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Zhang Y, Song X, Xie J, Hu J, Chen J, Li X, Zhang H, Zhou Q, Yuan L, Kong C, Shen Y, Wu J, Fang L, Dai Q. Large depth-of-field ultra-compact microscope by progressive optimization and deep learning. Nat Commun 2023; 14:4118. [PMID: 37433856 DOI: 10.1038/s41467-023-39860-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 06/28/2023] [Indexed: 07/13/2023] Open
Abstract
The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report an integrated microscope that achieves optical performance beyond a commercial microscope with a 5×, NA 0.1 objective but only at 0.15 cm3 and 0.5 g, whose size is five orders of magnitude smaller than that of a conventional microscope. To achieve this, a progressive optimization pipeline is proposed which systematically optimizes both aspherical lenses and diffractive optical elements with over 30 times memory reduction compared to the end-to-end optimization. By designing a simulation-supervision deep neural network for spatially varying deconvolution during optical design, we accomplish over 10 times improvement in the depth-of-field compared to traditional microscopes with great generalization in a wide variety of samples. To show the unique advantages, the integrated microscope is equipped in a cell phone without any accessories for the application of portable diagnostics. We believe our method provides a new framework for the design of miniaturized high-performance imaging systems by integrating aspherical optics, computational optics, and deep learning.
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Affiliation(s)
- Yuanlong Zhang
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, 100084, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100084, Beijing, China
| | - Xiaofei Song
- Tsinghua Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, China
| | - Jiachen Xie
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, 100084, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100084, Beijing, China
| | - Jing Hu
- State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, 310027, Hangzhou, China
| | - Jiawei Chen
- OPPO Research Institute, 518101, Shenzhen, China
| | - Xiang Li
- OPPO Research Institute, 518101, Shenzhen, China
| | - Haiyu Zhang
- OPPO Research Institute, 518101, Shenzhen, China
| | - Qiqun Zhou
- OPPO Research Institute, 518101, Shenzhen, China
| | - Lekang Yuan
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, 518055, Shenzhen, China
| | - Chui Kong
- School of Information Science and Technology, Fudan University, 200433, Shanghai, China
| | - Yibing Shen
- State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, 310027, Hangzhou, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, 100084, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China.
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, 100084, Beijing, China.
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100084, Beijing, China.
| | - Lu Fang
- Department of Electronic Engineering, Tsinghua University, 100084, Beijing, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, 100084, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China.
- Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, 100084, Beijing, China.
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, 100084, Beijing, China.
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2
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Xiao M, Xu N, He A, Yu Z, Chen B, Jin B, Jiang L, Yi C. A smartphone-based fluorospectrophotometer and ratiometric fluorescence nanoprobe for on-site quantitation of pesticide residue. iScience 2023; 26:106553. [PMID: 37123231 PMCID: PMC10139973 DOI: 10.1016/j.isci.2023.106553] [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: 11/15/2022] [Revised: 02/11/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
Cost-effective and user-friendly quantitation at points-of-need plays an important role in food safety inspection, environmental monitoring, and biomedical analysis. This study reports a stand-alone smartphone-based fluorospectrophotometer (the SBS) installed with a custom-designed application (the SBS-App) for on-site quantitation of pesticide using a ratiometric sensing scheme. The SBS can collect fluorescence emission spectra in the wavelength range of 380-760 nm within 5 s. A ratiometric fluorescence probe is facilely prepared by directly mixing the blue-emissive carbon nanodots (the Fe3+-specific fluorometric indicator) and red-emissive quantum dots (the internal standard) at a ratio of 11.6 (w/w). Based on the acetylcholinesterase/choline oxidase dual enzyme-mediated cascade catalytic reactions of Fe2+/Fe3+ transformation, a ratiometric fluorescence sensing scheme is developed. The practicability of the SBS is validated by on-site quantitation of chlorpyrifos in apple and cabbage with a comparable accuracy to the GC-MS method, offering a scalable solution to establish a cost-effective surveillance system for pesticide pollution.
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Affiliation(s)
- Meng Xiao
- Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
- Department of Clinical Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510000, China
| | - Ningxia Xu
- Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Aitong He
- Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Zipei Yu
- Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Bo Chen
- Food Inspection and Quarantine Center, Shenzhen Customs, Shenzhen 518033, China
| | - Baohui Jin
- Food Inspection and Quarantine Center, Shenzhen Customs, Shenzhen 518033, China
| | - Lelun Jiang
- Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Changqing Yi
- Guangdong Provincial Engineering and Technology Center of Advanced and Portable Medical Devices, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
- Research Institute of Sun Yat-Sen University in Shenzhen, Shenzhen 518057, China
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3
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Yu Y, Wen H, Li S, Cao H, Li X, Ma Z, She X, Zhou L, Huang S. Emerging microfluidic technologies for microbiome research. Front Microbiol 2022; 13:906979. [PMID: 36051769 PMCID: PMC9424851 DOI: 10.3389/fmicb.2022.906979] [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] [Received: 03/29/2022] [Accepted: 07/26/2022] [Indexed: 11/13/2022] Open
Abstract
The importance of the microbiome is increasingly prominent. For example, the human microbiome has been proven to be strongly associated with health conditions, while the environmental microbiome is recognized to have a profound influence on agriculture and even the global climate. Furthermore, the microbiome can serve as a fascinating reservoir of genes that encode tremendously valuable compounds for industrial and medical applications. In the past decades, various technologies have been developed to better understand and exploit the microbiome. In particular, microfluidics has demonstrated its strength and prominence in the microbiome research. By taking advantage of microfluidic technologies, inherited shortcomings of traditional methods such as low throughput, labor-consuming, and high-cost are being compensated or bypassed. In this review, we will summarize a broad spectrum of microfluidic technologies that have addressed various needs in the field of microbiome research, as well as the achievements that were enabled by the microfluidics (or technological advances). Finally, how microfluidics overcomes the limitations of conventional methods by technology integration will also be discussed.
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Affiliation(s)
- Yue Yu
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hui Wen
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Sihong Li
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Haojie Cao
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xuefei Li
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhixin Ma
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiaoyi She
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lei Zhou
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shuqiang Huang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Shuqiang Huang,
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4
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Rabha D, Biswas S, Hatiboruah D, Das P, Rather MA, Mandal M, Nath P. An affordable, handheld multimodal microscopic system with onboard cell morphology and counting features on a mobile device. Analyst 2022; 147:2859-2869. [DOI: 10.1039/d1an02317a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A simple yet effective, handheld and flexible bright-field and fluorescence microscopic platform on a smartphone with varying optical magnifications is reported for morphological analysis and onboard cell counting features.
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Affiliation(s)
- Diganta Rabha
- Applied Photonics and Nanophotonics Laboratory, Department of Physics, Tezpur University, Sonitpur, Assam-784028, India
| | - Sritam Biswas
- Applied Photonics and Nanophotonics Laboratory, Department of Physics, Tezpur University, Sonitpur, Assam-784028, India
| | - Diganta Hatiboruah
- Applied Photonics and Nanophotonics Laboratory, Department of Physics, Tezpur University, Sonitpur, Assam-784028, India
| | - Priyanka Das
- Applied Photonics and Nanophotonics Laboratory, Department of Physics, Tezpur University, Sonitpur, Assam-784028, India
| | - Muzamil Ahmad Rather
- Department of Molecular Biology and Biotechnology, Tezpur University, Sonitpur, Assam-784028, India
| | - Manabendra Mandal
- Department of Molecular Biology and Biotechnology, Tezpur University, Sonitpur, Assam-784028, India
| | - Pabitra Nath
- Applied Photonics and Nanophotonics Laboratory, Department of Physics, Tezpur University, Sonitpur, Assam-784028, India
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5
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Rajendran VK, Bakthavathsalam P, Bergquist PL, Sunna A. Smartphone technology facilitates point-of-care nucleic acid diagnosis: a beginner's guide. Crit Rev Clin Lab Sci 2020; 58:77-100. [PMID: 32609551 DOI: 10.1080/10408363.2020.1781779] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The reliable detection of nucleic acids at low concentrations in clinical samples like blood, urine and saliva, and in food can be achieved by nucleic acid amplification methods. Several portable and hand-held devices have been developed to translate these laboratory-based methods to point-of-care (POC) settings. POC diagnostic devices could potentially play an important role in environmental monitoring, health, and food safety. Use of a smartphone for nucleic acid testing has shown promising progress in endpoint as well as real-time analysis of various disease conditions. The emergence of smartphone-based POC devices together with paper-based sensors, microfluidic chips and digital droplet assays are used currently in many situations to provide quantitative detection of nucleic acid targets. State-of-the-art portable devices are commercially available and rapidly emerging smartphone-based POC devices that allow the performance of laboratory-quality colorimetric, fluorescent and electrochemical detection are described in this review. We present a comprehensive review of smartphone-based POC sensing applications, specifically on microbial diagnostics, assess their performance and propose recommendations for the future.
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Affiliation(s)
| | - Padmavathy Bakthavathsalam
- School of Chemistry and Australian Centre for Nanomedicine, University of New South Wales, Sydney, Australia
| | - Peter L Bergquist
- Department of Molecular Sciences, Macquarie University, Sydney, Australia.,Department of Molecular Medicine & Pathology, University of Auckland, Auckland, New Zealand.,Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia
| | - Anwar Sunna
- Department of Molecular Sciences, Macquarie University, Sydney, Australia.,Biomolecular Discovery Research Centre, Macquarie University, Sydney, Australia
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6
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Guan T, He J, Liu D, Liang Z, Shu B, Chen Y, Liu Y, Shen X, Li X, Sun Y, Lei H. Open Surface Droplet Microfluidic Magnetosensor for Microcystin-LR Monitoring in Reservoir. Anal Chem 2020; 92:3409-3416. [PMID: 31948225 DOI: 10.1021/acs.analchem.9b05516] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Establishing rapid, simple, and in situ detection of microcystin-LR (MC-LR) in drinking water sources is of significant importance for human health. To ease the situation that current methods cannot address, an open surface droplet microfluidic magnetosensor was designed and validated to quantify MC-LR in reservoir water, which is capable of (1) MC-LR isolation via MC-LR antibody-conjugated magnetic beads, (2) parallel and multistep analytical procedures in 15-array power-free and reusable active droplet microfluidic chips, (3) immunoassay incubation and fluorescence excitation within a miniaturized multifunctional 3D-printing optosensing accessory, and (4) signal read-out and data analysis by a user-friendly Android app. The proposed smartphone-based fluorimetric magnetosensor exhibited a low limit of detection of 1.2 × 10-5 μg/L in the range of 10-4 μg/L to 100 μg/L. This integrated and high throughput platform was utilized to draw an MC-LR contamination map for six reservoirs distributed in the Pearl River delta, Guangdong Province. It promises to be a simple and successful quantification method for MC-LR field detection, bringing many benefits to rapid on-site screening.
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Affiliation(s)
- Tian Guan
- Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science , South China Agricultural University , Guangzhou 510642 , China.,Guangdong Laboratory for Lingnan Modern Agriculture , Guangzhou 510642 , China
| | - Jianfei He
- Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science , South China Agricultural University , Guangzhou 510642 , China
| | - Dayu Liu
- Department of Laboratory Medicine, Guangzhou First People's Hospital , Guangzhou Medical University , Guangzhou 510180 , China
| | - Zaoqing Liang
- Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science , South China Agricultural University , Guangzhou 510642 , China
| | - Bowen Shu
- Department of Laboratory Medicine, Guangzhou First People's Hospital , Guangzhou Medical University , Guangzhou 510180 , China
| | - Yiping Chen
- College of Food Science and Technology , Huazhong Agricultural University , Wuhan , 430070 , China
| | - Yingju Liu
- Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science , South China Agricultural University , Guangzhou 510642 , China
| | - Xing Shen
- Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science , South China Agricultural University , Guangzhou 510642 , China
| | - Xiangmei Li
- Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science , South China Agricultural University , Guangzhou 510642 , China
| | - Yuanming Sun
- Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science , South China Agricultural University , Guangzhou 510642 , China
| | - Hongtao Lei
- Key Laboratory of Food Quality and Safety of Guangdong Province, College of Food Science , South China Agricultural University , Guangzhou 510642 , China.,Guangdong Laboratory for Lingnan Modern Agriculture , Guangzhou 510642 , China
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7
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Mariani S, Robbiano V, Iglio R, La Mattina AA, Nadimi P, Wang J, Kim B, Kumeria T, Sailor MJ, Barillaro G. Moldless Printing of Silicone Lenses With Embedded Nanostructured Optical Filters. ADVANCED FUNCTIONAL MATERIALS 2020; 30:1906836. [PMID: 32377177 PMCID: PMC7202556 DOI: 10.1002/adfm.201906836] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Optical lenses are among the oldest technological innovations (3000 years ago) and they have enabled a multitude of applications in healthcare and in our daily lives. The primary function of optical lenses has changed little over time; they serve mainly as a light-collection (e.g. reflected, transmitted, diffracted) element, and the wavelength and/or intensity of the collected light is usually manipulated by coupling with various external optical filter elements or coatings. This generally results in losses associated with multiple interfacial reflections, and increases the complexity of design and construction. In this work we introduce a change in this paradigm, by integrating both light-shaping and image magnification into a single lens element using a moldless procedure that takes advantage of the physical and optical properties of mesoporous silicon (PSi) photonic crystal nanostructures. Casting of a liquid poly(dimethyl) siloxane (PDMS) pre-polymer solution onto a PSi film generates a droplet with contact angle that is readily controlled by the silicon nanostructure, and adhesion of the cured polymer to the PSi photonic crystal allows preparation of lightweight (10 mg) freestanding lenses (4.7 mm focal length) with an embedded optical component (e.g. optical rugate filter, resonant cavity, distributed Bragg reflector). Our fabrication process shows excellent reliability (yield 95%) and low cost and we expect our lens to have implications in a wide range of applications. As a proof-of-concept, using a single monolithic lens/filter element we demonstrate: fluorescence imaging of isolated human cancer cells with rejection of the blue excitation light, through a lens that is self-adhered to a commercial smartphone; shaping the emission spectrum of a white light emitting diode (LED) to tune the color from red through blue; and selection of a narrow wavelength band (bandwidth 5 nm) from a fluorescent molecular probe.
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Affiliation(s)
- Stefano Mariani
- Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122, Italy
| | - Valentina Robbiano
- Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122, Italy
| | - Rossella Iglio
- Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122, Italy
| | - Antonino A La Mattina
- Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122, Italy
| | - Pantea Nadimi
- Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122, Italy
| | - Joanna Wang
- Materials Science and Engineering Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Byungji Kim
- Materials Science and Engineering Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Tushar Kumeria
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Michael J Sailor
- Department of Chemistry and Biochemistry, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Giuseppe Barillaro
- Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122, Italy
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8
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Leonard H, Colodner R, Halachmi S, Segal E. Recent Advances in the Race to Design a Rapid Diagnostic Test for Antimicrobial Resistance. ACS Sens 2018; 3:2202-2217. [PMID: 30350967 DOI: 10.1021/acssensors.8b00900] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Even with advances in antibiotic therapies, bacterial infections persistently plague society and have amounted to one of the most prevalent issues in healthcare today. Moreover, the improper and excessive administration of antibiotics has led to resistance of many pathogens to prescribed therapies, rendering such antibiotics ineffective against infections. While the identification and detection of bacteria in a patient's sample is critical for point-of-care diagnostics and in a clinical setting, the consequent determination of the correct antibiotic for a patient-tailored therapy is equally crucial. As a result, many recent research efforts have been focused on the development of sensors and systems that correctly guide a physician to the best antibiotic to prescribe for an infection, which can in turn, significantly reduce the instances of antibiotic resistance and the evolution of bacteria "superbugs." This review details the advantages and shortcomings of the recent advances (focusing from 2016 and onward) made in the developments of antimicrobial susceptibility testing (AST) measurements. Detection of antibiotic resistance by genomic AST techniques relies on the prediction of antibiotic resistance via extracted bacterial DNA content, while phenotypic determinations typically track physiological changes in cells and/or populations exposed to antibiotics. Regardless of the method used for AST, factors such as cost, scalability, and assay time need to be weighed into their design. With all of the expansive innovation in the field, which technology and sensing systems demonstrate the potential to detect antimicrobial resistance in a clinical setting?
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Affiliation(s)
- Heidi Leonard
- Department of Biotechnology and Food Engineering, Technion − Israel Institute of Technology, Haifa, Israel 3200003
| | - Raul Colodner
- Laboratory of Clinical Microbiology, Emek Medical Center, Afula, Israel 18101
| | - Sarel Halachmi
- Department of Urology, Bnai Zion Medical Center, Haifa, Israel 3104800
| | - Ester Segal
- Department of Biotechnology and Food Engineering, Technion − Israel Institute of Technology, Haifa, Israel 3200003
- The Russell Berrie Nanotechnology Institute, Technion − Israel Institute of Technology, Haifa, Israel, 3200003
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9
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Huang X, Xu D, Chen J, Liu J, Li Y, Song J, Ma X, Guo J. Smartphone-based analytical biosensors. Analyst 2018; 143:5339-5351. [DOI: 10.1039/c8an01269e] [Citation(s) in RCA: 171] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
With the rapid development, mass production, and pervasive distribution of smartphones in recent years, they have provided people with portable, cost-effective, and easy-to-operate platforms to build analytical biosensors for point-of-care (POC) applications and mobile health.
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Affiliation(s)
- Xiwei Huang
- Ministry of Education Key Lab of RF Circuits and Systems
- Hangzhou Dianzi University
- Hangzhou 310018
- P. R. China
| | - Dandan Xu
- State Key Lab of Advanced Welding and Joining
- Harbin Institute of Technology (Shenzhen)
- Shenzhen 518055
- P. R. China
- Ministry of Education Key Lab of Micro-systems and Micro-structures Manufacturing
| | - Jin Chen
- Ministry of Education Key Lab of RF Circuits and Systems
- Hangzhou Dianzi University
- Hangzhou 310018
- P. R. China
| | - Jixuan Liu
- Ministry of Education Key Lab of RF Circuits and Systems
- Hangzhou Dianzi University
- Hangzhou 310018
- P. R. China
| | - Yangbo Li
- Ministry of Education Key Lab of RF Circuits and Systems
- Hangzhou Dianzi University
- Hangzhou 310018
- P. R. China
| | - Jing Song
- School of Economics and Management
- Tsinghua University
- Beijing 100084
- P. R. China
| | - Xing Ma
- State Key Lab of Advanced Welding and Joining
- Harbin Institute of Technology (Shenzhen)
- Shenzhen 518055
- P. R. China
- Ministry of Education Key Lab of Micro-systems and Micro-structures Manufacturing
| | - Jinhong Guo
- School of Communication and Information Engineering
- University of Electronic Science and Technology of China
- Chengdu 611731
- P. R. China
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