dimensions (3D) is one of such advancements. 3D Models allow for close mimicry of cellular interactions and tissue microenvironments thereby improving the accuracy of results. The advent of bioprinting for fabrication of tissues has shown potential to improve 3D culture models. Areas covered: The present review provides a comprehensive update on a wide range of bioprinted tissue models and appraise them for their potential use in drug discovery research. Expert opinion: Efficiency, reproducibility, and standardization are some impediments of the bioprinted models. Vascularization of the constructs has to be addressed in the near future. While much progress has already been made with several seminal works, the next milestone will be the commercialization of these models after due regulatory approval.
Collapse
Affiliation(s)
- Aishwarya Satpathy
- a Centre for Healthcare Science and Technology , Indian Institute of Engineering Science and Technology Shibpur , Howrah , India
| | - Pallab Datta
- a Centre for Healthcare Science and Technology , Indian Institute of Engineering Science and Technology Shibpur , Howrah , India
| | - Yang Wu
- b Engineering Science and Mechanics Department , Penn State University , University Park , PA , USA
- c The Huck Institutes of the Life Sciences, Penn State University , USA
| | - Bugra Ayan
- b Engineering Science and Mechanics Department , Penn State University , University Park , PA , USA
- c The Huck Institutes of the Life Sciences, Penn State University , USA
| | - Ertugrul Bayram
- d Medical Oncology Department , Agri State Hospital , Agri , Turkey
| | - Ibrahim T Ozbolat
- b Engineering Science and Mechanics Department , Penn State University , University Park , PA , USA
- c The Huck Institutes of the Life Sciences, Penn State University , USA
- e Biomedical Engineering Department , Penn State University , University Park , PA , USA
- f Materials Research Institute, Penn State University , USA
| |
Collapse
|
31
|
Ienca M, Ferretti A, Hurst S, Puhan M, Lovis C, Vayena E. Considerations for ethics review of big data health research: A scoping review. PLoS One 2018; 13:e0204937. [PMID: 30308031 PMCID: PMC6181558 DOI: 10.1371/journal.pone.0204937] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 09/17/2018] [Indexed: 12/15/2022] Open
Abstract
Big data trends in biomedical and health research enable large-scale and multi-dimensional aggregation and analysis of heterogeneous data sources, which could ultimately result in preventive, diagnostic and therapeutic benefit. The methodological novelty and computational complexity of big data health research raises novel challenges for ethics review. In this study, we conducted a scoping review of the literature using five databases to identify and map the major challenges of health-related big data for Ethics Review Committees (ERCs) or analogous institutional review boards. A total of 1093 publications were initially identified, 263 of which were included in the final synthesis after abstract and full-text screening performed independently by two researchers. Both a descriptive numerical summary and a thematic analysis were performed on the full-texts of all articles included in the synthesis. Our findings suggest that while big data trends in biomedicine hold the potential for advancing clinical research, improving prevention and optimizing healthcare delivery, yet several epistemic, scientific and normative challenges need careful consideration. These challenges have relevance for both the composition of ERCs and the evaluation criteria that should be employed by ERC members when assessing the methodological and ethical viability of health-related big data studies. Based on this analysis, we provide some preliminary recommendations on how ERCs could adaptively respond to those challenges. This exploration is designed to synthesize useful information for researchers, ERCs and relevant institutional bodies involved in the conduction and/or assessment of health-related big data research.
Collapse
Affiliation(s)
- Marcello Ienca
- Health Ethics and Policy Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Agata Ferretti
- Health Ethics and Policy Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Samia Hurst
- Institute for Ethics, History and the Humanities, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Milo Puhan
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - Christian Lovis
- Division of Medical Information Sciences, Department of Radiology and Medical Informatics, University Hospital of Geneva, Geneva, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| |
Collapse
|
32
|
Li H, Wang X, Yu H, Zhu J, Jin H, Wang A, Yang Z. Combining in vitro and in silico Approaches to Find New Candidate Drugs Targeting the Pathological Proteins Related to the Alzheimer's Disease. Curr Neuropharmacol 2018; 16:758-768. [PMID: 29086699 PMCID: PMC6080099 DOI: 10.2174/1570159x15666171030142108] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 09/24/2017] [Accepted: 10/10/2017] [Indexed: 01/10/2023] Open
Abstract
Background: Alzheimer’s disease (AD) as the most common cause of dementia among older people has aroused the universal concern of the whole world. However, until now there is still none effective treatments. Consequently, the development of new drugs targeting this complicated brain disorder is urgent and needs more efforts. In this review, we detailed the current state of knowledge about new candidate drugs targeting the pathological proteins especially the drugs which are employed using the combined methods of in vitro and in silico. Methods: We looked up and reviewed online papers related to the pathogenesis and new drugs development of AD. Then, articles up to the requirements were respectively analyzed and summaried to provide the latest knowledge about the pathogenic effect and the new candidate drugs targeting Aβ and Tau proteins. Results: New candidate drugs targeting the Aβ include decreasing the production, promoting the clearence and preventing aggregation. However these drugs have mostly failed in Phase III clinical trial stage due to the unsuccessful of reversing cognition symptoms. As to tau protein, the prevention of tau aggregation and propagation is a promising strategy to synthesize/design mechanism-based drugs against tauopathies. Some candidate drugs are under research. Moreover, because of the complex pathogenesis of AD, multi-target drugs have also shed light on the treatment of AD. Conclusion: Given to the consecutive failure of Aβ-directed drugs and the feasibilities of tau-targeted therapy, more and more researchers suggested that the AD treatment should be moved from Aβ to tau or focused on considering the soluble form of Aβ and tau as a whole. Moreover, the novel in silico methods also have great potential in drug discovery, drug repositioning, virtual screening of chemical libraries. No matter how many difficulties and challenges in prevention and treatment of AD, we firmly believe that the effective and safe drugs will be found using the combined methods in the immediate future with the global effort.
Collapse
Affiliation(s)
- Hui Li
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaobing Wang
- Tumor Marker Research Center, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Hongmei Yu
- China-Japan Union Hospital of Jilin University, Changchun, Jilin, 130021, China
| | - Jing Zhu
- College of Pharmacy, The Ohio State University, Columbus, Ohio, 43210, United States
| | - Hongtao Jin
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Aiping Wang
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Zhaogang Yang
- Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio, 43210, United States
| |
Collapse
|
33
|
Tretter F. From mind to molecules and back to mind-Metatheoretical limits and options for systems neuropsychiatry. CHAOS (WOODBURY, N.Y.) 2018; 28:106325. [PMID: 30384654 DOI: 10.1063/1.5040174] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 10/01/2018] [Indexed: 06/08/2023]
Abstract
Psychiatric illnesses like dementia are increasingly relevant for public health affairs. Neurobiology promises progress in diagnosis and treatment of these illnesses and exhibits a rapid increase of knowledge by new neurotechnologies. In order to find generic patterns in huge neurobiological data sets and by exploring formal brain models, non-linear science offers many examples of fruitful insights into the complex dynamics of neuronal information processing. However, it should be minded that neurobiology neither can bridge the explanatory gap between brain and mind nor can substitute psychological and psychiatric categories and knowledge. For instance, volition is impaired in many mental disorders. In experimental setups, a "preactional" brain potential was discovered that occurs 0.5 s before a consciously evoked motor action. Neglecting the specific experimental conditions, this finding was over-interpreted as the empirical falsification of the philosophical (!) concept of "free volition/will." In contrast, the psychology of volition works with models that are composed of several stage-related hierarchically nested mental process cycles that were never tested in obviously "theory-free" neurobiology. As currently neurobiology shows a network turn (or systemic turn), this is one good reason to enhance systemic approaches in theoretical psychology, independently from neurobiology that still lacks "theory." Cybernetic control loop models and system models should be integrated and elaborated and in turn could give new impulses to neuropsychology and neuropsychiatry that conceptually can more easily connect to a network-oriented neurobiology. In this program, the conceptual background of nonlinear science is essential to bridge gaps between neurobiology and psychiatry, defining a real "theoretical" field of neuropsychiatry.
Collapse
Affiliation(s)
- Felix Tretter
- Bertalanffy Center for the Study of Systems Science, Paulanergasse 13 / door 5, A 1040 Vienna, Austria
| |
Collapse
|
34
|
Hassan M, Abbas Q, Seo SY, Shahzadi S, Ashwal HA, Zaki N, Iqbal Z, Moustafa AA. Computational modeling and biomarker studies of pharmacological treatment of Alzheimer's disease (Review). Mol Med Rep 2018; 18:639-655. [PMID: 29845262 PMCID: PMC6059694 DOI: 10.3892/mmr.2018.9044] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 07/05/2017] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is a complex and multifactorial disease. In order to understand the genetic influence in the progression of AD, and to identify novel pharmaceutical agents and their associated targets, the present study discusses computational modeling and biomarker evaluation approaches. Based on mechanistic signaling pathway approaches, various computational models, including biochemical and morphological models, are discussed to explore the strategies that may be used to target AD treatment. Different biomarkers are interpreted on the basis of morphological and functional features of amyloid β plaques and unstable microtubule‑associated tau protein, which is involved in neurodegeneration. Furthermore, imaging and cerebrospinal fluids are also considered to be key methods in the identification of novel markers for AD. In conclusion, the present study reviews various biochemical and morphological computational models and biomarkers to interpret novel targets and agonists for the treatment of AD. This review also highlights several therapeutic targets and their associated signaling pathways in AD, which may have potential to be used in the development of novel pharmacological agents for the treatment of patients with AD. Computational modeling approaches may aid the quest for the development of AD treatments with enhanced therapeutic efficacy and reduced toxicity.
Collapse
Affiliation(s)
- Mubashir Hassan
- Department of Biology, College of Natural Sciences, Kongju National University, Gongju, Chungcheongnam 32588, Republic of Korea
- Institute of Molecular Science and Bioinformatics, Dyal Singh Trust Library, Lahore 54000, Pakistan
| | - Qamar Abbas
- Department of Physiology, University of Sindh, Jamshoro 76080, Pakistan
| | - Sung-Yum Seo
- Department of Biology, College of Natural Sciences, Kongju National University, Gongju, Chungcheongnam 32588, Republic of Korea
| | - Saba Shahzadi
- Institute of Molecular Science and Bioinformatics, Dyal Singh Trust Library, Lahore 54000, Pakistan
- Department of Bioinformatics, Virtual University Davis Road Campus, Lahore 54000, Pakistan
| | - Hany Al Ashwal
- College of Information Technology, United Arab Emirates University, Al-Ain 15551, United Arab Emirates
| | - Nazar Zaki
- College of Information Technology, United Arab Emirates University, Al-Ain 15551, United Arab Emirates
| | - Zeeshan Iqbal
- Institute of Molecular Science and Bioinformatics, Dyal Singh Trust Library, Lahore 54000, Pakistan
| | - Ahmed A. Moustafa
- School of Social Sciences and Psychology, Western Sydney University, Sydney, NSW 2751, Australia
- MARCS Institute for Brain, Behavior and Development, Western Sydney University, Sydney, NSW 2751, Australia
| |
Collapse
|
35
|
Ding X, Bucholc M, Wang H, Glass DH, Wang H, Clarke DH, Bjourson AJ, Dowey LRC, O'Kane M, Prasad G, Maguire L, Wong-Lin K. A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data. Sci Rep 2018; 8:9774. [PMID: 29950585 PMCID: PMC6021389 DOI: 10.1038/s41598-018-27997-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 06/12/2018] [Indexed: 12/20/2022] Open
Abstract
There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.
Collapse
Affiliation(s)
- Xuemei Ding
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK.
- Faculty of Mathematics and Informatics, Fujian Normal University, Fuzhou, China.
| | - Magda Bucholc
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK
| | - Haiying Wang
- School of Computing and Mathematics, Ulster University, Jordanstown Campus, Northern Ireland, UK
| | - David H Glass
- School of Computing and Mathematics, Ulster University, Jordanstown Campus, Northern Ireland, UK
| | - Hui Wang
- School of Computing and Mathematics, Ulster University, Jordanstown Campus, Northern Ireland, UK
| | - Dave H Clarke
- Clarke Analytics Ltd., 6 Dernville, Annabella Mallow, Cork, Ireland
| | - Anthony John Bjourson
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, C-TRIC, Ulster University, Altnagelvin Hospital, Derry~Londonderry, Northern Ireland, UK
| | - Le Roy C Dowey
- C-TRIC, Altnagelvin Hospital campus, Derry~Londonderry, Northern Ireland, UK
- School of Biomedical Sciences, Ulster University, Coleraine Campus, Northern Ireland, UK
| | - Maurice O'Kane
- C-TRIC, Altnagelvin Hospital campus, Derry~Londonderry, Northern Ireland, UK
| | - Girijesh Prasad
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK
| | - Liam Maguire
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland, UK.
| |
Collapse
|
36
|
Mehta N, Pandit A. Concurrence of big data analytics and healthcare: A systematic review. Int J Med Inform 2018; 114:57-65. [PMID: 29673604 DOI: 10.1016/j.ijmedinf.2018.03.013] [Citation(s) in RCA: 121] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Accepted: 03/23/2018] [Indexed: 01/02/2023]
Abstract
BACKGROUND The application of Big Data analytics in healthcare has immense potential for improving the quality of care, reducing waste and error, and reducing the cost of care. PURPOSE This systematic review of literature aims to determine the scope of Big Data analytics in healthcare including its applications and challenges in its adoption in healthcare. It also intends to identify the strategies to overcome the challenges. DATA SOURCES A systematic search of the articles was carried out on five major scientific databases: ScienceDirect, PubMed, Emerald, IEEE Xplore and Taylor & Francis. The articles on Big Data analytics in healthcare published in English language literature from January 2013 to January 2018 were considered. STUDY SELECTION Descriptive articles and usability studies of Big Data analytics in healthcare and medicine were selected. DATA EXTRACTION Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. RESULTS A total of 58 articles were selected as per the inclusion criteria and analyzed. The analyses of these articles found that: (1) researchers lack consensus about the operational definition of Big Data in healthcare; (2) Big Data in healthcare comes from the internal sources within the hospitals or clinics as well external sources including government, laboratories, pharma companies, data aggregators, medical journals etc.; (3) natural language processing (NLP) is most widely used Big Data analytical technique for healthcare and most of the processing tools used for analytics are based on Hadoop; (4) Big Data analytics finds its application for clinical decision support; optimization of clinical operations and reduction of cost of care (5) major challenge in adoption of Big Data analytics is non-availability of evidence of its practical benefits in healthcare. CONCLUSION This review study unveils that there is a paucity of information on evidence of real-world use of Big Data analytics in healthcare. This is because, the usability studies have considered only qualitative approach which describes potential benefits but does not take into account the quantitative study. Also, majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data analytics in developing countries.
Collapse
Affiliation(s)
| | - Anil Pandit
- Symbiosis Institute of Health Sciences, Pune, India
| |
Collapse
|
37
|
Wu CHK, Luk SMH, Holder RL, Rodrigues Z, Ahmed F, Murdoch I. How do paper and electronic records compare for completeness? A three centre study. Eye (Lond) 2018. [PMID: 29515216 DOI: 10.1038/s41433-018-0065-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVES Medical records are legal documentation of patients' care hence must be accurate and complete for both medical and legal purposes. Electronic patient record (EPR) systems aim to improve the accuracy of documentation, provide better organisation and access of data. This study compares the completeness of traditional note records and EPR in glaucoma patients. METHODS Using criteria from the April 2009 National Institute for Health and Care Excellence (NICE) guidelines completeness of data entry was compared between EPR and paper notes in three units. Moorfields Eye Hospital (City Road) uses the Openeyes EPR. Bedford Hospital (Moorfields Eye Centre) and Western Eye Hospital use the Medisoft EPR. The standard was set at 100% compliance for predetermined parameters. RESULTS One hundred seventy paper notes and 270 electronic records were analysed. With the exception of central corneal thickness (p = 0.31), all other key parameters were more consistently recorded in the paper records than in the EPR. Intraocular pressure (p = 0.004), anterior chamber configuration and depth assessments using gonioscopy (p < 0.001), fundus examination (p = 0.015), past medical history (p < 0.001), medication including glaucoma medication (p < 0.001) and drug allergies (p < 0.001). CONCLUSIONS Our results show that paper records are significantly more complete than EPR. This is the case for two different EPRs and three separate sites. We propose additional training to aid data-collection; improving the design of EPRs by investigating factors such as layout and use of forced choice fields.
Collapse
Affiliation(s)
| | - Sheila M H Luk
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | | | - Faisal Ahmed
- Imperial College Healthcare NHS Trust, London, UK
| | - Ian Murdoch
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| |
Collapse
|
38
|
Miquel S, Champ C, Day J, Aarts E, Bahr BA, Bakker M, Bánáti D, Calabrese V, Cederholm T, Cryan J, Dye L, Farrimond JA, Korosi A, Layé S, Maudsley S, Milenkovic D, Mohajeri MH, Sijben J, Solomon A, Spencer JPE, Thuret S, Vanden Berghe W, Vauzour D, Vellas B, Wesnes K, Willatts P, Wittenberg R, Geurts L. Poor cognitive ageing: Vulnerabilities, mechanisms and the impact of nutritional interventions. Ageing Res Rev 2018; 42:40-55. [PMID: 29248758 DOI: 10.1016/j.arr.2017.12.004] [Citation(s) in RCA: 122] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 12/08/2017] [Accepted: 12/08/2017] [Indexed: 12/21/2022]
Abstract
BACKGROUND Ageing is a highly complex process marked by a temporal cascade of events, which promote alterations in the normal functioning of an individual organism. The triggers of normal brain ageing are not well understood, even less so the factors which initiate and steer the neuronal degeneration, which underpin disorders such as dementia. A wealth of data on how nutrients and diets may support cognitive function and preserve brain health are available, yet the molecular mechanisms underlying their biological action in both normal ageing, age-related cognitive decline, and in the development of neurodegenerative disorders have not been clearly elucidated. OBJECTIVES This review aims to summarise the current state of knowledge of vulnerabilities that predispose towards dysfunctional brain ageing, highlight potential protective mechanisms, and discuss dietary interventions that may be used as therapies. A special focus of this paper is on the impact of nutrition on neuroprotection and the underlying molecular mechanisms, and this focus reflects the discussions held during the 2nd workshop 'Nutrition for the Ageing Brain: Functional Aspects and Mechanisms' in Copenhagen in June 2016. The present review is the most recent in a series produced by the Nutrition and Mental Performance Task Force under the auspice of the International Life Sciences Institute Europe (ILSI Europe). CONCLUSION Coupling studies of cognitive ageing with studies investigating the effect of nutrition and dietary interventions as strategies targeting specific mechanisms, such as neurogenesis, protein clearance, inflammation, and non-coding and microRNAs is of high value. Future research on the impact of nutrition on cognitive ageing will need to adopt a longitudinal approach and multimodal nutritional interventions will likely need to be imposed in early-life to observe significant impact in older age.
Collapse
Affiliation(s)
- Sophie Miquel
- Mars-Wrigley, 1132 W. Blackhawk Street, Chicago, IL 60642, United States
| | - Claire Champ
- Human Appetite Research Unit, School of Psychology, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Jon Day
- Cerebrus Associates Limited, The White House, 2 Meadrow, Godalming, Surrey, GU7 3HN, United Kingdom
| | - Esther Aarts
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Kapittelweg 29, 6525 EN Nijmegen, The Netherlands
| | - Ben A Bahr
- Biotechnology Research and Training Centre, University of North Carolina - Pembroke, United States
| | - Martijntje Bakker
- The Netherlands Organisation for Health Research and Development, Laan van Nieuw Oost-Indië 334, 2593 CE The Hague, The Netherlands
| | - Diána Bánáti
- International Life Sciences Institute, Europe (ILSI Europe), Av E. Mounier 83, Box 6, 1200 Brussels, Belgium
| | - Vittorio Calabrese
- University of Catania, Department of Biomedical and Biotechnological Sciences, Biological Tower - Via Santa Sofia, 97, Catania, Italy
| | - Tommy Cederholm
- University of Uppsala, Institutionen för folkhälso- och vårdvetenskap, Klinisk nutrition och metabolism, Uppsala Science Park, 751 85 Uppsala, Sweden
| | - John Cryan
- Anatomy & Neuroscience, University College Cork, 386 Western Gateway Building, Cork, Ireland
| | - Louise Dye
- Human Appetite Research Unit, School of Psychology, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | | | - Aniko Korosi
- Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, The Netherlands
| | - Sophie Layé
- Nutrition et Neurobiologie Intégrée, INRA Bordeaux University, 146 rue Léo Saignat, 33076 Bordeaux cedex, France
| | - Stuart Maudsley
- Department of Biomedical Research and VIB-UAntwerp Center for Molecular Neurology, University of Antwerp, Gebouw V, Campus Drie Eiken, Universiteitsplein 1, 2610 Antwerpen, Belgium
| | - Dragan Milenkovic
- INRA, Human Nutrition Unit, UCA, F-63003, Clermont-Ferrand, France; Department of Internal Medicine, Division of Cardiovascular Medicine, School of Medicine, University of California Davis, Davis, CA 95616, United States
| | - M Hasan Mohajeri
- DSM Nutritional Products Ltd., Wurmisweg 576, Kaiseraugst 4303, Switzerland
| | - John Sijben
- Nutricia Research, Nutricia Advanced Medical Nutrition, PO Box 80141, 3508TC, Utrecht, The Netherlands
| | - Alina Solomon
- Aging Research Center, Karolinska Institutet, Gävlegatan 16, SE-113 30 Stockholm, Sweden
| | - Jeremy P E Spencer
- Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research, Department of Food and Nutritional Sciences, University of Reading, Whiteknights, Reading, RG6 6AP, United Kingdom
| | - Sandrine Thuret
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, The Maurice Wohl Clinical Neuroscience Institute,125 Coldharbour Lane, SE5 9NU London, United Kingdom
| | - Wim Vanden Berghe
- PPES, Department Biomedical Sciences, University Antwerp, Campus Drie Eiken, Universiteitsplein 1, 2610 Antwerp, Belgium
| | - David Vauzour
- University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, United Kingdom
| | - Bruno Vellas
- Department of Geriatric Medicine, CHU Toulouse, Gerontopole, Toulouse, France
| | - Keith Wesnes
- Wesnes Cognition Limited, Little Paddock, Streatley on Thames, RG8 9RD, United Kingdom; Medical School, University of Exeter, Exeter, United Kingdom; Department of Psychology, Northumbria University, Newcastle, United Kingdom; Centre for Human Psychopharmacology, Swinburne University, Melbourne, Australia; Medicinal Plant Research Group, Newcastle University, United Kingdom
| | - Peter Willatts
- School of Psychology, University of Dundee Nethergate, Dundee, DD1 4HN, United Kingdom
| | - Raphael Wittenberg
- London School of Economics and Political Science, Personal Social Services Research Unit, London, United Kingdom
| | - Lucie Geurts
- International Life Sciences Institute, Europe (ILSI Europe), Av E. Mounier 83, Box 6, 1200 Brussels, Belgium.
| |
Collapse
|
39
|
Ienca M, Vayena E, Blasimme A. Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy. Front Med (Lausanne) 2018; 5:13. [PMID: 29468161 PMCID: PMC5808247 DOI: 10.3389/fmed.2018.00013] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 01/16/2018] [Indexed: 11/13/2022] Open
Abstract
Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy.
Collapse
Affiliation(s)
- Marcello Ienca
- Health Ethics and Policy Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| |
Collapse
|
40
|
Hampel H, Toschi N, Babiloni C, Baldacci F, Black KL, Bokde AL, Bun RS, Cacciola F, Cavedo E, Chiesa PA, Colliot O, Coman CM, Dubois B, Duggento A, Durrleman S, Ferretti MT, George N, Genthon R, Habert MO, Herholz K, Koronyo Y, Koronyo-Hamaoui M, Lamari F, Langevin T, Lehéricy S, Lorenceau J, Neri C, Nisticò R, Nyasse-Messene F, Ritchie C, Rossi S, Santarnecchi E, Sporns O, Verdooner SR, Vergallo A, Villain N, Younesi E, Garaci F, Lista S. Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology. J Alzheimers Dis 2018; 64:S47-S105. [PMID: 29562524 PMCID: PMC6008221 DOI: 10.3233/jad-179932] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.
Collapse
Affiliation(s)
- Harald Hampel
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Department of Radiology, “Athinoula A. Martinos” Center for Biomedical Imaging, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, University of Rome “La Sapienza”, Rome, Italy
- Institute for Research and Medical Care, IRCCS “San Raffaele Pisana”, Rome, Italy
| | - Filippo Baldacci
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Keith L. Black
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland
| | - René S. Bun
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Francesco Cacciola
- Unit of Neurosurgery, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Enrica Cavedo
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
- IRCCS “San Giovanni di Dio-Fatebenefratelli”, Brescia, Italy
| | - Patrizia A. Chiesa
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Olivier Colliot
- Inserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de Recherche de Paris, France; Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Paris, France
| | - Cristina-Maria Coman
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Bruno Dubois
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
| | - Stanley Durrleman
- Inserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de Recherche de Paris, France
| | - Maria-Teresa Ferretti
- IREM, Institute for Regenerative Medicine, University of Zurich, Zürich, Switzerland
- ZNZ Neuroscience Center Zurich, Zürich, Switzerland
| | - Nathalie George
- Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle Épinière, ICM, Ecole Normale Supérieure, ENS, Centre MEG-EEG, F-75013, Paris, France
| | - Remy Genthon
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Marie-Odile Habert
- Département de Médecine Nucléaire, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris, France
- Laboratoire d’Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France
| | - Karl Herholz
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Wolfson Molecular Imaging Centre, Manchester, UK
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Foudil Lamari
- AP-HP, UF Biochimie des Maladies Neuro-métaboliques, Service de Biochimie Métabolique, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | | | - Stéphane Lehéricy
- Centre de NeuroImagerie de Recherche - CENIR, Institut du Cerveau et de la Moelle Épinière - ICM, F-75013, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, F-75013, Paris, France
| | - Jean Lorenceau
- Institut de la Vision, INSERM, Sorbonne Universités, UPMC Univ Paris 06, UMR_S968, CNRS UMR7210, Paris, France
| | - Christian Neri
- Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, CNRS UMR 8256, Institut de Biologie Paris-Seine (IBPS), Place Jussieu, F-75005, Paris, France
| | - Robert Nisticò
- Department of Biology, University of Rome “Tor Vergata” & Pharmacology of Synaptic Disease Lab, European Brain Research Institute (E.B.R.I.), Rome, Italy
| | - Francis Nyasse-Messene
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Craig Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Simone Rossi
- Department of Medicine, Surgery and Neurosciences, Unit of Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN Lab.), University of Siena, Siena, Italy
- Department of Medicine, Surgery and Neurosciences, Section of Human Physiology University of Siena, Siena, Italy
| | - Emiliano Santarnecchi
- Department of Medicine, Surgery and Neurosciences, Unit of Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN Lab.), University of Siena, Siena, Italy
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- IU Network Science Institute, Indiana University, Bloomington, IN, USA
| | | | - Andrea Vergallo
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Nicolas Villain
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | | | - Francesco Garaci
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Casa di Cura “San Raffaele Cassino”, Cassino, Italy
| | - Simone Lista
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| |
Collapse
|
41
|
Baldacci F, Lista S, O'Bryant SE, Ceravolo R, Toschi N, Hampel H. Blood-Based Biomarker Screening with Agnostic Biological Definitions for an Accurate Diagnosis Within the Dimensional Spectrum of Neurodegenerative Diseases. Methods Mol Biol 2018; 1750:139-155. [PMID: 29512070 DOI: 10.1007/978-1-4939-7704-8_9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The discovery, development, and validation of novel candidate biomarkers in Alzheimer's disease (AD) and other neurodegenerative diseases (NDs) are increasingly gaining momentum. As a result, evolving diagnostic research criteria of NDs are beginning to integrate biofluid and neuroimaging indicators of pathophysiological mechanisms. More than 10% of people aged over 65 suffer from NDs. There is an urgent need for a refined two-stage diagnostic model to first initiate an early, sensitive, and noninvasive process in primary care settings. Individuals that meet detection criteria will then be channeled to more specific, costly (positron-emission tomography), and invasive (cerebrospinal fluid) assessment methods for confirmatory biological characterization and diagnosis.A reliable and sensitive blood test for AD and other NDs is not yet established; however, it would provide the golden screening gate for an efficient primary care management. A limitation to the development of a large-scale blood-screening biomarker-based test is the traditional application of clinically descriptive criteria for the categorization of single late-stage ND constructs. These are genetically and biologically heterogeneous, reflected in multiple pathophysiological mechanisms and subsequent pathologies throughout a dimensional continuum. Evidence suggests that a shared, "open-source" integrated multilevel categorization of NDs that clusters individuals based on descriptive clinical phenotypes and pathophysiological biomarker signatures will provide the next incremental step toward an improved diagnostic process of NDs. This intermediate objective toward unbiased biomarker-guided early detection of individuals at risk for NDs is currently carried out by the international pilot Alzheimer Precision Medicine Initiative Cohort Program (APMI-CP).
Collapse
Affiliation(s)
- Filippo Baldacci
- AXA Research Fund & UPMC Chair, F-75013, Paris, France.,Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l'hôpital, F-75013, Paris, France.,Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'hôpital, F-75013, Paris, France.,Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l'hôpital, F-75013, Paris, France.,Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Simone Lista
- AXA Research Fund & UPMC Chair, F-75013, Paris, France. .,Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l'hôpital, F-75013, Paris, France. .,Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'hôpital, F-75013, Paris, France. .,Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l'hôpital, F-75013, Paris, France.
| | - Sid E O'Bryant
- Institute for Healthy Aging, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Roberto Ceravolo
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy.,Department of Radiology"Athinoula A. Martinos", Center for Biomedical Imaging, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Harald Hampel
- AXA Research Fund & UPMC Chair, F-75013, Paris, France.,Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l'hôpital, F-75013, Paris, France.,Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l'hôpital, F-75013, Paris, France.,Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l'hôpital, F-75013, Paris, France
| | | |
Collapse
|
42
|
Agoston DV, Langford D. Big Data in traumatic brain injury; promise and challenges. Concussion 2017; 2:CNC45. [PMID: 30202589 PMCID: PMC6122694 DOI: 10.2217/cnc-2016-0013] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 05/25/2017] [Indexed: 01/14/2023] Open
Abstract
Traumatic brain injury (TBI) is a spectrum disease of overwhelming complexity, the research of which generates enormous amounts of structured, semi-structured and unstructured data. This resulting big data has tremendous potential to be mined for valuable information regarding the "most complex disease of the most complex organ". Big data analyses require specialized big data analytics applications, machine learning and artificial intelligence platforms to reveal associations, trends, correlations and patterns not otherwise realized by current analytical approaches. The intersection of potential data sources between experimental TBI and clinical TBI research presents inherent challenges for setting parameters for the generation of common data elements and to mine existing legacy data that would allow highly translatable big data analyses. In order to successfully utilize big data analyses in TBI, we must be willing to accept the messiness of data, collect and store all data and give up causation for correlation. In this context, coupling the big data approach to established clinical and pre-clinical data sources will transform current practices for triage, diagnosis, treatment and prognosis into highly integrated evidence-based patient care.
Collapse
Affiliation(s)
- Denes V Agoston
- Department of Anatomy, Physiology & Genetics, Uniformed Services University, Bethesda, MD 20814, USA.,Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.,Department of Anatomy, Physiology & Genetics, Uniformed Services University, Bethesda, MD 20814, USA.,Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Dianne Langford
- Department of Neuroscience, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA.,Department of Neuroscience, Lewis Katz School of Medicine, Temple University, Philadelphia, PA 19140, USA
| |
Collapse
|
43
|
Geerts H, Spiros A, Roberts P, Carr R. Towards the virtual human patient. Quantitative Systems Pharmacology in Alzheimer's disease. Eur J Pharmacol 2017; 817:38-45. [PMID: 28583429 DOI: 10.1016/j.ejphar.2017.05.062] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2016] [Revised: 05/05/2017] [Accepted: 05/31/2017] [Indexed: 12/26/2022]
Abstract
Development of successful therapeutic interventions in Central Nervous Systems (CNS) disorders is a daunting challenge with a low success rate. Probable reasons include the lack of translation from preclinical animal models, the individual variability of many pathological processes converging upon the same clinical phenotype, the pharmacodynamical interaction of various comedications and last but not least the complexity of the human brain. This paper argues for a re-engineering of the pharmaceutical CNS Research & Development strategy using ideas focused on advanced computer modeling and simulation from adjacent engineering-based industries. We provide examples that such a Quantitative Systems Pharmacology approach based on computer simulation of biological processes and that combines the best of preclinical research with actual clinical outcomes can enhance translation to the clinical situation. We will expand upon (1) the need to go from Big Data to Smart Data and develop predictive and quantitative algorithms that are actionable for the pharma industry, (2) using this platform as a "knowledge machine" that captures community-wide expertise in an active hypothesis-testing approach, (3) learning from failed clinical trials and (4) the need to go beyond simple linear hypotheses and embrace complex non-linear hypotheses. We will propose a strategy for applying these concepts to the substantial individual variability of AD patient subgroups and the treatment of neuropsychiatric problems in AD. Quantitative Systems Pharmacology is a new 'humanized' tool for supporting drug discovery and development in general and CNS disorders in particular.
Collapse
Affiliation(s)
- Hugo Geerts
- In Silico Biosciences, Lexington, MA, USA; Perelman School of Medicine, Univ. of Pennsylvania, Philadelphia, PA, USA.
| | | | - Patrick Roberts
- Department of Biomedical Engineering, Oregon Health & Science University, Portland OR, USA
| | | |
Collapse
|
44
|
Paradells-Navarro S, Benlloch-Navarro MS, Almansa Frias MI, Garcia-Esparza MA, Broccoli V, Miranda M, Soria JM. Neuroprotection of Brain Cells by Lipoic Acid Treatment after Cellular Stress. ACS Chem Neurosci 2017; 8:569-577. [PMID: 27935686 DOI: 10.1021/acschemneuro.6b00306] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
We have previously observed that in vivo lipoic acid (LA) treatment induced a protective effect onto primary cortical neurons after brain injury. In an effort to better understand LA action mechanism in the brain, in the present study, we stressed brain cells in vitro and ex vivo and then analyzed by inmmunocytochemistry and biochemical assays, the changes induced by LA on cell survival and on the concentration of oxidative stress markers, such as glutathione (GSH), oxidized glutathione (GSSG), and malondialdehyde (MDA). The stressors used were lipopolysaccharide (LPS), dopamine, and l-buthionine-S,R-sulfoximine (BSO). Our results showed that LA decreased cell death and increased GSH/GSSG ratio in cells stressed by LPS + dopamine, suggesting that the mechanism underlying LA action is regeneration of GSSG to GSH. When cells were stressed by BSO, LA diminished cell death and decreased GSH/GSSG ratio. In this case, it could be concluded that, due to the low GSH basal levels, GSSG reduction is not possible and therefore it might be thought that cell death prevention might be mediated through other mechanisms. Finally, we induced chemical oxidative damage in brain homogenate. After LA treatment, GSH and GSH/GSSG ratio increased and MDA concentration decreased, demonstrating again that LA was not able to increase de novo GSH synthesis but is able to increase GSSG conversion to GSH.
Collapse
|
45
|
Hampel H, O’Bryant SE, Durrleman S, Younesi E, Rojkova K, Escott-Price V, Corvol JC, Broich K, Dubois B, Lista S. A Precision Medicine Initiative for Alzheimer’s disease: the road ahead to biomarker-guided integrative disease modeling. Climacteric 2017; 20:107-118. [DOI: 10.1080/13697137.2017.1287866] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- H. Hampel
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - S. E. O’Bryant
- Institute for Healthy Aging, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - S. Durrleman
- ARAMIS Lab, Inria Paris, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du cerveau et la moelle (ICM), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, Paris, France
| | - E. Younesi
- European Society for Translational Medicine, Vienna, Austria
| | - K. Rojkova
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - V. Escott-Price
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - J-C. Corvol
- Département de Neurologie, Sorbonne Université, Université Pierre et Marie Curie, Paris 06 UMR S 1127, Institut National de Santé et en Recherche Médicale (INSERM) U 1127 and CIC-1422, Centre National de Recherche Scientifique U 7225, Institut du Cerveau et de la Moelle Epinière, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France
| | - K. Broich
- President, Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | - B. Dubois
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - S. Lista
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | | |
Collapse
|
46
|
Chiba-Falek O, Lutz MW. Towards precision medicine in Alzheimer's disease: deciphering genetic data to establish informative biomarkers. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2017; 2:47-55. [PMID: 28944295 DOI: 10.1080/23808993.2017.1286227] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Developing biomarker tools for identification of individuals at high-risk for late-onset Alzheimer's disease (LOAD) is important for prognosis and early treatment. This review focuses on genetic factors and their potential role for precision medicine in LOAD. AREAS COVERED APOEe4 is the strongest genetic risk factor for non-Mendelian LOAD, and the APOE-linkage disequilibrium (LD) region has produced the most significant association signal in multi-center genome-wide-association-studies (GWAS). Consideration of extended haplotypes in the APOE-LD region and specifically, non-coding variants in putative enhancer elements, such as the TOMM40-polyT, in-addition to the coding variants that comprise the APOE-genotypes, may be useful for predicting subjects at high-risk of developing LOAD and estimating age-of-onset of early disease-stage symptoms. A genetic-biomarker based on APOE-TOMM40-polyT haplotypes, and age is currently applied in a clinical trial for prevention/delay of LOAD onset. Additionally, we discuss LOAD-GWAS discoveries and the development of new genetic risk scores based on LOAD-GWAS findings other than the APOE-LD region. EXPERT COMMENTARY Deciphering the precise causal genetic-variants within LOAD-GWAS regions will advance the development of genetic-biomarkers to complement and refine the APOE-LD region based prediction model. Collectively, the genetic-biomarkers will be translational for early diagnosis and enrichment of clinical trials with subjects at high-risk.
Collapse
Affiliation(s)
- Ornit Chiba-Falek
- Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA.,Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC 27710, USA
| | - Michael W Lutz
- Department of Neurology, Duke University Medical Center, Durham, NC 27710, USA
| |
Collapse
|
47
|
Cummings J, Aisen PS, DuBois B, Frölich L, Jack CR, Jones RW, Morris JC, Raskin J, Dowsett SA, Scheltens P. Drug development in Alzheimer's disease: the path to 2025. Alzheimers Res Ther 2016; 8:39. [PMID: 27646601 PMCID: PMC5028936 DOI: 10.1186/s13195-016-0207-9] [Citation(s) in RCA: 286] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The global impact of Alzheimer's disease (AD) continues to increase, and focused efforts are needed to address this immense public health challenge. National leaders have set a goal to prevent or effectively treat AD by 2025. In this paper, we discuss the path to 2025, and what is feasible in this time frame given the realities and challenges of AD drug development, with a focus on disease-modifying therapies (DMTs). Under the current conditions, only drugs currently in late Phase 1 or later will have a chance of being approved by 2025. If pipeline attrition rates remain high, only a few compounds at best will meet this time frame. There is an opportunity to reduce the time and risk of AD drug development through an improvement in trial design; better trial infrastructure; disease registries of well-characterized participant cohorts to help with more rapid enrollment of appropriate study populations; validated biomarkers to better detect disease, determine risk and monitor disease progression as well as predict disease response; more sensitive clinical assessment tools; and faster regulatory review. To implement change requires efforts to build awareness, educate and foster engagement; increase funding for both basic and clinical research; reduce fragmented environments and systems; increase learning from successes and failures; promote data standardization and increase wider data sharing; understand AD at the basic biology level; and rapidly translate new knowledge into clinical development. Improved mechanistic understanding of disease onset and progression is central to more efficient AD drug development and will lead to improved therapeutic approaches and targets. The opportunity for more than a few new therapies by 2025 is small. Accelerating research and clinical development efforts and bringing DMTs to market sooner would have a significant impact on the future societal burden of AD. As these steps are put in place and plans come to fruition, e.g., approval of a DMT, it can be predicted that momentum will build, the process will be self-sustaining, and the path to 2025, and beyond, becomes clearer.
Collapse
Affiliation(s)
- Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV USA
| | - Paul S. Aisen
- University of Southern California, San Diego, CA USA
| | - Bruno DuBois
- Institute for Memory and Alzheimer’s Disease (IM2A) and ICM, Salpêtrière University Hospital, Paris University, Paris, France
| | - Lutz Frölich
- Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | | | - Roy W. Jones
- The Research Institute for the Care of Older People (RICE), Royal United Hospital, Bath, UK
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St Louis, MO USA
| | | | | | - Philip Scheltens
- Department of Neurology & Alzheimer Center, VU University Medical Center, Amsterdam, Netherlands
| |
Collapse
|
48
|
Fasano M, Monti C, Alberio T. A systems biology-led insight into the role of the proteome in neurodegenerative diseases. Expert Rev Proteomics 2016; 13:845-55. [PMID: 27477319 DOI: 10.1080/14789450.2016.1219254] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
INTRODUCTION Multifactorial disorders are the result of nonlinear interactions of several factors; therefore, a reductionist approach does not appear to be appropriate. Proteomics is a global approach that can be efficiently used to investigate pathogenetic mechanisms of neurodegenerative diseases. AREAS COVERED Here, we report a general introduction about the systems biology approach and mechanistic insights recently obtained by over-representation analysis of proteomics data of cellular and animal models of Alzheimer's disease, Parkinson's disease and other neurodegenerative disorders, as well as of affected human tissues. Expert commentary: As an inductive method, proteomics is based on unbiased observations that further require validation of generated hypotheses. Pathway databases and over-representation analysis tools allow researchers to assign an expectation value to pathogenetic mechanisms linked to neurodegenerative diseases. The systems biology approach based on omics data may be the key to unravel the complex mechanisms underlying neurodegeneration.
Collapse
Affiliation(s)
- Mauro Fasano
- a Department of Science and High Technology and Center of Neuroscience , University of Insubria , Busto Arsizio , Italy
| | - Chiara Monti
- a Department of Science and High Technology and Center of Neuroscience , University of Insubria , Busto Arsizio , Italy
| | - Tiziana Alberio
- a Department of Science and High Technology and Center of Neuroscience , University of Insubria , Busto Arsizio , Italy
| |
Collapse
|
49
|
Resnick HE, Lathan CE. From battlefield to home: a mobile platform for assessing brain health. Mhealth 2016; 2:30. [PMID: 28293603 PMCID: PMC5344163 DOI: 10.21037/mhealth.2016.07.02] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 07/05/2016] [Indexed: 11/06/2022] Open
Abstract
Cognitive testing batteries have been used for decades to diagnose deficits associated with conditions such as head injury, age-related cognitive decline, and stroke, and they have also been used extensively for educational evaluation and planning. Cognitive testing is generally office-based, administered by professionals, uses paper and pencil testing modalities, reports results as summary scores, and is a "one shot deal" whose primary objective is to identify the presence and severity of cognitive deficit. This paper explores innovative departures from historical cognitive testing strategies and paradigms. The report explores (I) a shift from disease diagnosis in the office setting to mobile tracking of cognitive health and wellness in any setting; (II) the strength of computer-based cognitive measures and their role in facilitating development of new computational methods; and (III) using cognitive testing to inform on individual-level outcomes over time rather than dichotomous metrics at a single point in time.
Collapse
|