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Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong WK, Stanworth SJ. Machine learning in transfusion medicine: A scoping review. Transfusion 2024; 64:162-184. [PMID: 37950535 DOI: 10.1111/trf.17582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Suzanne Maynard
- Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHSBT and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Joseph Farrington
- Institute of Health Informatics, University College London, London, UK
| | - Samah Alimam
- Haematology Department, University College London Hospitals NHS Foundation Trust, London, UK
| | - Hayley Evans
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Kezhi Li
- Institute of Health Informatics, University College London, London, UK
| | - Wai Keong Wong
- Director of Digital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Simon J Stanworth
- Medical Sciences Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- NHSBT and Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Recktenwald SM, Lopes MGM, Peter S, Hof S, Simionato G, Peikert K, Hermann A, Danek A, van Bentum K, Eichler H, Wagner C, Quint S, Kaestner L. Erysense, a Lab-on-a-Chip-Based Point-of-Care Device to Evaluate Red Blood Cell Flow Properties With Multiple Clinical Applications. Front Physiol 2022; 13:884690. [PMID: 35574449 PMCID: PMC9091344 DOI: 10.3389/fphys.2022.884690] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 04/11/2022] [Indexed: 11/18/2022] Open
Abstract
In many medical disciplines, red blood cells are discovered to be biomarkers since they "experience" various conditions in basically all organs of the body. Classical examples are diabetes and hypercholesterolemia. However, recently the red blood cell distribution width (RDW), is often referred to, as an unspecific parameter/marker (e.g., for cardiac events or in oncological studies). The measurement of RDW requires venous blood samples to perform the complete blood cell count (CBC). Here, we introduce Erysense, a lab-on-a-chip-based point-of-care device, to evaluate red blood cell flow properties. The capillary chip technology in combination with algorithms based on artificial neural networks allows the detection of very subtle changes in the red blood cell morphology. This flow-based method closely resembles in vivo conditions and blood sample volumes in the sub-microliter range are sufficient. We provide clinical examples for potential applications of Erysense as a diagnostic tool [here: neuroacanthocytosis syndromes (NAS)] and as cellular quality control for red blood cells [here: hemodiafiltration (HDF) and erythrocyte concentrate (EC) storage]. Due to the wide range of the applicable flow velocities (0.1-10 mm/s) different mechanical properties of the red blood cells can be addressed with Erysense providing the opportunity for differential diagnosis/judgments. Due to these versatile properties, we anticipate the value of Erysense for further diagnostic, prognostic, and theragnostic applications including but not limited to diabetes, iron deficiency, COVID-19, rheumatism, various red blood cell disorders and anemia, as well as inflammation-based diseases including sepsis.
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Affiliation(s)
| | - Marcelle G. M. Lopes
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Cysmic GmbH, Saarbruecken, Germany
| | - Stephana Peter
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Theoretical Medicine and Biosciences, Saarland University, Saarbruecken, Germany
| | - Sebastian Hof
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Theoretical Medicine and Biosciences, Saarland University, Saarbruecken, Germany
| | - Greta Simionato
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Institute for Clinical and Experimental Surgery, Saarland University, Campus University Hospital, Homburg, Germany
| | - Kevin Peikert
- Translational Neurodegeneration Section “Albrecht-Kossel”, Department of Neurology, University Medical Center Rostock, University of Rostock, Rostock, Germany
| | - Andreas Hermann
- Translational Neurodegeneration Section “Albrecht-Kossel”, Department of Neurology, University Medical Center Rostock, University of Rostock, Rostock, Germany
- DZNE, Deutsches Zentrum für Neurodegenerative Erkrankungen, Research Site Rostock/Greifswald, Rostock, Germany
- Center for Transdisciplinary Neurosciences Rostock (CTNR), University Medical Center Rostock, University of Rostock, Rostock, Germany
| | - Adrian Danek
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-University, Munich, Germany
| | | | - Hermann Eichler
- Institute for Clinical Hemostaseology and Transfusion Medicine, Saarland University and Saarland University Hospital, Homburg, Germany
| | - Christian Wagner
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Department of Physics and Materials Science, University of Luxembourg, Luxembourg City, Luxembourg
| | - Stephan Quint
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Cysmic GmbH, Saarbruecken, Germany
| | - Lars Kaestner
- Experimental Physics, Saarland University, Saarbruecken, Germany
- Theoretical Medicine and Biosciences, Saarland University, Saarbruecken, Germany
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