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1. An insertable glucose sensor using a compact and cost-effective phosphorescence lifetime imager and machine learning

Author

Goncharov, Artem, Gorocs, Zoltan, Pradhan, Ridhi, Ko, Brian, Ajmal, Ajmal, Rodriguez, Andres, Baum, David, Veszpremi, Marcell, Yang, Xilin, Pindrys, Maxime, Zheng, Tianle, Wang, Oliver, Ramella-Roman, Jessica C., McShane, Michael J., and Ozcan, Aydogan

Subjects
Physics - Medical Physics, Computer Science - Machine Learning, Physics - Applied Physics, Physics - Biological Physics
Abstract

Optical continuous glucose monitoring (CGM) systems are emerging for personalized glucose management owing to their lower cost and prolonged durability compared to conventional electrochemical CGMs. Here, we report a computational CGM system, which integrates a biocompatible phosphorescence-based insertable biosensor and a custom-designed phosphorescence lifetime imager (PLI). This compact and cost-effective PLI is designed to capture phosphorescence lifetime images of an insertable sensor through the skin, where the lifetime of the emitted phosphorescence signal is modulated by the local concentration of glucose. Because this phosphorescence signal has a very long lifetime compared to tissue autofluorescence or excitation leakage processes, it completely bypasses these noise sources by measuring the sensor emission over several tens of microseconds after the excitation light is turned off. The lifetime images acquired through the skin are processed by neural network-based models for misalignment-tolerant inference of glucose levels, accurately revealing normal, low (hypoglycemia) and high (hyperglycemia) concentration ranges. Using a 1-mm thick skin phantom mimicking the optical properties of human skin, we performed in vitro testing of the PLI using glucose-spiked samples, yielding 88.8% inference accuracy, also showing resilience to random and unknown misalignments within a lateral distance of ~4.7 mm with respect to the position of the insertable sensor underneath the skin phantom. Furthermore, the PLI accurately identified larger lateral misalignments beyond 5 mm, prompting user intervention for re-alignment. The misalignment-resilient glucose concentration inference capability of this compact and cost-effective phosphorescence lifetime imager makes it an appealing wearable diagnostics tool for real-time tracking of glucose and other biomarkers., Comment: 24 Pages, 4 Figures

Published
2024
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16. Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning

Author

Yang, Xilin, Bai, Bijie, Zhang, Yijie, Aydin, Musa, Selcuk, Sahan Yoruc, Guo, Zhen, Fishbein, Gregory A., Atlan, Karine, Wallace, William Dean, Pillar, Nir, and Ozcan, Aydogan

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Physics - Optics
Abstract

Systemic amyloidosis is a group of diseases characterized by the deposition of misfolded proteins in various organs and tissues, leading to progressive organ dysfunction and failure. Congo red stain is the gold standard chemical stain for the visualization of amyloid deposits in tissue sections, as it forms complexes with the misfolded proteins and shows a birefringence pattern under polarized light microscopy. However, Congo red staining is tedious and costly to perform, and prone to false diagnoses due to variations in the amount of amyloid, staining quality and expert interpretation through manual examination of tissue under a polarization microscope. Here, we report the first demonstration of virtual birefringence imaging and virtual Congo red staining of label-free human tissue to show that a single trained neural network can rapidly transform autofluorescence images of label-free tissue sections into brightfield and polarized light microscopy equivalent images, matching the histochemically stained versions of the same samples. We demonstrate the efficacy of our method with blind testing and pathologist evaluations on cardiac tissue where the virtually stained images agreed well with the histochemically stained ground truth images. Our virtually stained polarization and brightfield images highlight amyloid birefringence patterns in a consistent, reproducible manner while mitigating diagnostic challenges due to variations in the quality of chemical staining and manual imaging processes as part of the clinical workflow., Comment: 20 Pages, 5 Figures

Published
2024
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21. Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network

Author

Ma, Guangdong, Yang, Xilin, Bai, Bijie, Li, Jingxi, Li, Yuhang, Gan, Tianyi, Shen, Che-Yung, Zhang, Yijie, Li, Yuzhu, Jarrahi, Mona, and Ozcan, Aydogan

Subjects
Physics - Optics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing
Abstract

Large-scale and high-dimensional permutation operations are important for various applications in e.g., telecommunications and encryption. Here, we demonstrate the use of all-optical diffractive computing to execute a set of high-dimensional permutation operations between an input and output field-of-view through layer rotations in a diffractive optical network. In this reconfigurable multiplexed material designed by deep learning, every diffractive layer has four orientations: 0, 90, 180, and 270 degrees. Each unique combination of these rotatable layers represents a distinct rotation state of the diffractive design tailored for a specific permutation operation. Therefore, a K-layer rotatable diffractive material is capable of all-optically performing up to 4^K independent permutation operations. The original input information can be decrypted by applying the specific inverse permutation matrix to output patterns, while applying other inverse operations will lead to loss of information. We demonstrated the feasibility of this reconfigurable multiplexed diffractive design by approximating 256 randomly selected permutation matrices using K=4 rotatable diffractive layers. We also experimentally validated this reconfigurable diffractive network using terahertz radiation and 3D-printed diffractive layers, providing a decent match to our numerical results. The presented rotation-multiplexed diffractive processor design is particularly useful due to its mechanical reconfigurability, offering multifunctional representation through a single fabrication process., Comment: 37 Pages, 10 Figures

Published
2024
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35. Highly potent and broadly neutralizing anti-CD4 trimeric nanobodies inhibit HIV-1 infection by inducing CD4 conformational alteration

Author

Zhu, Linjing, Huang, Bilian, Wang, Xiangyao, Ni, Fengfeng, Ao, Mingjun, Wang, Ruoke, Zheng, Bin, Chen, Chen, Xue, Jing, Zhu, Lin, Yang, Chenbo, Shi, Lingen, Geng, Shengya, Hu, Jiaqian, Yang, Mengshi, Zhang, Doudou, Yang, Ping, Li, Miaomiao, Li, Yuncheng, Hu, Qinxue, Ye, Sheng, Zheng, Peng, Wei, Hongxia, Wu, Zhiwei, Zhang, Linqi, Wang, Yaxin, Liu, Yalan, and Wu, Xilin

Published
2024
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