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1. Synthesis, photophysical properties, and self-dimerization studies of 2-λ ⁵ -phosphaquinolin-2-ones

   https://doi.org/10.1039/c9qo00199a  

   Bard, Jeremy P.; Deng, Chun-Lin; Richardson, Hannah C.; Odulio, Jacob M.; Barker, Joshua E.; Zakharov, Lev N.; Cheong, Paul H.-Y.; Johnson, Darren W.; Haley, Michael M. (April 2019, Organic Chemistry Frontiers)

   We have rationally designed and synthesized a library of phosphaquinolinone derivatives containing various electron-donating and -withdrawing groups on two positions of the scaffold. Distinct trends are observed between the substituents on R 1 and R 2 with both the photophysical properties of the molecules and their dimerization strengths. With withdrawing groups upon the scaffold, dimerization constants surpass 500 M −1 in H 2 O-saturated CDCl 3 . Computational studies on the dimeric structures corroborate the experimental findings. 

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2. Nondestructive, quantitative viability analysis of 3D tissue cultures using machine learning image segmentation

   https://doi.org/10.1063/5.0189222  

   Trettner, Kylie_J; Hsieh, Jeremy; Xiao, Weikun; Lee, Jerry_S_H; Armani, Andrea_M (March 2024, APL Bioengineering)

   Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research. 

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3. ¹⁵ N-Azides as practical and effective tags for developing long-lived hyperpolarized agents

   https://doi.org/10.1039/D1SC04647K  

   Bae, Junu; Zhang, Guannan; Park, Hyejin; Warren, Warren S.; Wang, Qiu (November 2021, Chemical Science)

   Azide moieties, unique linear species containing three nitrogen atoms, represent an attractive class of molecular tag for hyperpolarized magnetic resonance imaging (HP-MRI). Here we demonstrate ( 15 N) 3 -azide-containing molecules exhibit long-lasting hyperpolarization lifetimes up to 9.8 min at 1 T with remarkably high polarization levels up to 11.6% in water, thus establishing ( 15 N) 3 -azide as a powerful spin storage for hyperpolarization. A single ( 15 N)-labeled azide has also been examined as an effective alternative tag with long-lived hyperpolarization. A variety of biologically important molecules are studied in this work, including choline, glucose, amino acid, and drug derivatives, demonstrating great potential of 15 N-labeled azides as universal hyperpolarized tags for nuclear magnetic resonance imaging applications. 

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4. Automated cell properties toolbox from 3D bioprinted hydrogel scaffolds via deep learning and optical coherence tomography

   https://doi.org/10.1364/BOE.550401  

   Babaei, Mahdi; Shamouil, Aaron; Wang, Jiaying; Khare, Deepak; Wang, Tianyuanye; Shih, Meijie; Yu, Xiaojun; Gan, Yu (April 2025, Biomedical Optics Express)

   Accurately assessing cell viability and morphological properties within 3D bioprinted hydrogel scaffolds is essential for tissue engineering but remains challenging due to the limitations of existing invasive and threshold-based methods. We present a computational toolbox that automates cell viability analysis and quantifies key properties such as elongation, flatness, and surface roughness. This framework integrates optical coherence tomography (OCT) with deep learning-based segmentation, achieving a mean segmentation precision of 88.96%. By leveraging OCT’s high-resolution imaging with deep learning-based segmentation, our novel approach enables non-invasive, quantitative analysis, which can advance rapid monitoring of 3D cell cultures for regenerative medicine and biomaterial research. 

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5. Deep learning provides high accuracy in automated chondrocyte viability assessment in articular cartilage using nonlinear optical microscopy

   https://doi.org/10.1364/BOE.417478  

   Chen, Xun; Li, Yang; Wyman, Nicole; Zhang, Zheng; Fan, Hongming; Le, Michael; Gannon, Steven; Rose, Chelsea; Zhang, Zhao; Mercuri, Jeremy; et al (April 2021, Biomedical Optics Express)

   Chondrocyte viability is a crucial factor in evaluating cartilage health. Most cell viability assays rely on dyes and are not applicable forin vivoor longitudinal studies. We previously demonstrated that two-photon excited autofluorescence and second harmonic generation microscopy provided high-resolution images of cells and collagen structure; those images allowed us to distinguish live from dead chondrocytes by visual assessment or by the normalized autofluorescence ratio. However, both methods require human involvement and have low throughputs. Methods for automated cell-based image processing can improve throughput. Conventional image processing algorithms do not perform well on autofluorescence images acquired by nonlinear microscopes due to low image contrast. In this study, we compared conventional, machine learning, and deep learning methods in chondrocyte segmentation and classification. We demonstrated that deep learning significantly improved the outcome of the chondrocyte segmentation and classification. With appropriate training, the deep learning method can achieve 90% accuracy in chondrocyte viability measurement. The significance of this work is that automated imaging analysis is possible and should not become a major hurdle for the use of nonlinear optical imaging methods in biological or clinical studies. 

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