Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

[1]  Gaojie Yang,et al.  Thermal immuno-nanomedicine in cancer , 2023, Nature Reviews Clinical Oncology.

[2]  Xingcai Zhang,et al.  Lipid nanomaterials-based RNA therapy and cancer treatment , 2022, Acta pharmaceutica Sinica. B.

[3]  Jiashen Meng,et al.  Nir-Ii-Enhanced Single-Atom-Nanozyme for Sustainable Accelerating Bacteria-Infected Wound Healing , 2023, SSRN Electronic Journal.

[4]  W. Wen,et al.  Split-Ring Structured All-Inorganic Perovskite Photodetector Arrays for Masterly Internet of Things , 2022, Nano-Micro Letters.

[5]  Xingcai Zhang,et al.  Chip-Based High-Dimensional Optical Neural Network , 2022, Nano-Micro Letters.

[6]  Yihai Cao,et al.  The landscape of mRNA nanomedicine , 2022, Nature Medicine.

[7]  M. Guo,et al.  In Situ Forming Epidermal Bioelectronics for Daily Monitoring and Comprehensive Exercise. , 2022, ACS nano.

[8]  D. Peer,et al.  Nanotechnology-based strategies against SARS-CoV-2 variants , 2022, Nature Nanotechnology.

[9]  Xingcai Zhang,et al.  Layered double hydroxide-based nanomaterials for biomedical applications. , 2022, Chemical Society reviews.

[10]  M. Guo,et al.  Click chemistry extracellular vesicle/peptide/chemokine nanocarriers for treating central nervous system injuries , 2022, Acta pharmaceutica Sinica. B.

[11]  Bifeng Liu,et al.  A magnet-actuated microfluidic array chip for high-throughput pretreatment and amplification and detection of multiple pathogens. , 2022, The Analyst.

[12]  Bifeng Liu,et al.  Microfluidics-based strategies for molecular diagnostics of infectious diseases , 2022, Military Medical Research.

[13]  Xingcai Zhang,et al.  Microalgae-based oral microcarriers for gut microbiota homeostasis and intestinal protection in cancer radiotherapy , 2022, Nature Communications.

[14]  Jiashen Meng,et al.  Antibacterial Cascade Catalytic Glutathione-Depleting MOF Nanoreactors. , 2022, ACS applied materials & interfaces.

[15]  Bifeng Liu,et al.  Hand-powered vacuum-driven microfluidic gradient generator for high-throughput antimicrobial susceptibility testing. , 2022, Biosensors & bioelectronics.

[16]  S. Chi,et al.  Harnessing GLUT1 Targeted Pro-oxidant Ascorbate for Synergistic Phototherapeutics. , 2022, Angewandte Chemie.

[17]  Xingcai Zhang,et al.  DNA-Damage-Response-Targeting Mitochondria-Activated Multifunctional Prodrug Strategy for Self-defensive Tumor Therapy. , 2022, Angewandte Chemie.

[18]  Yan Liu,et al.  Imparting reusable and SARS-CoV-2 inhibition properties to standard masks through metal-organic nanocoatings , 2022, Journal of Hazardous Materials.

[19]  Bifeng Liu,et al.  Multi-reagents dispensing centrifugal microfluidics for point-of-care testing. , 2022, Biosensors & bioelectronics.

[20]  J. Bonventre,et al.  From Bench to the Clinic: The Path to Translation of Nanotechnology-Enabled mRNA SARS-CoV-2 Vaccines , 2022, Nano-Micro Letters.

[21]  Sheng Xu,et al.  Three-dimensional transistor arrays for intra- and inter-cellular recording , 2021, Nature Nanotechnology.

[22]  Xingcai Zhang,et al.  Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis. , 2021, Nanoscale.

[23]  Xingcai Zhang,et al.  Orally deliverable strategy based on microalgal biomass for intestinal disease treatment , 2021, Science advances.

[24]  Bifeng Liu,et al.  Wettability-patterned microchip for emerging biomedical materials and technologies , 2021, Materials Today.

[25]  Yufeng Zheng,et al.  2D MOF Periodontitis Photodynamic Ion Therapy. , 2021, Journal of the American Chemical Society.

[26]  JeongGil Ko,et al.  Fluorescent sensor array for high-precision pH classification with machine learning-supported mobile devices , 2021 .

[27]  N. Zhang,et al.  A non-printed integrated-circuit textile for wireless theranostics , 2021, Nature Communications.

[28]  Qilin Duan,et al.  Surface Plasmonic Sensors: Sensing Mechanism and Recent Applications , 2021, Sensors.

[29]  Sebastian J. F. Fudickar,et al.  Mask R-CNN Based C. Elegans Detection with a DIY Microscope , 2021, Biosensors.

[30]  Xueji Zhang,et al.  Luminescent wearable biosensors based on gold nanocluster networks for "turn-on" detection of Uric acid, glucose and alcohol in sweat. , 2021, Biosensors & bioelectronics.

[31]  Xingcai Zhang,et al.  Instrumentation-Compact Digital Microfluidic Reaction Interface-Extended Loop-Mediated Isothermal Amplification for Sample-to-Answer Testing of Vibrio parahaemolyticus. , 2021, Analytical chemistry.

[32]  Q. Wei,et al.  Cellphone enabled point-of-care assessment of breast tumor cytology and molecular HER2 expression from fine-needle aspirates , 2021, NPJ breast cancer.

[33]  J. Collins,et al.  CRISPR-based diagnostics , 2021, Nature Biomedical Engineering.

[34]  Aydogan Ozcan,et al.  Machine learning and computation-enabled intelligent sensor design , 2021, Nature Machine Intelligence.

[35]  D. Pillay,et al.  Deep learning of HIV field-based rapid tests , 2021, Nature Medicine.

[36]  D. Kuritzkes,et al.  Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images , 2021, Nature Biomedical Engineering.

[37]  Jianwei Shuai,et al.  Machine-learning micropattern manufacturing , 2021, Nano Today.

[38]  E. Elinav,et al.  Machine learning in clinical decision making. , 2021, Med.

[39]  M. Kanakasabapathy,et al.  Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory , 2021, Journal of Assisted Reproduction and Genetics.

[40]  Qi Liu,et al.  A self-powered rapid loading microfluidic chip for vector-borne viruses detection using RT-LAMP , 2021 .

[41]  Kai Heinrich,et al.  Machine learning and deep learning , 2021, Electron. Mark..

[42]  Amjad J. Humaidi,et al.  Review of deep learning: concepts, CNN architectures, challenges, applications, future directions , 2021, Journal of Big Data.

[43]  Y. S. Zhang,et al.  Targeting Hypoxic Tumors with Hybrid Nanobullets for Oxygen-Independent Synergistic Photothermal and Thermodynamic Therapy , 2021, Nano-micro letters.

[44]  Amjad J. Humaidi,et al.  Review of deep learning: concepts, CNN architectures, challenges, applications, future directions , 2021, Journal of Big Data.

[45]  Jian Pei,et al.  Model complexity of deep learning: a survey , 2021, Knowledge and Information Systems.

[46]  Xingcai Zhang,et al.  Capturing functional two-dimensional nanosheets from sandwich-structure vermiculite for cancer theranostics , 2021, Nature Communications.

[47]  E. Mcleod,et al.  High-Speed Lens-Free Holographic Sensing of Protein Molecules Using Quantitative Agglutination Assays. , 2021, ACS sensors.

[48]  Hakho Lee,et al.  Kaleidoscopic fluorescent arrays for machine-learning-based point-of-care chemical sensing. , 2021, Sensors and actuators. B, Chemical.

[49]  N. Shomron,et al.  Machine learning-based prediction of COVID-19 diagnosis based on symptoms , 2021, npj Digital Medicine.

[50]  Z. Tian,et al.  Immunogenic-cell-killing and immunosuppression-inhibiting nanomedicine , 2020, Bioactive materials.

[51]  Lionel M. Ni,et al.  Generalizing from a Few Examples , 2020, ACM Comput. Surv..

[52]  Zhanyu Ma,et al.  A Concise Review of Recent Few-shot Meta-learning Methods , 2020, Neurocomputing.

[53]  Yeahia Sarker,et al.  Graph Neural Network: A Comprehensive Review on Non-Euclidean Space , 2021, IEEE Access.

[54]  Charles S. Henry,et al.  NFC-enabling smartphone-based portable amperometric immunosensor for hepatitis B virus detection , 2021 .

[55]  J. Landers,et al.  Digital postprocessing and image segmentation for objective analysis of colorimetric reactions , 2020, Nature Protocols.

[56]  Mohamed S Draz,et al.  Virus detection using nanoparticles and deep neural network–enabled smartphone system , 2020, Science advances.

[57]  L. Deng,et al.  Biologically modified nanoparticles as theranostic bionanomaterials , 2020 .

[58]  Manoj Kumar Kanakasabapathy,et al.  Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning. , 2020, ACS nano.

[59]  Xingcai Zhang,et al.  Bio‐Inspired Ionic Skin for Theranostics , 2020, Advanced Functional Materials.

[60]  A. L. González,et al.  Machine Learning for Predicting the Surface Plasmon Resonance of Perfect and Concave Gold Nanocubes , 2020 .

[61]  Z. Tian,et al.  Fighting Immune Cold and Reprogramming Immunosuppressive Tumor Microenvironment with Red Blood Cell Membrane-Camouflaged Nanobullets. , 2020, ACS nano.

[62]  Xingcai Zhang,et al.  Insights from nanotechnology in COVID-19 treatment , 2020, Nano Today.

[63]  Wei Gao,et al.  Wearable electrochemical biosensors in North America. , 2020, Biosensors & bioelectronics.

[64]  Julio Cartier Maia Gomes,et al.  SmartSPR sensor: Machine learning approaches to create intelligent surface plasmon based sensors. , 2020, Biosensors & bioelectronics.

[65]  J. D. den Toonder,et al.  Wearable sweat sensing for prolonged, semicontinuous, and nonobtrusive health monitoring , 2020 .

[66]  R. Langer,et al.  A materials-science perspective on tackling COVID-19 , 2020, Nature Reviews Materials.

[67]  Jong-ryul Choi,et al.  Machine learning-based design of meta-plasmonic biosensors with negative index metamaterials. , 2020, Biosensors & bioelectronics.

[68]  Sam Emaminejad,et al.  A programmable epidermal microfluidic valving system for wearable biofluid management and contextual biomarker analysis , 2020, Nature Communications.

[69]  Wei Gao,et al.  Wireless battery-free wearable sweat sensor powered by human motion , 2020, Science Advances.

[70]  B. Neupane,et al.  A smartphone microscopic method for simultaneous detection of (oo)cysts of Cryptosporidium and Giardia , 2020, PLoS neglected tropical diseases.

[71]  Daniel Citterio,et al.  All-printed semiquantitative paper-based analytical devices relying on QR code array readout. , 2020, The Analyst.

[72]  Sina Ardalan,et al.  Towards smart personalized perspiration analysis: An IoT-integrated cellulose-based microfluidic wearable patch for smartphone fluorimetric multi-sensing of sweat biomarkers. , 2020, Biosensors & bioelectronics.

[73]  Ciarán M Lee,et al.  Improving the accuracy of medical diagnosis with causal machine learning , 2020, Nature Communications.

[74]  Xingcai Zhang,et al.  Microenvironment-Controlled Micropatterned Microfluidic Model (MMMM) for Biomimetic In Situ Studies. , 2020, ACS nano.

[75]  Yunwen Tao,et al.  Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma , 2020, Nature Communications.

[76]  Daniel Neil,et al.  Applications of machine learning to diagnosis and treatment of neurodegenerative diseases , 2020, Nature Reviews Neurology.

[77]  Jun Zhou,et al.  One-step rapid quantification of SARS-CoV-2 virus particles via low-cost nanoplasmonic sensors in generic microplate reader and point-of-care device , 2020, bioRxiv.

[78]  Manoj Kumar Kanakasabapathy,et al.  Performance of a deep learning based neural network in the selection of human blastocysts for implantation , 2020, eLife.

[79]  Tao Wang,et al.  Design of high-performance plasmonic nanosensors by particle swarm optimization algorithm combined with machine learning , 2020, Nanotechnology.

[80]  Maha Alafeef,et al.  Selective Naked-Eye Detection of SARS-CoV-2 Mediated by N Gene Targeted Antisense Oligonucleotide Capped Plasmonic Nanoparticles , 2020, ACS nano.

[81]  Yoon‐Kyoung Cho,et al.  A fidget spinner for the point-of-care diagnosis of urinary tract infection , 2020, Nature Biomedical Engineering.

[82]  Zachary S. Ballard,et al.  Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors , 2020, npj Digital Medicine.

[83]  W. Liang,et al.  Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography , 2020, Cell.

[84]  Aaron D. Ames,et al.  Biofuel-powered soft electronic skin with multiplexed and wireless sensing for human-machine interfaces , 2020, Science Robotics.

[85]  Laura M. Heiser,et al.  How Machine Learning Will Transform Biomedicine , 2020, Cell.

[86]  J. Ioannidis,et al.  Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies , 2020, BMJ.

[87]  C. Yi,et al.  A smartphone-based sensing system for on-site quantitation of multiple heavy metal ions using fluorescent carbon nanodots-based microarrays. , 2020, ACS sensors.

[88]  Qing Yang,et al.  Scalable Fabrication of Quasi-One-Dimensional Au Nanoribbons for Plasmonic Sensing. , 2020, Nano letters.

[89]  Ling Yu,et al.  On-chip RT-LAMP and colorimetric detection of the prostate cancer 3 biomarker with an integrated thermal and imaging box. , 2020, Talanta.

[90]  Jungyoup Han,et al.  A new microchannel capillary flow assay (MCFA) platform with lyophilized chemiluminescence reagents for a smartphone-based POCT detecting malaria , 2020, Microsystems & nanoengineering.

[91]  Hongda Chen,et al.  Smartphone Biosensor System with Multi-Testing Unit Based on Localized Surface Plasmon Resonance Integrated with Microfluidics Chip , 2020, Sensors.

[92]  Derek Tseng,et al.  Automated screening of sickle cells using a smartphone-based microscope and deep learning , 2019, 2020 Conference on Lasers and Electro-Optics (CLEO).

[93]  S. Sia,et al.  Biosensors for Personal Mobile Health: A System Architecture Perspective , 2019, Advanced materials technologies.

[94]  G. Dreyfuss,et al.  U1 snRNP regulates cancer cell migration and invasion , 2019, bioRxiv.

[95]  F. Hu,et al.  Smartphone-based droplet digital LAMP device with rapid nucleic acid isolation for highly sensitive point-of-care detection. , 2019, Analytical chemistry.

[96]  Ahmad B. A. Hassanat,et al.  Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review , 2019, Big Data.

[97]  Zhaoping Li,et al.  A laser-engraved wearable sensor for sensitive detection of uric acid and tyrosine in sweat , 2019, Nature Biotechnology.

[98]  Manoj Kumar Kanakasabapathy,et al.  Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology. , 2019, Lab on a chip.

[99]  Michael V. D’Ambrosio,et al.  A smart tele-cytology point-of-care platform for oral cancer screening , 2019, PloS one.

[100]  Yi Wang,et al.  Protein binding kinetics quantification via coupled plasmonic-photonic resonance nanosensors in generic microplate reader. , 2019, Biosensors & bioelectronics.

[101]  Zhao Li,et al.  Quantitation of Femtomolar Protein Biomarkers Using a Simple Microbubbling Digital Assay via Bright-field Smartphone Imaging. , 2019, Angewandte Chemie.

[102]  Hui Chen,et al.  Quantitation of Femtomolar‐Level Protein Biomarkers Using a Simple Microbubbling Digital Assay and Bright‐Field Smartphone Imaging , 2019, Angewandte Chemie.

[103]  M. Toma,et al.  Plasmonic coloration of silver nanodome arrays for a smartphone-based plasmonic biosensor , 2019, Nanoscale advances.

[104]  Gwiyeong Moon,et al.  Deep Learning Approach for Enhanced Detection of Surface Plasmon Scattering. , 2019, Analytical chemistry.

[105]  Qianming Xu,et al.  A low‐cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning , 2019, Journal of biophotonics.

[106]  Xiangjian He,et al.  Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges , 2019, Journal of Digital Imaging.

[107]  William Graf,et al.  Deep learning for cellular image analysis , 2019, Nature Methods.

[108]  Nan Jiang,et al.  Lateral and Vertical Flow Assays for Point‐of‐Care Diagnostics , 2019, Advanced healthcare materials.

[109]  Taesup Kim,et al.  Edge-Labeling Graph Neural Network for Few-Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[110]  Baolin Guo,et al.  Polyphenol and self-assembly: metal polyphenol nanonetwork for drug delivery and pharmaceutical applications , 2019, Future Drug Discovery.

[111]  Wei Gao,et al.  Wearable and flexible electronics for continuous molecular monitoring. , 2019, Chemical Society reviews.

[112]  Sebastian J. F. Fudickar,et al.  Mass Surveilance of C. elegans—Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection , 2019, Sensors.

[113]  Wei Gao,et al.  Flexible Electronics toward Wearable Sensing. , 2019, Accounts of chemical research.

[114]  Sakirevupalli Venkatramana Reddy,et al.  Structural, Optical and Magnetic Properties of (Ni, Al) Co-Doped ZnO Nanoparticles , 2019, Advances in Materials.

[115]  Michael R. Thomas,et al.  Taking connected mobile-health diagnostics of infectious diseases to the field , 2019, Nature.

[116]  Vinod Kumar Chauhan,et al.  Problem formulations and solvers in linear SVM: a review , 2019, Artificial Intelligence Review.

[117]  Ah Chung Tsoi,et al.  The Vapnik-Chervonenkis dimension of graph and recursive neural networks , 2018, Neural Networks.

[118]  Y. Hu,et al.  pH-Sensitive Poly(β-amino ester)s Nanocarriers Facilitate the Inhibition of Drug Resistance in Breast Cancer Cells , 2018, Nanomaterials.

[119]  Hailin Tang,et al.  Breast cancer subtypes and the risk of distant metastasis at initial diagnosis: a population-based study , 2018, Cancer management and research.

[120]  Weibo Fang,et al.  Smartphone-based mobile digital PCR device for DNA quantitative analysis with high accuracy. , 2018, Biosensors & bioelectronics.

[121]  Kamyar Mehrabi Kochehbyoki,et al.  DNA engineered micromotors powered by metal nanoparticles for motion based cellphone diagnostics , 2018, Nature Communications.

[122]  Yankai Cao,et al.  Machine Learning Algorithms for Liquid Crystal-Based Sensors. , 2018, ACS sensors.

[123]  R. Meagher,et al.  Colorimetric-Luminance Readout for Quantitative Analysis of Fluorescence Signals with a Smartphone CMOS Sensor. , 2018, Analytical chemistry.

[124]  Matt J. Whitfield,et al.  Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning , 2018, Nature Biomedical Engineering.

[125]  C. Seymour,et al.  Point-of-care sensors for the management of sepsis , 2018, Nature Biomedical Engineering.

[126]  Conor Liston,et al.  New machine-learning technologies for computer-aided diagnosis , 2018, Nature Medicine.

[127]  Seema Shah,et al.  A Review of Machine Learning and Deep Learning Applications , 2018, 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA).

[128]  John A Rogers,et al.  A fluorometric skin-interfaced microfluidic device and smartphone imaging module for in situ quantitative analysis of sweat chemistry. , 2018, Lab on a chip.

[129]  Cesar M. Castro,et al.  Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning , 2018, Nature Biomedical Engineering.

[130]  Anish Vasan,et al.  Motion-Based Immunological Detection of Zika Virus Using Pt-Nanomotors and a Cellphone. , 2018, ACS nano.

[131]  Xingcai Zhang,et al.  Nano-carriers for targeted delivery and biomedical imaging enhancement. , 2018, Therapeutic delivery.

[132]  Jixiao Liu,et al.  A low cost and portable smartphone microscopic device for cell counting , 2018 .

[133]  Juhwan Park,et al.  Finger-actuated microfluidic device for the blood cross-matching test. , 2018, Lab on a chip.

[134]  A. Somboonkaew,et al.  Rh blood phenotyping (D, E, e, C, c) microarrays using multichannel surface plasmon resonance imaging. , 2018, Biosensors & bioelectronics.

[135]  Daniel Irimia,et al.  Diagnosis of sepsis from a drop of blood by measurement of spontaneous neutrophil motility in a microfluidic assay , 2018, Nature Biomedical Engineering.

[136]  Stephen Yip,et al.  Machine learning classifies cancer , 2018, Nature.

[137]  A. Goldenberg,et al.  Learning from Everyday Images Enables Expert-like Diagnosis of Retinal Diseases , 2018, Cell.

[138]  Quanshi Zhang,et al.  Visual interpretability for deep learning: a survey , 2018, Frontiers of Information Technology & Electronic Engineering.

[139]  Gazihan Alankus,et al.  Quantifying colorimetric tests using a smartphone app based on machine learning classifiers , 2018 .

[140]  Dan Du,et al.  A portable smart-phone device for rapid and sensitive detection of E. coli O157:H7 in Yoghurt and Egg. , 2018, Biosensors & bioelectronics.

[141]  Yibo Zhang,et al.  Deep learning enhanced mobile-phone microscopy , 2017, ACS Photonics.

[142]  Tao Xiang,et al.  Learning to Compare: Relation Network for Few-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[143]  Syed Muhammad Anwar,et al.  Medical Image Analysis using Convolutional Neural Networks: A Review , 2017, Journal of Medical Systems.

[144]  M. Kanakasabapathy,et al.  An inexpensive smartphone-based device for point-of-care ovulation testing. , 2018, Lab on a chip.

[145]  Manoj Kumar Kanakasabapathy,et al.  Rapid, label-free CD4 testing using a smartphone compatible device. , 2017, Lab on a chip.

[146]  V. B. Surya Prasath,et al.  Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier - A Review , 2017, Big Data.

[147]  V. Jaiganesh,et al.  A Literature Review on Supervised Machine Learning Algorithms and Boosting Process , 2017 .

[148]  Kenji Suzuki,et al.  Overview of deep learning in medical imaging , 2017, Radiological Physics and Technology.

[149]  Ke Li,et al.  A smartphone-based point-of-care diagnosis of H1N1 with microfluidic convection PCR , 2017 .

[150]  Hua-Zhong Ying,et al.  New Epigallocatechin Gallate (EGCG) Nanocomplexes Co-Assembled with 3-Mercapto-1-Hexanol and β-Lactoglobulin for Improvement of Antitumor Activity , 2017 .

[151]  Steve Feng,et al.  Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy , 2017 .

[152]  Derek Tseng,et al.  Evaluation of a Mobile Phone-Based Microscope for Screening of Schistosoma haematobium Infection in Rural Ghana. , 2017, The American journal of tropical medicine and hygiene.

[153]  Manoj Kumar Kanakasabapathy,et al.  An automated smartphone-based diagnostic assay for point-of-care semen analysis , 2017, Science Translational Medicine.

[154]  Luke P. Lee,et al.  Self-powered integrated microfluidic point-of-care low-cost enabling (SIMPLE) chip , 2017, Science Advances.

[155]  Sungho Ko,et al.  A smartphone-based optical platform for colorimetric analysis of microfluidic device , 2017 .

[156]  Aydogan Ozcan,et al.  Computational Sensing Using Low-Cost and Mobile Plasmonic Readers Designed by Machine Learning. , 2017, ACS nano.

[157]  Derek Tseng,et al.  Targeted DNA sequencing and in situ mutation analysis using mobile phone microscopy , 2017, Nature Communications.

[158]  Xinhao Wang,et al.  Self-Referenced Smartphone-Based Nanoplasmonic Imaging Platform for Colorimetric Biochemical Sensing. , 2017, Analytical chemistry.

[159]  M. Saad Bhamla,et al.  Hand-powered ultralow-cost paper centrifuge , 2017, Nature Biomedical Engineering.

[160]  M. Daneel,et al.  Achievements and Challenges , 2017 .

[161]  Euiwon Bae,et al.  Colorimetric analysis of saliva–alcohol test strips by smartphone-based instruments using machine-learning algorithms , 2017 .

[162]  Pierre Parrend,et al.  Cerberus, an Access Control Scheme for Enforcing Least Privilege in Patient Cohort Study Platforms , 2017, Journal of Medical Systems.

[163]  Daniel Filippini,et al.  A 3D printed device for quantitative enzymatic detection using cell phones , 2016 .

[164]  Martin K. Nielsen,et al.  Automated parasite faecal egg counting using fluorescence labelling, smartphone image capture and computational image analysis. , 2016, International journal for parasitology.

[165]  Harvey Friedman,et al.  Smart Cup: A Minimally-Instrumented, Smartphone-Based Point-of-Care Molecular Diagnostic Device. , 2016, Sensors and actuators. B, Chemical.

[166]  Shizhi Qian,et al.  A smartphone-based point-of-care diagnosis of H1N1 with microfluidic convection PCR , 2016, Microsystem Technologies.

[167]  Sam Emaminejad,et al.  Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis , 2016, Nature.

[168]  Udayan Khurana,et al.  Automating Feature Engineering , 2016 .

[169]  Gerard L. Coté,et al.  Malaria Diagnosis Using a Mobile Phone Polarized Microscope , 2015, Scientific Reports.

[170]  Nuno M Reis,et al.  Portable smartphone quantitation of prostate specific antigen (PSA) in a fluoropolymer microfluidic device. , 2015, Biosensors & bioelectronics.

[171]  Daniel Filippini,et al.  Autonomous Chemical Sensing Interface for Universal Cell Phone Readout. , 2015, Angewandte Chemie.

[172]  Daniel A. Fletcher,et al.  Point-of-care quantification of blood-borne filarial parasites with a mobile phone microscope , 2015, Science Translational Medicine.

[173]  Hakho Lee,et al.  Digital diffraction analysis enables low-cost molecular diagnostics on a smartphone , 2015, Proceedings of the National Academy of Sciences.

[174]  Steve Feng,et al.  Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning. , 2015, Lab on a chip.

[175]  A. Rai,et al.  A smartphone dongle for diagnosis of infectious diseases at the point of care , 2015, Science Translational Medicine.

[176]  Liyun Guan,et al.  Barcode-like paper sensor for smartphone diagnostics: an application of blood typing. , 2014, Analytical chemistry.

[177]  Luke P. Lee,et al.  Self-powered Imbibing Microfluidic Pump by Liquid Encapsulation: SIMPLE. , 2014, Lab on a chip.

[178]  L. Capitán-Vallvey,et al.  Smartphone-based simultaneous pH and nitrite colorimetric determination for paper microfluidic devices. , 2014, Analytical chemistry.

[179]  Liwei Lin,et al.  Finger-powered microfluidic systems using multilayer soft lithography and injection molding processes. , 2014, Lab on a chip.

[180]  Changhuei Yang,et al.  A smartphone-based chip-scale microscope using ambient illumination. , 2014, Lab on a chip.

[181]  Daniel A. Fletcher,et al.  Low-Cost Mobile Phone Microscopy with a Reversed Mobile Phone Camera Lens , 2014, PloS one.

[182]  Jacek M. Zurada,et al.  Review and performance comparison of SVM- and ELM-based classifiers , 2014, Neurocomputing.

[183]  Li Jiang,et al.  Solar thermal polymerase chain reaction for smartphone-assisted molecular diagnostics , 2014, Scientific Reports.

[184]  N. Abbott,et al.  Analysis of the internal configurations of droplets of liquid crystal using flow cytometry. , 2013, Analytical chemistry.

[185]  D. Erickson,et al.  Smartphone based health accessory for colorimetric detection of biomarkers in sweat and saliva. , 2013, Lab on a chip.

[186]  Hongying Zhu,et al.  Cost-effective and rapid blood analysis on a cell-phone. , 2013, Lab on a chip.

[187]  Li Shen,et al.  Point-of-care colorimetric detection with a smartphone. , 2012, Lab on a chip.

[188]  J. Hernandez-Ortiz,et al.  Liquid-crystal-mediated self-assembly at nanodroplet interfaces , 2012, Nature.

[189]  Tao Chen,et al.  Squeeze-chip: a finger-controlled microfluidic flow network device and its application to biochemical assays. , 2012, Lab on a chip.

[190]  Patrick S. Noonan,et al.  Surfactant–DNA interactions at the liquid crystal–aqueous interface , 2012 .

[191]  Sotiris B. Kotsiantis,et al.  Decision trees: a recent overview , 2011, Artificial Intelligence Review.

[192]  Hongying Zhu,et al.  Optofluidic fluorescent imaging cytometry on a cell phone. , 2011, Analytical chemistry.

[193]  S. Yeh,et al.  DNA detection using commercial mobile phones. , 2011, Biosensors & bioelectronics.

[194]  Daniel Malamud,et al.  An isothermal amplification reactor with an integrated isolation membrane for point-of-care detection of infectious diseases. , 2011, The Analyst.

[195]  A. Ozcan,et al.  Holographic pixel super-resolution in portable lensless on-chip microscopy using a fiber-optic array. , 2011, Lab on a chip.

[196]  Amy L. Gryshuk,et al.  Cell-Phone-Based Platform for Biomedical Device Development and Education Applications , 2011, PloS one.

[197]  Guoan Zheng,et al.  Color-capable sub-pixel resolving optofluidic microscope for on-chip cell imaging , 2010, IEEE Winter Topicals 2011.

[198]  Derek Tseng,et al.  Lensfree microscopy on a cellphone. , 2010, Lab on a chip.

[199]  G. Whitesides,et al.  Simple telemedicine for developing regions: camera phones and paper-based microfluidic devices for real-time, off-site diagnosis. , 2008, Analytical chemistry.

[200]  Ruibo Lu,et al.  Liquid-crystal imaging of molecular-tilt ordering in self-assembled lipid tubules. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[201]  A. Föhrenbach,et al.  SIMPLE++ , 2000, OR Spectr..

[202]  H. Baxter Williams,et al.  A Survey , 1992 .

[203]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[204]  Rapid Determination of Phase Diagrams for Biomolecular LiquidLiquid Phase Separation with Microfluidics , 2022 .