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arXiv 2025年

作者： Cho, Hyein Kim, Jeonghoon Lim, Hocheol The Interdisciplinary Graduate Program in Integrative Biotechnology & Translational Medicine Yonsei University Incheon Korea Republic of Incheon Korea Republic of

Accurate amine property prediction is essential for optimizing CO2 capture efficiency in postcombustion processes. Quantum machine learning (QML) can enhance predictive modeling by leveraging superposition, entanglement, and interference to capture complex correlations. In this study, we developed hybrid quantum neural networks (HQNN) to improve quantitative structure-property relationship modeling for CO2-capturing amines. By integrating variational quantum regressors with classical multi-layer perceptrons and graph neural networks, quantum advantages were explored in physicochemical property prediction under noiseless conditions and robustness was evaluated against quantum hardware noise using IBM quantum systems. Our results showed that HQNNs improve predictive accuracy for key solvent properties, including basicity, viscosity, boiling point, melting point, and vapor pressure. The fine-tuned and frozen pre-trained HQNN models with 9 qubits consistently achieved the highest rankings, highlighting the benefits of integrating quantum layers with pretrained classical models. Furthermore, simulations under hardware noise confirmed the robustness of HQNNs, maintaining predictive performance. Overall, these findings emphasize the potential of hybrid quantum-classical architectures in molecular modeling. As quantum hardware and QML algorithms continue to advance, practical quantum benefits in QSPR modeling and materials discovery are expected to become increasingly attainable, driven by improvements in quantum circuit design, noise mitigation, and scalable architectures. © 2025, CC BY-NC-ND.

关键词： Quantum entanglement

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SAGE-Amine: Generative Amine Design with Multi-Property Optimization for Efficient CO2 Capture

arXiv

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arXiv 2025年

作者： Lim, Hocheol Cho, Hyein Kim, Jeonghoon Incheon21983 Korea Republic of The Interdisciplinary Graduate Program in Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of

Efficient CO2 capture is vital for mitigating climate change, with amine-based solvents being widely used due to their strong reactivity with CO2. However, optimizing key properties such as basicity, viscosity, and absorption capacity remains challenging, as traditional methods rely on labor-intensive experimentation and predefined chemical databases, limiting the exploration of novel solutions. Here, SAGE-Amine was introduced, a generative modeling approach that integrates Scoring-Assisted Generative Exploration (SAGE) with quantitative structure-property relationship models to design new amines tailored for CO2 capture. Unlike conventional virtual screening restricted to existing compounds, SAGE-Amine generates novel amines by leveraging autoregressive natural language processing models trained on amine datasets. SAGE-Amine identified known amines for CO2 capture from scratch and successfully performed single-property optimization, increasing basicity or reducing viscosity or vapor pressure. Furthermore, it facilitated multi-property optimization, simultaneously achieving high basicity with low viscosity and vapor pressure. The 10 top-ranked amines were suggested using SAGE-Amine and their thermodynamic properties were further assessed using COSMO-RS simulations, confirming their potential for CO2 capture. These results highlight the potential of generative modeling in accelerating the discovery of amine solvents and expanding the possibilities for industrial CO2 capture applications. © 2025, CC BY-NC-ND.

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Evaluating Crosslinking Efficiency and Cytocompatibility of Three Commonly Used Photoinitiators Across Different Cell-Compatible Hydrogel Platforms

SSRN

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SSRN 2025年

作者： Gavish Moscovitz, Aya Simaan Yameen, Haneen Bar-Am, Orit Seliktar, Dror The Faculty of Biomedical Engineering Technion-Israel Institute of Technology Haifa Israel The Interdisciplinary Program in Biotechnology Technion–Israel Institute of Technology Haifa Israel

Biomedical hydrogels that undergo a rapid liquid-to-solid phase transition often use a photopolymerization strategy to crosslink the polymer network. In applications such as 3D bioprinting or tissue engineering that involve cell-laden hydrogels, or where in-vivo polymerization is desired (i.e., in-situ), cytotoxicity of the hydrogel crosslinking reaction becomes an important issue. There are only a few cytocompatible photoinitiators (PIs) that are commonly used for crosslinking biomedical hydrogels, including Irgacure2959 (I2959), lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP) and EosinY (EY). These PIs are illuminated at 365nm, 405nm and 525nm, respectively. The photopolymerization conditions, including PI concentration and illumination intensity may affect both the cytotoxicity of the reaction and the crosslinking of the hydrogel. Herein we tested these three PIs to optimize the maximum crosslinking efficiency while minimizing cell death associated with the photochemical reaction. Testing was performed on three different types of cell-compatible hydrogels used for cell encapsulation and 3D cell growth, including a synthetic material (poly(ethylene glycol)-diacrylate, PEG-DA), a semi-synthetic material (PEG-fibrinogen, PF) and a modified biological material (methacrylated fibrinogen, FibMA). Strain-rate controlled rheology was used to characterize the crosslinking reaction efficiency, both in terms of maximum shear storage modulus, G’, and crosslinking kinetics. Viability was examined on human dermal fibroblasts in 3D culture within photopolymerized PF hydrogels crosslinked using each of the PIs. The results showed that PI concentration and the illumination intensity had significant impact on the crosslinking efficiency, with each material system demonstrating different responses to the PI parameters. Optimal photo-crosslinking conditions, considering both crosslinking efficiency and cell viability were not the same for the modified protein hydrogels, as co

关键词： Cell death

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Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils

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Computers in Biology and Medicine 2025年 184卷 109410-109410页

作者： Park, Jaehee Cheong, Da Yeon Lee, Gyudo Han, Cheol E. Department of Electronics and Information Engineering Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and degradation mechanisms of abnormal proteins implicated in various diseases, such as Alzheimer's disease, Parkinson's disease, type II diabetes, and various forms of amyloidosis. Atomic force microscopy (AFM) stands as the most representative method for studying amyloid fibril morphology. However, obstacles in AFM images, including noise, salt, and amorphous aggregates, often impede accurate sample quantification. In this study, we developed denoising software employing a U-Net deep learning architecture to address this issue. The software efficiently eliminated various impediments that interfere with fibril analysis in noisy AFM images, thereby facilitating precise quantification of amyloid fibrils. We also developed automated fibril analysis technologies using the denoised AFM images, leading to quicker, more precise, and more objective assessments of fibril morphology. Furthermore, we presented a method for fibril stiffness extraction from a modulus image through mask creation based on a denoised height image. Our approach secures time efficiency and precision in analyzing amyloid morphology, and we believe it will significantly advance the currently stagnant research on amyloid-related diseases. © 2024 The Authors

关键词： Neurodegenerative diseases

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Scaling Up Synthetic Cell Production Using Robotics and Machine Learning Toward Therapeutic Applications

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Advanced Biology 2025年第0期 e2400671页

作者： Sharf-Pauker, Noga Galil, Ido Kfir, Omer Chen, Gal Menachem, Rotem Shklover, Jeny Schroeder, Avi Ackerman, Shanny The Louis Family Laboratory for Targeted Drug Delivery and Personalized Medicine Technologies Department of Chemical Engineering Technion – Israel Institute of Technology Haifa32000 Israel The Norman Seiden Multidisciplinary Program for Nanoscience and Nanotechnology Technion – Israel Institute of Technology Haifa32000 Israel Faculty of Computer Science Technion – Israel Institute of Technology Haifa32000 Israel The Interdisciplinary Program for Biotechnology Technion – Israel Institute of Technology Haifa32000 Israel Cell Biology and Cancer Science Rappaport Faculty of Medicine Technion – Israel Institute of Technology Haifa32000 Israel

Synthetic cells (SCs), developed through bottom-up synthetic biology, hold great potential for biomedical applications, with the promise of replacing malfunctioning natural cells and treating diseases with spatiotemporal control. Currently, most SC synthesis and characterization processes are manual, limiting scalability and efficiency. In this study, an automated method is developed for large-scale production of protein-producing SCs for therapeutic applications. The optimized process, compatible with a robotic liquid handling system (LiHa), reduces production time by half. Additionally, incorporation of an automated tissue dissociator-based emulsification increases batch size 30-fold while preserving SC characteristics. To assess SC quality and protein synthesis, artificial intelligence (AI)-based image analysis is employed, allowing for automated, accurate and high-throughput SC characterization. Large-scale luciferase-expressing SCs from a single homogeneous batch are administered to mice, allowing for real-time monitoring of protein expression and reducing experimental variability. By troubleshooting several central steps in SC synthesis, it is demonstrated that automation and computerized quality control can significantly improve the process of SC synthesis for preclinical and clinical applications. © 2025 The Author(s). Advanced Biology published by Wiley-VCH GmbH.

关键词： AI artificial Intelligence automation machine learning robotics synthetic biology synthetic cells

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Robust temporal knowledge inference via pathway snapshots with liquid neural network

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Methods (San Diego, Calif.) 2025年 241卷 24-32页

作者： Peifu Han Jianmin Wang Dayan Liu Lin Liu Tao Song College of Computer Science and Technology China University of Petroleum Qingdao 266580 China. The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon 21983 Republic of Korea. Department of Stomatology The First Medical Center Chinese PLA General Hospital Beijing 100853 China. Electronic address: liulin768@***. College of Computer Science and Technology China University of Petroleum Qingdao 266580 China. Electronic address: tsong@***.

Static graphs play a pivotal role in modeling and analyzing biological and biomedical data. However, many real-world scenarios-such as disease progression and drug pharmacokinetic processes-exhibit dynamic behaviors. Consequently, static graph methods often struggle to robustly address new environments characterized by complex and previously unseen relationship changes. Here, we propose a method for constructing temporal knowledge inference agents tailored to disease pathways, enabling effective relation reasoning beyond their training environment under complex shifts. To achieve this, we developed an imitation learning framework using liquid neural networks, a class of continuous-time neural models inspired by the brain function that are causal and adaptable to changing conditions. Our findings indicate that liquid agents can distill the essential tasks from knowledge graph inputs while accounting temporal evolution, thereby enabling the transfer of temporal skills to novel time nodes. Compared to state-of-the-art deep reinforcement learning agents, experiments demonstrate that temporal robustness in decision-making emerges uniquely in liquid networks.

关键词： Disease Pathways Liquid Neural Networks Relation Reasoning Robust Decision-Making Temporal Knowledge Inference

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Sestrin2 is a central regulator of mitochondrial stress responses in disease and aging

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Ageing Research Reviews 2025年 109卷 102762-102762页

作者： Machado, Ivo F. Palmeira, Carlos M. Rolo, Anabela P. CNC-UC – Center for Neuroscience and Cell Biology University of Coimbra Coimbra Portugal CiBB – Center for Innovative Biomedicine and Biotechnology University of Coimbra Coimbra Portugal Institute of Interdisciplinary Research Doctoral Program in Experimental Biology and Biomedicine (PDBEB) University of Coimbra Coimbra Portugal Department of Life Sciences University of Coimbra Coimbra Portugal

Mitochondria supply most of the energy for cellular functions and coordinate numerous cellular pathways. Their dynamic nature allows them to adjust to stress and cellular metabolic demands, thus ensuring the preservation of cellular homeostasis. Loss of normal mitochondrial function compromises cell survival and has been implicated in the development of many diseases and in aging. Although exposure to continuous or severe stress has adverse effects on cells, mild mitochondrial stress enhances mitochondrial function and potentially extends health span through mitochondrial adaptive responses. Over the past few decades, sestrin2 (SESN2) has emerged as a pivotal regulator of stress responses. For instance, SESN2 responds to genotoxic, oxidative, and metabolic stress, promoting cellular defense against stress-associated damage. Here, we focus on recent findings that establish SESN2 as an orchestrator of mitochondrial stress adaptation, which is supported by its involvement in the integrated stress response, mitochondrial biogenesis, and mitophagy. Additionally, we discuss the integral role of SESN2 in mediating the health benefits of exercise as well as its impact on skeletal muscle, liver and heart injury, and aging. © 2025

关键词： Aging Liver Mitochondria Mitohormesis Muscle Sestrin2

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Pseudomonas aeruginosa-derived DnaJ induces TLR2 expression through TLR10-mediated activation of the PI3K-SGK1 pathway in macrophages

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Microbes and Infection 2025年 105481页

作者： Lee, Jaehoo Jin, Yongxin Wu, Weihui Lee, Yeji Ha, Un-Hwan Department of Biotechnology and Bioinformatics Korea University Sejong 30019 South Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong 30019 South Korea State Key Laboratory of Medicinal Chemical Biology Key Laboratory of Molecular Microbiology and Technology of the Ministry of Education Department of Microbiology Nankai University Tianjin 300071 China

TLR2 is a key component of the innate immune system, responsible for recognizing Gram-positive bacterial components and initiating inflammatory signaling cascades that activate defense responses. However, little is known about the regulatory effects of Pseudomonas aeruginosa (P. aeruginosa) on TLR2 expression. In this study, we investigated the potential link between P. aeruginosa-derived DnaJ and TLR2 expression in macrophages, as well as the activation of downstream signaling pathways. Our findings revealed that DnaJ significantly induced TLR2 expression in a dose- and time-dependent manner, predominantly affecting TLR2 with minimal impact on other TLRs, such as TLR4 and TLR5, which detect bacterial PAMPs. The DnaJ-mediated TLR2 induction was driven by activation of the PI3K-SGK1 signaling pathway, with TLR10 playing a crucial role in facilitating these effects. This increase in TLR2 expression led to enhanced production of inflammatory cytokines in response to secondary Staphylococcus aureus infections, indicating a role in boosting host defense mechanisms. In conclusion, these findings suggest that P. aeruginosa-derived DnaJ promotes TLR2 expression via TLR10-mediated activation of the PI3K-SGK1 pathway, thereby enhancing host immune responses against Gram-positive bacterial infections. © 2025 Institut Pasteur

关键词： DnaJ Pseudomonas aeruginosa SGK1 TLR10 TLR2

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EXCESSIVE SUCROSE EXACERBATES HIGH FAT DIET-INDUCED HEPATIC INFLAMMATION AND FIBROSIS PROMOTING OSTEOARTHRITIS IN MICE MODEL

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Osteoarthritis and Cartilage 2025年 33卷 S139-S140页

作者： Yunhui Min Yunji Heo Dinesh Suminda Godagama Gamaarachchige minhye Kim myeongyeon shin Md. M. Ansari Young-Ok Son Interdisciplinary Graduate Program in Advanced Convergence Technology and Science Jeju National University Jeju Korea (the Republic of) Department of Animal Biotechnology Faculty of Biotechnology College of Applied Life Sciences Jeju National University Jeju-si Jeju-do Korea (the Republic of) Interdisciplinary Graduate Program in Advanced Convergence Technology and Science Jeju national University Jeju-si Jeju-do Korea (the Republic of)

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New clutter rejection method using time-domain averaging for ultrasound color doppler imaging

New clutter rejection method using time-domain averaging for...

引用

2009 IEEE International Ultrasonics Symposium, IUS 2009

作者： Lee, Jaejin Cho, Jeong Yoo, Yang Mo Song, Tai-Kyong Department of Electronic Engineering Interdisciplinary Program of Integrated Biotechnology Sogang University Seoul Korea Republic of

ISBN: (纸本)9781424443895

In ultrasound color Doppler imaging, effective rejection of clutter which is originated from surrounding tissues and slowly-moving vessel walls is important to improve the sensitivity in visualizing the blood flow. To adequately remove clutter, highpass filters, such as initialized infinite impulse response (IIR), are typically utilized. In this paper, we present a new clutter rejection method which can provide comparable clutter rejection to projection-initialized IIR filtering while significantly reducing hardware complexity for a low-cost handheld ultrasound system. ©2009 IEEE.

关键词： Impulse response

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