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Liu J, Du H, Huang L, Xie W, Liu K, Zhang X, Chen S, Zhang Y, Li D, Pan H. AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery. ACS APPLIED MATERIALS & INTERFACES 2024; 16:38832-38851. [PMID: 39016521 DOI: 10.1021/acsami.4c07665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.
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Affiliation(s)
- Junchi Liu
- Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China
| | - Hanze Du
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Lei Huang
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Wangni Xie
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Kexuan Liu
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Xue Zhang
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Shi Chen
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Yuan Zhang
- Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China
| | - Daowei Li
- Jilin Provincial Key Laboratory of Tooth Development and Bone Remodeling, School and Hospital of Stomatology, Jilin University, 1500 Qinghua Road, Changchun 130012, China
| | - Hui Pan
- Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China
- Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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Zhou K, Xiao Z, Liu Q, Wang X, Huo J, Wu X, Zhao X, Feng X, Fu B, Xu P, Deng Y, Xiao W, Sun T, Da L. Comprehensive application of AI algorithms with TCR NGS data for glioma diagnosis. Sci Rep 2024; 14:15361. [PMID: 38965388 PMCID: PMC11224284 DOI: 10.1038/s41598-024-65305-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 06/19/2024] [Indexed: 07/06/2024] Open
Abstract
T-cell receptor (TCR) detection can examine the extent of T-cell immune responses. Therefore, the article analyzed characteristic data of glioma obtained by DNA-based TCR high-throughput sequencing, to predict the disease with fewer biomarkers and higher accuracy. We downloaded data online and obtained six TCR-related diversity indices to establish a multidimensional classification system. By comparing actual presence of the 602 correlated sequences, we obtained two-dimensional and multidimensional datasets. Multiple classification methods were utilized for both datasets with the classification accuracy of multidimensional data slightly less to two-dimensional datasets. This study reduced the TCR β sequences through feature selection methods like RFECV (Recursive Feature Elimination with Cross-Validation). Consequently, using only the presence of these three sequences, the classification AUC value of 96.67% can be achieved. The combination of the three correlated TCR clones obtained at a source data threshold of 0.1 is: CASSLGGNTEAFF_TRBV12_TRBJ1-1, CASSYSDTGELFF_TRBV6_TRBJ2-2, and CASSLTGNTEAFF_TRBV12_TRBJ1-1. At 0.001, the combination is: CASSLGETQYF_TRBV12_TRBJ2-5, CASSLGGNQPQHF_TRBV12_TRBJ1-5, and CASSLSGNTIYF_TRBV12_TRBJ1-3. This method can serve as a potential diagnostic and therapeutic tool, facilitating diagnosis and treatment of glioma and other cancers.
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Affiliation(s)
- Kaiyue Zhou
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Zhengliang Xiao
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Qi Liu
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Xu Wang
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Jiaxin Huo
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Xiaoqi Wu
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Xiaoxiao Zhao
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Xiaohan Feng
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Baoyi Fu
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China
| | - Pengfei Xu
- Hangzhou ImmuQuad Biotechnologies, LLC, Hangzhou, China
| | - Yunyun Deng
- Hangzhou ImmuQuad Biotechnologies, LLC, Hangzhou, China
| | - Wenwen Xiao
- Hangzhou ImmuQuad Biotechnologies, LLC, Hangzhou, China
| | - Tao Sun
- Hangzhou ImmuQuad Biotechnologies, LLC, Hangzhou, China.
- Institute of Wenzhou, Zhejiang University, Wenzhou, China.
| | - Lin Da
- Department of Mathematics, School of Mathematical Sciences, Inner Mongolia University, Hohhot, China.
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Liu Y, Zheng X, Wang D, Fang F, Chen Y, Hong M, Wu J, Zhang C, Qiao Y, Izevbaye I, Zhang S. Performance evaluation and clinical application of the UA-5600 automatic dry chemistry urine analyzer in renal diseases. Transl Androl Urol 2024; 13:1024-1036. [PMID: 38983473 PMCID: PMC11228674 DOI: 10.21037/tau-24-189] [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: 04/18/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024] Open
Abstract
Background Urine testing as a routine screening programme, abnormal test results can be suggestive to clinicians but can sometimes be overlooked, and the establishment of a diagnostic model can better assist clinicians in identifying potential problems. BLD (blood), LEU (leukocyte), PRO (protein) and GLU (glucose) are the four most important parameters in urine testing, and the accuracy of their results is a key concern for clinicians, so it is essential to verify the accuracy of their results. In this study, we evaluated the analytical and clinical performance of Mindray's automatic urine dry chemistry analyzer, the UA-5600 (Hereinafter referred to as the (UA-5600), and the test strips configured with the instrument, and developed a machine-learning (ML) model for kidney disease screening from the results of 11 parameters output from the UA-5600 with the aim of detecting abnormal urine test results. Methods Urine samples from outpatients and inpatients at The First Affiliated Hospital of Sun Yat-sen University were collected from August to September 2022 to evaluate the performance of the Mindray UA-5600 dry chemistry analyzer and test strips. The evaluation of the UA-5600 and its test strips focused on the agreement of the urine BLD and LEU readings with the RBC (red blood cell) and WBC (white blood cell) counts obtained by the Mindray EH-2090 urine formed element analyzer. We also compared the PRO and GLU readings with the results of the Mindray BS-2800M biochemistry analyzer. Urine samples from outpatients and inpatients were retrospectively analysed and grouped according to LIS diagnosis. Additionally, eight ML models for kidney disease screening were developed using 11 parameters measured by the UA-5600. And the model was validated by the validation set. Results The UA-5600 had an 89.55% concordance rate for BLD and a 91.04% concordance rate for LEU compared to the EH-2090 analyzer. When benchmarked against the BS-2800M, the concordance rates for PRO and GLU were 94.14% and 95.20%, respectively. A total of 1,691 samples were used for the construction of the ML models, of which 346 patients (135 males and 211 females, age range: 18 to 98 years) diagnosed with renal disease, and 1,345 patients (397 males and 948 females, age range: 18 to 92 years) with non-renal disease diagnosed with other conditions. Notably, the Naïve Bayes (NB) model, which was built from the UA-5600 parameters, demonstrated superior predictive capabilities for renal disease, with an area under the receiver operating characteristic curve of 0.9470, a sensitivity of 0.7767, and a specificity of 0.9457. Conclusions The Mindray UA-5600 demonstrates robust detection abilities for both BLD and LEU, and its results for PRO and GLU align closely with those obtained from the chemistry analyzer. The NB model has a good screening ability and shows promise as an effective screening tool.
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Affiliation(s)
- Yin Liu
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaohe Zheng
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Dong Wang
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Fei Fang
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yao Chen
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Mengzhi Hong
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiali Wu
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chi Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yangyang Qiao
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Iyare Izevbaye
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Grady Health System, Atlanta, GA, USA
| | - Shihong Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Kim H, Hur M, Yi JH, Lee GH, Lee S, Moon HW, Yun YM. Detection of blasts using flags and cell population data rules on Beckman Coulter DxH 900 hematology analyzer in patients with hematologic diseases. Clin Chem Lab Med 2024; 62:958-966. [PMID: 38000045 DOI: 10.1515/cclm-2023-0932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/01/2023] [Indexed: 11/26/2023]
Abstract
OBJECTIVES White blood cell (WBC)-related flags are essential for detecting abnormal cells including blasts in automated hematology analyzers (AHAs). Cell population data (CPD) may characterize each WBC population, and customized CPD rules can be also useful for detecting blasts. We evaluated the performance of WBC-related flags, customized CPD rules, and their combination for detecting blasts on the Beckman Coulter DxH 900 AHA (DxH 900, Beckman Coulter, Miami, Florida, USA). METHODS In a total of 239 samples from patients with hematologic diseases, complete blood count on DxH 900 and manual slide review (MSR) were conducted. The sensitivity, specificity, and efficiency of the five WBC-related flags, nine customized CPD rules, and their combination were evaluated for detecting blasts, in comparison with MSR. RESULTS Blasts were detected by MSR in 40 out of 239 (16.7 %) samples. The combination of flags and CPD rules showed the highest sensitivity compared with each of flags and CPD rules for detecting blasts (97.5 vs. 72.5 % vs. 92.5 %). Compared with any flag, the combination of flags and CPD rules significantly reduced false-negative samples from 11 to one for detecting blasts (27.5 vs. 2.5 %, p=0.002). CONCLUSIONS This is the first study that evaluated the performance of both flags and CPD rules on DxH 900. The customized CPD rules as well as the combination of flags and CPD rules outperformed WBC-related flags for detecting blasts on DxH 900. The customized CPD rules can play a complementary role for improving the capability of blast detection on DxH 900.
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Affiliation(s)
- Hanah Kim
- Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Korea
| | - Mina Hur
- Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Korea
| | - Jong-Ho Yi
- Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Korea
| | - Gun-Hyuk Lee
- Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Korea
| | - Seungho Lee
- Department of Preventive Medicine, College of Medicine, Dong-A University, Busan, Korea
| | - Hee-Won Moon
- Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Korea
| | - Yeo-Min Yun
- Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul, Korea
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Kim SY, Shin SY, Saeed M, Ryu JE, Kim JS, Ahn J, Jung Y, Moon JM, Choi CH, Choi HK. Prediction of Clinical Remission with Adalimumab Therapy in Patients with Ulcerative Colitis by Fourier Transform-Infrared Spectroscopy Coupled with Machine Learning Algorithms. Metabolites 2023; 14:2. [PMID: 38276292 PMCID: PMC10818421 DOI: 10.3390/metabo14010002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/06/2023] [Accepted: 12/12/2023] [Indexed: 01/27/2024] Open
Abstract
We aimed to develop prediction models for clinical remission associated with adalimumab treatment in patients with ulcerative colitis (UC) using Fourier transform-infrared (FT-IR) spectroscopy coupled with machine learning (ML) algorithms. This prospective, observational, multicenter study enrolled 62 UC patients and 30 healthy controls. The patients were treated with adalimumab for 56 weeks, and clinical remission was evaluated using the Mayo score. Baseline fecal samples were collected and analyzed using FT-IR spectroscopy. Various data preprocessing methods were applied, and prediction models were established by 10-fold cross-validation using various ML methods. Orthogonal partial least squares-discriminant analysis (OPLS-DA) showed a clear separation of healthy controls and UC patients, applying area normalization and Pareto scaling. OPLS-DA models predicting short- and long-term remission (8 and 56 weeks) yielded area-under-the-curve values of 0.76 and 0.75, respectively. Logistic regression and a nonlinear support vector machine were selected as the best prediction models for short- and long-term remission, respectively (accuracy of 0.99). In external validation, prediction models for short-term (logistic regression) and long-term (decision tree) remission performed well, with accuracy values of 0.73 and 0.82, respectively. This was the first study to develop prediction models for clinical remission associated with adalimumab treatment in UC patients by fecal analysis using FT-IR spectroscopy coupled with ML algorithms. Logistic regression, nonlinear support vector machines, and decision tree were suggested as the optimal prediction models for remission, and these were noninvasive, simple, inexpensive, and fast analyses that could be applied to personalized treatments.
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Affiliation(s)
- Seok-Young Kim
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea; (S.-Y.K.); (M.S.); (J.E.R.); (J.-S.K.); (J.A.); (Y.J.)
| | - Seung Yong Shin
- Department of Internal Medicine, College of Medicine, Chung-Ang University, Seoul 06973, Republic of Korea; (S.Y.S.); (J.M.M.)
| | - Maham Saeed
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea; (S.-Y.K.); (M.S.); (J.E.R.); (J.-S.K.); (J.A.); (Y.J.)
| | - Ji Eun Ryu
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea; (S.-Y.K.); (M.S.); (J.E.R.); (J.-S.K.); (J.A.); (Y.J.)
| | - Jung-Seop Kim
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea; (S.-Y.K.); (M.S.); (J.E.R.); (J.-S.K.); (J.A.); (Y.J.)
| | - Junyoung Ahn
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea; (S.-Y.K.); (M.S.); (J.E.R.); (J.-S.K.); (J.A.); (Y.J.)
| | - Youngmi Jung
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea; (S.-Y.K.); (M.S.); (J.E.R.); (J.-S.K.); (J.A.); (Y.J.)
| | - Jung Min Moon
- Department of Internal Medicine, College of Medicine, Chung-Ang University, Seoul 06973, Republic of Korea; (S.Y.S.); (J.M.M.)
| | - Chang Hwan Choi
- Department of Internal Medicine, College of Medicine, Chung-Ang University, Seoul 06973, Republic of Korea; (S.Y.S.); (J.M.M.)
| | - Hyung-Kyoon Choi
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea; (S.-Y.K.); (M.S.); (J.E.R.); (J.-S.K.); (J.A.); (Y.J.)
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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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Lin E, Fuda F, Luu HS, Cox AM, Fang F, Feng J, Chen M. Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology. Semin Diagn Pathol 2023; 40:88-94. [PMID: 36801182 DOI: 10.1053/j.semdp.2023.02.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/04/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023]
Abstract
Digital pathology has a crucial role in diagnostic pathology and is increasingly a technological requirement in the field. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond the microscopic slide and enable true integration of knowledge and expertise. There is clear potential for artificial intelligence (AI) breakthroughs in pathology and hematopathology. In this review article, we discuss the approach of using machine learning in the diagnosis, classification, and treatment guidelines of hematolymphoid disease, as well as recent progress of artificial intelligence in flow cytometric analysis of hematolymphoid diseases. We review these topics specifically through the potential clinical applications of CellaVision, an automated digital image analyzer of peripheral blood, and Morphogo, a novel artificial intelligence-based bone marrow analyzing system. Adoption of these new technologies will allow pathologists to streamline workflow and achieve faster turnaround time in diagnosing hematological disease.
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Affiliation(s)
- Elisa Lin
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America
| | - Franklin Fuda
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America
| | - Hung S Luu
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America
| | - Andrew M Cox
- Cell & Molecular Biology
- Luda Hill Department of Bioinformatics, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America
| | - Fengqi Fang
- Department of Oncology, The First Hospital of Dalian Medical University, Dalian, China
| | - Junlin Feng
- Division of Medical Technology Development, Hangzhou Zhiwei Information & Technology Ltd., Hangzhou, China
| | - Mingyi Chen
- Department of Pathology and Laboratory Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, United States of America.
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Rocha MCDS, de Azevedo VD, dos Santos MDFL, Soares RDDA, Santos VEP, de Azevedo IC. Elements for assistance to patients with hematological malignancies to propose care lines: a scoping review. Rev Bras Enferm 2023; 76:e20220152. [PMID: 36753254 PMCID: PMC9901355 DOI: 10.1590/0034-7167-2022-0152] [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: 03/10/2022] [Accepted: 10/02/2022] [Indexed: 02/09/2023] Open
Abstract
OBJECTIVES to identify the elements for assistance to patients with hematological malignancies to propose a care line. METHODS this is a scoping review, anchored in the JBI theoretical framework, with searches carried out in April 2021, in eight electronic databases and 10 repositories of theses and dissertations. RESULTS the final sample consisted of 93 studies, and the main forms of assistance provided that can support a care line for this public were imaging tests, immunophenotyping, chemotherapy regimens, radiotherapy, infection management, assessment of nutritional status, maintenance of oral function, symptom management and screening for second malignancies. CONCLUSIONS the elaboration of a care line for onco-hematologic patients is necessary, considering the complexity surrounding the diagnosis and treatment of hematologic malignancies, in addition to the difficulties that are imposed in relation to access and continuity of care in the network.
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Obstfeld AE. Hematology and Machine Learning. J Appl Lab Med 2023; 8:129-144. [PMID: 36610431 DOI: 10.1093/jalm/jfac108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/18/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND Substantial improvements in computational power and machine learning (ML) algorithm development have vastly increased the limits of what autonomous machines are capable of. Since its beginnings in the 19th century, laboratory hematology has absorbed waves of progress yielding improvements in both of accuracy and efficiency. The next wave of change in laboratory hematology will be the result of the ML revolution that has already touched many corners of healthcare and society at large. CONTENT This review will describe the manifestations of ML and artificial intelligence (AI) already utilized in the clinical hematology laboratory. This will be followed by a topical summary of the innovative and investigational applications of this technology in each of the major subdomains within laboratory hematology. SUMMARY Application of this technology to laboratory hematology will increase standardization and efficiency by reducing laboratory staff involvement in automatable activities. This will unleash time and resources for focus on more meaningful activities such as the complexities of patient care, research and development, and process improvement.
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Affiliation(s)
- Amrom E Obstfeld
- Department of Pathology & Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA.,Department of Pathology & Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
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Rocha MCDS, Azevedo VDD, Santos MDFLD, Soares RDDA, Santos VEP, Azevedo ICD. Elementos para assistência a pacientes com neoplasias hematológicas para propor linhas de cuidado: scoping review. Rev Bras Enferm 2023. [DOI: 10.1590/0034-7167-2022-0152pt] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
RESUMO Objetivos: identificar os elementos para assistência a pacientes com neoplasias hematológicas para propor uma linha de cuidado. Métodos: trata-se de uma scoping review, ancorada no referencial teórico do JBI, com buscas realizadas em abril de 2021 em oito bases de dados eletrônicas e 10 repositórios de teses e dissertações. Resultados: a amostra final foi composta por 93 estudos, e as principais formas de assistências prestadas que podem embasar uma linha de cuidado para esse público foram exames de imagem, imunofenotipagem, regimes quimioterápicos, radioterapia, gestão de infecções, avaliação do estado nutricional, manutenção da função oral, gerenciamento de sintomas e rastreio para segundas neoplasias. Conclusões: a elaboração de uma linha de cuidados para pacientes onco-hematológicos se faz necessária, tendo em vista a complexidade que cerca o diagnóstico e tratamento das neoplasias hematológicas, além das dificuldades que se impõem em relação ao acesso e continuidade do cuidado em rede.
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Rabbani N, Kim GYE, Suarez CJ, Chen JH. Applications of machine learning in routine laboratory medicine: Current state and future directions. Clin Biochem 2022; 103:1-7. [PMID: 35227670 PMCID: PMC9007900 DOI: 10.1016/j.clinbiochem.2022.02.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/04/2022] [Accepted: 02/23/2022] [Indexed: 01/27/2023]
Abstract
Machine learning is able to leverage large amounts of data to infer complex patterns that are otherwise beyond the capabilities of rule-based systems and human experts. Its application to laboratory medicine is particularly exciting, as laboratory testing provides much of the foundation for clinical decision making. In this article, we provide a brief introduction to machine learning for the medical professional in addition to a comprehensive literature review outlining the current state of machine learning as it has been applied to routine laboratory medicine. Although still in its early stages, machine learning has been used to automate laboratory tasks, optimize utilization, and provide personalized reference ranges and test interpretation. The published literature leads us to believe that machine learning will be an area of increasing importance for the laboratory practitioner. We envision the laboratory of the future will utilize these methods to make significant improvements in efficiency and diagnostic precision.
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Affiliation(s)
- Naveed Rabbani
- Department of Clinical Informatics, Lucile Packard Children's Hospital, Palo Alto, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
| | - Grace Y E Kim
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Carlos J Suarez
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jonathan H Chen
- Stanford Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA; Division of Hospital Medicine, Stanford University School of Medicine, Stanford, CA, USA
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12
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AIM in Haematology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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13
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Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021; 10:1048. [PMID: 34451513 PMCID: PMC8399076 DOI: 10.3390/pathogens10081048] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 08/11/2021] [Accepted: 08/11/2021] [Indexed: 12/15/2022] Open
Abstract
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
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Affiliation(s)
- Gunjan Arora
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Jayadev Joshi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA;
| | - Rahul Shubhra Mandal
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Nitisha Shrivastava
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Richa Virmani
- Confo Therapeutics, Technologiepark 94, 9052 Ghent, Belgium;
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, New Delhi 110020, India;
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Arora G, Joshi J, Mandal RS, Shrivastava N, Virmani R, Sethi T. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. Pathogens 2021; 10:1048. [PMID: 34451513 PMCID: PMC8399076 DOI: 10.3390/pathogens10081048,] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.
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Affiliation(s)
- Gunjan Arora
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
- Correspondence: or
| | - Jayadev Joshi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44106, USA;
| | - Rahul Shubhra Mandal
- Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Nitisha Shrivastava
- Department of Pathology, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, NY 10461, USA;
| | - Richa Virmani
- Confo Therapeutics, Technologiepark 94, 9052 Ghent, Belgium;
| | - Tavpritesh Sethi
- Indraprastha Institute of Information Technology, New Delhi 110020, India;
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15
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Davids J, Ashrafian H. AIM in Haematology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_182-1] [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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16
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Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning. PLoS Negl Trop Dis 2020; 14:e0008960. [PMID: 33362244 PMCID: PMC7757819 DOI: 10.1371/journal.pntd.0008960] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 11/08/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue. METHODOLOGY/PRINCIPAL FINDINGS Out of 1,581 patients in the National Cheng Kung University Hospital with suspected dengue infections and subjected to NS1 antigen, IgM and IgG, and qRT-PCR tests, 798 patients including 138 severe cases were enrolled in the study. The primary target outcome was severe dengue. Machine learning models were trained and tested using the patient dataset that included demographic information and qualitative laboratory test results collected on day 1 when they sought medical advice. To develop prognostic models, we applied various machine learning methods, including logistic regression, random forest, gradient boosting machine, support vector classifier, and artificial neural network, and compared the performance of the methods. The artificial neural network showed the highest average discrimination area under the receiver operating characteristic curve (0.8324 ± 0.0268) and balance accuracy (0.7523 ± 0.0273). According to the model explainer that analyzed the contributions/co-contributions of the different factors, patient age and dengue NS1 antigenemia were the two most important risk factors associated with severe dengue. Additionally, co-existence of anti-dengue IgM and IgG in patients with dengue increased the probability of severe dengue. CONCLUSIONS/SIGNIFICANCE We developed prognostic models for the prediction of dengue severity in patients, using machine learning. The discriminative ability of the artificial neural network exhibited good performance for severe dengue prognosis. This model could help clinicians obtain a rapid prognosis during dengue outbreaks. However, the model requires further validation using external cohorts in future studies.
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An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria. ELECTRONICS 2020. [DOI: 10.3390/electronics9111810] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells’ criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells’ criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%.
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Establishing Cost-effective Strategies for Predicting Outcomes of Pediatric Leukemia. J Pediatr Hematol Oncol 2020; 42:451. [PMID: 32852397 DOI: 10.1097/mph.0000000000001902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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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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