Correction for 'Nanoparticle-based photoacoustic analysis for highly sensitive lateral flow assays' by Yunfei Zhao et al., Nanoscale, 2016, 8, 19204-19210.
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Photoacoustic Imaging with Attention-Guided Deep Learning for Predicting Axillary Lymph Node Status in Breast Cancer.
Acad Radiol
January 2025
Department of Ultrasound, The Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen 518020, Guangdong, China (G.L., S.T., Z.H., M.W., S.M., J.X., F.D.); Department of Ultrasound, The First Affiliated Hospital, Southern University of Science and Technology (Shenzhen People's Hospital), Shenzhen 518020, Guangdong, China (H.T., H.W., J.X., F.D.). Electronic address:
Rationale And Objectives: Preoperative assessment of axillary lymph node (ALN) status is essential for breast cancer management. This study explores the use of photoacoustic (PA) imaging combined with attention-guided deep learning (DL) for precise prediction of ALN status.
Materials And Methods: This retrospective study included patients with histologically confirmed early-stage breast cancer from 2022 to 2024, randomly divided (8:2) into training and test cohorts.
Integrated analysis of cellulose structure and properties using solid-state low-field H-NMR and photoacoustic spectroscopy.
Sci Rep
January 2025
Nonprofitable Organization Touche NPO, Sapporo, 060-004, Japan.
In this study, we explore the structural intricacies of cellulose, a polymer composed of glucose monomers arranged in a linear chain, primarily investigated through solid-state NMR techniques. Specifically, we employ low-field proton nuclear magnetic resonance (H-NMR) to delve into the diverse hydrogen atom types within the cellulose molecule. The low-field H-NMR technique allows us to discern these hydrogen atoms based on their distinct chemical shifts, providing valuable insights into the various functional groups present in cellulose.
View Article and Find Full Text PDFAxially multifocal metalens for 3D volumetric photoacoustic imaging of neuromelanin in live brain organoid.
Sci Adv
January 2025
Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Optical resolution photoacoustic imaging of uneven samples without z-scanning is transformative for the fast analysis and diagnosis of diseases. However, current approaches to elongate the depth of field (DOF) typically imply cumbersome postprocessing procedures, bulky optical element ensembles, or substantial excitation beam side lobes. Metasurface technology allows for the phase modulation of light and the miniaturization of imaging systems to wavelength-size thickness.
View Article and Find Full Text PDFUse of pattern recognition in photoacoustic imaging to identify neuronal ensembles in the prefrontal cortex of rats undergoing conditioned fear learning.
Photoacoustics
February 2025
Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States.
Pattern recognition analysis in brain research has improved understanding of sensory processing and led to the identification of default brain networks in neuroimaging studies. The current study uses pattern recognition analysis to extend our previous findings showing conditioned fear learning and novelty-exposure (i.e.
View Article and Find Full Text PDFMethane, Ethane, and Propane Detection Using a Quartz-Enhanced Photoacoustic Sensor for Natural Gas Composition Analysis.
Energy Fuels
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PolySense Lab, Dipartimento Interateneo di Fisica, University and Polytechnic of Bari, Via Amendola 173, Bari 70126, Italy.
A compact and portable gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) for the detection of methane (C1), ethane (C2), and propane (C3) in natural gas (NG)-like mixtures is reported. An interband cascade laser (ICL) emitting at 3367 nm is employed to target absorption features of the three alkanes, and partial least-squares regression analysis is employed to filter out spectral interferences and matrix effects characterizing the examined gas mixtures. Spectra of methane, ethane, and propane mixtures diluted in nitrogen are employed to train and test the regression algorithm, achieving a prediction accuracy of ∼98%, ∼96%, and ∼93% on C1, C2, and C3, respectively.
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