1
|
Zheng H, Zhao N, Xu S, He J, Ospina R, Qiu Z, Liu Y. Deep Learning-Based Automated Cell Detection-Facilitated Meat Quality Evaluation. Foods 2024; 13:2270. [PMID: 39063354 PMCID: PMC11276255 DOI: 10.3390/foods13142270] [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: 06/04/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024] Open
Abstract
Meat consumption is increasing globally. The safety and quality of meat are considered important issues for human health. During evaluations of meat quality and freshness, microbiological parameters are often analyzed. Counts of indicator cells can provide important references for meat quality. In order to eliminate the error of manual operation and improve detection efficiency, this paper proposed a Convolutional Neural Network (CNN) with a backbone called Detect-Cells-Rapidly-Net (DCRNet), which can identify and count stained cells automatically. The DCRNet replaces the single channel of residual blocks with the aggregated residual blocks to learn more features with fewer parameters. The DCRNet combines the deformable convolution network to fit flexible shapes of stained animal cells. The proposed CNN with DCRNet is self-adaptive to different resolutions of images. The experimental results indicate that the proposed CNN with DCRNet achieves an Average Precision of 81.2% and is better than traditional neural networks for this task. The difference between the results of the proposed method and manual counting is less than 0.5% of the total number of cells. The results indicate that DCRNet is a promising solution for cell detection and can be equipped in future meat quality monitoring systems.
Collapse
Affiliation(s)
- Hui Zheng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (H.Z.); (N.Z.); (Z.Q.)
| | - Nan Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (H.Z.); (N.Z.); (Z.Q.)
| | - Saifei Xu
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (S.X.); (J.H.)
| | - Jin He
- College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; (S.X.); (J.H.)
| | - Ricardo Ospina
- Research Faculty of Agriculture, Hokkaido University, Sapporo 060-8589, Japan;
| | - Zhengjun Qiu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (H.Z.); (N.Z.); (Z.Q.)
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; (H.Z.); (N.Z.); (Z.Q.)
| |
Collapse
|
2
|
Klimek A, Mondal D, Block S, Sharma P, Netz RR. Data-driven classification of individual cells by their non-Markovian motion. Biophys J 2024; 123:1173-1183. [PMID: 38515300 PMCID: PMC11140416 DOI: 10.1016/j.bpj.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 03/11/2024] [Accepted: 03/18/2024] [Indexed: 03/23/2024] Open
Abstract
We present a method to differentiate organisms solely by their motion based on the generalized Langevin equation (GLE) and use it to distinguish two different swimming modes of strongly confined unicellular microalgae Chlamydomonas reinhardtii. The GLE is a general model for active or passive motion of organisms and particles that can be derived from a time-dependent general many-body Hamiltonian and in particular includes non-Markovian effects (i.e., the trajectory memory of its past). We extract all GLE parameters from individual cell trajectories and perform an unbiased cluster analysis to group them into different classes. For the specific cell population employed in the experiments, the GLE-based assignment into the two different swimming modes works perfectly, as checked by control experiments. The classification and sorting of single cells and organisms is important in different areas; our method, which is based on motion trajectories, offers wide-ranging applications in biology and medicine.
Collapse
Affiliation(s)
- Anton Klimek
- Fachbereich Physik, Freie Universität Berlin, Berlin, Germany
| | - Debasmita Mondal
- Department of Physics, Indian Institute of Science, Bangalore, India; James Franck Institute, University of Chicago, Chicago, Illinois
| | - Stephan Block
- Institut für Chemie und Biochemie, Freie Universität Berlin, Berlin, Germany
| | - Prerna Sharma
- Department of Physics, Indian Institute of Science, Bangalore, India; Department of Bioengineering, Indian Institute of Science, Bangalore, India
| | - Roland R Netz
- Fachbereich Physik, Freie Universität Berlin, Berlin, Germany.
| |
Collapse
|
3
|
Bellos T, Manolitsis I, Katsimperis S, Juliebø-Jones P, Feretzakis G, Mitsogiannis I, Varkarakis I, Somani BK, Tzelves L. Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review. Cancers (Basel) 2024; 16:1775. [PMID: 38730727 PMCID: PMC11083167 DOI: 10.3390/cancers16091775] [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: 02/26/2024] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery". Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ''master-slave'' robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.
Collapse
Affiliation(s)
- Themistoklis Bellos
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Ioannis Manolitsis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Stamatios Katsimperis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | | | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece;
| | - Iraklis Mitsogiannis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Ioannis Varkarakis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Bhaskar K. Somani
- Department of Urology, University of Southampton, Southampton SO16 6YD, UK;
| | - Lazaros Tzelves
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| |
Collapse
|
4
|
Li X, Zhu H, Gu B, Yao C, Gu Y, Xu W, Zhang J, He J, Liu X, Li D. Advancing Intelligent Organ-on-a-Chip Systems with Comprehensive In Situ Bioanalysis. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2305268. [PMID: 37688520 DOI: 10.1002/adma.202305268] [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: 06/02/2023] [Revised: 08/03/2023] [Indexed: 09/11/2023]
Abstract
In vitro models are essential to a broad range of biomedical research, such as pathological studies, drug development, and personalized medicine. As a potentially transformative paradigm for 3D in vitro models, organ-on-a-chip (OOC) technology has been extensively developed to recapitulate sophisticated architectures and dynamic microenvironments of human organs by applying the principles of life sciences and leveraging micro- and nanoscale engineering capabilities. A pivotal function of OOC devices is to support multifaceted and timely characterization of cultured cells and their microenvironments. However, in-depth analysis of OOC models typically requires biomedical assay procedures that are labor-intensive and interruptive. Herein, the latest advances toward intelligent OOC (iOOC) systems, where sensors integrated with OOC devices continuously report cellular and microenvironmental information for comprehensive in situ bioanalysis, are examined. It is proposed that the multimodal data in iOOC systems can support closed-loop control of the in vitro models and offer holistic biomedical insights for diverse applications. Essential techniques for establishing iOOC systems are surveyed, encompassing in situ sensing, data processing, and dynamic modulation. Eventually, the future development of iOOC systems featuring cross-disciplinary strategies is discussed.
Collapse
Affiliation(s)
- Xiao Li
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Hui Zhu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Bingsong Gu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Cong Yao
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Yuyang Gu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Wangkai Xu
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jia Zhang
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jiankang He
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xinyu Liu
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, M5S 3G8, Canada
| | - Dichen Li
- State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
- NMPA Key Laboratory for Research and Evaluation of Additive Manufacturing Medical Devices, Xi'an Jiaotong University, Xi'an, 710049, China
| |
Collapse
|
5
|
Ko J, Hyung S, Cheong S, Chung Y, Li Jeon N. Revealing the clinical potential of high-resolution organoids. Adv Drug Deliv Rev 2024; 207:115202. [PMID: 38336091 DOI: 10.1016/j.addr.2024.115202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/01/2024] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
The symbiotic interplay of organoid technology and advanced imaging strategies yields innovative breakthroughs in research and clinical applications. Organoids, intricate three-dimensional cell cultures derived from pluripotent or adult stem/progenitor cells, have emerged as potent tools for in vitro modeling, reflecting in vivo organs and advancing our grasp of tissue physiology and disease. Concurrently, advanced imaging technologies such as confocal, light-sheet, and two-photon microscopy ignite fresh explorations, uncovering rich organoid information. Combined with advanced imaging technologies and the power of artificial intelligence, organoids provide new insights that bridge experimental models and real-world clinical scenarios. This review explores exemplary research that embodies this technological synergy and how organoids reshape personalized medicine and therapeutics.
Collapse
Affiliation(s)
- Jihoon Ko
- Department of BioNano Technology, Gachon University, Gyeonggi 13120, Republic of Korea
| | - Sujin Hyung
- Precision Medicine Research Institute, Samsung Medical Center, Seoul 08826, Republic of Korea; Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University, Samsung Medical Center, Seoul 08826, Republic of Korea
| | - Sunghun Cheong
- Interdisciplinary Program in Bioengineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Yoojin Chung
- Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin 17035, Republic of Korea
| | - Noo Li Jeon
- Interdisciplinary Program in Bioengineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; Department of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; Institute of Advanced Machines and Design, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; Qureator, Inc., San Diego, CA, USA.
| |
Collapse
|
6
|
Mei T, Wang T, Zhou Q. Multi-omics and artificial intelligence predict clinical outcomes of immunotherapy in non-small cell lung cancer patients. Clin Exp Med 2024; 24:60. [PMID: 38554212 PMCID: PMC10981593 DOI: 10.1007/s10238-024-01324-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/05/2024] [Indexed: 04/01/2024]
Abstract
In recent years, various types of immunotherapy, particularly the use of immune checkpoint inhibitors targeting programmed cell death 1 or programmed death ligand 1 (PD-L1), have revolutionized the management and prognosis of non-small cell lung cancer. PD-L1 is frequently used as a biomarker for predicting the likely benefit of immunotherapy for patients. However, some patients receiving immunotherapy have high response rates despite having low levels of PD-L1. Therefore, the identification of this group of patients is extremely important to improve prognosis. The tumor microenvironment contains tumor, stromal, and infiltrating immune cells with its composition differing significantly within tumors, between tumors, and between individuals. The omics approach aims to provide a comprehensive assessment of each patient through high-throughput extracted features, promising a more comprehensive characterization of this complex ecosystem. However, features identified by high-throughput methods are complex and present analytical challenges to clinicians and data scientists. It is thus feasible that artificial intelligence could assist in the identification of features that are beyond human discernment as well as in the performance of repetitive tasks. In this paper, we review the prediction of immunotherapy efficacy by different biomarkers (genomic, transcriptomic, proteomic, microbiomic, and radiomic), together with the use of artificial intelligence and the challenges and future directions of these fields.
Collapse
Affiliation(s)
- Ting Mei
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, 610000, China
| | - Ting Wang
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, 610000, China
| | - Qinghua Zhou
- Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, 610000, China.
| |
Collapse
|
7
|
Wang S, Han J, Huang J, Islam K, Shi Y, Zhou Y, Kim D, Zhou J, Lian Z, Liu Y, Huang J. Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors. Stem Cell Res Ther 2024; 15:74. [PMID: 38475857 PMCID: PMC10935795 DOI: 10.1186/s13287-024-03682-8] [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: 09/08/2023] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction. METHODS In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images. RESULTS After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs, short-term HSCs, and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments. CONCLUSION Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into the molecular mechanisms underlying stem cell self-renewal.
Collapse
Affiliation(s)
- Shen Wang
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, USA
| | - Jianzhong Han
- Coriell Institute for Medical Research, Camden, NJ, USA
| | - Jingru Huang
- Shanghai Key Laboratory of Medical Epigenetics, Laboratory of Cancer Epigenetics, Institutes of Biomedical Sciences, Medical College of Fudan University, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China
| | - Khayrul Islam
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, USA
| | - Yuheng Shi
- Shanghai Key Laboratory of Medical Epigenetics, Laboratory of Cancer Epigenetics, Institutes of Biomedical Sciences, Medical College of Fudan University, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China
| | - Yuyuan Zhou
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Dongwook Kim
- Coriell Institute for Medical Research, Camden, NJ, USA
| | - Jane Zhou
- Health and Human Biology, Brown University, Providence, RI, USA
| | - Zhaorui Lian
- Coriell Institute for Medical Research, Camden, NJ, USA
| | - Yaling Liu
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, USA.
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
| | - Jian Huang
- Coriell Institute for Medical Research, Camden, NJ, USA.
- Cooper Medical School of Rowan University, Camden, NJ, USA.
- Center for Metabolic Disease Research, Temple University Lewis Katz School of Medicine, Philadelphia, PA, USA.
| |
Collapse
|
8
|
Wu Z, Park J, Steiner PR, Zhu B, Zhang JXJ. Generative Adversarial Network Model to Classify Human Induced Pluripotent Stem Cell-Cardiomyocytes based on Maturation Level. RESEARCH SQUARE 2024:rs.3.rs-4061531. [PMID: 38559233 PMCID: PMC10980104 DOI: 10.21203/rs.3.rs-4061531/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Objective Our study develops a generative adversarial network (GAN)-based method that generates faithful synthetic image data of human cardiomyocytes at varying stages in their maturation process, as a tool to significantly enhance the classification accuracy of cells and ultimately assist the throughput of computational analysis of cellular structure and functions. Methods Human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) were cultured on micropatterned collagen coated hydrogels of physiological stiffnesses to facilitate maturation and optical measurements were performed for their structural and functional analyses. Control groups were cultured on collagen coated glass well plates. These image recordings were used as the real data to train the GAN model. Results The results show the GAN approach is able to replicate true features from the real data, and inclusion of such synthetic data significantly improves the classification accuracy compared to usage of only real experimental data that is often limited in scale and diversity. Conclusion The proposed model outperformed four conventional machine learning algorithms with respect to improved data generalization ability and data classification accuracy by incorporating synthetic data. Significance This work demonstrates the importance of integrating synthetic data in situations where there are limited sample sizes and thus, effectively addresses the challenges imposed by data availability.
Collapse
Affiliation(s)
- Ziqian Wu
- Thayer School of Engineering, Dartmouth College, Hanover, NH USA
| | - Jiyoon Park
- Thayer School of Engineering, Dartmouth College, Hanover, NH USA
| | | | - Bo Zhu
- Department of Computer Science, Dartmouth College, Hanover, NH USA. He is now with the School of Interactive Computing, Georgia Institute of Technology, GA USA
| | - John X J Zhang
- Thayer School of Engineering, Dartmouth College, Hanover, NH USA
| |
Collapse
|
9
|
Ko J, Song J, Choi N, Kim HN. Patient-Derived Microphysiological Systems for Precision Medicine. Adv Healthc Mater 2024; 13:e2303161. [PMID: 38010253 DOI: 10.1002/adhm.202303161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Indexed: 11/29/2023]
Abstract
Patient-derived microphysiological systems (P-MPS) have emerged as powerful tools in precision medicine that provide valuable insight into individual patient characteristics. This review discusses the development of P-MPS as an integration of patient-derived samples, including patient-derived cells, organoids, and induced pluripotent stem cells, into well-defined MPSs. Emphasizing the necessity of P-MPS development, its significance as a nonclinical assessment approach that bridges the gap between traditional in vitro models and clinical outcomes is highlighted. Additionally, guidance is provided for engineering approaches to develop microfluidic devices and high-content analysis for P-MPSs, enabling high biological relevance and high-throughput experimentation. The practical implications of the P-MPS are further examined by exploring the clinically relevant outcomes obtained from various types of patient-derived samples. The construction and analysis of these diverse samples within the P-MPS have resulted in physiologically relevant data, paving the way for the development of personalized treatment strategies. This study describes the significance of the P-MPS in precision medicine, as well as its unique capacity to offer valuable insights into individual patient characteristics.
Collapse
Affiliation(s)
- Jihoon Ko
- Department of BioNano Technology, Gachon University, Seongnam-si, Gyeonggi-do, 13120, Republic of Korea
| | - Jiyoung Song
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Nakwon Choi
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
- Division of Bio-Medical Science & Technology, KIST School, Seoul, 02792, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Hong Nam Kim
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
- Division of Bio-Medical Science & Technology, KIST School, Seoul, 02792, Republic of Korea
- School of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea
- Yonsei-KIST Convergence Research Institute, Yonsei University, Seoul, 03722, Republic of Korea
| |
Collapse
|
10
|
Burguete-Lopez A, Makarenko M, Bonifazi M, Menezes de Oliveira BN, Getman F, Tian Y, Mazzone V, Li N, Giammona A, Liberale C, Fratalocchi A. Real-time simultaneous refractive index and thickness mapping of sub-cellular biology at the diffraction limit. Commun Biol 2024; 7:154. [PMID: 38321111 PMCID: PMC10847501 DOI: 10.1038/s42003-024-05839-w] [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: 04/28/2023] [Accepted: 01/20/2024] [Indexed: 02/08/2024] Open
Abstract
Mapping the cellular refractive index (RI) is a central task for research involving the composition of microorganisms and the development of models providing automated medical screenings with accuracy beyond 95%. These models require significantly enhancing the state-of-the-art RI mapping capabilities to provide large amounts of accurate RI data at high throughput. Here, we present a machine-learning-based technique that obtains a biological specimen's real-time RI and thickness maps from a single image acquired with a conventional color camera. This technology leverages a suitably engineered nanostructured membrane that stretches a biological analyte over its surface and absorbs transmitted light, generating complex reflection spectra from each sample point. The technique does not need pre-existing sample knowledge. It achieves 10-4 RI sensitivity and sub-nanometer thickness resolution on diffraction-limited spatial areas. We illustrate practical application by performing sub-cellular segmentation of HCT-116 colorectal cancer cells, obtaining complete three-dimensional reconstruction of the cellular regions with a characteristic length of 30 μm. These results can facilitate the development of real-time label-free technologies for biomedical studies on microscopic multicellular dynamics.
Collapse
Affiliation(s)
- Arturo Burguete-Lopez
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Maksim Makarenko
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Marcella Bonifazi
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Physik-Institut, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
| | - Barbara Nicoly Menezes de Oliveira
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Fedor Getman
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yi Tian
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Valerio Mazzone
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Physik-Institut, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
| | - Ning Li
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Alessandro Giammona
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Segrate, Italy
| | - Carlo Liberale
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Andrea Fratalocchi
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
| |
Collapse
|
11
|
Islam K, Paul R, Wang S, Liu Y. [WITHDRAWN] MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems. ARXIV 2024:arXiv:2309.08421v2. [PMID: 37744465 PMCID: PMC10516114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
This paper has been withdrawn by Khayrul Islam.
Collapse
|
12
|
Wang K, Song L, Wang C, Ren Z, Zhao G, Dou J, Di J, Barbastathis G, Zhou R, Zhao J, Lam EY. On the use of deep learning for phase recovery. LIGHT, SCIENCE & APPLICATIONS 2024; 13:4. [PMID: 38161203 PMCID: PMC10758000 DOI: 10.1038/s41377-023-01340-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/13/2023] [Accepted: 11/16/2023] [Indexed: 01/03/2024]
Abstract
Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.
Collapse
Affiliation(s)
- Kaiqiang Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China.
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Li Song
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Chutian Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Zhenbo Ren
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China
| | - Guangyuan Zhao
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jiazhen Dou
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - Jianglei Di
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - George Barbastathis
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Renjie Zhou
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jianlin Zhao
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China.
| | - Edmund Y Lam
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
| |
Collapse
|
13
|
Zhang Z, Chen X, Tang R, Zhu Y, Guo H, Qu Y, Xie P, Lian IY, Wang Y, Lo YH. Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry. Sci Rep 2023; 13:20533. [PMID: 37996496 PMCID: PMC10667244 DOI: 10.1038/s41598-023-46782-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 11/04/2023] [Indexed: 11/25/2023] Open
Abstract
A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations. Using the DCAEC model, we achieve a balanced accuracy of 91.9% for human white blood cell (WBC) clustering and 97.9% for WBC/leukemia clustering using the 3D IFC images and 3D DCAEC model. Above all, although no human recognizable features can separate the clusters of cells with protein localization, we demonstrate the fused DCAEC model can achieve a cluster balanced accuracy of 85.3% from the label-free 2D transmission and 3D side scattering images. To reveal how the neural network recognizes features beyond human ability, we use the gradient-weighted class activation mapping method to discover the cluster-specific visual patterns automatically. Evaluation results show that the automatically identified salient image regions have strong cluster-specific visual patterns for different clusters, which we believe is a stride for the interpretable neural network for cell analysis with high-throughput IFCs.
Collapse
Affiliation(s)
- Zunming Zhang
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Xinyu Chen
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Rui Tang
- NanoCellect Biomedical, Inc., San Diego, CA, 92121, USA
| | - Yuxuan Zhu
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Han Guo
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Yunjia Qu
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0435, USA
| | - Pengtao Xie
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Ian Y Lian
- Department of Biology, Lamar University, Beaumont, TX, 77710, USA
| | - Yingxiao Wang
- Department of Bioengineering, Institute of Engineering in Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093-0435, USA
| | - Yu-Hwa Lo
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
| |
Collapse
|
14
|
Wang S, Han J, Huang J, Islam K, Shi Y, Zhou Y, Kim D, Zhou J, Lian Z, Liu Y, Huang J. Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors. RESEARCH SQUARE 2023:rs.3.rs-3332530. [PMID: 38014055 PMCID: PMC10680918 DOI: 10.21203/rs.3.rs-3332530/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Background Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction. Methods In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images. Results After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs (LT-HSCs), short-term HSCs (ST-HSCs), and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments. Conclusion Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. With ongoing advancements in model algorithms and their integration into various imaging systems, deep learning stands poised to become an invaluable tool, significantly impacting stem cell research.
Collapse
Affiliation(s)
- Shen Wang
- Lehigh University Department of Mechanical Engineering and Mechanics
| | | | | | - Khayrul Islam
- Lehigh University Department of Mechanical Engineering and Mechanics
| | - Yuheng Shi
- Shanghai Medical College of Fudan University: Fudan University School of Basic Medical Sciences
| | | | | | | | | | | | | |
Collapse
|
15
|
Park J, Bai B, Ryu D, Liu T, Lee C, Luo Y, Lee MJ, Huang L, Shin J, Zhang Y, Ryu D, Li Y, Kim G, Min HS, Ozcan A, Park Y. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat Methods 2023; 20:1645-1660. [PMID: 37872244 DOI: 10.1038/s41592-023-02041-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/11/2023] [Indexed: 10/25/2023]
Abstract
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.
Collapse
Affiliation(s)
- Juyeon Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Bijie Bai
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chungha Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Yi Luo
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mahn Jae Lee
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jeongwon Shin
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | | | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
- Tomocube, Daejeon, Republic of Korea.
| |
Collapse
|
16
|
Lee J, Moon G, Ka S, Toh KA, Kim D. Deep Learning Approach for the Localization and Analysis of Surface Plasmon Scattering. SENSORS (BASEL, SWITZERLAND) 2023; 23:8100. [PMID: 37836930 PMCID: PMC10575049 DOI: 10.3390/s23198100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/24/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
Surface plasmon resonance microscopy (SPRM) combines the principles of traditional microscopy with the versatility of surface plasmons to develop label-free imaging methods. This paper describes a proof-of-principles approach based on deep learning that utilized the Y-Net convolutional neural network model to improve the detection and analysis methodology of SPRM. A machine-learning based image analysis technique was used to provide a method for the one-shot analysis of SPRM images to estimate scattering parameters such as the scatterer location. The method was assessed by applying the approach to SPRM images and reconstructing an image from the network output for comparison with the original image. The results showed that deep learning can localize scatterers and predict other variables of scattering objects with high accuracy in a noisy environment. The results also confirmed that with a larger field of view, deep learning can be used to improve traditional SPRM such that it localizes and produces scatterer characteristics in one shot, considerably increasing the detection capabilities of SPRM.
Collapse
Affiliation(s)
| | | | | | | | - Donghyun Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Republic of Korea; (J.L.); (G.M.); (S.K.); (K.-A.T.)
| |
Collapse
|
17
|
Julian T, Tang T, Hosokawa Y, Yalikun Y. Machine learning implementation strategy in imaging and impedance flow cytometry. BIOMICROFLUIDICS 2023; 17:051506. [PMID: 37900052 PMCID: PMC10613093 DOI: 10.1063/5.0166595] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/06/2023] [Indexed: 10/31/2023]
Abstract
Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.
Collapse
Affiliation(s)
- Trisna Julian
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan
| | - Tao Tang
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Yoichiroh Hosokawa
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan
| | | |
Collapse
|
18
|
Piansaddhayanon C, Koracharkornradt C, Laosaengpha N, Tao Q, Ingrungruanglert P, Israsena N, Chuangsuwanich E, Sriswasdi S. Label-free tumor cells classification using deep learning and high-content imaging. Sci Data 2023; 10:570. [PMID: 37634014 PMCID: PMC10460430 DOI: 10.1038/s41597-023-02482-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/16/2023] [Indexed: 08/28/2023] Open
Abstract
Many studies have shown that cellular morphology can be used to distinguish spiked-in tumor cells in blood sample background. However, most validation experiments included only homogeneous cell lines and inadequately captured the broad morphological heterogeneity of cancer cells. Furthermore, normal, non-blood cells could be erroneously classified as cancer because their morphology differ from blood cells. Here, we constructed a dataset of microscopic images of organoid-derived cancer and normal cell with diverse morphology and developed a proof-of-concept deep learning model that can distinguish cancer cells from normal cells within an unlabeled microscopy image. In total, more than 75,000 organoid-drived cells from 3 cholangiocarcinoma patients were collected. The model achieved an area under the receiver operating characteristics curve (AUROC) of 0.78 and can generalize to cell images from an unseen patient. These resources serve as a foundation for an automated, robust platform for circulating tumor cell detection.
Collapse
Affiliation(s)
- Chawan Piansaddhayanon
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
- Chula Intelligent and Complex Systems, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Chonnuttida Koracharkornradt
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Napat Laosaengpha
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Qingyi Tao
- NVIDIA AI Technology Center, Singapore, Singapore
| | - Praewphan Ingrungruanglert
- Center of Excellence for Stem Cell and Cell Therapy, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Nipan Israsena
- Center of Excellence for Stem Cell and Cell Therapy, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
- Department of Pharmacology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
| | - Ekapol Chuangsuwanich
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330, Thailand.
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
| | - Sira Sriswasdi
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
- Center for Artificial Intelligence in Medicine, Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, 10330, Thailand.
| |
Collapse
|
19
|
Dudaie M, Barnea I, Nissim N, Shaked NT. On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning. Sci Rep 2023; 13:12370. [PMID: 37524884 PMCID: PMC10390541 DOI: 10.1038/s41598-023-38160-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/04/2023] [Indexed: 08/02/2023] Open
Abstract
We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming. Here, we present a new approach for label-free classification of individual cells based directly on the raw off-axis holographic images, each of which contains the complete complex wavefront (amplitude and quantitative phase profiles) of the cell. To obtain this, we built a convolutional neural network, which is invariant to the spatial frequencies and directions of the interference fringes of the off-axis holograms. We demonstrate the effectiveness of this approach using four types of cancer cells. This approach has the potential to significantly improve both speed and robustness of imaging flow cytometry, enabling real-time label-free classification of individual cells.
Collapse
Affiliation(s)
- Matan Dudaie
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Itay Barnea
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Noga Nissim
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Natan T Shaked
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel.
| |
Collapse
|
20
|
Goktas P, Simon Carbajo R. PPSW-SHAP: Towards Interpretable Cell Classification Using Tree-Based SHAP Image Decomposition and Restoration for High-Throughput Bright-Field Imaging. Cells 2023; 12:1384. [PMID: 37408219 DOI: 10.3390/cells12101384] [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: 02/26/2023] [Revised: 04/28/2023] [Accepted: 05/09/2023] [Indexed: 07/07/2023] Open
Abstract
Advancements in high-throughput microscopy imaging have transformed cell analytics, enabling functionally relevant, rapid, and in-depth bioanalytics with Artificial Intelligence (AI) as a powerful driving force in cell therapy (CT) manufacturing. High-content microscopy screening often suffers from systematic noise, such as uneven illumination or vignetting artifacts, which can result in false-negative findings in AI models. Traditionally, AI models have been expected to learn to deal with these artifacts, but success in an inductive framework depends on sufficient training examples. To address this challenge, we propose a two-fold approach: (1) reducing noise through an image decomposition and restoration technique called the Periodic Plus Smooth Wavelet transform (PPSW) and (2) developing an interpretable machine learning (ML) platform using tree-based Shapley Additive exPlanations (SHAP) to enhance end-user understanding. By correcting artifacts during pre-processing, we lower the inductive learning load on the AI and improve end-user acceptance through a more interpretable heuristic approach to problem solving. Using a dataset of human Mesenchymal Stem Cells (MSCs) cultured under diverse density and media environment conditions, we demonstrate supervised clustering with mean SHAP values, derived from the 'DFT Modulus' applied to the decomposition of bright-field images, in the trained tree-based ML model. Our innovative ML framework offers end-to-end interpretability, leading to improved precision in cell characterization during CT manufacturing.
Collapse
Affiliation(s)
- Polat Goktas
- UCD School of Computer Science, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- CeADAR: Ireland's Centre for Applied Artificial Intelligence, Clonskeagh, D04 V2N9 Dublin, Ireland
| | - Ricardo Simon Carbajo
- UCD School of Computer Science, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
- CeADAR: Ireland's Centre for Applied Artificial Intelligence, Clonskeagh, D04 V2N9 Dublin, Ireland
| |
Collapse
|
21
|
Pirone D, Montella A, Sirico DG, Mugnano M, Villone MM, Bianco V, Miccio L, Porcelli AM, Kurelac I, Capasso M, Iolascon A, Maffettone PL, Memmolo P, Ferraro P. Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry. Sci Rep 2023; 13:6042. [PMID: 37055398 PMCID: PMC10101968 DOI: 10.1038/s41598-023-32110-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/21/2023] [Indexed: 04/15/2023] Open
Abstract
Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D phase-contrast tomograms of each single cell. In fact, we show that discrimination of tumor cells against white blood cells is potentially achievable with the aid of artificial intelligence in a label-free flow-cyto-tomography method. We propose a hierarchical machine learning decision-maker, working on a set of features calculated from the 3D tomograms of the cells' refractive index. We prove that 3D morphological features are adequately distinctive to identify tumor cells versus the white blood cell background in the first stage and, moreover, in recognizing the tumor type at the second decision step. Proof-of-concept experiments are shown, in which two different tumor cell lines, namely neuroblastoma cancer cells and ovarian cancer cells, are used against monocytes. The reported results allow claiming the identification of tumor cells with a success rate higher than 97% and with an accuracy over 97% in discriminating between the two cancer cell types, thus opening in a near future the route to a new Liquid Biopsy tool for detecting and classifying circulating tumor cells in blood by stain-free method.
Collapse
Affiliation(s)
- Daniele Pirone
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy
| | - Annalaura Montella
- CEINGE Advanced Biotechnologies, Naples, Italy
- DMMBM, Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Naples, Italy
| | - Daniele G Sirico
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy
| | - Martina Mugnano
- Department of Chemical, Materials and Production Engineering, DICMaPI, University of Naples "Federico II", Piazzale Tecchio 80, 80125, Naples, Italy
| | - Massimiliano M Villone
- Department of Chemical, Materials and Production Engineering, DICMaPI, University of Naples "Federico II", Piazzale Tecchio 80, 80125, Naples, Italy
| | - Vittorio Bianco
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy
| | - Anna Maria Porcelli
- Department of Pharmacy and Biotechnology (FABIT), University of Bologna, Bologna, Italy
- Interdepartmental Centre for Industrial Research 'Scienze Della Vita e Tecnologie per La Salute', University of Bologna, Bologna, Italy
- Centre for Applied Biomedical Research (CRBA), University of Bologna, Bologna, Italy
| | - Ivana Kurelac
- Centre for Applied Biomedical Research (CRBA), University of Bologna, Bologna, Italy
- DIMEC, Department of Medical and Surgical Sciences, Centro di Studio e Ricerca Sulle Neoplasie (CSR) Ginecologiche, Alma Mater Studiorum-University of Bologna, 40138, Bologna, Italy
| | - Mario Capasso
- CEINGE Advanced Biotechnologies, Naples, Italy
- DMMBM, Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Naples, Italy
| | - Achille Iolascon
- CEINGE Advanced Biotechnologies, Naples, Italy
- DMMBM, Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Naples, Italy
| | - Pier Luca Maffettone
- Department of Chemical, Materials and Production Engineering, DICMaPI, University of Naples "Federico II", Piazzale Tecchio 80, 80125, Naples, Italy
| | - Pasquale Memmolo
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy.
| | - Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy.
| |
Collapse
|
22
|
Asaad M, Lu SC, Hassan AM, Kambhampati P, Mitchell D, Chang EI, Yu P, Hanasono MM, Sidey-Gibbons C. The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction. Ann Surg Oncol 2023; 30:2343-2352. [PMID: 36719569 DOI: 10.1245/s10434-022-13053-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 12/22/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND Machine learning has been increasingly used for surgical outcome prediction, yet applications in head and neck reconstruction are not well-described. In this study, we developed and evaluated the performance of ML algorithms in predicting postoperative complications in head and neck free-flap reconstruction. METHODS We conducted a comprehensive review of patients who underwent microvascular head and neck reconstruction between January 2005 and December 2018. Data were used to develop and evaluate nine supervised ML algorithms in predicting overall complications, major recipient-site complication, and total flap loss. RESULTS We identified 4000 patients who met inclusion criteria. Overall, 33.7% of patients experienced a complication, 26.5% experienced a major recipient-site complication, and 1.7% suffered total flap loss. The k-nearest neighbors algorithm demonstrated the best overall performance for predicting any complication (AUROC = 0.61, sensitivity = 0.60). Regularized regression had the best performance for predicting major recipient-site complications (AUROC = 0.68, sensitivity = 0.66), and decision trees were the best predictors of total flap loss (AUROC = 0.66, sensitivity = 0.50). CONCLUSIONS ML accurately identified patients at risk of experiencing postsurgical complications, including total flap loss. Predictions from ML models may provide insight in the perioperative setting and facilitate shared decision making.
Collapse
Affiliation(s)
- Malke Asaad
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sheng-Chieh Lu
- Department of Symptom Research, MD Anderson Center for INSPiRED Cancer Care, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Abbas M Hassan
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Praneeth Kambhampati
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Baylor College of Medicine, Houston, TX, USA
| | - David Mitchell
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- McGovern Medical School, Houston, TX, USA.
| | - Edward I Chang
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peirong Yu
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Matthew M Hanasono
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - C Sidey-Gibbons
- Department of Symptom Research, MD Anderson Center for INSPiRED Cancer Care, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| |
Collapse
|
23
|
Hourani T, Perez-Gonzalez A, Khoshmanesh K, Luwor R, Achuthan AA, Baratchi S, O'Brien-Simpson NM, Al-Hourani A. Label-free macrophage phenotype classification using machine learning methods. Sci Rep 2023; 13:5202. [PMID: 36997576 PMCID: PMC10061362 DOI: 10.1038/s41598-023-32158-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 03/23/2023] [Indexed: 04/01/2023] Open
Abstract
Macrophages are heterogeneous innate immune cells that are functionally shaped by their surrounding microenvironment. Diverse macrophage populations have multifaceted differences related to their morphology, metabolism, expressed markers, and functions, where the identification of the different phenotypes is of an utmost importance in modelling immune response. While expressed markers are the most used signature to classify phenotypes, multiple reports indicate that macrophage morphology and autofluorescence are also valuable clues that can be used in the identification process. In this work, we investigated macrophage autofluorescence as a distinct feature for classifying six different macrophage phenotypes, namely: M0, M1, M2a, M2b, M2c, and M2d. The identification was based on extracted signals from multi-channel/multi-wavelength flow cytometer. To achieve the identification, we constructed a dataset containing 152,438 cell events each having a response vector of 45 optical signals fingerprint. Based on this dataset, we applied different supervised machine learning methods to detect phenotype specific fingerprint from the response vector, where the fully connected neural network architecture provided the highest classification accuracy of 75.8% for the six phenotypes compared simultaneously. Furthermore, by restricting the number of phenotypes in the experiment, the proposed framework produces higher classification accuracies, averaging 92.0%, 91.9%, 84.2%, and 80.4% for a pool of two, three, four, five phenotypes, respectively. These results indicate the potential of the intrinsic autofluorescence for classifying macrophage phenotypes, with the proposed method being quick, simple, and cost-effective way to accelerate the discovery of macrophage phenotypical diversity.
Collapse
Affiliation(s)
- Tetiana Hourani
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, 3050, Australia
| | - Alexis Perez-Gonzalez
- Melbourne Cytometry Platform, Department of Microbiology and Immunology, The University of Melbourne, at The Peter Doherty Institute of Infection and Immunity, Parkville, VIC, 3010, Australia
| | | | - Rodney Luwor
- Department of Surgery, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, 3050, Australia
- Fiona Elsey Cancer Research Institute, Ballarat, Victoria, 3350, Australia
- Federation University Australia, Ballarat, Victoria, 3350, Australia
| | - Adrian A Achuthan
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, 3050, Australia
| | - Sara Baratchi
- School of Health & Biomedical Sciences, RMIT University, Bundoora, Victoria, 3083, Australia
| | - Neil M O'Brien-Simpson
- ACTV Research Group, Division of Basic and Clinical Oral Sciences, Centre for Oral Health Research, Melbourne Dental School, Royal Dental Hospital, The University of Melbourne, 720 Swanston Street, Carlton, VIC, 3010, Australia
| | - Akram Al-Hourani
- School of Engineering, RMIT University, Melbourne, Victoria, 3000, Australia.
| |
Collapse
|
24
|
Guo Z, Lin T, Jing D, Wang W, Sui Y. A method for real-time mechanical characterisation of microcapsules. Biomech Model Mechanobiol 2023:10.1007/s10237-023-01712-7. [PMID: 36964429 PMCID: PMC10366294 DOI: 10.1007/s10237-023-01712-7] [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: 10/26/2022] [Accepted: 03/09/2023] [Indexed: 03/26/2023]
Abstract
Characterising the mechanical properties of flowing microcapsules is important from both fundamental and applied points of view. In the present study, we develop a novel multilayer perceptron (MLP)-based machine learning (ML) approach, for real-time simultaneous predictions of the membrane mechanical law type, shear and area-dilatation moduli of microcapsules, from their camera-recorded steady profiles in tube flow. By MLP, we mean a neural network where many perceptrons are organised into layers. A perceptron is a basic element that conducts input-output mapping operation. We test the performance of the present approach using both simulation and experimental data. We find that with a reasonably high prediction accuracy, our method can reach an unprecedented low prediction latency of less than 1 millisecond on a personal computer. That is the overall computational time, without using parallel computing, from a single experimental image to multiple capsule mechanical parameters. It is faster than a recently proposed convolutional neural network-based approach by two orders of magnitude, for it only deals with the one-dimensional capsule boundary instead of the entire two-dimensional capsule image. Our new approach may serve as the foundation of a promising tool for real-time mechanical characterisation and online active sorting of deformable microcapsules and biological cells in microfluidic devices.
Collapse
Affiliation(s)
- Ziyu Guo
- School of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, United Kingdom
| | - Tao Lin
- School of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, United Kingdom
| | - Dalei Jing
- School of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, United Kingdom
| | - Wen Wang
- School of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, United Kingdom
| | - Yi Sui
- School of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, United Kingdom.
| |
Collapse
|
25
|
Neo YT, Chia WY, Lim SS, Ngan CL, Kurniawan TA, Chew KW. Smart systems in producing algae-based protein to improve functional food ingredients industries. Food Res Int 2023; 165:112480. [PMID: 36869493 DOI: 10.1016/j.foodres.2023.112480] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/29/2022] [Accepted: 01/11/2023] [Indexed: 01/15/2023]
Abstract
Production and extraction systems of algal protein and handling process of functional food ingredients need to control several parameters such as temperature, pH, intensity, and turbidity. Many researchers have investigated the Internet of Things (IoT) approach for enhancing the yield of microalgae biomass and machine learning for identifying and classifying microalgae. However, there have been few specific studies on using IoT and artificial intelligence (AI) for production and extraction of algal protein as well as functional food ingredients processing. In order to improve the production of algal protein and functional food ingredients, the implementation of smart system is a must to have real-time monitoring, remote control system, quick response to sudden events, prediction and characterisation. Techniques of IoT and AI are expected to help functional food industries to have a big breakthrough in the future. Manufacturing and implementation of beneficial smart systems are important to provide convenience and to increase the efficiency of work by using the interconnectivity of IoT devices to have good capturing, processing, archiving, analyzing, and automation. This review investigates the possibilities of implementation of IoT and AI in production and extraction of algal protein and processing of functional food ingredients.
Collapse
Affiliation(s)
- Yi Ting Neo
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Wen Yi Chia
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Siew Shee Lim
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Cheng Loong Ngan
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang, Selangor Darul Ehsan, Malaysia
| | | | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62, Nanyang Drive, Singapore 637459, Singapore.
| |
Collapse
|
26
|
Darrin M, Samudre A, Sahun M, Atwell S, Badens C, Charrier A, Helfer E, Viallat A, Cohen-Addad V, Giffard-Roisin S. Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study. Sci Rep 2023; 13:745. [PMID: 36639503 PMCID: PMC9839696 DOI: 10.1038/s41598-023-27718-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 01/06/2023] [Indexed: 01/15/2023] Open
Abstract
The fraction of red blood cells adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease. Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples (out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. Moreover, these videos are of different durations (from 6 to more than 100 frames). We present a two-stage end-to-end machine learning pipeline able to automatically classify cell motions in videos with a high class imbalance. By extending, comparing, and combining two state-of-the-art methods, a convolutional neural network (CNN) model and a recurrent CNN, we are able to automatically discard 97% of the unreliable cell sequences (first stage) and classify highly and poorly deformable red cell sequences with 97% accuracy and an F1-score of 0.94 (second stage). Dataset and codes are publicly released for the community.
Collapse
Affiliation(s)
| | | | - Maxime Sahun
- Aix Marseille Univ, CNRS, CINAM, Marseille, France
| | - Scott Atwell
- Aix Marseille Univ, CNRS, CINAM, Marseille, France
| | - Catherine Badens
- Aix Marseille University, INSERM, Marseille Medical Genetics (MMG), 13005, Marseille, France
| | | | | | | | | | - Sophie Giffard-Roisin
- Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IRD, IFSTTAR, ISTerre, Grenoble, France.
| |
Collapse
|
27
|
Liu G, Zhao J, Tian G, Li S, Lu Y. Visualizing knowledge evolution trends and research hotspots of artificial intelligence in colorectal cancer: A bibliometric analysis. Front Oncol 2022; 12:925924. [PMID: 36518311 PMCID: PMC9742812 DOI: 10.3389/fonc.2022.925924] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 11/11/2022] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND In recent years, the rapid development of artificial intelligence (AI) technology has created a new diagnostic and therapeutic opportunity for colorectal cancer (CRC). Numerous academic and clinical studies have demonstrated that high-level auxiliary diagnosis and treatment systems based on AI technology can significantly improve the readability of medical data, objectively provide a reliable and comprehensive reference for physicians, reduce the experience gap between physicians, and aid physicians in making more accurate diagnosis decisions. In this study, we used bibliometric techniques to visually analyze the literature about AI in the CRC field and summarize the current situation and research hotspots in this field. METHODS The relevant literature on AI in the field of CRC research was obtained from the Web of Science Core Collection (WoSCC) database. The software CiteSpace was utilized to analyze the number of papers, countries, institutions, authors, journals, cited literature, and keywords of the included literature and generate a visual knowledge map. The present study aims to evaluate the origin, current hotspots, and research trends of AI in CRC using bibliometric analysis. RESULTS As of March 2022, 64 nations/regions, 230 institutions, 245 journals, and 300 authors had published 562 AI-related articles in the field of CRC. Since 2016, each year has seen an exponential increase. China and the United States were the largest contributors, with the largest number of beneficial research institutions and the closest collaboration relationship. The World Journal of Gastroenterology is this field's most widely published journal. Diagnosis and treatment research, gene and immunology research, intestinal polyp research, tumor grading research, gastrointestinal endoscopy research, and prognosis research comprised the six topics derived from high-frequency keyword cluster analysis. CONCLUSION In recent years, field research has been a popular topic of discussion. The results of our bibliometric analysis allow us to comprehend better the current situation and trend of this research field, and the quantitative data indicators can serve as a guide for the research and application of global scholars.
Collapse
Affiliation(s)
- Guangwei Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao University, Qingdao, Shandong, China
| | - Jun Zhao
- Department of Pharmacy, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Guangye Tian
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Shuai Li
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Yun Lu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, Qingdao University, Qingdao, Shandong, China
| |
Collapse
|
28
|
Wei Z, Liu X, Yan R, Sun G, Yu W, Liu Q, Guo Q. Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging. Front Genet 2022; 13:1002327. [PMID: 36386823 PMCID: PMC9644055 DOI: 10.3389/fgene.2022.1002327] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 09/26/2022] [Indexed: 01/25/2023] Open
Abstract
Complex intracellular organizations are commonly represented by dividing the metabolic process of cells into different organelles. Therefore, identifying sub-cellular organelle architecture is significant for understanding intracellular structural properties, specific functions, and biological processes in cells. However, the discrimination of these structures in the natural organizational environment and their functional consequences are not clear. In this article, we propose a new pixel-level multimodal fusion (PLMF) deep network which can be used to predict the location of cellular organelle using label-free cell optical microscopy images followed by deep-learning-based automated image denoising. It provides valuable insights that can be of tremendous help in improving the specificity of label-free cell optical microscopy by using the Transformer-Unet network to predict the ground truth imaging which corresponds to different sub-cellular organelle architectures. The new prediction method proposed in this article combines the advantages of a transformer's global prediction and CNN's local detail analytic ability of background features for label-free cell optical microscopy images, so as to improve the prediction accuracy. Our experimental results showed that the PLMF network can achieve over 0.91 Pearson's correlation coefficient (PCC) correlation between estimated and true fractions on lung cancer cell-imaging datasets. In addition, we applied the PLMF network method on the cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new way for the time-resolved study of subcellular components in different cells, especially for cancer cells.
Collapse
Affiliation(s)
- Zhihao Wei
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Xi Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Ruiqing Yan
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Guocheng Sun
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China,School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Weiyong Yu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Qiang Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Qianjin Guo
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China,School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing, China,*Correspondence: Qianjin Guo,
| |
Collapse
|
29
|
Zare Harofte S, Soltani M, Siavashy S, Raahemifar K. Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2203169. [PMID: 36026569 DOI: 10.1002/smll.202203169] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/16/2022] [Indexed: 05/14/2023]
Abstract
Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.
Collapse
Affiliation(s)
- Samaneh Zare Harofte
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
| | - Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi University of Technology, Tehran, 14176-14411, Iran
- Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, 14197-33141, Iran
| | - Saeed Siavashy
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), Penn State University, State College, PA, 16801, USA
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada
- Department of Chemical Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada
| |
Collapse
|
30
|
Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells. Int J Mol Sci 2022; 23:ijms231810827. [PMID: 36142736 PMCID: PMC9504098 DOI: 10.3390/ijms231810827] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/10/2022] [Accepted: 09/13/2022] [Indexed: 11/23/2022] Open
Abstract
Stimulated Raman Scattering Microscopy (SRS) is a powerful tool for label-free detailed recognition and investigation of the cellular and subcellular structures of living cells. Determining subcellular protein localization from the cell level of SRS images is one of the basic goals of cell biology, which can not only provide useful clues for their functions and biological processes but also help to determine the priority and select the appropriate target for drug development. However, the bottleneck in predicting subcellular protein locations of SRS cell imaging lies in modeling complicated relationships concealed beneath the original cell imaging data owing to the spectral overlap information from different protein molecules. In this work, a multiple parallel fusion network, MPFnetwork, is proposed to study the subcellular locations from SRS images. This model used a multiple parallel fusion model to construct feature representations and combined multiple nonlinear decomposing algorithms as the automated subcellular detection method. Our experimental results showed that the MPFnetwork could achieve over 0.93 dice correlation between estimated and true fractions on SRS lung cancer cell datasets. In addition, we applied the MPFnetwork method to cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new method for the time-resolved study of subcellular components in different cells, especially cancer cells.
Collapse
|
31
|
Raji H, Tayyab M, Sui J, Mahmoodi SR, Javanmard M. Biosensors and machine learning for enhanced detection, stratification, and classification of cells: a review. Biomed Microdevices 2022; 24:26. [PMID: 35953679 DOI: 10.1007/s10544-022-00627-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2022] [Indexed: 12/16/2022]
Abstract
Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another, therefore, is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in the analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.
Collapse
Affiliation(s)
- Hassan Raji
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Muhammad Tayyab
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Jianye Sui
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Seyed Reza Mahmoodi
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA
| | - Mehdi Javanmard
- Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.
| |
Collapse
|
32
|
Wang Q, Lu J, Fan K, Xu Y, Xiong Y, Sun Z, Zhai M, Zhang Z, Zhang S, Song Y, Luo J, You M, Guo M, Zhang X. High-throughput "read-on-ski" automated imaging and label-free detection system for toxicity screening of compounds using personalised human kidney organoids. J Zhejiang Univ Sci B 2022; 23:564-577. [PMID: 35794686 DOI: 10.1631/jzus.b2100701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Organoid models are used to study kidney physiology, such as the assessment of nephrotoxicity and underlying disease processes. Personalized human pluripotent stem cell-derived kidney organoids are ideal models for compound toxicity studies, but there is a need to accelerate basic and translational research in the field. Here, we developed an automated continuous imaging setup with the "read-on-ski" law of control to maximize temporal resolution with minimum culture plate vibration. High-accuracy performance was achieved: organoid screening and imaging were performed at a spatial resolution of 1.1 μm for the entire multi-well plate under 3 min. We used the in-house developed multi-well spinning device and cisplatin-induced nephrotoxicity model to evaluate the toxicity in kidney organoids using this system. The acquired images were processed via machine learning-based classification and segmentation algorithms, and the toxicity in kidney organoids was determined with 95% accuracy. The results obtained by the automated "read-on-ski" imaging device, combined with label-free and non-invasive algorithms for detection, were verified using conventional biological procedures. Taking advantage of the close-to-in vivo-kidney organoid model, this new development opens the door for further application of scaled-up screening using organoids in basic research and drug discovery.
Collapse
Affiliation(s)
- Qizheng Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Jun Lu
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510320, China
| | - Ke Fan
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Yiwei Xu
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Yucui Xiong
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Zhiyong Sun
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510320, China
| | - Man Zhai
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Zhizhong Zhang
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510320, China
| | - Sheng Zhang
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Yan Song
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Jianzhong Luo
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Mingliang You
- Hangzhou Cancer Institute, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou 310002, China
| | - Meijin Guo
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China. ,
| | - Xiao Zhang
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China. .,Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510320, China.
| |
Collapse
|
33
|
Cheng G, Zhang F, Xing Y, Hu X, Zhang H, Chen S, Li M, Peng C, Ding G, Zhang D, Chen P, Xia Q, Wu M. Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer. Front Immunol 2022; 13:893198. [PMID: 35844508 PMCID: PMC9286729 DOI: 10.3389/fimmu.2022.893198] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/27/2022] [Indexed: 12/12/2022] Open
Abstract
Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using immunohistochemistry staining remains challenging for pathologists. Here we developed a deep learning (DL)-based artificial intelligence (AI) model to automatically analyze the immunohistochemical expression of PD-L1 in lung cancer patients. A total of 1,288 patients with lung cancer were included in the study. The diagnostic ability of three different AI models (M1, M2, and M3) was assessed in both PD-L1 (22C3) and PD-L1 (SP263) assays. M2 and M3 showed improved performance in the evaluation of PD-L1 expression in the PD-L1 (22C3) assay, especially at 1% cutoff. Highly accurate performance in the PD-L1 (SP263) was also achieved, with accuracy and specificity of 96.4 and 96.8% in both M2 and M3, respectively. Moreover, the diagnostic results of these three AI-assisted models were highly consistent with those from the pathologist. Similar performances of M1, M2, and M3 in the 22C3 dataset were also obtained in lung adenocarcinoma and lung squamous cell carcinoma in both sampling methods. In conclusion, these results suggest that AI-assisted diagnostic models in PD-L1 expression are a promising tool for improving the efficiency of clinical pathologists.
Collapse
Affiliation(s)
- Guoping Cheng
- Department of Pathology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | | | | | - Xingyi Hu
- Department of Pathology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
- The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - He Zhang
- Department of Pathology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | | | | | | | - Guangtai Ding
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Dadong Zhang
- 3D Medicines Inc., Shanghai, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
| | - Peilin Chen
- 3D Medicines Inc., Shanghai, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
| | - Qingxin Xia
- Department of Pathology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
| | - Meijuan Wu
- Department of Pathology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
- *Correspondence: Dadong Zhang, ; Peilin Chen, ; Qingxin Xia, ; Meijuan Wu,
| |
Collapse
|
34
|
Moallem G, Pore AA, Gangadhar A, Sari-Sarraf H, Vanapalli SA. Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-210268RR. [PMID: 35831930 PMCID: PMC9278981 DOI: 10.1117/1.jbo.27.7.076003] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 06/09/2022] [Indexed: 05/15/2023]
Abstract
SIGNIFICANCE Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug testing and molecular characterization. AIM The goal is to develop and test deep learning (DL) approaches to detect unstained breast cancer cells in bright-field microscopy images that contain white blood cells (WBCs). APPROACH We tested two convolutional neural network (CNN) approaches. The first approach allows investigation of the prominent features extracted by CNN to discriminate in vitro cancer cells from WBCs. The second approach is based on faster region-based convolutional neural network (Faster R-CNN). RESULTS Both approaches detected cancer cells with higher than 95% sensitivity and 99% specificity with the Faster R-CNN being more efficient and suitable for deployment presenting an improvement of 4% in sensitivity. The distinctive feature that CNN uses for discrimination is cell size, however, in the absence of size difference, the CNN was found to be capable of learning other features. The Faster R-CNN was found to be robust with respect to intensity and contrast image transformations. CONCLUSIONS CNN-based DL approaches could be potentially applied to detect patient-derived CTCs from images of blood samples.
Collapse
Affiliation(s)
- Golnaz Moallem
- Texas Tech University, Department of Electrical and Computer Engineering, Lubbock, Texas, United States
| | - Adity A. Pore
- Texas Tech University, Department of Chemical Engineering, Lubbock, Texas, United States
| | - Anirudh Gangadhar
- Texas Tech University, Department of Chemical Engineering, Lubbock, Texas, United States
| | - Hamed Sari-Sarraf
- Texas Tech University, Department of Electrical and Computer Engineering, Lubbock, Texas, United States
- Address all correspondence to Hamed Sari-Sarraf, ; Siva A. Vanapalli,
| | - Siva A. Vanapalli
- Texas Tech University, Department of Chemical Engineering, Lubbock, Texas, United States
- Address all correspondence to Hamed Sari-Sarraf, ; Siva A. Vanapalli,
| |
Collapse
|
35
|
Machine learning-enabled cancer diagnostics with widefield polarimetric second-harmonic generation microscopy. Sci Rep 2022; 12:10290. [PMID: 35717344 PMCID: PMC9206659 DOI: 10.1038/s41598-022-13623-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 05/03/2022] [Indexed: 11/08/2022] Open
Abstract
The extracellular matrix (ECM) collagen undergoes major remodeling during tumorigenesis. However, alterations to the ECM are not widely considered in cancer diagnostics, due to mostly uniform appearance of collagen fibers in white light images of hematoxylin and eosin-stained (H&E) tissue sections. Polarimetric second-harmonic generation (P-SHG) microscopy enables label-free visualization and ultrastructural investigation of non-centrosymmetric molecules, which, when combined with texture analysis, provides multiparameter characterization of tissue collagen. This paper demonstrates whole slide imaging of breast tissue microarrays using high-throughput widefield P-SHG microscopy. The resulting P-SHG parameters are used in classification to differentiate tumor from normal tissue, resulting in 94.2% for both accuracy and F1-score, and 6.3% false discovery rate. Subsequently, the trained classifier is employed to predict tumor tissue with 91.3% accuracy, 90.7% F1-score, and 13.8% false omission rate. As such, we show that widefield P-SHG microscopy reveals collagen ultrastructure over large tissue regions and can be utilized as a sensitive biomarker for cancer diagnostics and prognostics studies.
Collapse
|
36
|
Hilzenrat G, Gill ET, McArthur SL. Imaging approaches for monitoring three-dimensional cell and tissue culture systems. JOURNAL OF BIOPHOTONICS 2022; 15:e202100380. [PMID: 35357086 DOI: 10.1002/jbio.202100380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/27/2022] [Accepted: 03/28/2022] [Indexed: 06/14/2023]
Abstract
The past decade has seen an increasing demand for more complex, reproducible and physiologically relevant tissue cultures that can mimic the structural and biological features of living tissues. Monitoring the viability, development and responses of such tissues in real-time are challenging due to the complexities of cell culture physical characteristics and the environments in which these cultures need to be maintained in. Significant developments in optics, such as optical manipulation, improved detection and data analysis, have made optical imaging a preferred choice for many three-dimensional (3D) cell culture monitoring applications. The aim of this review is to discuss the challenges associated with imaging and monitoring 3D tissues and cell culture, and highlight topical label-free imaging tools that enable bioengineers and biophysicists to non-invasively characterise engineered living tissues.
Collapse
Affiliation(s)
- Geva Hilzenrat
- Bioengineering Engineering Group, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, Victoria, Australia
| | - Emma T Gill
- Bioengineering Engineering Group, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, Victoria, Australia
| | - Sally L McArthur
- Bioengineering Engineering Group, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, Victoria, Australia
| |
Collapse
|
37
|
Abstract
With the continuous miniaturization of conventional integrated circuits, obstacles such as excessive cost, increased resistance to electronic motion, and increased energy consumption are gradually slowing down the development of electrical computing and constraining the application of deep learning. Optical neuromorphic computing presents various opportunities and challenges compared with the realm of electronics. Algorithms running on optical hardware have the potential to meet the growing computational demands of deep learning and artificial intelligence. Here, we review the development of optical neural networks and compare various research proposals. We focus on fiber-based neural networks. Finally, we describe some new research directions and challenges.
Collapse
|
38
|
Yamamoto M, Miyata S. Influences of Microscopic Imaging Conditions on Accuracy of Cell Morphology Discrimination Using Convolutional Neural Network of Deep Learning. MICROMACHINES 2022; 13:mi13050760. [PMID: 35630227 PMCID: PMC9145082 DOI: 10.3390/mi13050760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/05/2022] [Accepted: 05/09/2022] [Indexed: 02/01/2023]
Abstract
Recently, automated cell culture devices have become necessary for cell therapy applications. The maintenance of cell functions is critical for cell expansion. However, there are risks of losing these functions, owing to disturbances in the surrounding environment and culturing procedures. Therefore, there is a need for a non-invasive and highly accurate evaluation method for cell phenotypes. In this study, we focused on an automated discrimination technique using image processing with a deep learning algorithm. This study aimed to clarify the effects of the optical magnification of the microscope and cell size in each image on the discrimination accuracy for cell phenotypes and morphologies. Myoblast cells (C2C12 cell line) were cultured and differentiated into myotubes. Microscopic images of the cultured cells were acquired at magnifications of 40× and 100×. A deep learning architecture was constructed to discriminate between undifferentiated and differentiated cells. The discrimination accuracy exceeded 90% even at a magnification of 40× for well-developed myogenic differentiation. For the cells under immature myogenic differentiation, a high optical magnification of 100× was required to maintain a discrimination accuracy over 90%. The microscopic optical magnification should be adjusted according to the cell differentiation to improve the efficiency of image-based cell discrimination.
Collapse
Affiliation(s)
- Masashi Yamamoto
- Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Kanagawa, Japan;
| | - Shogo Miyata
- Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Kanagawa, Japan
- Correspondence:
| |
Collapse
|
39
|
Mukherjee P, Patino CA, Pathak N, Lemaitre V, Espinosa HD. Deep Learning-Assisted Automated Single Cell Electroporation Platform for Effective Genetic Manipulation of Hard-to-Transfect Cells. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2107795. [PMID: 35315229 PMCID: PMC9119920 DOI: 10.1002/smll.202107795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 02/03/2022] [Indexed: 05/03/2023]
Abstract
Genome engineering of cells using CRISPR/Cas systems has opened new avenues for pharmacological screening and investigating the molecular mechanisms of disease. A critical step in many such studies is the intracellular delivery of the gene editing machinery and the subsequent manipulation of cells. However, these workflows often involve processes such as bulk electroporation for intracellular delivery and fluorescence activated cell sorting for cell isolation that can be harsh to sensitive cell types such as human-induced pluripotent stem cells (hiPSCs). This often leads to poor viability and low overall efficacy, requiring the use of large starting samples. In this work, a fully automated version of the nanofountain probe electroporation (NFP-E) system, a nanopipette-based single-cell electroporation method is presented that provides superior cell viability and efficiency compared to traditional methods. The automated system utilizes a deep convolutional network to identify cell locations and a cell-nanopipette contact algorithm to position the nanopipette over each cell for the application of electroporation pulses. The automated NFP-E is combined with microconfinement arrays for cell isolation to demonstrate a workflow that can be used for CRISPR/Cas9 gene editing and cell tracking with potential applications in screening studies and isogenic cell line generation.
Collapse
Affiliation(s)
- Prithvijit Mukherjee
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60208, USA
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, IL, 60208, USA
- iNfinitesimal LLC, Skokie, IL, 60077, USA
| | - Cesar A Patino
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60208, USA
- iNfinitesimal LLC, Skokie, IL, 60077, USA
| | - Nibir Pathak
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60208, USA
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, IL, 60208, USA
| | | | - Horacio D Espinosa
- Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60208, USA
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, IL, 60208, USA
- iNfinitesimal LLC, Skokie, IL, 60077, USA
| |
Collapse
|
40
|
Liu TY, Mahjoubfar A, Prusinski D, Stevens L. Neuromorphic computing for content-based image retrieval. PLoS One 2022; 17:e0264364. [PMID: 35385477 PMCID: PMC8985975 DOI: 10.1371/journal.pone.0264364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 02/08/2022] [Indexed: 11/18/2022] Open
Abstract
Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency. Here, we explore the application of Loihi, a neuromorphic computing chip developed by Intel, for the computer vision task of image retrieval. We evaluated the functionalities and the performance metrics that are critical in content-based visual search and recommender systems using deep-learning embeddings. Our results show that the neuromorphic solution is about 2.5 times more energy-efficient compared with an ARM Cortex-A72 CPU and 12.5 times more energy-efficient compared with NVIDIA T4 GPU for inference by a lightweight convolutional neural network when batch size is 1 while maintaining the same level of matching accuracy. The study validates the potential of neuromorphic computing in low-power image retrieval, as a complementary paradigm to the existing von Neumann architectures.
Collapse
Affiliation(s)
- Te-Yuan Liu
- Target Corporation, Sunnyvale, California, United States of America
| | - Ata Mahjoubfar
- Target Corporation, Sunnyvale, California, United States of America
- * E-mail:
| | - Daniel Prusinski
- Target Corporation, Sunnyvale, California, United States of America
| | - Luis Stevens
- Target Corporation, Sunnyvale, California, United States of America
| |
Collapse
|
41
|
Wang X, Kittaka M, He Y, Zhang Y, Ueki Y, Kihara D. OC_Finder: Osteoclast Segmentation, Counting, and Classification Using Watershed and Deep Learning. FRONTIERS IN BIOINFORMATICS 2022; 2. [PMID: 35474753 PMCID: PMC9038109 DOI: 10.3389/fbinf.2022.819570] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Osteoclasts are multinucleated cells that exclusively resorb bone matrix proteins and minerals on the bone surface. They differentiate from monocyte/macrophage lineage cells in the presence of osteoclastogenic cytokines such as the receptor activator of nuclear factor-κB ligand (RANKL) and are stained positive for tartrate-resistant acid phosphatase (TRAP). In vitro osteoclast formation assays are commonly used to assess the capacity of osteoclast precursor cells for differentiating into osteoclasts wherein the number of TRAP-positive multinucleated cells is counted as osteoclasts. Osteoclasts are manually identified on cell culture dishes by human eyes, which is a labor-intensive process. Moreover, the manual procedure is not objective and results in lack of reproducibility. To accelerate the process and reduce the workload for counting the number of osteoclasts, we developed OC_Finder, a fully automated system for identifying osteoclasts in microscopic images. OC_Finder consists of cell image segmentation with a watershed algorithm and cell classification using deep learning. OC_Finder detected osteoclasts differentiated from wild-type and Sh3bp2KI/+ precursor cells at a 99.4% accuracy for segmentation and at a 98.1% accuracy for classification. The number of osteoclasts classified by OC_Finder was at the same accuracy level with manual counting by a human expert. OC_Finder also showed consistent performance on additional datasets collected with different microscopes with different settings by different operators. Together, successful development of OC_Finder suggests that deep learning is a useful tool to perform prompt and accurate unbiased classification and detection of specific cell types in microscopic images.
Collapse
Affiliation(s)
- Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | - Mizuho Kittaka
- Department of Biomedical Sciences and Comprehensive Care, Indiana University School of Dentistry, Indianapolis, IN, United States
- Indiana Center for Musculoskeletal Health, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Yilin He
- School of Software Engineering, Shandong University, Jinan, China
| | - Yiwei Zhang
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Yasuyoshi Ueki
- Department of Biomedical Sciences and Comprehensive Care, Indiana University School of Dentistry, Indianapolis, IN, United States
- Indiana Center for Musculoskeletal Health, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
- Purdue Cancer Research Institute, Purdue University, West Lafayette, IN, United States
- *Correspondence: Daisuke Kihara,
| |
Collapse
|
42
|
Rabbi F, Dabbagh SR, Angin P, Yetisen AK, Tasoglu S. Deep Learning-Enabled Technologies for Bioimage Analysis. MICROMACHINES 2022; 13:mi13020260. [PMID: 35208385 PMCID: PMC8880650 DOI: 10.3390/mi13020260] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 01/31/2022] [Accepted: 02/03/2022] [Indexed: 02/05/2023]
Abstract
Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.
Collapse
Affiliation(s)
- Fazle Rabbi
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
| | - Sajjad Rahmani Dabbagh
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey
- Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkey
| | - Pelin Angin
- Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey;
| | - Ali Kemal Yetisen
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK;
| | - Savas Tasoglu
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey; (F.R.); (S.R.D.)
- Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey
- Koc University Is Bank Artificial Intelligence Lab (KUIS AILab), Koç University, Sariyer, Istanbul 34450, Turkey
- Institute of Biomedical Engineering, Boğaziçi University, Çengelköy, Istanbul 34684, Turkey
- Physical Intelligence Department, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany
- Correspondence:
| |
Collapse
|
43
|
Light sheet based volume flow cytometry (VFC) for rapid volume reconstruction and parameter estimation on the go. Sci Rep 2022; 12:78. [PMID: 34997009 PMCID: PMC8741756 DOI: 10.1038/s41598-021-03902-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 12/06/2021] [Indexed: 11/08/2022] Open
Abstract
Optical imaging is paramount for disease diagnosis and to access its progression over time. The proposed optical flow imaging (VFC/iLIFE) is a powerful technique that adds new capabilities (3D volume visualization, organelle-level resolution, and multi-organelle screening) to the existing system. Unlike state-of-the-art point-illumination-based biomedical imaging techniques, the sheet-based VFC technique is capable of single-shot sectional visualization, high throughput interrogation, real-time parameter estimation, and instant volume reconstruction with organelle-level resolution of live specimens. The specimen flow system was realized on a multichannel (Y-type) microfluidic chip that enables visualization of organelle distribution in several cells in-parallel at a relatively high flow-rate (2000 nl/min). The calibration of VFC system requires the study of point emitters (fluorescent beads) at physiologically relevant flow-rates (500-2000 nl/min) for determining flow-induced optical aberration in the system point spread function (PSF). Subsequently, the recorded raw images and volumes were computationally deconvolved with flow-variant PSF to reconstruct the cell volume. High throughput investigation of the mitochondrial network in HeLa cancer cell was carried out at sub-cellular resolution in real-time and critical parameters (mitochondria count and size distribution, morphology, entropy, and cell strain statistics) were determined on-the-go. These parameters determine the physiological state of cells, and the changes over-time, revealing the metastatic progression of diseases. Overall, the developed VFC system enables real-time monitoring of sub-cellular organelle organization at a high-throughput with high-content capacity.
Collapse
|
44
|
Memmolo P, Aprea G, Bianco V, Russo R, Andolfo I, Mugnano M, Merola F, Miccio L, Iolascon A, Ferraro P. Differential diagnosis of hereditary anemias from a fraction of blood drop by digital holography and hierarchical machine learning. Biosens Bioelectron 2022; 201:113945. [PMID: 35032844 DOI: 10.1016/j.bios.2021.113945] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/17/2021] [Accepted: 12/28/2021] [Indexed: 01/25/2023]
Abstract
Anemia affects about the 25% of the global population and can provoke severe diseases, ranging from weakness and dizziness to pregnancy problems, arrhythmias and hearth failures. About 10% of the patients are affected by rare anemias of which 80% are hereditary. Early differential diagnosis of anemia enables prescribing patients a proper treatment and diet, which is effective to mitigate the associated symptoms. Nevertheless, the differential diagnosis of these conditions is often difficult due to shared and overlapping phenotypes. Indeed, the complete blood count and unaided peripheral blood smear observation cannot always provide a reliable differential diagnosis, so that biomedical assays and genetic tests are needed. These procedures are not error-free, require skilled personnel, and severely impact the financial resources of national health systems. Here we show a differential screening system for hereditary anemias that relies on holographic imaging and artificial intelligence. Label-free holographic imaging is aided by a hierarchical machine learning decider that works even in the presence of a very limited dataset but is enough accurate for discerning between different anemia classes with minimal morphological dissimilarities. It is worth to notice that only a few tens of cells from each patient are sufficient to obtain a correct diagnosis, with the advantage of significantly limiting the volume of blood drawn. This work paves the way to a wider use of home screening systems for point of care blood testing and telemedicine with lab-on-chip platforms.
Collapse
Affiliation(s)
- Pasquale Memmolo
- Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (ISASI-CNR), via Campi Flegrei 34, 80078, Pozzuoli, Napoli, Italy
| | - Genny Aprea
- Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (ISASI-CNR), via Campi Flegrei 34, 80078, Pozzuoli, Napoli, Italy
| | - Vittorio Bianco
- Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (ISASI-CNR), via Campi Flegrei 34, 80078, Pozzuoli, Napoli, Italy.
| | - Roberta Russo
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Federico II di Napoli, Italy; CEINGE-Biotecnologie Avanzate, Napoli, Italy
| | - Immacolata Andolfo
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Federico II di Napoli, Italy; CEINGE-Biotecnologie Avanzate, Napoli, Italy
| | - Martina Mugnano
- Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (ISASI-CNR), via Campi Flegrei 34, 80078, Pozzuoli, Napoli, Italy
| | - Francesco Merola
- Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (ISASI-CNR), via Campi Flegrei 34, 80078, Pozzuoli, Napoli, Italy
| | - Lisa Miccio
- Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (ISASI-CNR), via Campi Flegrei 34, 80078, Pozzuoli, Napoli, Italy
| | - Achille Iolascon
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Federico II di Napoli, Italy; CEINGE-Biotecnologie Avanzate, Napoli, Italy
| | - Pietro Ferraro
- Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (ISASI-CNR), via Campi Flegrei 34, 80078, Pozzuoli, Napoli, Italy
| |
Collapse
|
45
|
Li J, Chen J, Bai H, Wang H, Hao S, Ding Y, Peng B, Zhang J, Li L, Huang W. An Overview of Organs-on-Chips Based on Deep Learning. RESEARCH (WASHINGTON, D.C.) 2022; 2022:9869518. [PMID: 35136860 PMCID: PMC8795883 DOI: 10.34133/2022/9869518] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 12/08/2021] [Indexed: 12/15/2022]
Abstract
Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of "big data" and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.
Collapse
Affiliation(s)
- Jintao Li
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jie Chen
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electronics and Information Engineering, Anhui University, Hefei 230601, China
- 38th Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China
| | - Hua Bai
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Haiwei Wang
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shiping Hao
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yang Ding
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Bo Peng
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jing Zhang
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Lin Li
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech), Nanjing 211800, China
| | - Wei Huang
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech), Nanjing 211800, China
| |
Collapse
|
46
|
Witmer A, Bhanu B. Generative Adversarial Networks for Morphological-Temporal Classification of Stem Cell Images. SENSORS (BASEL, SWITZERLAND) 2021; 22:206. [PMID: 35009749 PMCID: PMC8749838 DOI: 10.3390/s22010206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/24/2021] [Accepted: 12/26/2021] [Indexed: 12/14/2022]
Abstract
Frequently, neural network training involving biological images suffers from a lack of data, resulting in inefficient network learning. This issue stems from limitations in terms of time, resources, and difficulty in cellular experimentation and data collection. For example, when performing experimental analysis, it may be necessary for the researcher to use most of their data for testing, as opposed to model training. Therefore, the goal of this paper is to perform dataset augmentation using generative adversarial networks (GAN) to increase the classification accuracy of deep convolutional neural networks (CNN) trained on induced pluripotent stem cell microscopy images. The main challenges are: 1. modeling complex data using GAN and 2. training neural networks on augmented datasets that contain generated data. To address these challenges, a temporally constrained, hierarchical classification scheme that exploits domain knowledge is employed for model learning. First, image patches of cell colonies from gray-scale microscopy images are generated using GAN, and then these images are added to the real dataset and used to address class imbalances at multiple stages of training. Overall, a 2% increase in both true positive rate and F1-score is observed using this method as compared to a straightforward, imbalanced classification network, with some greater improvements on a classwise basis. This work demonstrates that synergistic model design involving domain knowledge is key for biological image analysis and improves model learning in high-throughput scenarios.
Collapse
Affiliation(s)
- Adam Witmer
- Visualization and Intelligent Systems Laboratory, University of California, Riverside, CA 92521, USA;
- Department of Bioengineering, University of California, Riverside, CA 92521, USA
| | - Bir Bhanu
- Visualization and Intelligent Systems Laboratory, University of California, Riverside, CA 92521, USA;
- Department of Bioengineering, University of California, Riverside, CA 92521, USA
- Department of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA
| |
Collapse
|
47
|
Gerckens M, Schorpp K, Pelizza F, Wögrath M, Reichau K, Ma H, Dworsky AM, Sengupta A, Stoleriu MG, Heinzelmann K, Merl-Pham J, Irmler M, Alsafadi HN, Trenkenschuh E, Sarnova L, Jirouskova M, Frieß W, Hauck SM, Beckers J, Kneidinger N, Behr J, Hilgendorff A, Hadian K, Lindner M, Königshoff M, Eickelberg O, Gregor M, Plettenburg O, Yildirim AÖ, Burgstaller G. Phenotypic drug screening in a human fibrosis model identified a novel class of antifibrotic therapeutics. SCIENCE ADVANCES 2021; 7:eabb3673. [PMID: 34936468 PMCID: PMC8694600 DOI: 10.1126/sciadv.abb3673] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Fibrogenic processes instigate fatal chronic diseases leading to organ failure and death. Underlying biological processes involve induced massive deposition of extracellular matrix (ECM) by aberrant fibroblasts. We subjected diseased primary human lung fibroblasts to an advanced three-dimensional phenotypic high-content assay and screened a repurposing drug library of small molecules for inhibiting ECM deposition. Fibrotic Pattern Detection by Artificial Intelligence identified tranilast as an effective inhibitor. Structure-activity relationship studies confirmed N-(2-butoxyphenyl)-3-(phenyl)acrylamides (N23Ps) as a novel and highly potent compound class. N23Ps suppressed myofibroblast transdifferentiation, ECM deposition, cellular contractility, and altered cell shapes, thus advocating a unique mode of action. Mechanistically, transcriptomics identified SMURF2 as a potential therapeutic target network. Antifibrotic activity of N23Ps was verified by proteomics in a human ex vivo tissue fibrosis disease model, suppressing profibrotic markers SERPINE1 and CXCL8. Conclusively, N23Ps are a novel class of highly potent compounds inhibiting organ fibrosis in patients.
Collapse
Affiliation(s)
- Michael Gerckens
- Institute of Lung Biology and Disease (ILBD) and Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Kenji Schorpp
- Assay Development and Screening Platform, Institute of Molecular Toxicology and Pharmacology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Francesco Pelizza
- Chemical and Process Engineering, Strathclyde University, Glasgow, Scotland, UK
| | - Melanie Wögrath
- Institute of Lung Biology and Disease (ILBD) and Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
- CPC-M bioArchive, Helmholtz Zentrum München, Comprehensive Pneumology Center Munich DZL/CPC-M, Munich, Germany
| | - Kora Reichau
- Institute of Medicinal Chemistry, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Leibniz Universität Hannover, Institute of Organic Chemistry and Center for Biomolecular Drug Research (BMWZ), Hannover, Germany
| | - Huilong Ma
- Institute of Medicinal Chemistry, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Leibniz Universität Hannover, Institute of Organic Chemistry and Center for Biomolecular Drug Research (BMWZ), Hannover, Germany
| | - Armando-Marco Dworsky
- Institute of Lung Biology and Disease (ILBD) and Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
- CPC-M bioArchive, Helmholtz Zentrum München, Comprehensive Pneumology Center Munich DZL/CPC-M, Munich, Germany
| | - Arunima Sengupta
- Institute of Lung Biology and Disease (ILBD) and Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Mircea Gabriel Stoleriu
- Institute of Lung Biology and Disease (ILBD) and Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
- CPC-M bioArchive, Helmholtz Zentrum München, Comprehensive Pneumology Center Munich DZL/CPC-M, Munich, Germany
- Asklepios Fachkliniken Munich-Gauting, Munich, Germany
| | - Katharina Heinzelmann
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Comprehensive Pneumology Center (CPC), Research Unit Lung Repair and Regeneration, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Juliane Merl-Pham
- Research Unit Protein Science, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Neuherberg, Germany
| | - Martin Irmler
- Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Hani N. Alsafadi
- CPC-M bioArchive, Helmholtz Zentrum München, Comprehensive Pneumology Center Munich DZL/CPC-M, Munich, Germany
- Comprehensive Pneumology Center (CPC), Research Unit Lung Repair and Regeneration, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
- Wallenberg Center for Molecular Medicine (WCMM), Department of Experimental Medical Sciences, Lund University, Lund, Sweden
| | - Eduard Trenkenschuh
- Department of Pharmacy–Center for Drug Research, Pharmaceutical Technology and Biopharmaceutics, Ludwig-Maximillians University of Munich, Munich, Germany
| | - Lenka Sarnova
- Laboratory of Integrative Biology, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Marketa Jirouskova
- Laboratory of Integrative Biology, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Wolfgang Frieß
- Department of Pharmacy–Center for Drug Research, Pharmaceutical Technology and Biopharmaceutics, Ludwig-Maximillians University of Munich, Munich, Germany
| | - Stefanie M. Hauck
- Research Unit Protein Science, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Neuherberg, Germany
| | - Johannes Beckers
- Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), 85764 Neuherberg, Germany
- Chair of Experimental Genetics, Technische Universität München, 85354 Freising, Germany
| | - Nikolaus Kneidinger
- CPC-M bioArchive, Helmholtz Zentrum München, Comprehensive Pneumology Center Munich DZL/CPC-M, Munich, Germany
- Department of Internal Medicine V, Ludwig-Maximillians University of Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Jürgen Behr
- CPC-M bioArchive, Helmholtz Zentrum München, Comprehensive Pneumology Center Munich DZL/CPC-M, Munich, Germany
- Asklepios Fachkliniken Munich-Gauting, Munich, Germany
- Department of Internal Medicine V, Ludwig-Maximillians University of Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Anne Hilgendorff
- Institute of Lung Biology and Disease (ILBD) and Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
- CPC-M bioArchive, Helmholtz Zentrum München, Comprehensive Pneumology Center Munich DZL/CPC-M, Munich, Germany
| | - Kamyar Hadian
- Assay Development and Screening Platform, Institute of Molecular Toxicology and Pharmacology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Michael Lindner
- CPC-M bioArchive, Helmholtz Zentrum München, Comprehensive Pneumology Center Munich DZL/CPC-M, Munich, Germany
- Asklepios Fachkliniken Munich-Gauting, Munich, Germany
- Paracelsus Medical Private University, Salzburg, Austria
| | - Melanie Königshoff
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Comprehensive Pneumology Center (CPC), Research Unit Lung Repair and Regeneration, Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Oliver Eickelberg
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Martin Gregor
- Laboratory of Integrative Biology, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Oliver Plettenburg
- Institute of Medicinal Chemistry, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- Leibniz Universität Hannover, Institute of Organic Chemistry and Center for Biomolecular Drug Research (BMWZ), Hannover, Germany
- Institute for Lung Health (ILH), Justus Liebig University, Giessen, Germany
| | - Ali Önder Yildirim
- Institute of Lung Biology and Disease (ILBD) and Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Gerald Burgstaller
- Institute of Lung Biology and Disease (ILBD) and Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
- CPC-M bioArchive, Helmholtz Zentrum München, Comprehensive Pneumology Center Munich DZL/CPC-M, Munich, Germany
- Corresponding author.
| |
Collapse
|
48
|
Xin L, Xiao W, Che L, Liu J, Miccio L, Bianco V, Memmolo P, Ferraro P, Li X, Pan F. Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning. ACS OMEGA 2021; 6:31046-31057. [PMID: 34841147 PMCID: PMC8613806 DOI: 10.1021/acsomega.1c04204] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 10/29/2021] [Indexed: 05/13/2023]
Abstract
About 75% of epithelial ovarian cancer (EOC) patients suffer from relapsing and develop drug resistance after primary chemotherapy. The commonly used clinical examinations and biological tumor tissue models for chemotherapeutic sensitivity are time-consuming and expensive. Research studies showed that the cell morphology-based method is promising to be a new route for chemotherapeutic sensitivity evaluation. Here, we offer how the drug resistance of EOC cells can be assessed through a label-free and high-throughput microfluidic flow cytometer equipped with a digital holographic microscope reinforced by machine learning. It is the first time that such type of assessment is performed to the best of our knowledge. Several morphologic and texture features at a single-cell level have been extracted from the quantitative phase images. In addition, we compared four common machine learning algorithms, including naive Bayes, decision tree, K-nearest neighbors, support vector machine (SVM), and fully connected network. The result shows that the SVM classifier achieves the optimal performance with an accuracy of 92.2% and an area under the curve of 0.96. This study demonstrates that the proposed method achieves high-accuracy, high-throughput, and label-free assessment of the drug resistance of EOC cells. Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.
Collapse
Affiliation(s)
- Lu Xin
- Key
Laboratory of Precision Opto-mechatronics Technology, School of Instrumentation
& Optoelectronic Engineering, Beihang
University, Beijing 100191, China
| | - Wen Xiao
- Key
Laboratory of Precision Opto-mechatronics Technology, School of Instrumentation
& Optoelectronic Engineering, Beihang
University, Beijing 100191, China
| | - Leiping Che
- Key
Laboratory of Precision Opto-mechatronics Technology, School of Instrumentation
& Optoelectronic Engineering, Beihang
University, Beijing 100191, China
| | - JinJin Liu
- Department
of Obstetrics and Gynecology, Peking University
People’s Hospital, Beijing 100044, China
| | - Lisa Miccio
- CNR,
Institute of Applied Sciences & Intelligent Systems (ISASI) “E.
Caianiello”, via
Campi Flegrei 34, 80078 Pozzuoli, Italy
| | - Vittorio Bianco
- CNR,
Institute of Applied Sciences & Intelligent Systems (ISASI) “E.
Caianiello”, via
Campi Flegrei 34, 80078 Pozzuoli, Italy
| | - Pasquale Memmolo
- CNR,
Institute of Applied Sciences & Intelligent Systems (ISASI) “E.
Caianiello”, via
Campi Flegrei 34, 80078 Pozzuoli, Italy
| | - Pietro Ferraro
- CNR,
Institute of Applied Sciences & Intelligent Systems (ISASI) “E.
Caianiello”, via
Campi Flegrei 34, 80078 Pozzuoli, Italy
| | - Xiaoping Li
- Department
of Obstetrics and Gynecology, Peking University
People’s Hospital, Beijing 100044, China
| | - Feng Pan
- Key
Laboratory of Precision Opto-mechatronics Technology, School of Instrumentation
& Optoelectronic Engineering, Beihang
University, Beijing 100191, China
| |
Collapse
|
49
|
Movsesyan A, Besteiro LV, Wang Z, Govorov AO. Mie Sensing with Neural Networks: Recognition of Nano‐Object Parameters, the Invisibility Point, and Restricted Models. ADVANCED THEORY AND SIMULATIONS 2021. [DOI: 10.1002/adts.202100369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Artur Movsesyan
- Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 China
- Department of Physics and Astronomy Ohio University Athens OH 45701 USA
| | | | - Zhiming Wang
- Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 China
| | | |
Collapse
|
50
|
Otesteanu CF, Ugrinic M, Holzner G, Chang YT, Fassnacht C, Guenova E, Stavrakis S, deMello A, Claassen M. A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics. CELL REPORTS METHODS 2021; 1:100094. [PMID: 35474892 PMCID: PMC9017143 DOI: 10.1016/j.crmeth.2021.100094] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/21/2021] [Accepted: 09/14/2021] [Indexed: 12/21/2022]
Abstract
The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics. We demonstrate the capability of iCellCnn to achieve diagnosis of Sézary syndrome (SS) from patient samples on the basis of bright-field IFC images of T cells obtained after fluorescence-activated cell sorting of human peripheral blood mononuclear cell specimens. With a sample size of four healthy donors and five SS patients, iCellCnn achieved a 100% classification accuracy. As iCellCnn is not restricted to the diagnosis of SS, we expect such weakly supervised approaches to tap the diagnostic potential of IFC by providing automatic data-driven diagnosis of diseases with so-far unknown morphological manifestations.
Collapse
Affiliation(s)
- Corin F. Otesteanu
- Institute for Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Martina Ugrinic
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Gregor Holzner
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Yun-Tsan Chang
- Department of Dermatology, University Hospital Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Christina Fassnacht
- Department of Dermatology, University Hospital Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Emmanuella Guenova
- Department of Dermatology, University Hospital Zurich and Faculty of Medicine, University of Zurich, Zurich, Switzerland
- Department of Dermatology, Lausanne University Hospital (CHUV) and Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Stavros Stavrakis
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Andrew deMello
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
| | - Manfred Claassen
- Internal Medicine I, University Hospital Tübingen, Faculty of Medicine, University of Tübingen, Tübingen, Germany
| |
Collapse
|