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Weng Y, Shen H, Mei L, Liu L, Yao Y, Li R, Wei S, Yan R, Ruan X, Wang D, Wei Y, Deng Y, Zhou Y, Xiao T, Goda K, Liu S, Zhou F, Lei C. Typing of acute leukemia by intelligent optical time-stretch imaging flow cytometry on a chip. LAB ON A CHIP 2023; 23:1703-1712. [PMID: 36799214 DOI: 10.1039/d2lc01048h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Acute leukemia (AL) is one of the top life-threatening diseases. Accurate typing of AL can significantly improve its prognosis. However, conventional methods for AL typing often require cell staining, which is time-consuming and labor-intensive. Furthermore, their performance is highly limited by the specificity and availability of fluorescent labels, which can hardly meet the requirements of AL typing in clinical settings. Here, we demonstrate AL typing by intelligent optical time-stretch (OTS) imaging flow cytometry on a microfluidic chip. Specifically, we employ OTS microscopy to capture the images of cells in clinical bone marrow samples with a spatial resolution of 780 nm at a high flowing speed of 1 m s-1 in a label-free manner. Then, to show the clinical utility of our method for which the features of clinical samples are diverse, we design and construct a deep convolutional neural network (CNN) to analyze the cellular images and determine the AL type of each sample. We measure 30 clinical samples composed of 7 acute lymphoblastic leukemia (ALL) samples, 17 acute myelogenous leukemia (AML) samples, and 6 samples from healthy donors, resulting in a total of 227 620 images acquired. Results show that our method can distinguish ALL and AML with an accuracy of 95.03%, which, to the best of our knowledge, is a record in label-free AL typing. In addition to AL typing, we believe that the high throughput, high accuracy, and label-free operation of our method make it a potential solution for cell analysis in scientific research and clinical settings.
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Affiliation(s)
- Yueyun Weng
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- The Key Laboratory of Transients in Hydraulic Machinery of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, Wuhan, China
| | - Hui Shen
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Liye Mei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Li Liu
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Yifan Yao
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Rubing Li
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Shubin Wei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Ruopeng Yan
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Xiaolan Ruan
- Department of Hematology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Du Wang
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
| | - Yongchang Wei
- Department of Radiation & Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yunjie Deng
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Yuqi Zhou
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Tinghui Xiao
- Department of Chemistry, University of Tokyo, Tokyo, Japan
| | - Keisuke Goda
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- Department of Chemistry, University of Tokyo, Tokyo, Japan
- Department of bioengineering, University of California, Los Angeles, USA
| | - Sheng Liu
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- The Key Laboratory of Transients in Hydraulic Machinery of Ministry of Education, School of Power and Mechanical Engineering, Wuhan University, Wuhan, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.
| | - Cheng Lei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China.
- Department of Chemistry, University of Tokyo, Tokyo, Japan
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Yang S, Wang J, Chi H, Yang B. Distortion compensation in continuous-time photonic time-stretched ADC based on redundancy detection. APPLIED OPTICS 2021; 60:1646-1652. [PMID: 33690501 DOI: 10.1364/ao.417088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 01/18/2021] [Indexed: 06/12/2023]
Abstract
In continuous-time photonic time-stretched analog-to-digital conversion (PTS-ADC), a wavelength-division multiplexer (WDM) is widely used to separate the overlapped stretched signal segments in both the wavelength and time domains. However, the spectrum shape of the WDM is not an ideal rectangle, and signal distortion occurs when we reconstruct the stretched signal by connecting the segments carried by the rising or falling edges of the spectrum. A signal distortion compensation scheme based on redundancy detection is proposed in this paper. Two WDMs with complementary spectrum division are employed; if we properly set the central wavelength of each channel of both WDMs, the signal segments that might be distorted by inter-channel mismatch can be redundantly detected and the distortion can be completely avoided. Moreover, by adjusting the dispersion amount of the first dispersive medium, the optical carrier obtained by connecting the stretched chirped pulses is partly overlapped, which guarantees the redundant modulation of signal segments that might be distorted by inter-pulse mismatch. Accordingly, the signal distortion induced by inter-pulse mismatch can be perfectly removed. In addition, the effective bandwidth of each chirped optical pulse is given. The maximum system stretch factor is also derived in this paper, and experiments based on the proposed signal distortion compensation scheme are carried out. The results obtained provide instructive guidelines for the design of a continuous-time PTS-ADC with the desired performance.
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Weng Y, Mei L, Wu G, Chen S, Zhan B, Goda K, Liu S, Lei C. Analysis of signal detection configurations in optical time-stretch imaging. OPTICS EXPRESS 2020; 28:29272-29284. [PMID: 33114830 DOI: 10.1364/oe.403454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 09/07/2020] [Indexed: 06/11/2023]
Abstract
Optical time-stretch (OTS) imaging is effective for observing ultra-fast dynamic events in real time by virtue of its capability of acquiring images with high spatial resolution at high speed. In different implementations of OTS imaging, different configurations of its signal detection, i.e. fiber-coupled and free-space detection schemes, are employed. In this research, we quantitatively analyze and compare the two detection configurations of OTS imaging in terms of sensitivity and image quality with the USAF-1951 resolution chart and diamond films, respectively, providing a valuable guidance for the system design of OTS imaging in diverse fields.
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Isozaki A, Harmon J, Zhou Y, Li S, Nakagawa Y, Hayashi M, Mikami H, Lei C, Goda K. AI on a chip. LAB ON A CHIP 2020; 20:3074-3090. [PMID: 32644061 DOI: 10.1039/d0lc00521e] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as drug design, material discovery, speech recognition, self-driving cars, advertising, finance, medical imaging, and astronomical observation, where AI-produced outcomes have been proven to be comparable or even superior to the performance of human experts. In these applications, what is essentially important for the development of AI is the data needed for machine learning. Despite its prominent importance, the very first process of the AI development, namely data collection and data preparation, is typically the most laborious task and is often a limiting factor of constructing functional AI algorithms. Lab-on-a-chip technology, in particular microfluidics, is a powerful platform for both the construction and implementation of AI in a large-scale, cost-effective, high-throughput, automated, and multiplexed manner, thereby overcoming the above bottleneck. On this platform, high-throughput imaging is a critical tool as it can generate high-content information (e.g., size, shape, structure, composition, interaction) of objects on a large scale. High-throughput imaging can also be paired with sorting and DNA/RNA sequencing to conduct a massive survey of phenotype-genotype relations whose data is too complex to analyze with traditional computational tools, but is analyzable with the power of AI. In addition to its function as a data provider, lab-on-a-chip technology can also be employed to implement the developed AI for accurate identification, characterization, classification, and prediction of objects in mixed, heterogeneous, or unknown samples. In this review article, motivated by the excellent synergy between AI and lab-on-a-chip technology, we outline fundamental elements, recent advances, future challenges, and emerging opportunities of AI with lab-on-a-chip technology or "AI on a chip" for short.
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Affiliation(s)
- Akihiro Isozaki
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Kanagawa Institute of Industrial Science and Technology, Kanagawa 213-0012, Japan
| | - Jeffrey Harmon
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Yuqi Zhou
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Shuai Li
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and The Cambridge Centre for Data-Driven Discovery, Cambridge University, Cambridge CB3 0WA, UK
| | - Yuta Nakagawa
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Mika Hayashi
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Hideharu Mikami
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Cheng Lei
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Institute of Technological Sciences, Wuhan University, Hubei 430072, China
| | - Keisuke Goda
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Institute of Technological Sciences, Wuhan University, Hubei 430072, China and Department of Bioengineering, University of California, Los Angeles, California 90095, USA
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