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Fabrication of versatile poly(xylitol sebacate)-co-poly(ethylene glycol) hydrogels through multifunctional crosslinkers and dynamic bonds for wound healing

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Acta Biomaterialia 2023年 170卷 344-359页

作者： Yeh, Ying-Yu Lin, Yi-Yun Wang, Ting-Teng Yeh, Yu-Jia Chiu, Ting-Hsiang Wang, Reuben Bai, Meng-Yi Yeh, Yi-Cheun Institute of Polymer Science and Engineering National Taiwan University Taipei Taiwan Institute of Biomedical Engineering National Taiwan University Taipei Taiwan Biomedical Engineering Program Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei Taiwan Institute of Food Safety and Health National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan Adjunct Appointment to the Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Program National Taiwan University Taipei Taiwan GIP-TRIAD Master's Degree in Agro-Biomedical Science National Taiwan University Taipei Taiwan

Poly(polyol sebacate) (PPS) polymer family has been recognized as promising biomaterials for biomedical applications with their characteristics of easy production, elasticity, biodegradation, and cytocompatibility. Poly(xylitol sebacate)-co-poly(ethylene glycol) (PXS-co-PEG) has been developed to fabricate PPS-based hydrogels;however, current PXS-co-PEG hydrogels presented limited properties and functions due to the limitations of the crosslinkers and crosslinking chemistry used in the hydrogel formation. Here, we fabricate a new type of PXS-co-PEG hydrogels through the use of multifunctional crosslinkers as well as dynamic bonds. In our design, polyethyleneimine-polydopamine (PEI-PDA) macromers are utilized to crosslink aldehyde-functionalized PXS-co-PEG (APP) through imine bonds and hydrogen bonds. PEI-PDA/APP hydrogels present multiple functional properties (e.g., fluorescent, elastomeric, biodegradable, self-healing, bioadhesive, antioxidant, and antibacterial behaviors). These properties of PEI-PDA/APP hydrogels can be fine-tuned by changing the PDA grafting degrees in the PEI-PDA crosslinkers. Most importantly, PEI-PDA/APP hydrogels are considered promising wound dressings to promote tissue remodeling and prevent bacterial infection in vivo. Taken together, PEI-PDA/APP hydrogels have been demonstrated as versatile biomaterials to provide multiple tailorable properties and desirable functions to expand the utility of PPS-based hydrogels for advanced biomedical applications. Statement of significance: Various strategies have been developed to fabricate poly(polyol sebacate) (PPS)-based hydrogels. However, current PPS-based hydrogels present limited properties and functions due to the limitations of the crosslinkers and crosslinking chemistry used in the hydrogel formation. This work describes that co-engineering crosslinkers and interfacial crosslinking is a promising approach to synthesizing a new type of poly(xylitol sebacate)-co-poly(ethylene glycol) (PXS-co-

关键词： Polyols

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Ultrasonic interfacial crosslinking of TiO2-based nanocomposite hydrogels through thiol-norbornene reactions for sonodynamic antibacterial treatment

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Biomaterials science 2023年第12期11卷 4184-4199页

作者： Yang, Su-Rung Wang, Reuben Yan, Chen-Jie Lin, Yi-Yun Yeh, Yu-Jia Yeh, Ying-Yu Yeh, Yi-Cheun Institute of Polymer Science and Engineering National Taiwan University Taipei Taiwan Institute of Food Safety and Health National Taiwan University Taipei Taiwan Master of Public Health Program National Taiwan University Taipei Taiwan GIP-TRIAD Master's Degree in Agro-Biomedical Science National Taiwan University Taipei Taiwan

Nanocomposite (NC) hydrogels used for sonodynamic therapy (SDT) face challenges such as lacking interfacial interactions between the polymers and nanomaterials as well as presenting uneven dispersion of nanomaterials in the hydrogel network, reducing their mechanical properties and treatment efficiency. Here, we demonstrate a promising approach of co-engineering nanomaterials and interfacial crosslinking to expand the materials construction and biomedical applications of NC hydrogels in SDT. In this work, mesoporous silica-coated titanium dioxide nanoparticles with thiolated surface functionalization (TiO2@MS-SH) are utilized as crosslinkers to react with norbornene-functionalized dextran (Nor-Dex) through ultrasound-triggered thiol-norbornene reactions, forming TiO2@MS-SH/Nor-Dex NC hydrogels. The TiO2@MS-SH nanoparticles act not only as multivalent crosslinkers to improve the mechanical properties of hydrogels under ultrasound irradiation but also as reactive oxygen species (ROS) generators to allow the use of TiO2@MS-SH/Nor-Dex NC hydrogels in SDT applications. Particularly, the TiO2@MS-SH/Nor-Dex NC hydrogels present tailorable microstructures, properties, and sonodynamic killing of bacteria through the modulation of the ultrasound frequency. Taken together, a versatile TiO2-based NC hydrogel platform prepared under ultrasonic interfacial crosslinking reactions is developed for advancing the applications in SDT. © 2023 The Royal Society of Chemistry.

关键词： Titanium dioxide

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Design of a Multilingual Translation System with Personalized Voice Characteristics 21

Design of a Multilingual Translation System with Personalize...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Chen, Chen-Hua Lai, Guan-Yu Lee, Chuan Wu, Chang-Yu Liu, Kuan-Lin Lai, Shin-Chi Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. of Automation Engineering Huwei Taiwan National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan

ISBN: (纸本)9798350377088

This study combines AI voice synthesis with IoT to create a real-time multilingual translation system. Front-end devices capture the speaker's voice, which is then played back in both original and translated forms, enabling consecutive interpreting. This system is ideal for multilingual scenarios. After digitization, audio signals undergo speech-to-text conversion, multilingual translation, and text-to-speech processing. Speech synthesis matches the speaker's voice characteristics, allowing listeners to hear the translation in the speaker's voice. Both voice-to-text conversion and translated audio playback use Wi-Fi for data transmission, facilitating future applications. In validation, the average translation and transmission time for each speech segment were 410 ms and 3.38 s, respectively. The synthesized voice similarity analysis achieved an accuracy of 88%, showcasing the method's potential for system integration and development. © 2024 IEEE.

关键词： Wi-Fi

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Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis (vol 14, 3750, 2023)

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NATURE COMMUNICATIONS 2023年第1期14卷 1-16页

作者： Takada, Hitomi Sasagawa, Yohei Yoshimura, Mika Tanaka, Kaori Iwayama, Yoshimi Hayashi, Tetsutaro Isomura-Matoba, Ayako Nikaido, Itoshi Kurisaki, Akira Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan Master’s/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan

Defects in gastric progenitor cell differentiation are associated with various gastric disorders, including atrophic gastritis, intestinal metaplasia, and gastric cancer. However, the mechanisms underlying the multilineage differentiation of gastric progenitor cells during healthy homeostasis remain poorly understood. Here, using a single-cell RNA sequencing method, Quartz-Seq2, we analyzed the gene expression dynamics of progenitor cell differentiation toward pit cell, neck cell, and parietal cell lineages in healthy adult mouse corpus tissues. Enrichment analysis of pseudotime-dependent genes and a gastric organoid assay revealed that EGFR-ERK signaling promotes pit cell differentiation, whereas NF-κB signaling maintains gastric progenitor cells in an undifferentiated state. In addition, pharmacological inhibition of EGFR in vivo resulted in a decreased number of pit cells. Although activation of EGFR signaling in gastric progenitor cells has been suggested as one of the major inducers of gastric cancers, our findings unexpectedly identified that EGFR signaling exerts a differentiation-promoting function, not a mitogenic function, in normal gastric homeostasis.

关键词： Adult stem cells Stem-cell differentiation Stem-cell niche

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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A review of noncatalytic and catalytic pyrolysis and co-pyrolysis products from lignocellulosic and algal biomass using Py-GC/MS

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Journal of Industrial and engineering Chemistry 2024年 134卷 51-64页

作者： Chen, Wei-Hsin Ho, Kuan-Yu Aniza, Ria Sharma, Amit Kumar Saravanakumar, Ayyadurai Hoang, Anh Tuan Department of Aeronautics and Astronautics National Cheng Kung University Tainan701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung411 Taiwan International Doctoral Degree Program on Energy Engineering National Cheng Kung University Tainan701 Taiwan Uttarakhand Dehradun248007 India Directorate of Research SRM Institute of Science and Technology Kattankulathur - 603203 Chengalpattu District Tamil Nadu India Faculty of Automotive Engineering Dong A University Danang Viet Nam

Biomass pyrolysis has garnered significant attention as a sustainable energy production method utilizing various biomass feedstocks. Pyrolysate is any product generated from the pyrolysis process, including solid, liquid, and gas types. This review focuses on the application of analytical pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) in the context of four pyrolysis modes: single feedstock pyrolysis, co-pyrolysis, catalytic pyrolysis, and catalytic co-pyrolysis to gain insights into the characteristics of the pyrolysates. A comprehensive understanding of each pyrolysis mode's unique products, benefits, and limitations is achieved by analyzing the pyrolysates of different feedstocks, including lignocellulosic and algal biomass. Moreover, this study discusses the integration of Py-GC/MS with techniques such as density function theory (DFT), which focuses on estimating the reactions’ activation energies or kinetic studies concentrating on the reaction rate and mechanism to gain further insight into pyrolysis mechanisms. Lastly, design of experiment (DoE) techniques are proposed for pyrolysis optimization to obtain a more comprehensive assessment of the parameter's influence on pyrolysis factors and levels. © 2024 The Author(s)

关键词： Pyrolysis

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Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study

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BMC Medical Imaging 2024年第1期24卷 92页

作者： Chang, Joseph Lin, Bo-Ru Wang, Ti-Hao Chen, Chung-Ming Department of Biomedical Engineering College of Medicine and College of Engineering National Taiwan University No. 1 Sec. 1 Jen-Ai Road Taipei 100 Taipei 100 Taiwan EverFortune.AI Co. Ltd Taichung Taiwan The Data Science Degree Program College of Electrical Engineering and Computer Science National Taiwan University and Academia Sinica Taipei Taiwan Department of Radiation Oncology China Medical University Hospital Taichung Taiwan Department of Medicine China Medical University Taichung Taiwan

Background: The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States. Methods: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. Results: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong’s CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. Conclusion: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings. © The Author(s) 2024.

关键词： Active learning Chest radiographs Deep learning Pleural effusion X-rays

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A Technology-Organization-Environment (TOE) Framework Based on Scientometry for Understanding The Risk Factors in Sustainable Water Resources Management

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Water Resources Management 2023年第15期37卷 5849-5869页

作者： Lin, Chih-Hsien Chen, Wei-Hsiang Institute of Environmental Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Aerosol Science and Research Center National Sun Yat-sen University Kaohsiung804 Taiwan Center for Emerging Contaminants Research National Sun Yat-sen University Kaohsiung804 Taiwan Department of Public Health Kaohsiung Medical University Kaohsiung807 Taiwan Doctoral and Mater Degree Program in Toxicology Kaohsiung Medical University Kaohsiung807 Taiwan

The study of how water can be used sustainably through risk management has become increasingly complex due to the many different aspects involved. This study used a three-step approach to investigate the issue: trend analysis, bibliometric analysis, and the development of a conceptual TOE framework. The trend analysis identified water conservation and water crisis as the most frequently mentioned keywords, suggesting the need to address public concern for these issues. The bibliometric analysis found that 37% of the articles were published in the last three years, and environmental science and technology was the most common field of study. The TOE framework was used to link and group the keywords (risk factors) from the articles into three categories: technology, organization, and environment. This framework provides a way to understand the different factors that contribute to water scarcity and to develop strategies for addressing these risk factors. The three risk factors with the highest scores were statistical analysis (technology), climate change (environment), and uncertainty (environment). In the technology category, statistical analysis, sustainable development, and water quality were the most important risk factors. In the organization category, water management, governance, and water markets were the most important risk factors. In the environment category, climate change, uncertainty, and flood were the most important risk factors. This study is expected to provide valuable insights into the research trends that are important for achieving sustainability in water resource management, both in conventional and academic studies, over the past decade. Graphical Abstract: [Figure not available: see fulltext.]. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Sustainable development

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PS2MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry

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Analytical Chemistry 2024年第12期96卷 4835-4844页

作者： Lin, Yi-Ching Chien, Wei-Chen Wang, Yu-Xuan Wang, Ying-Hau Yang, Feng-Shuo Tseng, Li-Ping Hung, Jui-Hung Department of Laboratory Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung807 Taiwan Department of Laboratory Medicine School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung807 Taiwan Doctoral Degree Program of Toxicology College of Pharmacy Kaohsiung Medical University Kaohsiung807 Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung807 Taiwan Department of Computer Science National Yang Ming Chiao Tung University HsinChu300 Taiwan Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung807 Taiwan Program in Biomedical Artificial Intelligence National Tsing Hua University HsinChu300 Taiwan

The rapid proliferation of new psychoactive substances (NPS) poses significant challenges to conventional mass-spectrometry-based identification methods due to the absence of reference spectra for these emerging substances. This paper introduces PS2MS, an AI-powered predictive system designed specifically to address the limitations of identifying the emergence of unidentified novel illicit drugs. PS2MS builds a synthetic NPS database by enumerating feasible derivatives of known substances and uses deep learning to generate mass spectra and chemical fingerprints. When the mass spectrum of an analyte does not match any known reference, PS2MS simultaneously examines the chemical fingerprint and mass spectrum against the putative NPS database using integrated metrics to deduce possible identities. Experimental results affirm the effectiveness of PS2MS in identifying cathinone derivatives within real evidence specimens, signifying its potential for practical use in identifying emerging drugs of abuse for researchers and forensic experts. © 2024 The Authors. Published by American Chemical Society.

关键词： Mass spectrometry

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An Ultra-high Sensitivity Sensing Collimation Imager: Design, Modelling, and Assessment

An Ultra-high Sensitivity Sensing Collimation Imager: Design...

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Medical Imaging 2025: Physics of Medical Imaging

作者： Cai, Ling Aggarwal, Samarth Enninful, Kweku Chen, Yunlai Komarov, Sergey Thorek, Daniel Jha, Abhinav K. Tai, Yuan-Chuan Imaging Science Doctoral Program Washington University in St. Louis St. LouisMO United States Department of Biomedical Engineering Washington University in St. Louis St. LouisMO United States Mallinckrodt Institute of Radiology Washington University St. Louis United States

ISBN: (纸本)9781510685888

In this work, we discuss the design and development of an ultra-high sensitivity imaging system, Sensing Collimation Imager (SCI), dedicated to guide and optimize application of alpha particle-emitting radiopharmaceutical therapy (αRPT). Alpha particles are highly potent and unlike conventional external beam therapy can be used in metastatic settings. Currently, the lack of information of radionuclide distribution hinders estimated internal absorbed dose from αRPT, which calls for ultrasensitive SPECT technologies that can report reliable activity distribution under ultra-low count rate imaging conditions. The proposed SCI-SPECT is based on an innovative design that incorporates the geometry of both conventional pinhole collimator and coded aperture. Aimed to decouple the dependence of resolution and sensitivity, the heavy metal collimator is replaced by scintillator crystals to serve dual purposes of increasing detected events and providing spatial information. The detector module consists of an 8×8 array of crystal elements. Each crystal presents an alternating pattern of GAGG scintillator and acrylic. The depth of interaction (DOI) information is obtained through dual-ended readout system. Our experimental results are based on a working prototype SCI module that is fully functional under a broad range of energy (70 keV - more than 511 keV), which is well-suited for alpha particle emitter imaging applications. Furthermore, we modeled the system response function based on analytical derivation and validated it by comparing with Monte Carlo simulations. We carried out a preliminary imaging study of three rod sources filled with Tc99m. The image was successfully reconstructed with the derived system matrix, and the rods at different locations can be clearly resolved. We also compared the performance with Siemens Symbia Intevo scanner for high γ-ray energy imaging with Cu-64, the reconstructed image shows better resolution while potentially achieving 2 orders of magni

关键词： Alpha particles

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