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Gedefaw L, Liu CF, Ip RKL, Tse HF, Yeung MHY, Yip SP, Huang CL. Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders. Cells 2023; 12:1755. [PMID: 37443789 PMCID: PMC10340428 DOI: 10.3390/cells12131755] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/21/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.
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Affiliation(s)
- Lealem Gedefaw
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Chia-Fei Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Rosalina Ka Ling Ip
- Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China; (R.K.L.I.); (H.-F.T.)
| | - Hing-Fung Tse
- Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China; (R.K.L.I.); (H.-F.T.)
| | - Martin Ho Yin Yeung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Shea Ping Yip
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Chien-Ling Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
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Chen Z, Wang X, Chen X, Huang J, Wang C, Wang J, Wang Z. Accelerating therapeutic protein design with computational approaches toward the clinical stage. Comput Struct Biotechnol J 2023; 21:2909-2926. [PMID: 38213894 PMCID: PMC10781723 DOI: 10.1016/j.csbj.2023.04.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/11/2023] [Accepted: 04/27/2023] [Indexed: 01/13/2024] Open
Abstract
Therapeutic protein, represented by antibodies, is of increasing interest in human medicine. However, clinical translation of therapeutic protein is still largely hindered by different aspects of developability, including affinity and selectivity, stability and aggregation prevention, solubility and viscosity reduction, and deimmunization. Conventional optimization of the developability with widely used methods, like display technologies and library screening approaches, is a time and cost-intensive endeavor, and the efficiency in finding suitable solutions is still not enough to meet clinical needs. In recent years, the accelerated advancement of computational methodologies has ushered in a transformative era in the field of therapeutic protein design. Owing to their remarkable capabilities in feature extraction and modeling, the integration of cutting-edge computational strategies with conventional techniques presents a promising avenue to accelerate the progression of therapeutic protein design and optimization toward clinical implementation. Here, we compared the differences between therapeutic protein and small molecules in developability and provided an overview of the computational approaches applicable to the design or optimization of therapeutic protein in several developability issues.
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Affiliation(s)
- Zhidong Chen
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xinpei Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Xu Chen
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Juyang Huang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Chenglin Wang
- Shenzhen Qiyu Biotechnology Co., Ltd, Shenzhen 518107, China
| | - Junqing Wang
- School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
| | - Zhe Wang
- Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China
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Mo Q, Zhang T, Wu J, Wang L, Luo J. Identification of thrombopoiesis inducer based on a hybrid deep neural network model. Thromb Res 2023; 226:36-50. [PMID: 37119555 DOI: 10.1016/j.thromres.2023.04.011] [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: 12/08/2022] [Revised: 03/13/2023] [Accepted: 04/11/2023] [Indexed: 05/01/2023]
Abstract
Thrombocytopenia is a common haematological problem worldwide. Currently, there are no relatively safe and effective agents for the treatment of thrombocytopenia. To address this challenge, we propose a computational method that enables the discovery of novel drug candidates with haematopoietic activities. Based on different types of molecular representations, three deep learning (DL) algorithms, namely recurrent neural networks (RNNs), deep neural networks (DNNs), and hybrid neural networks (RNNs+DNNs), were used to develop classification models to distinguish between active and inactive compounds. The evaluation results illustrated that the hybrid DL model exhibited the best prediction performance, with an accuracy of 97.8 % and Matthews correlation coefficient of 0.958 on the test dataset. Subsequently, we performed drug discovery screening based on the hybrid DL model and identified a compound from the FDA-approved drug library that was structurally divergent from conventional drugs and showed a potential therapeutic action against thrombocytopenia. The novel drug candidate wedelolactone significantly promoted megakaryocyte differentiation in vitro and increased platelet levels and megakaryocyte differentiation in irradiated mice with no systemic toxicity. Overall, our work demonstrates how artificial intelligence can be used to discover novel drugs against thrombocytopenia.
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Affiliation(s)
- Qi Mo
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China
| | - Ting Zhang
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China
| | - Jianming Wu
- Basic Medical College, Southwest Medical University, Luzhou 646000, China.
| | - Long Wang
- Department of Pharmacology, School of Pharmacy, Southwest Medical University, Luzhou 646000, China.
| | - Jiesi Luo
- Basic Medical College, Southwest Medical University, Luzhou 646000, China; State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.
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Gorostiola González M, Janssen APA, IJzerman AP, Heitman LH, van Westen GJP. Oncological drug discovery: AI meets structure-based computational research. Drug Discov Today 2022; 27:1661-1670. [PMID: 35301149 DOI: 10.1016/j.drudis.2022.03.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 01/22/2022] [Accepted: 03/09/2022] [Indexed: 02/08/2023]
Abstract
The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.
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Affiliation(s)
- Marina Gorostiola González
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands; Oncode Institute, Utrecht, The Netherlands
| | - Antonius P A Janssen
- Oncode Institute, Utrecht, The Netherlands; Molecular Physiology, Leiden Institute of Chemistry, Leiden University, The Netherlands
| | - Adriaan P IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands
| | - Laura H Heitman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands; Oncode Institute, Utrecht, The Netherlands
| | - Gerard J P van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, The Netherlands.
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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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Sun Z. Low intensity pulsed ultrasound information technology intervention in diagnosis and prediction of Muscle Atrophy. Pak J Med Sci 2021; 37:1569-1573. [PMID: 34712284 PMCID: PMC8520380 DOI: 10.12669/pjms.37.6-wit.4839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 06/08/2021] [Accepted: 07/17/2021] [Indexed: 11/15/2022] Open
Abstract
Objective: To discuss the effects and function of LIPUS on muscle atrophy (MA), analysis from various aspects through the study of low-intensity pulsed ultrasound (LIPUS) information technology intervention (ITI) in diagnosis and the prediction of muscle atrophy.. Method: In this study conducted in our university, 74 healthy female SD rats aged three months, weighing 100-200g were selected. All rats were placed in sterile cages from June 2020 to September 2020. They were divided into three groups. In the OVO group and OVE group, the mice are treated with LIPUS, Finally, the changes of body weight, grasping power, biochemical indexes and glycogen content of gastrocnemius muscle were analyzed and recorded to explore the effect and value of LIPUS ITI combined with intermittent weight-bearing exercise in the treatment of MA Results: After weight-bearing running, the body weight of model (OVO) group, exercise (OVE) group and NC group had significant statistical significance (P<0.01). It was found that the weight of OVE group was much more as compared to OVO group. There was significant difference in body weight between OVO group and NC group (P<0.05). After LIPUS treatment, it was found that the weight of OVO group, OVE group, LIPUS group and OVE +LIPUS group increased. Compared with the NC group, there was significant statistical difference (P<0.01). Conclusion: Low intensity pulsed ultrasound ITI has a good effect on improving MA, so as to effectively improve the weight of gastrocnemius muscle. The combined application of the two is better for the improvement of muscular atrophy.
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Affiliation(s)
- Zhijun Sun
- Zhijun Sun, Master of Degree. Department of Physical Education Teaching, Tianjin University of Commerce, Beichen 300134, Tianjin, China
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Eckardt JN, Bornhäuser M, Wendt K, Middeke JM. Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects. Blood Adv 2020; 4:6077-6085. [PMID: 33290546 PMCID: PMC7724910 DOI: 10.1182/bloodadvances.2020002997] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 10/26/2020] [Indexed: 12/19/2022] Open
Abstract
Machine learning (ML) is rapidly emerging in several fields of cancer research. ML algorithms can deal with vast amounts of medical data and provide a better understanding of malignant disease. Its ability to process information from different diagnostic modalities and functions to predict prognosis and suggest therapeutic strategies indicates that ML is a promising tool for the future management of hematologic malignancies; acute myeloid leukemia (AML) is a model disease of various recent studies. An integration of these ML techniques into various applications in AML management can assure fast and accurate diagnosis as well as precise risk stratification and optimal therapy. Nevertheless, these techniques come with various pitfalls and need a strict regulatory framework to ensure safe use of ML. This comprehensive review highlights and discusses recent advances in ML techniques in the management of AML as a model disease of hematologic neoplasms, enabling researchers and clinicians alike to critically evaluate this upcoming, potentially practice-changing technology.
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Affiliation(s)
- Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Martin Bornhäuser
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
- National Center for Tumor Diseases, Dresden (NCT/UCC), Dresden, Germany
- German Consortium for Translational Cancer Research, DKFZ, Heidelberg, Germany; and
| | - Karsten Wendt
- Institute of Circuits and Systems, Technical University Dresden, Dresden, Germany
| | - Jan Moritz Middeke
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany
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Fu Y, Zhang Y, Khoo BL. Liquid biopsy technologies for hematological diseases. Med Res Rev 2020; 41:246-274. [PMID: 32929726 DOI: 10.1002/med.21731] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/10/2020] [Accepted: 09/02/2020] [Indexed: 12/18/2022]
Abstract
Since the discovery of circulating tumor cells in 1869, technological advances in studying circulating biomarkers from patients' blood have made the diagnosis of nonhematologic cancers less invasive. Technological advances in the detection and analysis of biomarkers provide new opportunities for the characterization of other disease types. When compared with traditional biopsies, liquid biopsy markers, such as exfoliated bladder cancer cells, circulating cell-free DNA (cfDNA), and extracellular vesicles (EV), are considered more convenient than conventional biopsies. Liquid biopsy markers undoubtedly have the potential to influence disease management and treatment dynamics. Our main focuses of this review will be the cell-based, gene-based, and protein-based key liquid biopsy markers (including EV and cfDNA) in disease detection, and discuss the research progress of these biomarkers used in conjunction with liquid biopsy. First, we highlighted the key technologies that have been broadly adopted used in hematological diseases. Second, we introduced the latest technological developments for the specific detection of cardiovascular disease, leukemia, and coronavirus disease. Finally, we concluded with perspectives on these research areas, focusing on the role of microfluidic technology and artificial intelligence in point-of-care medical applications. We believe that the noninvasive capabilities of these technologies have great potential in the development of diagnostics and can influence treatment options, thereby advancing precision disease management.
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Affiliation(s)
- Yatian Fu
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Yiyuan Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China
| | - Bee Luan Khoo
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong, China
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