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Tsagiopoulou M, Gut IG. Machine learning and multi-omics data in chronic lymphocytic leukemia: the future of precision medicine? Front Genet 2024; 14:1304661. [PMID: 38283149 PMCID: PMC10811210 DOI: 10.3389/fgene.2023.1304661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/27/2023] [Indexed: 01/30/2024] Open
Abstract
Chronic lymphocytic leukemia is a complex and heterogeneous hematological malignancy. The advance of high-throughput multi-omics technologies has significantly influenced chronic lymphocytic leukemia research and paved the way for precision medicine approaches. In this review, we explore the role of machine learning in the analysis of multi-omics data in this hematological malignancy. We discuss recent literature on different machine learning models applied to single omic studies in chronic lymphocytic leukemia, with a special focus on the potential contributions to precision medicine. Finally, we highlight the recently published machine learning applications in multi-omics data in this area of research as well as their potential and limitations.
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Affiliation(s)
| | - Ivo G. Gut
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain
- Universitat de Barcelona (UB), Barcelona, Spain
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Dey A, Ghosh S, Bhuniya T, Koley M, Bera A, Guha S, Chakraborty K, Muthu S, Gorai S, Vorn R, Vadivalagan C, Anand K. Clinical Theragnostic Signature of Extracellular Vesicles in Traumatic Brain Injury (TBI). ACS Chem Neurosci 2023; 14:2981-2994. [PMID: 37624044 PMCID: PMC10485905 DOI: 10.1021/acschemneuro.3c00386] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 08/16/2023] [Indexed: 08/26/2023] Open
Abstract
Traumatic brain injury (TBI) is a common cause of disability and fatality worldwide. Depending on the clinical presentation, it is a type of acquired brain damage that can be mild, moderate, or severe. The degree of patient's discomfort, prognosis, therapeutic approach, survival rates, and recurrence can all be strongly impacted by an accurate diagnosis made early on. The Glasgow Coma Scale (GCS), along with neuroimaging (MRI (Magnetic Resonance Imaging) and CT scan), is a neurological assessment tools used to evaluate and categorize the severity of TBI based on the patient's level of consciousness, eye opening, and motor response. Extracellular vesicles (EVs) are a growing domain, explaining neurological complications in a more detailed manner. EVs, in general, play a role in cellular communication. Its molecular signature such as DNA, RNA, protein, etc. contributes to the status (health or pathological stage) of the parental cell. Brain-derived EVs support more specific screening (diagnostic and prognostic) in TBI research. Therapeutic impact of EVs are more promising for aiding in TBI healing. It is nontoxic, biocompatible, and capable of crossing the blood-brain barrier (BBB) to transport therapeutic molecules. This review has highlighted the relationships between EVs and TBI theranostics, EVs and TBI-related clinical trials, and related research domain-associated challenges and solutions. This review motivates further exploration of associations between EVs and TBI and develops a better approach to TBI management.
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Affiliation(s)
- Anuvab Dey
- Department
of Biological Sciences and Biological Engineering, IIT Guwahati, North
Guwahati, Assam 781039, India
| | | | - Tiyasa Bhuniya
- Department
of Biotechnology, NIT Durgapur, Mahatma Gandhi Rd, A-Zone, Durgapur, West Bengal 713209, India
| | - Madhurima Koley
- Chemistry
and Chemical Biology department, IIT(ISM), Dhanbad 826004, India
| | - Aishi Bera
- Heritage
Institute of Technology, Chowbaga, Anandapur, Kolkata 700107, India
| | - Sudeepta Guha
- Chemistry
and Chemical Biology department, IIT(ISM), Dhanbad 826004, India
| | | | - Sathish Muthu
- Department
of Orthopaedics, Orthopaedic Research Group, Coimbatore 641045, Tamil Nadu, India
- Department
of Biotechnology, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore 641021, Tamil Nadu, India
| | - Sukhamoy Gorai
- Rush University
Medical Center, 1620 W Harrison St, Chicago, Illinois 60612, United States
| | - Rany Vorn
- School
of Nursing and Medicine, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Chithravel Vadivalagan
- Department
of Surgery, University of Michigan Medical
Center, Ann Arbor, Michigan 48109, United States
| | - Krishnan Anand
- Department
of Chemical Pathology, School of Pathology, Faculty of Health Sciences, University of the Free State, Bloemfontein 9300, South Africa
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Zelco A, Wapeesittipan P, Joshi A. Insights into Sex and Gender Differences in Brain and Psychopathologies Using Big Data. Life (Basel) 2023; 13:1676. [PMID: 37629533 PMCID: PMC10455614 DOI: 10.3390/life13081676] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 06/30/2023] [Accepted: 07/15/2023] [Indexed: 08/27/2023] Open
Abstract
The societal implication of sex and gender (SG) differences in brain are profound, as they influence brain development, behavior, and importantly, the presentation, prevalence, and therapeutic response to diseases. Technological advances have enabled speed up identification and characterization of SG differences during development and in psychopathologies. The main aim of this review is to elaborate on new technological advancements, such as genomics, imaging, and emerging biobanks, coupled with bioinformatics analyses of data generated from these technologies have facilitated the identification and characterization of SG differences in the human brain through development and psychopathologies. First, a brief explanation of SG concepts is provided, along with a developmental and evolutionary context. We then describe physiological SG differences in brain activity and function, and in psychopathologies identified through imaging techniques. We further provide an overview of insights into SG differences using genomics, specifically taking advantage of large cohorts and biobanks. We finally emphasize how bioinformatics analyses of big data generated by emerging technologies provides new opportunities to reduce SG disparities in health outcomes, including major challenges.
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Affiliation(s)
| | | | - Anagha Joshi
- Department of Clinical Science, Computational Biology Unit, University of Bergen, 5020 Bergen, Norway; (A.Z.); (P.W.)
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Alkhodari M, Xiong Z, Khandoker AH, Hadjileontiadis LJ, Leeson P, Lapidaire W. The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare. Expert Rev Cardiovasc Ther 2023; 21:531-543. [PMID: 37300317 DOI: 10.1080/14779072.2023.2223978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/06/2023] [Indexed: 06/12/2023]
Abstract
INTRODUCTION Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial intelligence (AI)-based techniques, developed from big patient datasets to provide personalized recommendations for preventive advice. AREAS COVERED In this narrative review, we discuss the impact of integrating AI and big data analysis for personalized cardiovascular care, focusing on the management of HDP. EXPERT OPINION The pathophysiological response of women to pregnancy varies, and deeper insight into each response can be gained through a deeper analysis of the medical history of pregnant women based on clinical records and imaging data. Further research is required to be able to implement AI for clinical cases using multi-modality and multi-organ assessment, and this could expand both knowledge on pregnancy-related disorders and personalized treatment planning.
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Affiliation(s)
- Mohanad Alkhodari
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
| | - Zhaohan Xiong
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ahsan H Khandoker
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
| | - Leontios J Hadjileontiadis
- Healthcare Engineering Innovation Center (HEIC), Department of Biomedical Engineering, Khalifa University of Science and Tehcnology, Abu Dhabi, UAE
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Paul Leeson
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Winok Lapidaire
- Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
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Babu A, Ramanathan G. Multi-omics insights and therapeutic implications in polycystic ovary syndrome: a review. Funct Integr Genomics 2023; 23:130. [PMID: 37079114 DOI: 10.1007/s10142-023-01053-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 04/04/2023] [Accepted: 04/08/2023] [Indexed: 04/21/2023]
Abstract
Polycystic ovary syndrome (PCOS) is a common gynecological disease that causes adverse effects in women in their reproductive phase. Nonetheless, the molecular mechanisms remain unclear. Over the last decade, sequencing and omics approaches have advanced at an increased pace. Omics initiatives have come to the forefront of biomedical research by presenting the significance of biological functions and processes. Thus, multi-omics profiling has yielded important insights into understanding the biology of PCOS by identifying potential biomarkers and therapeutic targets. Multi-omics platforms provide high-throughput data to leverage the molecular mechanisms and pathways involving genetic alteration, epigenetic regulation, transcriptional regulation, protein interaction, and metabolic alterations in PCOS. The purpose of this review is to outline the prospects of multi-omics technologies in PCOS research by revealing novel biomarkers and therapeutic targets. Finally, we address the knowledge gaps and emerging treatment strategies for the management of PCOS. Future PCOS research in multi-omics at the single-cell level may enhance diagnostic and treatment options.
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Affiliation(s)
- Achsha Babu
- Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India
| | - Gnanasambandan Ramanathan
- Department of Biomedical Sciences, School of Biosciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India.
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