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Oxygen-producing composite dressing activated by photothermal and piezoelectric effects for accelerated healing of infected wounds

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Chemical engineering Journal 2023年 476卷

作者： Lai, Yen-Han Roy Barman, Snigdha Ganguly, Anindita Pal, Arnab Yu, Jui-Han Chou, Syun-Hong Huang, E-Wen Lin, Zong-Hong Chen, San-Yuan Department of Materials Science and Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Biomedical Engineering National Tsing Hua University Hsinchu30013 Taiwan Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Science China Medical University Hospital Taichung40400 Taiwan School of Dentistry College of Dental Medicine Kaohsiung Medical University Kaohsiung80708 Taiwan International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan

The process of wound healing is often obstructed by the prevalence of bacterial infection at the wound site. Hence, innovative strategies to alleviate infection is extremely necessary to realize effective wound recovery in a timely manner. Here, we report a self-activated composite dressing consisting of piezoelectric and photothermal functional layers for combating wound infection and provide the desired treatment. The synergistic dressing is functionalized with piezoelectric Poly-L-lactic acid (PLLA) which under the effect of ultrasound irradiation controllably generates reactive oxygen species (ROS) for antibacterial activity, thus facilitating the dressing to inhibit the growth of bacteria at the wound bed. Further, the dressing is modified with calcium peroxide (CaO2) which can effectively convert the generated ROS into molecular oxygen (O2) in presence of catalase enzyme for treating chronic hypoxia in the infected wounds. The sustained generation of O2 by the wound dressing augmented the cell proliferation, migration and tissue regeneration aiding in wound healing. By combining the inherent photothermal activity of reduced graphene oxide (rGO), the synergistic dressing under NIR irradiation enhanced the wound recovery by upregulating the heat shock protein 90 (Hsp90) secretion owing to the heat generation on the wounds. The results obtained demonstrate that the self-triggered multifunctional wound dressing is a promising candidate for clinical treatment of infected wounds with a user-friendly interface. © 2023 Elsevier B.V.

关键词： Ultrasonics

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Short-Chain Dense Brush Pegylation on Rigid Nanocarriers Overcomes Anti-Peg Antibody Recognition for Immune-Stealth Drug Delivery

SSRN

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SSRN 2025年

作者： Chen, Ting-Yu Chang, Chi-Yuan Wen, Ting-Chun Xu, Li Lin, Yu-Han Peng, Cheng-Liang Yeh, Yi-Qi Chen, Chueh-Hsuan Chuang, Kuo-Hsiang Wu, Si-Han Graduate Institute of Pharmacognosy Taipei Medical University Taipei11031 Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University Taipei11031 Taiwan Department of Isotope Application National Atomic Research Institute Taoyuan Taiwan National Synchrotron Radiation Research Center Hsinchu Science Park Hsinchu30076 Taiwan Research Center for Applied Science Academia Sinica Taiwan PhD Program in Clinical Drug Development of Chinese Herbal Medicine Taipei Medical University Taipei Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan

The immunogenicity of polyethylene glycol (PEG) remains a critical challenge in nanomedicine, as pre-existing anti-PEG antibodies can compromise therapeutic efficacy through accelerated blood clearance (ABC) and life-threatening hypersensitivity. Here, we address this challenge by engineering the PEG architecture, creating short-chain dense brush PEGylation (PEG500) on rigid mesoporous silica nanoparticles (MSNs). Unlike conventional PEG2000-liposomes, MSN-PEG500 evades antibodies via three synergistic mechanisms: (1) short-chain PEG500 reduces epitope accessibility, (2) high grafting density (4.43 chains/nm2, according to TGA) forms an dense steric shield, and (3) the rigid silica surface prevents PEG burial. ELISA studies demonstrated that, at equivalent nanoparticle concentrations, MSN-PEG500 exhibits a 26-fold reduction in anti-PEG IgG (6.3) binding and a 1000-fold reduction in anti-PEG IgM (AGP3) binding compared to Lipodox (PEG2000), both of which target the PEG backbone. Furthermore, radiolabeled MSN-PEG500 achieved prolonged circulation and 25% tumor accumulation (ID/g) at 24 hours. In strongly immunized mice, doxorubicin-loaded MSN-PEG500 preserved therapeutic efficacy and 100% survival, whereas Lipodox induced fatal hypersensitivity. Mechanistic investigations revealed that complement activation drives PEG2000 immunotoxicity, and pretreatment with the C3 inhibitor compstatin effectively mitigated temperature drops from 10°C to 2°C in immunized hosts. By demonstrating that nanoscale control of polymer conformation dictates immunological fate, this work provides a clinically translatable blueprint for developing immune-evasive nanomedicines. © 2025, The Authors. All rights reserved.

关键词： Silica nanoparticles

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Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 303-314页

作者： Jia Ying Renee Cattell Tianyun Zhao Lan Lei Zhao Jiang Shahid M.Hussain Yi Gao H‑H.Sherry Chow Alison T.Stopeck Patricia A.Thompson Chuan Huang Department of Biomedical Engineering Stony Brook UniversityStony BrookNY 11794USA Department of Radiation Oncology Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA Department of Medicine Northside Hospital GwinnettLawrencevilleGA 30046USA Program of Public Health Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA Department of Radiology Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA Department of Biomedical Informatics Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA School of Biomedical Engineering Health Science CenterShenzhen UniversityShenzhen 518060China University of Arizona Cancer Center TucsonAZ 85719USA Department of Medicine Renaissance School of MedicineStony Brook UniversityStony BrookNY 11794USA Stony Brook Cancer Center Stony Brook UniversityStony BrookNY 11794USA Department of Medicine Cedar Sinai CancerCedars Sinai Medical CenterLos AngelesCA 90048USA

Presence of higher breast density(BD)and persistence over time are risk factors for breast cancer.A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is *** this study,we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD *** datasets of volunteers from two clinical trials were *** MR images were acquired on 3T Siemens Biograph mMR,Prisma,and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation *** whole-breast segmentation strategies,utiliz-ing image registration and 3D U-Net,were *** segmentation was performed.A task-based analysis was performed:a previously developed MR-based BD measure,MagDensity,was calculated and assessed using automated and manual *** mean squared error(MSE)and intraclass correlation coefficient(ICC)between MagDensity were evaluated using the manual segmentation as a *** test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures(Δ_(2-1)),MSE,and *** results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation,with ICCs of 0.986(95%CI:0.974-0.993)and 0.983(95%CI:0.961-0.992),*** test-retest analysis,MagDensity derived using the regis-tration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993(95%CI:0.982-0.997)when compared to other segmentation *** conclusion,the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating *** methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment,with the registration exhibiting superior performance for highly reproducibl

关键词： Breast cancer Breast density Breast segmentation Image registration Deep learning

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Gradient Boosting Model for Water Quality Forecasting

Gradient Boosting Model for Water Quality Forecasting

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Panda, Parnani Banerjee, Sushovan Mishra, Sushruta Shashidhar, Chitra de Albuquerque, Victor Hugo C. Reis, Marcello Singh, Pooja Kalinga Institute of Industrial Technology Deemed to Be University Bhubaneswar India Department of Commerce and Management Seshadripuram College Bengaluru560020 India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceará Fortaleza Brazil Federal University of Ceará Ceará Brazil Fort Lauderdale United States Lead of the Biomedical Data Analytics Research Group Bengaluru India Federal Institute of Ceará Ceará Brazil Meteora Consultoria Fortaleza Brazil Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819965465

The aim of our study is to evaluate the quality of water using machine learning methods. Potability is a subjective word that quantifies water quality. In this research, a variety of metrics were used to measure general potability of the water. The prescribed elements are applied like variable matrix to denote water quality. The article used three different classification methods, with success rates ranging from 84.27 to 93.16%, to predict the water quality class including ensemble approach like Random Forest (RFC), SVM for classification (SVC), as well as XGBoost. The Gradient Boosting approach, out of all the ones used, received the greatest accuracy (93.16%), precision (96.3%), recall (92.76%) and F-score (94.54%). Also, it achieved the least computational delay with a mean latency of 1.34 s. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Water quality

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Chronic citalopram effects on the brain neurochemical profile and perfusion in a rat model of depression detected by the NMR techniques – spectroscopy and perfusion

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Biomedicine and Pharmacotherapy 2024年 181卷 117656-117656页

作者： Harastova-Pavlova, Iveta Drazanova, Eva Kratka, Lucie Amchova, Petra Hrickova, Maria Macicek, Ondrej Vitous, Jiri Jirik, Radovan Ruda-Kucerova, Jana Institute of Scientific Instruments of the Czech Academy of Sciences Brno Czech Republic Department of Condensed Matter Physics Faculty of Science Masaryk University Brno Czech Republic Department of Pharmacology Faculty of Medicine Masaryk University Brno Czech Republic Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic

Background: Major depressive disorder (MDD) is a mental illness with a high worldwide prevalence and suboptimal pharmacological treatment, which necessitates the development of novel, more efficacious MDD medication. Nuclear magnetic resonance (NMR) can non-invasively provide insight into the neurochemical state of the brain using proton magnetic resonance spectroscopy (¹H MRS), and an assessment of regional cerebral blood flow (rCBF) by perfusion imaging. These methods may provide valuable in vivo markers of the pathological processes underlying MDD. Methods: This study examined the effects of the chronic antidepressant medication, citalopram, in a well-validated MDD model induced by bilateral olfactory bulbectomy (OB) in rats. ¹H MRS was utilized to assess key metabolite ratios in the dorsal hippocampus and sensorimotor cortex bilaterally, and arterial spin labelling was employed to estimate rCBF in several additional brain regions. Results: The ¹H MRS data results suggest lower hippocampal Cho/tCr and lower cortical NAA/tCr levels as a characteristic of the OB phenotype. Spectroscopy revealed lower hippocampal Tau/tCr in citalopram-treated rats, indicating a potentially deleterious effect of the drug. However, the significant OB model–citalopram treatment interaction was observed using ¹H MRS in hippocampal mI/tCr, Glx/tCr and Gln/tCr, indicating differential treatment effects in the OB and control groups. The perfusion data revealed higher rCBF in the whole brain, hippocampus and thalamus in the OB rats, while citalopram appeared to normalise it without affecting the control group. Conclusion: Collectively, ¹H MRS and rCBF approaches demonstrated their capacity to capture an OB-induced phenotype and chronic antidepressant treatment effect in multiple brain regions. © 2024 The Authors

关键词： Brain metabolites Cerebral perfusion Citalopram Depression Olfactory bulbectomy Rats

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Nu-Ri Park Min-Joong Kim Jongsu Lee Minah Seo Yong-Sang Ryu Myung-Ki Kim KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA School of Biomedical Engineering Korea University Seoul Republic of Korea Interdisciplinary Program in Precision Public Health Korea University Seoul Republic of Korea

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen (<3%... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Electric potential Accuracy Hydrogen Lasers and electrooptics Optical sensors Electro-optical waveguides

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PPARα affects hepatic lipid homeostasis by perturbing necroptosis signals in the intestinal epithelium

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Acta Pharmaceutica Sinica B 2024年第11期14卷 4858-4873页

作者： Shufang Na Yanjie Fan HongLei Chen Ling Li Guolin Li Furong Zhang Rongyan Wang Yafei Yang Zixia Shen Zhuang Peng Yafei Wu Yong Zhu Zheqiong Yang Guicheng Dong Qifa Ye Jiang Yue Department of Pharmacology Taikang Medical School(School of Basic Medical Sciences)Wuhan UniversityWuhan 430071China Zhongnan Hospital of Wuhan University Institute of Hepatobiliary Diseases of Wuhan UniversityTransplant Center of Wuhan UniversityNational Quality Control Center for Donated Organ ProcurementHubei Key Laboratory of Medical Technology on TransplantationHubei Provincial Clinical Research Center for Natural Polymer Biological LiverWuhan 430071China Department of Pathology Taikang Medical School(School of Basic Medical Sciences)Wuhan UniversityWuhan 430071China Center for Biomedical Aging National&Local Joint Engineering Laboratory of Animal Peptide Drug DevelopmentCollege of Life SciencesHunan Normal UniversityChangsha 410081China College of Life Science Inner Mongolia Agricultural UniversityHohhot 010011China Hubei Province Key Laboratory of Allergy and Immunology Wuhan 430060China

Rapid turnover of the intestinal epithelium is a critical strategy to balance the uptake of nutrients and defend against environmental insults, whereas inappropriate death promotes the spread of inflammation. PPARα is highly expressed in the small intestine and regulates the absorption of dietary lipids. However, as a key mediator of inflammation, the impact of intestinal PPARα signaling on cell death pathways is unknown. Here, we show that Pparα deficiency of intestinal epithelium up-regulates necroptosis signals, disrupts the gut vascular barrier, and promotes LPS translocation into the liver. Intestinal Pparα deficiency drives age-related hepatic steatosis and aggravates hepatic fibrosis induced by a high-fat plus high-sucrose diet (HFHS). PPARα levels correlate with TRIM38 and MLKL in the human ileum. Inhibition of PPARα up-regulates necroptosis signals in the intestinal organoids triggered by TNF-α and LPS stimuli via TRIM38/TRIF and CREB3L3/MLKL pathways. Butyric acid ameliorates hepatic steatosis induced by intestinal Pparα deficiency through the inhibition of necroptosis. Our data suggest that intestinal PPARα is essential for the maintenance of microenvironmental homeostasis and the spread of inflammation via the gut-liver axis.

关键词： Gut-liveraxis NAFLD PPARα Intestine LPS Liver

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Single-sequence protein structure prediction using language models from deep learning

Single-sequence protein structure prediction using language ...

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2021 AIChE Annual Meeting

作者： Chowdhury, Ratul Bouatta, Nazim Biswas, Surojit Rochereau, Charlotte Church, George M. Sorger, Peter K. Alquraishi, Mohammed Laboratory of Systems Pharmacology Program in Therapeutic Science Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Nabla Bio. Inc. United States Integrated Program in Cellular Molecular and Biomedical Studies Columbia University New YorkNY United States Department of Systems Biology Columbia University New YorkNY United States

ISBN: (纸本)9781713852834

AlphaFold2 and related systems use deep learning to predict protein structure from co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite dramatic, recent increases in accuracy, three challenges remain: (i) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated, (ii) rapid exploration of designed structures, and (iii) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) able to predict protein structure from single protein sequences without use of MSAs. This deep learning system has two novel elements: a protein language model (AminoBERT) that uses a Transformer to learn latent structural information from millions of unaligned proteins and a geometric module that compactly represents Cabackbone geometry. RGN2 outperforms AlphaFold2 and RoseTTAFold (as well as trRosetta) on orphan proteins and is competitive with designed sequences, while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction. © 2021 American Institute of Chemical Engineers. All rights reserved.

关键词： Proteins

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Cobalt-Chromium-Molybdenum Femoral Knee Implant Damage Correlates With Elevated Periprosthetic Metal Concentrations

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Journal of Arthroplasty 2025年第7期40卷 S315-S323页

作者： Kurtz, Peter W. Aslani, Shabnam Kurtz, Michael A. Taylor, Lilliana M. Barnes, Emma R. MacDonald, Daniel W. Piuzzi, Nicolas S. Mihalko, William M. Kurtz, Steven M. Gilbert, Jeremy L. Department of Bioengineering Clemson University Clemson SC United States The Clemson University-Medical University of South Carolina Bioengineering Program Charleston South Carolina United States Department of Biomedical Engineering Drexel University Implant Research Core Philadelphia Pennsylvania United States Department of Orthopaedic Surgery Cleveland Clinic Adult Reconstruction Research (CCARR) Cleveland Clinic Cleveland Ohio United States Department of Biomedical Engineering Cleveland Clinic Cleveland Ohio United States Campbell Clinic Department of Orthopaedic Surgery and Biomedical Engineering University of Tennessee Health Science Center Memphis TN United States

Background: Total knee arthroplasty (TKA) device systems frequently include cast cobalt chrome alloy (CoCrMo) femoral components. However, compared to total hip arthroplasty (THA), gaps persist in our understanding of the correlations between femoral component damage, local metal release, and potential biological effects. Additionally, it remains unclear how TKA metal release affects clinical success or patient satisfaction. In this study, we investigated the associations between implant damage and metal release in the periprosthetic tissues following TKA. We asked: (1) does damage severity correlate with increased metal concentrations within the periprosthetic tissue? and (2) does the magnitude of metal released from CoCrMo femoral components merit clinical concern? Methods: There were 51 CoCrMo femoral components and synovial samples that were prospectively collected by an institutional review board–exempt retrieval program. Devices received damage scores ranging from minimal (1) to severe (4). Tissue metal concentrations for cobalt (Co), chromium (Cr), molybdenum (Mo), and titanium (Ti) were quantified using inductively coupled plasma mass spectroscopy. Results: Visual damage correlated with increases in Co, Cr, and Mo concentrations in the periprosthetic tissue (P = 0.0008, 0.029, and 0.007, respectively). Within the tissue adjacent to severely damaged implants, we measured median Co, Cr, and Mo concentrations of 7.81, 5.26, and 0.713 μg/mL, respectively. For minimally damaged implants, we report median Co, Cr, and Mo concentrations of 0.111, 1.80, and 0.179 μg/mL, respectively. In several of the 51 (14%) tissue samples, we measured Co and Cr concentrations > 10 μg/mL. Additionally, within periprosthetic tissues of devices with Ti tibial trays, Ti concentrations increased (P = 0.0052) arising, in part, from tibial-femoral component contact during arthroplasty. Conclusions: We (1) showed elevated periprosthetic tissue metal concentrations in TKA patients and (2)

关键词： cobalt-chromium-molybdenum femoral component metal concentration titanium total knee arthroplasty

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