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Methacrylic acid/Tween 80 engineered amorphous calcium phosphate as an effective bioactive reinforcing nanofillers in dental adhesive

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Dental Materials Journal 2025年第2期44卷 190-200页

作者： Maimansomsuk, Siripatsorn Teangrom, Paremai Teanchai, Chayada Sinthuvanich, Chomdao Ruengket, Pakorn Attarataya, Jakrada Akkarachaneeyakorn, Khrongkhwan Department of Chemistry Faculty of Science Kasetsart University Bangkok10900 Thailand Center of Excellence for Innovation in Chemistry Faculty of Science Kasetsart University Bangkok10900 Thailand Research Center Faculty of Dentistry Mahidol University Bangkok10400 Thailand Department of Biochemistry Faculty of Science Kasetsart University Bangkok Thailand Faculty of Science Kasetsart University Bangkok Thailand Genetic Engineering and Bioinformatics Program Graduate School Kasetsart University Bangkok Thailand Nakhon Ratchasima30000 Thailand

Bioactive fillers in dental adhesives are designed to release beneficial ions, such as calcium and phosphate, to help in remineralization process, and preventing secondary caries. Their incorporation also aims to improve the longevity of dental restorations. Herein, CaP nanoparticles were synthesized through a water-in-oil emulsion method using Tween80 as a surfactant and methacrylic acid (MAA). MAA officiated as a co-surfactant and reduced the size of CaP nanoparticle, including improved compatibility with the components of dental adhesive. The incorporation of 2% T80/CaP/MAA nanofillers into dental adhesive demonstrated increased efficiency, as evidenced by a higher degree of conversion (%DC) and greater micro-tensile bond strength (μTBS). More importantly, T80/CaP/ MAA nanofillers were able to induce remineralization process in the dental structure, which was confirmed by EDX mapping and IR imaging. Finally, a cytotoxicity assessment of the dental adhesive containing T80/CaP/MAA showed no significant adverse effects on human fibroblast cells. © 2025, Japanese Society for Dental Materials and Devices. All rights reserved.

关键词： Stem cells

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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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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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A direct gene transferring system for Oncidium orchids, a difficult crop for genetic transformation

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Agriculture and Natural Resources 2018年第5期52卷 424-429页

作者： Niyomtham, Krittiya Bhinija, Kisana Huehne, Pattana S. Genetic Engineering and Bioinformatics Interdisciplinary Graduate Programs The Graduate School Kasetsart University Bangkok 10900 Thailand Laboratory of Biotechnology Chulabhorn Research Institute Bangkok 10210 Thailand Department of Genetics Faculty of Science Kasetsart University Bangkok 10900 Thailand

Oncidium orchids are susceptible to damage through viral infection caused by the Cymbidium mosaic virus (CymMV). Generating a virus-resistant plant is a major challenge. Unfortunately, gene transformation in Oncidium orchids is not easy. This study developed selection steps for a direct gene transferring system through particle bombardment. Optimized hygromycin screening was reported in the transformed protocorm-like bodies (PLBs) of two commercial Oncidium hybrids (Onc. Gower Ramsey and Onc. Sweet Sugar) with the RNAi construction of the coat protein gene of CymMV. The transgenic Onc. Sweet Sugar PLBs were regenerated on a modified Murashige and Skoog medium containing hygromycin. The effective selection system in exterminating non-transformed PLBs and the functional transgene in the transgenic lines was analyzed using reverse transcription polymerase chain reaction and quantitative polymerase chain reaction. The application of this study may help the gene transformation system of other orchid plant species. © 2018

关键词： Cymbidium mosaic virus Gene transfer Hygromycin Oncidium Particle bombardment

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Potential anti-biofilm producing marine actinomycetes isolated from sea sediments in Thailand

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Agriculture and Natural Resources 2018年第3期52卷 228-233页

作者： Leetanasaksakul, Kantinan Thamchaipenet, Arinthip Department of Genetics Faculty of Science Kasetsart University Bangkok 10900 Thailand Interdisciplinary Graduate Program in Genetic Engineering Graduate School Kasetsart University Bangkok 10900 Thailand Center for Advanced Studies in Tropical Natural Resources Kasetsart University Bangkok 10900 Thailand

Marine actinomycetes are well known as novel resources of bioactive compounds including anti-biofilm agents with various therapeutic applications. In this study, one hundred and one marine actinomycetes were isolated from sea sediments collected from Andaman sea and the Gulf of Thailand. Morphological study and partial 16S rDNA sequence analysis revealed that most of isolates belong to genus Streptomyces (n = 90, 89.11%) and the rest are rare actinomycetes (n = 11, 10.89%) including Actinopolymorpha, Actinomycetospora, Dietzia Nocardiopsis, Micromonospora and Mycobacterium. Among them, ten and thirteen strains significantly reduced more than 60% of biofilm formation of Escherichia coli and Staphylococcus aureus, respectively. But no such activity was found against Pseudomonas aeruginosa. Further analysis of their supernatants indicated that most of marine actinomycetes secreted non-toxic anti-biofilm agents and some accompanied with protease activity. These are preferred characteristics of anti-biofilm substances that potentially restricted biofilm forming of E. coli and S. aureus and simultaneously prevented bacteria to develop resistance. © 2018 Kasetsart University

关键词： Anti-biofilm Marine actinomycetes

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Integrated synchrotron radiation-based fourier transform infrared (SR-FTIR) microscopy and tandem-mass spectrometry (LC-MS/MS) used to elucidate the apoptotic effect of chamuangone in A549 cells

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Biochemical and Biophysical Research Communications 2025年 764卷 151826页

作者： Wanaragthai, Panatda Yingchutrakul, Yodying Panichayupakaranant, Pharkphoom Vongsvivut, Jitraporn Aonbangkhen, Chanat Yang, Meng Chieh Maiuthed, Arnatchai Chanvorachote, Pithi Wood, Bayden R. Choowongkomon, Kiattawee Krobthong, Sucheewin Interdisciplinary Program of Genetic Engineering and Bioinformatics Kasetsart University Bangkok Thailand National Center for Genetic Engineering and Biotechnology NSTDA Pathum Thani 12120 Thailand Phytomedicine and Pharmaceutical Biotechnology Excellence Center Faculty of Pharmaceutical Sciences Prince of Songkla University Hat-Yai Thailand ANSTO-Australian Synchrotron 800 Blackburn Road Clayton Victoria Australia Center of Excellence in Natural Products Chemistry (CENP) Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Department of Anatomy Faculty of Medicine Srinakharinwirot University Bangkok 10110 Thailand Centre of Biopharmaceutical Science for Healthy Ageing Faculty of Pharmacy Mahidol University Bangkok 10400 Thailand Department of Pharmacology Faculty of Pharmacy Mahidol University Bangkok 10400 Thailand Center of Excellence in Cancer Cell and Molecular Biology Faculty of Pharmaceutical Sciences Chulalongkorn University Bangkok 10330 Thailand Department of Pharmacology and Physiology Faculty of Pharmaceutical Sciences Chulalongkorn University Bangkok 10330 Thailand Bio-spectroscopy Group School of Chemistry Monash University Clayton Victoria 3800 Australia Department of Biochemistry Faculty of Science Kasetsart University Bangkok Thailand

Chamuangone, a natural compound extracted from Garcinia cowa leaves, has demonstrated potential in cancer therapeutics, but its effects on lung cancer cells remain unclear. This study investigates the apoptotic effects of Chamuangone on human lung adenocarcinoma cells (A549). The A549 cells were treated with Chamuangone, and the cytotoxic effects were evaluated using an MTT assay, revealing a dose-dependent inhibition of cell proliferation with an IC₅₀ value of 19.43 μM. Annexin V assays further confirmed that Chamuangone induces apoptosis in A549 cells, showing increased levels of late apoptosis with higher concentrations. Synchrotron radiation-based Fourier transform infrared (SR-FTIR) microscopy provided insights into macromolecular changes, highlighting significant alterations in proteins, lipids, and nucleic acids. These structural changes in key cellular macromolecules were supported by proteomic analysis, which identified the upregulation of apoptosis-related proteins, including Peroxiredoxin-2 and Na⁺/H⁺ exchange regulatory cofactor NHE-RF1. Canonical pathway analysis indicated that Chamuangone affects oxidative phosphorylation and mitochondrial dysfunction, both crucial pathways for apoptosis. Additionally, upstream regulator analysis demonstrated significant inhibition of the epidermal growth factor receptor (EGFR), a key player in lung cancer progression. These findings suggest that Chamuangone triggers apoptosis through mitochondrial pathways and EGFR inhibition, positioning it as a promising therapeutic candidate for lung cancer treatment. © 2025 Elsevier Inc.

关键词： Epidermal growth factor receptor Garcinia cowa Mitochondrial dysfunction Oxidative phosphorylation Proteomics

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Searching for and analysis of bacterial blight resistance genes from Thailand rice germplasm

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Agriculture and Natural Resources 2017年第5期51卷 365-375页

作者： Sombunjitt, Siriporn Sriwongchai, Tanee Kuleung, Chatuporn Hongtrakul, Vipa Interdisciplinary Graduate Program in Genetic Engineering The Graduate School Kasetsart University Bangkok Thailand Department of Agronomy Faculty of Agriculture Kasetsart University Bangkok Thailand Department of Genetics Faculty of Science Kasetsart University Bangkok Thailand Center of Advanced Studies in Tropical Natural Resources NRU-KU Kasetsart University Bangkok Thailand

In Thailand, rice (Oryza sativa L.) is the main food and agricultural product for export. However, the production of rice has faced many problems and bacterial blight disease is one of the problems in rice growing. Bacterial blight resistance Xa4, Xa7, xa5 and xa13 genes were discovered in 155 Thai rice cultivars using polymerase chain reaction-based gene-linked and gene-specific markers and the specific allele was compared with susceptible rice cultivars and with rice carrying each resistance gene. The results showed the same alleles as in the Xa4 resistance gene (89.68%), followed by the Xa7 resistance gene (11.61%), while the same alleles as xa5 and xa13 resistance genes were not found in the Thai rice germplasm studied. A new allele of Xa7 was found and part of the DNA sequence was analyzed and compared to alleles obtained from resistant and susceptible rice lines. Sixteen cultivars carried the same alleles as the Xa4 and Xa7 resistant alleles. Phenotyping of 12 rice cultivars was performed using the leaf-clipping method and inoculation with three isolates of Xoo (TB0002, Xoo5 and Xoo6). Three rice cultivars (Kan Phu Daeng, Phuyai Li and RD23) had resistance to all three Xoo isolates. These cultivars will be useful as genetic sources in rice breeding programs in the future. © 2017 Kasetsart University

关键词： Bacterial blight Resistance genes Thailand rice germplasm

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QTL Mapping for Partial Resistance to Southern Corn Rust Using RILs of Tropical Sweet Corn

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American Journal of Plant Sciences 2013年第4期4卷 878-889页

作者： Kitti Wanlayaporn Jetsada Authrapun Apichat Vanavichit Somvong Tragoonrung Department of Agonomy Faculty of Agriculture Kasetsart University Nakhon Pathom Thailand Genome Institute National Center for Genetic Engineering and Biotechnology Pathum Thani Thailand Interdisciplinary Graduate Program in Genetic Engineering Kasetsart University Bangkok Thailand

Southern corn rust is one of destructive diseases in maize caused by Puccinia polysora Undrew. A mapping population of tropical sweet corn recombinant inbred lines (RILs) derived from a cross between hA9104 and hA9035 inbred lines were set up to detect quantitative trait loci (QTLs) involved in partial resistance to southern corn rust. Eighty nine RILs were used to evaluate resistance levels using nine-point relative scale (1-9) at Sweet Seeds, Suwan Farm, Thailand include combined analysis. A genetic linkage map was constructed with 157 SSR markers, with a total length of 2123.1 cM, covering 10 chromosomes. Broad-sense heritability of individual location ranged from 0.76 and 0.82 and combined across locations was 0.87. Multiple QTL mapping (MQM) was applied for the identification of the QTLs. Fifteen QTLs were detected on chromosome 1, 2, 5, 6, 9 and 10 in both locations and combined across locations. QTLs on chromosome 1, 5 and 6 were contributed by alleles of resistant parent hA9104 while others were contributed by alleles from the susceptible parent, hA9035. Phenotypic variance of each QTL explained ranged from 6.1% to 41.8% with a total of 69.8% - 81.9%. QTL on chromosome 1, 6 and 10 were stable QTLs detected in both locations.

关键词： Sweet Corn Southern Corn Rust Partial Resistance Quantitative Trait Loci (QTLs) Combined across Locations

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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A Drug-Induced Liver Injury Prediction Model using Transcriptional Response Data with Graph Neural Network

A Drug-Induced Liver Injury Prediction Model using Transcrip...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Doyeong Hwang Minji Jeon Jaewoo Kang Department of Computer Science and Engineering Korea University Seoul Republic of Korea Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Republic of Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Drug-Induced Liver Injury (DILI) is a major cause of failed drug candidates in clinical trials and withdrawal of approved drugs from the market. Therefore, machine learning-based DILI prediction can be key in increasing the success rate of drug discovery because drug candidates that are predicted to potentially induce liver injury can be rejected before clinical trials. However, existing DILI prediction models mainly focus on the chemical structures of drugs. Since we cannot determine whether a drug will cause liver injury based solely on its structure, DILI prediction based on the transcriptional effect of a drug on a cell is necessary. In this paper, we propose GLIT which is a model that uses transcriptional response data and chemical structures and can be used for drug-induced liver injury prediction. GLIT learns the embedding vectors of drug structures and drug-induced gene expression profiles using graph attention networks in a biological knowledge graph for predicting DILI. GLIT outperformed a baseline model that uses only drug structure information by 7% and 19.2% in terms of correct classification rate (CCR) and Matthews correlation coefficient (MCC), respectively. In addition, we conducted a literature survey to confirm whether the class labels of drugs, in the unknown DILI class, predicted by GLIT are correct.

关键词： Drugs Gene expression Neural networks Injuries Liver Predictive models Biological system modeling

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