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Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/14/2022]
Abstract
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
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Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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52
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Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts. Nat Commun 2022; 13:1590. [PMID: 35338121 PMCID: PMC8956598 DOI: 10.1038/s41467-022-28423-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 01/17/2022] [Indexed: 01/27/2023] Open
Abstract
Drug discovery for diseases such as Parkinson's disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinson's disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platform's robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinson's disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.
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53
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[Molecular diagnostics and molecular tumor board in uro-oncology : Precision medicine using the example of metastatic castration-resistant prostate cancer]. Urologe A 2022; 61:311-322. [PMID: 35157098 DOI: 10.1007/s00120-022-01784-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2022] [Indexed: 10/19/2022]
Abstract
Novel approaches to molecular tumor profiling evaluate DNA, RNA and protein alterations to create a detailed molecular map that enables precise and personalized treatment decisions. As the field of molecular profiling is constantly evolving, the training and networking of doctors is of decisive importance. Through the establishment of precision medicine with precision oncological consultations supported by interdisciplinary molecular tumor boards, many patients with difficult to treat tumor diseases can be advised and treated. Many pathophysiological relationships in progressive tumors can be elucidated resulting in new therapeutic options for the profiled patients; however, understanding the complex mutational profiles remains a very demanding task that requires a suitably trained and committed team that should be in close contact with the scientific advancements in precision oncology.
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54
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Alves VM, Korn D, Pervitsky V, Thieme A, Capuzzi SJ, Baker N, Chirkova R, Ekins S, Muratov EN, Hickey A, Tropsha A. Knowledge-based approaches to drug discovery for rare diseases. Drug Discov Today 2022; 27:490-502. [PMID: 34718207 PMCID: PMC9124594 DOI: 10.1016/j.drudis.2021.10.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/13/2021] [Accepted: 10/21/2021] [Indexed: 02/03/2023]
Abstract
The conventional drug discovery pipeline has proven to be unsustainable for rare diseases. Herein, we discuss recent advances in biomedical knowledge mining applied to discovering therapeutics for rare diseases. We summarize current chemogenomics data of relevance to rare diseases and provide a perspective on the effectiveness of machine learning (ML) and biomedical knowledge graph mining in rare disease drug discovery. We illustrate the power of these methodologies using a chordoma case study. We expect that a broader application of knowledge graph mining and artificial intelligence (AI) approaches will expedite the discovery of viable drug candidates against both rare and common diseases.
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Affiliation(s)
- Vinicius M Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA; UNC Catalyst for Rare Diseases, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Daniel Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Vera Pervitsky
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Andrew Thieme
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Stephen J Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Nancy Baker
- ParlezChem, 123 W Union Street, Hillsborough, NC 27278, USA
| | - Rada Chirkova
- Department of Computer Science, North Carolina State University, Raleigh, NC 27695-8206, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB, Brazil
| | - Anthony Hickey
- UNC Catalyst for Rare Diseases, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
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55
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Nussinov R, Tsai CJ, Jang H. How can same-gene mutations promote both cancer and developmental disorders? SCIENCE ADVANCES 2022; 8:eabm2059. [PMID: 35030014 PMCID: PMC8759737 DOI: 10.1126/sciadv.abm2059] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/22/2021] [Indexed: 05/05/2023]
Abstract
The question of how same-gene mutations can drive both cancer and neurodevelopmental disorders has been puzzling. It has also been puzzling why those with neurodevelopmental disorders have a high risk of cancer. Ras, MEK, PI3K, PTEN, and SHP2 are among the oncogenic proteins that can harbor mutations that encode diseases other than cancer. Understanding why some of their mutations can promote cancer, whereas others promote neurodevelopmental diseases, and why even the same mutations may promote both phenotypes, has important clinical ramifications. Here, we review the literature and address these tantalizing questions. We propose that cell type–specific expression of the mutant protein, and of other proteins in the respective pathway, timing of activation (during embryonic development or sporadic emergence), and the absolute number of molecules that the mutations activate, alone or in combination, are pivotal in determining the pathological phenotypes—cancer and (or) developmental disorders.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD 21702, USA
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56
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Alabi RO, Almangush A, Elmusrati M, Mäkitie AA. Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine. FRONTIERS IN ORAL HEALTH 2022; 2:794248. [PMID: 35088057 PMCID: PMC8786902 DOI: 10.3389/froh.2021.794248] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/13/2021] [Indexed: 12/21/2022] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Pathology, University of Helsinki, Helsinki, Finland
- Institute of Biomedicine, Pathology, University of Turku, Turku, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Antti A. Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Otorhinolaryngology – Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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57
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Artificial Intelligence for Autism Spectrum Disorders. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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58
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Nature-inspired dynamic gene-loaded nanoassemblies for the treatment of brain diseases. Adv Drug Deliv Rev 2022; 180:114029. [PMID: 34752841 DOI: 10.1016/j.addr.2021.114029] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 09/03/2021] [Accepted: 10/27/2021] [Indexed: 12/14/2022]
Abstract
Gene therapy has great potential to treat brain diseases. However, genetic drugs need to overcome a cascade of barriers for their full potential. The conventional delivery systems often struggle to meet expectations. Natural biological particles that are highly optimized for specific functions in body, can inspire optimization of dynamic gene-loaded nanoassemblies (DGN). The DGN refer to gene loaded nanoassemblies whose functions and structures are changeable in response to the biological microenvironments or can dynamically interact with tissues or cells. The nature-inspired DGN can meet the needs in brain diseases treatment, including i) Non-elimination in blood (N), ii) Across the blood-brain barrier (A), iii) Targeting cells (T), iv) Efficient uptake (U), v) Controllable release (R), vi) Eyeable (E)-abbreviated as the "NATURE". In this Review, from nature to "NATURE", we mainly summarize the specific application of nature-inspired DGN in the "NATURE" cascade process. Furthermore, the Review provides an outlook for this field.
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59
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AIM in Neurology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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60
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How Technological Innovation Affect China's Pharmaceutical Smart Manufacturing Industrial Upgrading. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:3342153. [PMID: 34868514 PMCID: PMC8642005 DOI: 10.1155/2021/3342153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/02/2021] [Indexed: 11/29/2022]
Abstract
In recent years, a new generation of information technology has provided sufficient technical support for the smart manufacturing industry. In order to promote the upgrading of China's pharmaceutical smart manufacturing industry, the direction of industrial upgrading and transformation will be discussed from the perspective of technological innovation. According to the input and output data of technological innovation in China's pharmaceutical manufacturing industry from 2007 to 2019, the DEA method is used to analyze the allocation of innovative resources in China's pharmaceutical manufacturing industry in recent years. The study found that the efficiency of technological innovation in China's pharmaceutical manufacturing industry fluctuated greatly from 2007 to 2019, with a low overall level and varying degrees of wasted resources. On this basis, an in-depth analysis of the system architecture of the pharmaceutical smart manufacturing industry under the Industry 4.0 environment was performed. Finally, four paths for the digital transformation of China's pharmaceutical manufacturing industry are proposed. Chinese pharmaceutical manufacturing companies need to use new technologies to carry out comprehensive intelligent upgrading and digital transformation to improve innovation efficiency.
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61
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Asiimwe R, Lam S, Leung S, Wang S, Wan R, Tinker A, McAlpine JN, Woo MMM, Huntsman DG, Talhouk A. From biobank and data silos into a data commons: convergence to support translational medicine. J Transl Med 2021; 19:493. [PMID: 34863191 PMCID: PMC8645144 DOI: 10.1186/s12967-021-03147-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/16/2021] [Indexed: 11/30/2022] Open
Abstract
Background To drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and effective use of data. In this article, we describe the journey of British Columbia’s Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcomes unit, and a collection of data silos, into an integrated data commons to support data standardization and resource sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients. Results Through several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: (1) biospecimen collections, (2) molecular and genomics data, and (3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community. Conclusions Proper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing frameworks, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03147-z.
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Affiliation(s)
- Rebecca Asiimwe
- Department of Molecular Oncology, BC Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada.,BC Children's Hospital Research Institute, 938 West 28th Avenue, Vancouver, BC, V5Z 4H4, Canada
| | - Stephanie Lam
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Samuel Leung
- Department of Molecular Oncology, BC Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada.,Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Shanzhao Wang
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Rachel Wan
- Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada.,BC Cancer, 600 West 10th Avenue, Vancouver CentreVancouver, BC, V5Z 4E6, Canada
| | - Anna Tinker
- Department of Medicine, Faculty of Medicine, Division of Medical Oncology, University of British Columbia, 2775 Laurel Street, Vancouver, BC, V5Z 1M9, Canada.,BC Cancer, 600 West 10th Avenue, Vancouver CentreVancouver, BC, V5Z 4E6, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Jessica N McAlpine
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Michelle M M Woo
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, Canada.,OVCARE Research Program, Vancouver, Canada
| | - David G Huntsman
- Department of Molecular Oncology, BC Cancer Research Centre, 675 West 10th Avenue, Vancouver, BC, V5Z 1L3, Canada.,Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada.,Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, Canada.,OVCARE Research Program, Vancouver, Canada
| | - Aline Talhouk
- OVCARE Research Program, Vancouver, Canada. .,Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of British Columbia, 5th Floor (593), 828 West 10th Ave, Vancouver, BC, V5Z 1M9, Canada.
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Liu L, Bi M, Wang Y, Liu J, Jiang X, Xu Z, Zhang X. Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis. NANOSCALE 2021; 13:19352-19366. [PMID: 34812823 DOI: 10.1039/d1nr06195j] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Artificial intelligence (AI) is an emerging technology with great potential, and its robust calculation and analysis capabilities are unmatched by traditional calculation tools. With the promotion of deep learning and open-source platforms, the threshold of AI has also become lower. Combining artificial intelligence with traditional fields to create new fields of high research and application value has become a trend. AI has been involved in many disciplines, such as medicine, materials, energy, and economics. The development of AI requires the support of many kinds of data, and microfluidic systems can often mine object data on a large scale to support AI. Due to the excellent synergy between the two technologies, excellent research results have emerged in many fields. In this review, we briefly review AI and microfluidics and introduce some applications of their combination, mainly in nanomedicine and material synthesis. Finally, we discuss the development trend of the combination of the two technologies.
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Affiliation(s)
- Linbo Liu
- John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
| | - Mingcheng Bi
- Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China
| | - Yunhua Wang
- John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
| | - Junfeng Liu
- Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China
| | - Xiwen Jiang
- College of Biological Science and Engineering, Fuzhou university, Fuzhou 350108, P.R. China
| | - Zhongbin Xu
- Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China
| | - Xingcai Zhang
- John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
- School of Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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63
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Obafemi-Ajayi T, Perkins A, Nanduri B, Wunsch II DC, Foster JA, Peckham J. No-boundary thinking: a viable solution to ethical data-driven AI in precision medicine. AI AND ETHICS 2021; 2:635-643. [PMID: 34870283 PMCID: PMC8628283 DOI: 10.1007/s43681-021-00118-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/02/2021] [Indexed: 10/28/2022]
Abstract
Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on models that capture and use pertinent details of the population or phenomenon being analyzed. For any application area, more importantly in precision medicine which directly impacts human lives, the data upon which algorithms are run must be procured, cleaned, and organized well to assure reliable and interpretable results, and to assure that they do not perpetrate or amplify human prejudices. This must be done without violating basic assumptions of the algorithms in use. Algorithmic results need to be clearly communicated to stakeholders and domain experts to enable sound conclusions. Our position is that AI holds great promise for supporting precision medicine, but we need to move forward with great care, with consideration for possible ethical implications. We make the case that a no-boundary or convergent approach is essential to support sound and ethical decisions. No-boundary thinking supports problem definition and solving with teams of experts possessing diverse perspectives. When dealing with AI and the data needed to use AI, there is a spectrum of activities that needs the attention of a no-boundary team. This is necessary if we are to draw viable conclusions and develop actions and policies based on the AI, the data, and the scientific foundations of the domain in question.
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Affiliation(s)
| | - Andy Perkins
- Department of Computer Science and Engineering, Mississippi State University, Starkville, MS USA
| | - Bindu Nanduri
- Department of Comparative Biomedical Sciences, College of Veterinary Medicine, Mississippi State University, Starkville, MS USA
| | - Donald C. Wunsch II
- Electrical & Computer Engineering Department, Missouri University of Science and Technology, Rolla, MO USA
| | - James A. Foster
- Biological Sciences Department, University of Idaho, Moscow, ID USA
| | - Joan Peckham
- Computer Science & Statistics Department, University of Rhode Island, Kingston, RI USA
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Abstract
The search for biomarkers for autism spectrum disorder (henceforth autism) has received a lot of attention due to their potential clinical relevance. The clinical and aetiological heterogeneity of autism suggests the presence of subgroups. The lack of identification of a valid diagnostic biomarker for autism, and the inconsistencies seen in studies assessing differences between autism and typically developing control groups, may be partially explained by the vast heterogeneity observed in autism. The focus now is to better understand the clinical and biological heterogeneity and identify stratification biomarkers, which are measures that describe subgroups of individuals with shared biology. Using stratification approaches to assess treatment within pre-defined subgroups could clarify who may benefit from different treatments and therapies, and ultimately lead to more effective individualised treatment plans.
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65
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Alvari G, Coviello L, Furlanello C. EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders. Brain Sci 2021; 11:1555. [PMID: 34942856 PMCID: PMC8699076 DOI: 10.3390/brainsci11121555] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/04/2021] [Accepted: 11/19/2021] [Indexed: 12/26/2022] Open
Abstract
The high level of heterogeneity in Autism Spectrum Disorder (ASD) and the lack of systematic measurements complicate predicting outcomes of early intervention and the identification of better-tailored treatment programs. Computational phenotyping may assist therapists in monitoring child behavior through quantitative measures and personalizing the intervention based on individual characteristics; still, real-world behavioral analysis is an ongoing challenge. For this purpose, we designed EYE-C, a system based on OpenPose and Gaze360 for fine-grained analysis of eye-contact episodes in unconstrained therapist-child interactions via a single video camera. The model was validated on video data varying in resolution and setting, achieving promising performance. We further tested EYE-C on a clinical sample of 62 preschoolers with ASD for spectrum stratification based on eye-contact features and age. By unsupervised clustering, three distinct sub-groups were identified, differentiated by eye-contact dynamics and a specific clinical phenotype. Overall, this study highlights the potential of Artificial Intelligence in categorizing atypical behavior and providing translational solutions that might assist clinical practice.
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Affiliation(s)
- Gianpaolo Alvari
- Department of Psychology and Cognitive Sciences, University of Trento, Corso Bettini 84, 38068 Rovereto, Italy
- DSH Research Unit, Bruno Kessler Foundation, Via Sommarive 8, 38123 Trento, Italy
| | - Luca Coviello
- University of Trento, 38122 Trento, Italy;
- Enogis, Via al Maso Visintainer 8, 38122 Trento, Italy
| | - Cesare Furlanello
- HK3 Lab, Piazza Manifatture 1, 38068 Rovereto, Italy;
- Orobix Life, Via Camozzi 145, 24121 Bergamo, Italy
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Nassir N, Bankapur A, Samara B, Ali A, Ahmed A, Inuwa IM, Zarrei M, Safizadeh Shabestari SA, AlBanna A, Howe JL, Berdiev BK, Scherer SW, Woodbury-Smith M, Uddin M. Single-cell transcriptome identifies molecular subtype of autism spectrum disorder impacted by de novo loss-of-function variants regulating glial cells. Hum Genomics 2021; 15:68. [PMID: 34802461 PMCID: PMC8607722 DOI: 10.1186/s40246-021-00368-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 11/05/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In recent years, several hundred autism spectrum disorder (ASD) implicated genes have been discovered impacting a wide range of molecular pathways. However, the molecular underpinning of ASD, particularly from the point of view of 'brain to behaviour' pathogenic mechanisms, remains largely unknown. METHODS We undertook a study to investigate patterns of spatiotemporal and cell type expression of ASD-implicated genes by integrating large-scale brain single-cell transcriptomes (> million cells) and de novo loss-of-function (LOF) ASD variants (impacting 852 genes from 40,122 cases). RESULTS We identified multiple single-cell clusters from three distinct developmental human brain regions (anterior cingulate cortex, middle temporal gyrus and primary visual cortex) that evidenced high evolutionary constraint through enrichment for brain critical exons and high pLI genes. These clusters also showed significant enrichment with ASD loss-of-function variant genes (p < 5.23 × 10-11) that are transcriptionally highly active in prenatal brain regions (visual cortex and dorsolateral prefrontal cortex). Mapping ASD de novo LOF variant genes into large-scale human and mouse brain single-cell transcriptome analysis demonstrate enrichment of such genes into neuronal subtypes and are also enriched for subtype of non-neuronal glial cell types (astrocyte, p < 6.40 × 10-11, oligodendrocyte, p < 1.31 × 10-09). CONCLUSION Among the ASD genes enriched with pathogenic de novo LOF variants (i.e. KANK1, PLXNB1), a subgroup has restricted transcriptional regulation in non-neuronal cell types that are evolutionarily conserved. This association strongly suggests the involvement of subtype of non-neuronal glial cells in the pathogenesis of ASD and the need to explore other biological pathways for this disorder.
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Affiliation(s)
- Nasna Nassir
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
| | - Asma Bankapur
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
| | - Bisan Samara
- Biomedical Engineering Department, McGill University, Montréal, QC, Canada
| | - Abdulrahman Ali
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
| | - Awab Ahmed
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
| | - Ibrahim M Inuwa
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
| | - Mehdi Zarrei
- The Centre for Applied Genomics (TCAG), The Hospital for Sick Children, Toronto, ON, Canada.,Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | | | - Ammar AlBanna
- Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE.,The Mental Health Center of Excellence, Al Jalila Children's Speciality Hospital, Dubai, UAE
| | - Jennifer L Howe
- The Centre for Applied Genomics (TCAG), The Hospital for Sick Children, Toronto, ON, Canada.,Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Bakhrom K Berdiev
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE
| | - Stephen W Scherer
- The Centre for Applied Genomics (TCAG), The Hospital for Sick Children, Toronto, ON, Canada.,Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.,Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Marc Woodbury-Smith
- The Centre for Applied Genomics (TCAG), The Hospital for Sick Children, Toronto, ON, Canada.,Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Mohammed Uddin
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE. .,Cellular Intelligence (Ci) Lab, GenomeArc Inc., Toronto, ON, Canada.
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Xu X, Seijo-Rabina A, Awad A, Rial C, Gaisford S, Basit AW, Goyanes A. Smartphone-enabled 3D printing of medicines. Int J Pharm 2021; 609:121199. [PMID: 34673166 DOI: 10.1016/j.ijpharm.2021.121199] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/07/2021] [Accepted: 10/08/2021] [Indexed: 12/12/2022]
Abstract
3D printing is a manufacturing technique that is transforming numerous industrial sectors, particularly where it is key tool in the development and fabrication of medicinees that are personalised to the individual needs of patients. Most 3D printers are relatively large, require trained operators and must be located in a pharmaceutical setting to manufacture dosage forms. In order to realise fully the potential of point-of-care manufacturing of medicines, portable printers that are easy to operate are required. Here, we report the development of a 3D printer that operates using a mobile smartphone. The printer, operating on stereolithographic principles, uses the light from the smartphone's screen to photopolymerise liquid resins and create solid structures. The shape of the printed dosage form is determined using a custom app on the smartphone. Warfarin-loaded Printlets (3D printed tablets) of various sizes and patient-centred shapes (caplet, triangle, diamond, square, pentagon, torus, and gyroid lattices) were successfully printed to a high resolution and with excellent dimensional precision using different photosensitive resins. The drug was present in an amorphous form, and the Printlets displayed sustained release characterises. The promising proof-of-concept results support the future potential of this compact, user-friendly and interconnected smartphone-based system for point-of-care manufacturing of personalised medications.
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Affiliation(s)
- Xiaoyan Xu
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Alejandro Seijo-Rabina
- Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Atheer Awad
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK
| | - Carlos Rial
- FabRx Ltd., 7B North Lane, Canterbury CT2 7EB, UK
| | - Simon Gaisford
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; FabRx Ltd., 7B North Lane, Canterbury CT2 7EB, UK
| | - Abdul W Basit
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; FabRx Ltd., 7B North Lane, Canterbury CT2 7EB, UK.
| | - Alvaro Goyanes
- Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, UK; Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, I+D Farma (GI-1645), Facultad de Farmacia, and Health Research Institute of Santiago de Compostela (IDIS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain; FabRx Ltd., 7B North Lane, Canterbury CT2 7EB, UK.
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Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models. Neurobiol Dis 2021; 161:105558. [PMID: 34767943 PMCID: PMC10112395 DOI: 10.1016/j.nbd.2021.105558] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.
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69
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Kim H, Lee JH, Kim HJ, Park CM, Wu HG, Goo JM. Extended application of a CT-based artificial intelligence prognostication model in patients with primary lung cancer undergoing stereotactic ablative radiotherapy. Radiother Oncol 2021; 165:166-173. [PMID: 34748856 DOI: 10.1016/j.radonc.2021.10.022] [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: 05/21/2021] [Revised: 09/24/2021] [Accepted: 10/28/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND PURPOSE To validate a computed tomography (CT)-based deep learning prognostication model, originally developed for a surgical cohort, in patients with primary lung cancer undergoing stereotactic ablative radiotherapy (SABR). MATERIALS AND METHODS This retrospective study identified patients with clinical stage T1-2N0M0 lung cancer treated with SABR between 2013 and 2018. The outcomes were local recurrence-free survival (LRFS), disease-free survival (DFS), and overall survival (OS). The discrimination performance of the model, which extracted a quantitative score of cumulative risk for an adverse event up to 900 days, was evaluated using time-dependent receiver operating characteristic curve analysis. Multivariable Cox regression was performed to investigate the independent prognostic value of the model output adjusting for clinical factors including age, sex, smoking status, and clinical T category. RESULTS In total, 135 patients (median age, 78 years; 101 men; 78 [57.8%] adenocarcinomas and 57 [42.2%] squamous cell carcinomas) were evaluated. Most patients (117/135) were treated with 48-60 Gy in four fractions. Median biologically effective dose was 150.0 Gy (interquartile range, 126.9, 150.0 Gy). For LRFS, the area under the curve (AUC) was 0.72 (95% confidence interval [CI]: 0.58, 0.87). The AUCs were 0.70 (95% CI: 0.60, 0.81) for DFS and 0.66 (95% CI: 0.54, 0.77) for OS. Model output was associated with LRFS (adjusted hazard ratio [HR], 1.043; 95% CI: 1.003, 1.085; P = 0.04), DFS (adjusted HR, 1.03; 95% CI: 1.01, 1.05; P = 0.008), and OS (adjusted HR, 1.025; 95% CI: 1.002, 1.047; P = 0.03). CONCLUSION This study showed external validity and transportability of the CT-based deep learning prognostication model for radiotherapy candidates.
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Affiliation(s)
- Hyungjin Kim
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea
| | - Joo Ho Lee
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Republic of Korea; Cancer Research Institute, Seoul National University, Republic of Korea.
| | - Hak Jae Kim
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Republic of Korea; Cancer Research Institute, Seoul National University, Republic of Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Republic of Korea; Cancer Research Institute, Seoul National University, Republic of Korea
| | - Hong-Gyun Wu
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Republic of Korea; Cancer Research Institute, Seoul National University, Republic of Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Republic of Korea; Cancer Research Institute, Seoul National University, Republic of Korea
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Kontopodis EE, Papadaki E, Trivizakis E, Maris TG, Simos P, Papadakis GZ, Tsatsakis A, Spandidos DA, Karantanas A, Marias K. Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review). Exp Ther Med 2021; 22:1149. [PMID: 34504594 PMCID: PMC8393268 DOI: 10.3892/etm.2021.10583] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/29/2021] [Indexed: 12/18/2022] Open
Abstract
Computer-aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence-based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.
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Affiliation(s)
- Eleftherios E Kontopodis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Efrosini Papadaki
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Eleftherios Trivizakis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Thomas G Maris
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Panagiotis Simos
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Psychiatry and Behavioral Sciences, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Georgios Z Papadakis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Aristidis Tsatsakis
- Centre of Toxicology Science and Research, Faculty of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Demetrios A Spandidos
- Laboratory of Clinical Virology, Medical School, University of Crete, 71003 Heraklion, Greece
| | - Apostolos Karantanas
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Kostas Marias
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
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Abstract
Health is often qualitatively defined as a status free from disease and its quantitative definition requires finding the boundary separating health from pathological conditions. Since many complex diseases have a strong genetic component, substantial efforts have been made to sequence large-scale personal genomes; however, we are not yet able to effectively quantify health status from personal genomes. Since mutational impacts are ultimately manifested at the protein level, we envision that introducing a panoramic proteomic view of complex diseases will allow us to mechanistically understand the molecular etiologies of human diseases. In this perspective article, we will highlight key proteomic approaches to identify pathogenic mutations and map their convergent pathways underlying disease pathogenesis and the integration of omics data at multiple levels to define the borderline between health and disease.
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Affiliation(s)
- Mara Zilocchi
- Department of Biochemistry, University of Regina, Regina, Saskatchewan S4S 0A2, Canada
| | - Cheng Wang
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, the Bakar Computational Health Sciences Institute, the Parker Institute for Cancer Immunotherapy, and the Department of Neurology, School of Medicine, University of California, San Francisco, CA, USA
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, Saskatchewan S4S 0A2, Canada
| | - Jingjing Li
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, the Bakar Computational Health Sciences Institute, the Parker Institute for Cancer Immunotherapy, and the Department of Neurology, School of Medicine, University of California, San Francisco, CA, USA
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Bahado-Singh RO, Vishweswaraiah S, Aydas B, Radhakrishna U. Artificial intelligence and placental DNA methylation: newborn prediction and molecular mechanisms of autism in preterm children. J Matern Fetal Neonatal Med 2021; 35:8150-8159. [PMID: 34404318 DOI: 10.1080/14767058.2021.1963704] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND Autism Spectrum Disorder (ASD) represents a heterogeneous group of disorders with a complex genetic and epigenomic etiology. DNA methylation is the most extensively studied epigenomic mechanism and correlates with altered gene expression. Artificial intelligence (AI) is a powerful tool for group segregation and for handling the large volume of data generated in omics experiments. METHODS We performed genome-wide methylation analysis for differential methylation of cytosine nucleotide (CpG) was performed in 20 postpartum placental tissue samples from preterm births. Ten newborns went on to develop autism (Autistic Disorder subtype) and there were 10 unaffected controls. AI including Deep Learning (AI-DL) platforms were used to identify and rank cytosine methylation markers for ASD detection. Ingenuity Pathway Analysis (IPA) to identify genes and molecular pathways that were dysregulated in autism. RESULTS We identified 4870 CpG loci comprising 2868 genes that were significantly differentially methylated in ASD compared to controls. Of these 431 CpGs met the stringent EWAS threshold (p-value <5 × 10-8) along with ≥10% methylation difference between CpGs in cases and controls. DL accurately predicted autism with an AUC (95% CI) of 1.00 (1-1) and sensitivity and specificity of 100% using a combination of 5 CpGs [cg13858611 (NRN1), cg09228833 (ZNF217), cg06179765 (GPNMB), cg08814105 (NKX2-5), cg27092191 (ZNF267)] CpG markers. IPA identified five prenatally dysregulated molecular pathways linked to ASD. CONCLUSIONS The present study provides substantial evidence that epigenetic differences in placental tissue are associated with autism development and raises the prospect of early and accurate detection of the disorder.
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Affiliation(s)
- Ray O Bahado-Singh
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | - Sangeetha Vishweswaraiah
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
| | - Buket Aydas
- Department of Healthcare Analytics, Meridian Health Plans, Detroit, MI, USA
| | - Uppala Radhakrishna
- Department of Obstetrics and Gynecology, Oakland University William Beaumont School of Medicine, Royal Oak, MI, USA
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Dinneen TJ, Ghrálaigh FN, Walsh R, Lopez LM, Gallagher L. How does genetic variation modify ND-CNV phenotypes? Trends Genet 2021; 38:140-151. [PMID: 34364706 DOI: 10.1016/j.tig.2021.07.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/30/2021] [Accepted: 07/06/2021] [Indexed: 02/05/2023]
Abstract
Rare copy-number variants (CNVs) associated with neurodevelopmental disorders (NDDs), i.e., ND-CNVs, provide an insight into the neurobiology of NDDs and, potentially, a link between biology and clinical outcomes. However, ND-CNVs are characterised by incomplete penetrance resulting in heterogeneous carrier phenotypes, ranging from non-affected to multimorbid psychiatric, neurological, and physical phenotypes. Recent evidence indicates that other variants in the genome, or 'other hits', may partially explain the variable expressivity of ND-CNVs. These may be other rare variants or the aggregated effects of common variants that modify NDD risk. Here we discuss the recent findings, current questions, and future challenges relating to other hits research in the context of ND-CNVs and their potential for improved clinical diagnostics and therapeutics for ND-CNV carriers.
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Affiliation(s)
- Thomas J Dinneen
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland.
| | - Fiana Ní Ghrálaigh
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland; Department of Biology, National University of Ireland Maynooth, Maynooth, Ireland
| | - Ruth Walsh
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - Lorna M Lopez
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland; Department of Biology, National University of Ireland Maynooth, Maynooth, Ireland
| | - Louise Gallagher
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland.
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Fernandez-Luque L, Al Herbish A, Al Shammari R, Argente J, Bin-Abbas B, Deeb A, Dixon D, Zary N, Koledova E, Savage MO. Digital Health for Supporting Precision Medicine in Pediatric Endocrine Disorders: Opportunities for Improved Patient Care. Front Pediatr 2021; 9:715705. [PMID: 34395347 PMCID: PMC8358399 DOI: 10.3389/fped.2021.715705] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 06/17/2021] [Indexed: 12/16/2022] Open
Abstract
Digitalization of healthcare delivery is rapidly fostering development of precision medicine. Multiple digital technologies, known as telehealth or eHealth tools, are guiding individualized diagnosis and treatment for patients, and can contribute significantly to the objectives of precision medicine. From a basis of "one-size-fits-all" healthcare, precision medicine provides a paradigm shift to deliver a more nuanced and personalized approach. Genomic medicine utilizing new technologies can provide precision analysis of causative mutations, with personalized understanding of mechanisms and effective therapy. Education is fundamental to the telehealth process, with artificial intelligence (AI) enhancing learning for healthcare professionals and empowering patients to contribute to their care. The Gulf Cooperation Council (GCC) region is rapidly implementing telehealth strategies at all levels and a workshop was convened to discuss aspirations of precision medicine in the context of pediatric endocrinology, including diabetes and growth disorders, with this paper based on those discussions. GCC regional investment in AI, bioinformatics and genomic medicine, is rapidly providing healthcare benefits. However, embracing precision medicine is presenting some major new design, installation and skills challenges. Genomic medicine is enabling precision and personalization of diagnosis and therapy of endocrine conditions. Digital education and communication tools in the field of endocrinology include chatbots, interactive robots and augmented reality. Obesity and diabetes are a major challenge in the GCC region and eHealth tools are increasingly being used for management of care. With regard to growth failure, digital technologies for growth hormone (GH) administration are being shown to enhance adherence and response outcomes. While technical innovations become more affordable with increasing adoption, we should be aware of sustainability, design and implementation costs, training of HCPs and prediction of overall healthcare benefits, which are essential for precision medicine to develop and for its objectives to be achieved.
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Affiliation(s)
| | | | - Riyad Al Shammari
- National Center for Artificial Intelligence, Saudi Data and Artificial Intelligence Authority, Riyadh, Saudi Arabia
| | - Jesús Argente
- Department of Pediatrics & Pediatric Endocrinology, Hospital Infantil Universitario Niño Jesús, Instituto de Investigación La Princesa, Universidad Autónoma de Madrid, Madrid, Spain
- Centro de Investigación Biomédica en Red (CIBER) de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- IMDEA Food Institute, CEIUAM+CSIC, Madrid, Spain
| | - Bassam Bin-Abbas
- King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Asma Deeb
- Paediatric Endocrine Division, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates
| | - David Dixon
- Connected Health and Devices, Merck, Ares Trading SA, Aubonne, Switzerland
| | - Nabil Zary
- Institute for Excellence in Health Professions Education, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | | | - Martin O. Savage
- Department of Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine & Dentistry, London, United Kingdom
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76
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Quantitative neurogenetics: applications in understanding disease. Biochem Soc Trans 2021; 49:1621-1631. [PMID: 34282824 DOI: 10.1042/bst20200732] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/11/2021] [Accepted: 06/21/2021] [Indexed: 12/31/2022]
Abstract
Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.
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Rahaman MA, Lopa M, Uddin KMF, Baqui MA, Keya SP, Faruk MO, Sarker S, Basiruzzaman M, Islam M, AlBanna A, Jahan N, Chowdhury MAKA, Saha N, Hussain M, Colombi C, O'Rielly D, Woodbury-Smith M, Ghaziuddin M, Rahman MM, Uddin M. An Exploration of Physical and Phenotypic Characteristics of Bangladeshi Children with Autism Spectrum Disorder. J Autism Dev Disord 2021; 51:2392-2401. [PMID: 32975665 DOI: 10.1007/s10803-020-04703-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This study explored the physical and clinical phenotype of Bangladeshi children with autism spectrum disorder (ASD). A totally of 283 children who were referred for screening and administered Module 1 of the Autism Diagnostic Observation Schedule (ADOS) were included. Overall, 209 met the ADOS algorithmic cutoff for ASD. A trend for greater weight and head circumference was observed in children with ASD versus non-ASD. Head circumference was significantly (p < 0.03) larger in ASD males compared with non-ASD males. A trend was also observed for symptom severity, higher in females than males (p = 0.068), with further analyses demonstrating that social reciprocity (p < 0.014) and functional play (p < 0.03) were significantly more impaired in ASD females than males. The findings help understand sex differences in ASD.
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Affiliation(s)
- Md Ashiquir Rahaman
- Centre for Precision Therapeutics, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Maksuda Lopa
- Centre for Precision Therapeutics, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - K M Furkan Uddin
- Centre for Precision Therapeutics, NeuroGen Children's Healthcare, Dhaka, Bangladesh.,Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh.,Holy Family Red Crescent Medical College, Dhaka, Bangladesh
| | - Md Abdul Baqui
- Centre for Precision Therapeutics, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Selina Parvin Keya
- Centre for Precision Therapeutics, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Md Omar Faruk
- Centre for Precision Therapeutics, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Shaoli Sarker
- Centre for Precision Therapeutics, NeuroGen Children's Healthcare, Dhaka, Bangladesh.,Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh.,Department of Paediatric Neuroscience, Dhaka Shishu Hospital, Dhaka, Bangladesh
| | - Mohammed Basiruzzaman
- Centre for Precision Therapeutics, NeuroGen Children's Healthcare, Dhaka, Bangladesh.,Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Mazharul Islam
- Centre for Precision Therapeutics, NeuroGen Children's Healthcare, Dhaka, Bangladesh.,Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Ammar AlBanna
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE.,Al Jalila Specialty Children's Hospital, Dubai, UAE
| | - Nargis Jahan
- Centre for Precision Therapeutics, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - M A K Azad Chowdhury
- Neonatology, Bangladesh Institute of Child Health, Dhaka Shishu Hospital, Dhaka, Bangladesh
| | - Narayan Saha
- Department of Paediatric Neurology, National Institute of Neurosciences, Dhaka, Bangladesh
| | - Manzoor Hussain
- Department of Paediatric Cardiology, Dhaka Shishu Hospital, Dhaka, Bangladesh
| | - Costanza Colombi
- Department of Paediatric Cardiology, Dhaka Shishu Hospital, Dhaka, Bangladesh
| | - Darren O'Rielly
- Faculty of Medicine, Centre for Translational Genomics, Memorial University, St. Johns, Canada
| | - Marc Woodbury-Smith
- Department of Genetics and Genome Biology, The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada.,Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | | | - Mohammad Mizanur Rahman
- Department of Paediatric Neurology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
| | - Mohammed Uddin
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE. .,Department of Genetics and Genome Biology, The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada.
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78
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Lee J, Kwon J, Kim D, Park M, Kim K, Bae I, Kim H, Kong J, Kim Y, Shin U, Kim E. Gene Expression Profiles Associated with Radio-Responsiveness in Locally Advanced Rectal Cancer. BIOLOGY 2021; 10:biology10060500. [PMID: 34205090 PMCID: PMC8226560 DOI: 10.3390/biology10060500] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/14/2021] [Accepted: 06/01/2021] [Indexed: 12/13/2022]
Abstract
Simple Summary Standard treatment of locally advanced rectal cancer (LARC) consists of chemotherapy, radiotherapy, and surgery. Identification of radio-resistant (RR) and radio-sensitive (RS) LARC has been a major hurdle for patient-specific treatment. The development of biomarkers that can discriminate radio-responsiveness before surgery could improve standard treatment and minimize unwanted side effects. Abstract LARC patients were sorted according to their radio-responsiveness and patient-derived organoids were established from the respective cancer tissues. Expression profiles for each group were obtained using RNA-seq. Biological and bioinformatic analysis approaches were used in deciphering genes and pathways that participate in the radio-resistance of LARC. Thirty candidate genes encoding proteins involved in radio-responsiveness–related pathways, including the immune system, DNA repair and cell-cycle control, were identified. Interestingly, one of the candidate genes, cathepsin E (CTSE), exhibited differential methylation at the promoter region that was inversely correlated with the radio-resistance of patient-derived organoids, suggesting that methylation status could contribute to radio-responsiveness. On the basis of these results, we plan to pursue development of a gene chip for diagnosing the radio-responsiveness of LARC patients, with the hope that our efforts will ultimately improve the prognosis of LARC patients.
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Affiliation(s)
- Jeeyong Lee
- Division of Radiation Biomedical Research, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (J.L.); (D.K.); (K.K.); (I.B.)
| | - Junhye Kwon
- Department of Radiological & Clinical Research, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (J.K.); (M.P.); (H.K.); (Y.K.)
| | - DaYeon Kim
- Division of Radiation Biomedical Research, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (J.L.); (D.K.); (K.K.); (I.B.)
- Department of Radiological and Medico-Oncological Sciences, University of Science and Technology, Daejeon 34113, Korea
| | - Misun Park
- Department of Radiological & Clinical Research, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (J.K.); (M.P.); (H.K.); (Y.K.)
| | - KwangSeok Kim
- Division of Radiation Biomedical Research, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (J.L.); (D.K.); (K.K.); (I.B.)
- Department of Radiological and Medico-Oncological Sciences, University of Science and Technology, Daejeon 34113, Korea
| | - InHwa Bae
- Division of Radiation Biomedical Research, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (J.L.); (D.K.); (K.K.); (I.B.)
| | - Hyunkyung Kim
- Department of Radiological & Clinical Research, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (J.K.); (M.P.); (H.K.); (Y.K.)
- Department of Radiological and Medico-Oncological Sciences, University of Science and Technology, Daejeon 34113, Korea
| | - JoonSeog Kong
- Department of Pathology, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea;
| | - Younjoo Kim
- Department of Radiological & Clinical Research, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (J.K.); (M.P.); (H.K.); (Y.K.)
- Department of Internal Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea
| | - UiSup Shin
- Department of Radiological & Clinical Research, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (J.K.); (M.P.); (H.K.); (Y.K.)
- Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea
- Correspondence: (U.S.); (E.K.)
| | - EunJu Kim
- Division of Radiation Biomedical Research, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Korea; (J.L.); (D.K.); (K.K.); (I.B.)
- Department of Radiological and Medico-Oncological Sciences, University of Science and Technology, Daejeon 34113, Korea
- Correspondence: (U.S.); (E.K.)
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Deif R, Salama M. Depression From a Precision Mental Health Perspective: Utilizing Personalized Conceptualizations to Guide Personalized Treatments. Front Psychiatry 2021; 12:650318. [PMID: 34045980 PMCID: PMC8144285 DOI: 10.3389/fpsyt.2021.650318] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 04/13/2021] [Indexed: 11/13/2022] Open
Abstract
Modern research has proven that the "typical patient" requiring standardized treatments does not exist, reflecting the need for more personalized approaches for managing individual clinical profiles rather than broad diagnoses. In this regard, precision psychiatry has emerged focusing on enhancing prevention, diagnosis, and treatment of psychiatric disorders through identifying clinical subgroups, suggesting personalized evidence-based interventions, assessing the effectiveness of different interventions, and identifying risk and protective factors for remission, relapse, and vulnerability. Literature shows that recent advances in the field of precision psychiatry are rapidly becoming more data-driven reflecting both the significance and the continuous need for translational research in mental health. Different etiologies underlying depression have been theorized and some factors have been identified including neural circuitry, biotypes, biopsychosocial markers, genetics, and metabolomics which have shown to explain individual differences in pathology and response to treatment. Although the precision approach may prove to enhance diagnosis and treatment decisions, major challenges are hindering its clinical translation. These include the clinical diversity of psychiatric disorders, the technical complexity and costs of multiomics data, and the need for specialized training in precision health for healthcare staff, besides ethical concerns such as protecting the privacy and security of patients' data and maintaining health equity. The aim of this review is to provide an overview of recent findings in the conceptualization and treatment of depression from a precision mental health perspective and to discuss potential challenges and future directions in the application of precision psychiatry for the treatment of depression.
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Affiliation(s)
- Reem Deif
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, Cairo, Egypt
| | - Mohamed Salama
- Institute of Global Health and Human Ecology, School of Sciences and Engineering, The American University in Cairo, Cairo, Egypt
- Faculty of Medicine, Mansoura University, Mansoura, Egypt
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
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80
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Chakravarty K, Antontsev V, Bundey Y, Varshney J. Driving success in personalized medicine through AI-enabled computational modeling. Drug Discov Today 2021; 26:1459-1465. [PMID: 33609781 DOI: 10.1016/j.drudis.2021.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/26/2021] [Accepted: 02/10/2021] [Indexed: 12/29/2022]
Abstract
The development of successful drugs is expensive and time-consuming because of high clinical attrition rates. This is caused partially by the rupture seen in the translatability of the drug from the bench to the clinic in the context of personalized medicine. Artificial intelligence (AI)-driven platforms integrated with mechanistic modeling have become instrumental in accelerating the drug development process by leveraging data ubiquitously across the various phases. AI can counter the deficiencies and ambiguities that arise during the classical drug development process while reducing human intervention and bridging the translational gap in discovering the connections between drugs and diseases.
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Affiliation(s)
| | - Victor Antontsev
- VeriSIM Life Inc., 1 Sansome St. Suite 3500, San Francisco, CA 94104, USA
| | - Yogesh Bundey
- VeriSIM Life Inc., 1 Sansome St. Suite 3500, San Francisco, CA 94104, USA
| | - Jyotika Varshney
- VeriSIM Life Inc., 1 Sansome St. Suite 3500, San Francisco, CA 94104, USA.
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81
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Akter H, Hossain MS, Dity NJ, Rahaman MA, Furkan Uddin KM, Nassir N, Begum G, Hameid RA, Islam MS, Tusty TA, Basiruzzaman M, Sarkar S, Islam M, Jahan S, Lim ET, Woodbury-Smith M, Stavropoulos DJ, O'Rielly DD, Berdeiv BK, Nurun Nabi AHM, Ahsan MN, Scherer SW, Uddin M. Whole exome sequencing uncovered highly penetrant recessive mutations for a spectrum of rare genetic pediatric diseases in Bangladesh. NPJ Genom Med 2021; 6:14. [PMID: 33594065 PMCID: PMC7887195 DOI: 10.1038/s41525-021-00173-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 01/06/2021] [Indexed: 01/31/2023] Open
Abstract
Collectively, rare genetic diseases affect a significant number of individuals worldwide. In this study, we have conducted whole-exome sequencing (WES) and identified underlying pathogenic or likely pathogenic variants in five children with rare genetic diseases. We present evidence for disease-causing autosomal recessive variants in a range of disease-associated genes such as DHH-associated 46,XY gonadal dysgenesis (GD) or 46,XY sex reversal 7, GNPTAB-associated mucolipidosis II alpha/beta (ML II), BBS1-associated Bardet-Biedl Syndrome (BBS), SURF1-associated Leigh Syndrome (LS) and AP4B1-associated spastic paraplegia-47 (SPG47) in unrelated affected members from Bangladesh. Our analysis pipeline detected three homozygous mutations, including a novel c. 863 G > C (p.Pro288Arg) variant in DHH, and two compound heterozygous variants, including two novel variants: c.2972dupT (p.Met991Ilefs*) in GNPTAB and c.229 G > C (p.Gly77Arg) in SURF1. All mutations were validated by Sanger sequencing. Collectively, this study adds to the genetic heterogeneity of rare genetic diseases and is the first report elucidating the genetic profile of (consanguineous and nonconsanguineous) rare genetic diseases in the Bangladesh population.
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Affiliation(s)
- Hosneara Akter
- Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | | | - Nushrat Jahan Dity
- Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Md Atikur Rahaman
- Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - K M Furkan Uddin
- Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Nasna Nassir
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Science, Dubai, UAE
| | - Ghausia Begum
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Science, Dubai, UAE
| | - Reem Abdel Hameid
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Science, Dubai, UAE
| | | | - Tahrima Arman Tusty
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka, Bangladesh
| | - Mohammad Basiruzzaman
- Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh
- Department of Child Neurology, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Shaoli Sarkar
- Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh
- Department of Child Neurology, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Mazharul Islam
- Genetics and Genomic Medicine Centre, NeuroGen Children's Healthcare, Dhaka, Bangladesh
- Department of Child Neurology, NeuroGen Children's Healthcare, Dhaka, Bangladesh
| | - Sharmin Jahan
- Department of Endocrinology & Metabolism, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
| | - Elaine T Lim
- Department of Genetics, Harvard Medical School, Boston, USA
| | - Marc Woodbury-Smith
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Dimitri James Stavropoulos
- Genome Diagnostics, Department of Pediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Canada
| | | | - Bakhrom K Berdeiv
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Science, Dubai, UAE
| | - A H M Nurun Nabi
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Mohammed Nazmul Ahsan
- Department of Genetic Engineering & Biotechnology, University of Dhaka, Dhaka, Bangladesh
| | - Stephen W Scherer
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Canada
- McLaughlin Centre and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Mohammed Uddin
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Science, Dubai, UAE.
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Rostam Niakan Kalhori S, Tanhapour M, Gholamzadeh M. Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine. J Biomed Inform 2021; 115:103687. [PMID: 33497811 DOI: 10.1016/j.jbi.2021.103687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/05/2020] [Accepted: 01/18/2021] [Indexed: 10/22/2022]
Abstract
INTRODUCTION Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM. METHODS PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019. RESULTS From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease. CONCLUSION This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.
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Affiliation(s)
- Sharareh Rostam Niakan Kalhori
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mozhgan Tanhapour
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Marsa Gholamzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
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Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic. SUSTAINABLE OPERATIONS AND COMPUTERS 2021; 2. [PMCID: PMC8052508 DOI: 10.1016/j.susoc.2021.04.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Background and aims Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart. Methods We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure. Results AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed. Conclusions Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.
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84
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Artificial Intelligence for Autism Spectrum Disorders. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_249-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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85
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Ilan Y. Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes. Front Digit Health 2020; 2:569178. [PMID: 34713042 PMCID: PMC8521820 DOI: 10.3389/fdgth.2020.569178] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 10/02/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.
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86
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Isozaki A, Harmon J, Zhou Y, Li S, Nakagawa Y, Hayashi M, Mikami H, Lei C, Goda K. AI on a chip. LAB ON A CHIP 2020; 20:3074-3090. [PMID: 32644061 DOI: 10.1039/d0lc00521e] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as drug design, material discovery, speech recognition, self-driving cars, advertising, finance, medical imaging, and astronomical observation, where AI-produced outcomes have been proven to be comparable or even superior to the performance of human experts. In these applications, what is essentially important for the development of AI is the data needed for machine learning. Despite its prominent importance, the very first process of the AI development, namely data collection and data preparation, is typically the most laborious task and is often a limiting factor of constructing functional AI algorithms. Lab-on-a-chip technology, in particular microfluidics, is a powerful platform for both the construction and implementation of AI in a large-scale, cost-effective, high-throughput, automated, and multiplexed manner, thereby overcoming the above bottleneck. On this platform, high-throughput imaging is a critical tool as it can generate high-content information (e.g., size, shape, structure, composition, interaction) of objects on a large scale. High-throughput imaging can also be paired with sorting and DNA/RNA sequencing to conduct a massive survey of phenotype-genotype relations whose data is too complex to analyze with traditional computational tools, but is analyzable with the power of AI. In addition to its function as a data provider, lab-on-a-chip technology can also be employed to implement the developed AI for accurate identification, characterization, classification, and prediction of objects in mixed, heterogeneous, or unknown samples. In this review article, motivated by the excellent synergy between AI and lab-on-a-chip technology, we outline fundamental elements, recent advances, future challenges, and emerging opportunities of AI with lab-on-a-chip technology or "AI on a chip" for short.
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Affiliation(s)
- Akihiro Isozaki
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Kanagawa Institute of Industrial Science and Technology, Kanagawa 213-0012, Japan
| | - Jeffrey Harmon
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Yuqi Zhou
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Shuai Li
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and The Cambridge Centre for Data-Driven Discovery, Cambridge University, Cambridge CB3 0WA, UK
| | - Yuta Nakagawa
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Mika Hayashi
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Hideharu Mikami
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Cheng Lei
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Institute of Technological Sciences, Wuhan University, Hubei 430072, China
| | - Keisuke Goda
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Institute of Technological Sciences, Wuhan University, Hubei 430072, China and Department of Bioengineering, University of California, Los Angeles, California 90095, USA
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Woodbury-Smith M. Conceptualising social and communication vulnerabilities among detainees in the criminal justice system. RESEARCH IN DEVELOPMENTAL DISABILITIES 2020; 100:103611. [PMID: 32109817 DOI: 10.1016/j.ridd.2020.103611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 02/11/2020] [Accepted: 02/11/2020] [Indexed: 06/10/2023]
Abstract
More people with autism spectrum disorder (ASD) are now being identified in the criminal justice system, and in parallel with this increase, the prevalence of ASD in the community has risen more than 150 % in the same time period. In this article, I will argue that this increase is due to a reclassification of those individuals whose social, communicative and behavioural function is at the lower end of the normal range. Put simply, extremes of these quantitative traits are now being conceptualised as 'disorder'. This has particular relevance for the criminal justice system as such traits are over-represented in this population: as such, it is likely that increasing numbers of people who are incarcerated will receive an ASD diagnosis. This will have major implications for where best, and how best, to manage such individuals using a framework of 'disorder' versus 'difference'.
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