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Fabrication of amino acid conjugated polymeric micelles for controlled anticancer drug delivery using radiation and pH-stimuli-triggering systems

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Journal of drug delivery Science and technology 2023年 80卷

作者： Addisu, Kefyalew dagnew Lee, Wen-Yi Tsai, Hsieh-Chih Wu, Szu-Yuan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei 106 Taiwan Faculty of Chemical and Food Engineering Bahir Dar University P. O. Box 26 Bahir Dar Ethiopia Advance Membrane Materials Center National Taiwan University of Science and Technology Taipei 106 Taiwan R&D Center for Membrane Technology Chung Yuan Christian University Chungli Taoyuan 320 Taiwan Department of Food Nutrition and Health Biotechnology College of Medical and Health Science Asia University Taichung 413 Taiwan Big Data Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan 256 Taiwan Division of Radiation Oncology Department of Medicine Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan 256 Taiwan Department of Healthcare Administration College of Medical and Health Science Asia University Taichung 413 Taiwan Cancer Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan 256 Taiwan Graduate Institute of Business Administration Fu Jen Catholic University Taipei 242 Taiwan Centers for Regional Anesthesia and Pain Medicine Taipei Municipal Wan Fang Hospital Taipei Medical University Taipei 110 Taiwan

drug delivery to the tumor site and the minimization of adverse effects on surrounding tissues remain major issues in pharmaceutical research. In this study, pH- and radiation-responsive PLGA-PEG-PLGA amphiphilic copolymers were terminally conjugated with tryptophan (Try), tyrosine (Tyr), or histidine (His), utilizing PEG as an initiator and Sn (Oct) 2 as a catalyst. Micelles having an inner core of hydrophobic blocks (PLGA and Try/Tyr/His) and a corona of hydrophilic PEG were developed to improve stability. The structural destabilization of the micelles caused by radiation and pH was characterized by an increase in dOX fluorescence intensities and a shift in particle size. Both Try-PLGA-PEG-PLGA-Try and Tyr-PLGA-PEG-PLGA-Tyr micelles displayed low cytotoxicity in MTT experiments with NIH-3T3 and HeLa cell lines at 100 μg/mL concentrations, with cell viabilities of more than 95.33% and 92.33%, respectively. Samples containing 10 μg/mL dOX, on the other hand, demonstrated significant anticancer efficacy against HeLa cells (28.54% and 29.97% of the cells remained viable after treatment with Tyr and Try, respectively). The cellular uptake experiment also revealed that dOX-loaded micelles were up taken by the cell and that irradiation and pH-sensitive stimuli induced drug release in the cytoplasm and nucleus of the cancer cell. Finally, these findings imply that the synthesized copolymers could be used as stimuli-responsive carriers for anti-cancer drug delivery. © 2023

关键词： dual-responsive PLGA-PEG-PLGA radiation-sensitive Tryptophan Tyrosine

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OncoEnrichr: cancer-dedicated gene set interpretation

arXiv

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arXiv 2021年

作者： Nakken, Sigve Gundersen, Sveinung Bernal, Fabian L.M. Polychronopoulos, dimitris Hovig, Eivind Wesche, Jørgen Department of Tumor Biology Institute for Cancer Research Oslo University Hospital Norway Centre for Cancer Cell Reprogramming Institute of Clinical Medicine Faculty of Medicine University of Oslo Norway Centre for Bioinformatics Department of Informatics University of Oslo Norway University Center for Information Technology University of Oslo Norway Early Data Science and Computational Oncology Research and Early Development Oncology R&D AstraZeneca Cambridge United Kingdom

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichr, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichr differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations, and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichr through interrogation of two candidate lists from proteomic and CrISPr screens. oncoEnrichr is freely available as a web-based workflow hosted by the Galaxy platform (https://***), and can also be accessed as a stand-alone r package (https://***/sigven/oncoEnrichr). © 2021, CC BY-NC-SA.

关键词： Tumors

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disturbance of questionable publishing to academia

arXiv

引用

arXiv 2021年

作者： You, Taekho Park, Jinseo Lee, June Young Yun, Jinhyuk Jung, Woo-Sung School of AI Convergence Soongsil University 369 Sangdo-ro Dongjak-gu Seoul06978 Korea Republic of Department of Industrial and Management Engineering Pohang University of Science and Technology 77 Cheongam-ro Pohang Gyeongsangbukdo 37673 Korea Republic of Center for Global R&D Data Analysis Korea Institute of Science and Technology Information 66 Heogi-ro Dongdaemun-gu Seoul02456 Korea Republic of Department of Physics Pohang University of Science and Technology 77 Cheongam-ro Pohang Gyeongsangbukdo 37673 Korea Republic of

Questionable publications have been accused of "greedy" practices;however, their influence on academia has not been gauged. Here, we probe the impact of questionable publications through a systematic and comprehensive analysis with various participants from academia and compare the results with those of their unaccused counterparts using billions of citation records, including liaisons, i.e., journals and publishers, and prosumers, i.e., authors. Questionable publications attribute publisher-level self-citations to their journals while limiting journal-level self-citations;yet, conventional journal-level metrics are unable to detect these publisher-level self-citations. We propose a hybrid journal-publisher metric for detecting self-favouring citations among QJs from publishers. Additionally, we demonstrate that the questionable publications were less disruptive and influential than their counterparts. Our findings indicate an inflated citation impact of suspicious academic publishers. The findings provide a basis for actionable policy-making against questionable publications. Copyright © 2021, The Authors. All rights reserved.

关键词： Publishing

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Causal discovery with Cascade Nonlinear Additive Noise Models

arXiv

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arXiv 2019年

作者： Cai, ruichu Qiao, Jie Zhang, Kun Zhang, Zhenjie Hao, Zhifeng School of Computers Guangdong University of Technology China Department of philosophy Carnegie Mellon University Singapore R&D Yitu Technology Ltd School of Mathematics and Big Data Foshan University China

Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class is not transitive–even if each direct causal relation follows this model, indirect causal influences, which result from omitted intermediate causal variables and are frequently encountered in practice, do not necessarily follow the model constraints;as a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a cascade nonlinear additive noise model to represent such causal influences–each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured intermediate variables, from data, under the variational auto-encoder framework. Our theoretical results show that with our model, causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases. Copyright © 2019, The Authors. All rights reserved.

关键词： Additives

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Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

arXiv

引用

arXiv 2023年

作者： Cheng, Sibo Quilodrán-Casas, César Ouala, Said Farchi, Alban Liu, Che Tandeo, Pierre Fablet, ronan Lucor, didier Iooss, Bertrand Brajard, Julien Xiao, dunhui Janjic, Tijana ding, Weiping Guo, Yike Carrassi, Alberto Bocquet, Marc Arcucci, rossella Data Science Institute Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Department of Earth Science and Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Computer Science and Engineering Hong Kong University of Science and Technology 999077 Hong Kong IMT Atlantique Lab-STICC UMR CNRS 6285 France and Odyssey Inria/IMT France RIKEN Center for Computational Science Kobe Japan CEREA École des Ponts and EDF R&D île-de-France France The Laboratoire Interdisciplinaire des Sciences du Numérique CNRS Paris-Saclay University OrsayF-91403 France 78401 Chatou France Institut de Mathématiques de Toulouse Toulouse31062 France SINCLAIR AI Lab Saclay France Bergen Norway School of Mathematical Sciences Tongji University Shanghai200092 China Mathematical Institute for Machine Learning and Data Science KU Eichstätt-Ingolstadt Bavaria Germany School of Information Science and Technology Nantong University Nantong226019 China Department of Physics and Astronomy Augusto Righi University of Bologna Bologna40124 Italy

data Assimilation (dA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFd) to geoscience and climate systems. recently, much effort has been given in combining dA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of the state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review aims at ML scientists who attempt to apply dA and UQ techniques to improve the accuracy and the interpretability of their models, but also at dA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of dA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed. © 2023, CC BY-NC-SA.

关键词： dynamical systems

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An effective POS-CNN-based adaptive model for classifying brain tumour from MrI images

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AIP Conference Proceedings 2023年第1期2914卷

作者： Sajeev ram Arumugam L. Thanga Mariappan K. Makanyadevi r. Balakrishna Sankar Ganesh Karuppasamy Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Smart Computing School of Information Technology and Engineering Vellore Institute of Technology vellore India Department of Computer science and Engineering M. Kumarasamy College of Engineering Karur India Department of Artificial Intelligence and Data Science Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

One of the crucial reasons for death in people is a brain tumour. It will likely develop into malignancy if not appropriately and promptly treated. As a result, early brain tumour diagnosis is a crucial requirement. The Convolutional Neural Network and Particle Swarm Optimization are combined to construct a proposed framework for Brain Tumour detection from MrI Images. PSO is used to increase segmentation precision and to train the proposed system using CNN. Along with PSO, Scale Invariant Feature Transform (SIFT) is employed as a feature descriptor to improve the system’s classification accuracy. Brain tumour MrI images from the BraTS 2018 dataset were used as input sets for this research. A diagnosis accuracy of 98.76 % was obtained from the results. The proposed technique has better accuracy when the assessment features of the proposed system are evaluated with a few existing approaches.

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Meta mask correction for nuclei segmentation in histopathological image

arXiv

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arXiv 2021年

作者： Shi, Jiangbo Jia, Chang Gao, Zeyu Gong, Tieliang Wang, Chunbao Li, Chen National Engineering Lab for Big Data Analytics Xi'an Jiao Tong University Shaanxi Xi'an710049 China School of Computer Science and Technology Xi'an Jiao tong University Shaanxi Xi'an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.R&D Xi'an Jiao tong University Shaanxi Xi'an710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Shaanxi Xi'an710061 China

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings. © 2021, CC BY.

关键词： deep learning

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Exponential Signal reconstruction with deep Hankel Matrix Factorization

arXiv

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arXiv 2020年

作者： Huang, Yihui Zhao, Jinkui Wang, Zi Orekhov, Vladislav Guo, di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center National Institute for Data Science in Health and Medicine Xiamen University Xiamen China The School of Computer and Information Engineering Xiamen University of Technology Xiamen China The Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg Sweden

Exponential function is a basic form of temporal signals, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing. To achieve this goal, partial data may be acquired but result in severe artifacts in its spectrum, which is the Fourier transform of exponentials. Thus, reliable spectrum reconstruction is highly expected in the fast data acquisition in many applications, such as chemistry, biology, and medical imaging. In this work, we propose a deep learning method whose neural network structure is designed by imitating the iterative process in the model-based state-of-the-art exponentials reconstruction method with low-rank Hankel matrix factorization. With the experiments on synthetic data and realistic biological magnetic resonance signals, we demonstrate that the new method yields much lower reconstruction errors and preserves the low-intensity signals much better than compared methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Matrix algebra

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Toward Accurate Post-Born-Oppenheimer Molecular Simulations on Quantum Computers: An Adaptive Variational Eigensolver with Nuclear-Electronic Frozen Natural Orbitals

arXiv

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arXiv 2023年

作者： Nykänen, Anton Miller, Aaron Talarico, Walter Knecht, Stefan Kovyrshin, Arseny Skogh, Mårten Tornberg, Lars Broo, Anders Mensa, Stefano Symons, Benjamin C.B. Sahin, Emre Crain, Jason Tavernelli, Ivano Pavošević, Fabijan Algorithmiq Ltd. Kanavakatu 3C HelsinkiFI-00160 Finland School of Physics Trinity College Dublin College Green Dublin 2 Ireland Department of Applied Physics QTF Centre of Excellence Center for Quantum Engineering Aalto University School of Science AaltoFIN-00076 Finland ETH Zürich Department of Chemistry and Applied Life Sciences Vladimir-Prelog-Weg 1-5/10 Zürich8093 Switzerland Data Science and Modelling Pharmaceutical Sciences R&D AstraZeneca Gothenburg Pepparedsleden 1 MolndalSE-431 83 Sweden Department of Chemistry and Chemical Engineering Chalmers University of Technology Gothenburg Sweden The Hartree Centre STFC Sci-Tech Daresbury WarringtonWA4 4AD United Kingdom IBM Research Europe Hartree Centre STFC Laboratory Sci-Tech Daresbury WarringtonWA4 4AD United Kingdom Department of Biochemistry University of Oxford OxfordOX1 3QU United Kingdom IBM Quantum IBM Research - Zürich Rüschlikon8803 Switzerland

Nuclear quantum effects such as zero-point energy and hydrogen tunnelling play a central role in many biological and chemical processes. The nuclear-electronic orbital (NEO) approach captures these effects by treating selected nuclei quantum mechanically on the same footing as electrons. On classical computers, the resources required for an exact solution of NEO-based models grow exponentially with system size. By contrast, quantum computers offer a means of solving this problem with polynomial scaling. However, due to the limitations of current quantum devices, NEO simulations are confined to the smallest systems described by minimal basis sets whereas realistic simulations beyond the Born-Oppenheimer approximation require more sophisticated basis sets. For this purpose, we herein extend a hardware-efficient AdAPT-VQE method to the NEO framework in the frozen natural orbital (FNO) basis. We demonstrate on H2 and d2 molecules that the NEO-FNO-AdAPT-VQE method reduces the CNOT count by a several orders of magnitude relative to the NEO unitary coupled cluster method with singles and doubles (NEO-UCCSd) while maintaining the desired accuracy. This extreme reduction in the CNOT gate count is sufficient to permit practical computations employing the NEO method - an important step toward accurate simulations involving non-classical nuclei and non-Born-Oppenheimer effects on near-term quantum devices. We further show that the method can capture isotope effects and we demonstrate that inclusion of correlation energy systematically improves the prediction of ∆ZPE. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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One-dimensional deep low-rank and sparse network for accelerated MrI

arXiv

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arXiv 2021年

作者： Wang, Zi Qian, Chen Guo, di Sun, Hongwei Li, rushuai Zhao, Bo Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China School of Computer and Information Engineering Xiamen University of Technology Xiamen China United Imaging Research Institute of Intelligent Imaging Beijing China Department of Nuclear Medicine Nanjing First Hospital Nanjing Medical University Nanjing China Department of Biomedical Engineering Oden Institute for Computational Engineering and Sciences University of Texas at Austin Austin United States

deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MrI). Most state-of-the-art deep learning reconstructions adopt the powerful convolutional neural network and perform 2d convolution since many magnetic resonance images or their corresponding k-space are in 2d. In this work, we present a new approach that explores the 1d convolution, making the deep network much easier to be trained and generalized. We further integrate the 1d convolution into the proposed deep network, named as One-dimensional deep Low-rank and Sparse network (OdLS), which unrolls the iteration procedure of a low-rank and sparse reconstruction model. Extensive results on in vivo knee and brain datasets demonstrate that, the proposed OdLS is very suitable for the case of limited training subjects and provides improved reconstruction performance than state-of-the-art methods both visually and quantitatively. Additionally, OdLS also shows nice robustness to different undersampling scenarios and some mismatches between the training and test data. In summary, our work demonstrates that the 1d deep learning scheme is memory-efficient and robust in fast MrI. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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