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Biosensors, 2023 Label-free biosensors have become an indispensable tool for analyzing intrinsic molecular properties, such as mass, and quantifying molecular interactions without interference from labels, which is critical for the screening of drugs, detecting disease ...
Pengfei Zhang, Rui Wang
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Microsystems & Nanoengineering, 2022 Passive and label-free microfluidic devices have no complex external accessories or detection-interfering label particles. These devices are now widely used in medical and bioresearch applications, including cell focusing and cell separation.
Hao Tang +4 more
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A review on label free biosensors
Biosensors and Bioelectronics: X, 2022 Label-free biosensing has advanced significantly in recent years due to its capacity for quick and inexpensive bio-detection in small volumes. Additionally, they have developed into lab-on-a-chip technology and may be able to perform real-time analysis ...
Vimala Rani Samuel, K.Jagajjanani Rao
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Label‐Free Single‐Molecule Immunoassay [PDF]
Advanced ScienceSingle‐molecule immunoassay is a reliable technique for the detection and quantification of low‐abundance blood biomarkers, which are essential for early disease diagnosis and biomedical research.
Xiaoyan Zhou +11 more
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Label-Free Single-Molecule Conalbumin Analysis [PDF]
MicromachinesNanoaperture optical tweezers (NOTs) were used to analyze conalbumin in various forms. By analyzing the power spectrum of the NOT-transmitted laser signal, differences between iron and iron-free conalbumin were observed; the corner frequency extrapolated
Tianyu Zhao, Xi Ren, Reuven Gordon
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Label-Free Concept Bottleneck Models [PDF]
International Conference on Learning Representations, 2023 Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to human-understandable concepts.
Tuomas P. Oikarinen +3 more
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Label-free Node Classification on Graphs with Large Language Models (LLMS) [PDF]
International Conference on Learning Representations, 2023 In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance.
Zhikai Chen +7 more
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Towards Label-free Scene Understanding by Vision Foundation Models [PDF]
Neural Information Processing Systems, 2023 Vision foundation models such as Contrastive Vision-Language Pre-training (CLIP) and Segment Anything (SAM) have demonstrated impressive zero-shot performance on image classification and segmentation tasks.
Runnan Chen +7 more
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Label-Free Liver Tumor Segmentation [PDF]
Computer Vision and Pattern Recognition, 2023 We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical ...
Qixing Hu +6 more
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Chip-based label-free incoherent super-resolution optical microscopy [PDF]
Light: Science & ApplicationsThe photo-kinetics of fluorescent molecules have enabled the circumvention of the far-field optical diffraction limit. Despite its enormous potential, the necessity to label the sample may adversely influence the delicate biology under investigation ...
Nikhil Jayakumar +8 more
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