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The Bacterial Outer Membrane Vesicle-Cloaked Immunostimulatory Nanoplatform Reinvigorates T Cell Function and Reprograms Tumor Immunity

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ACS Nano 2025年 2025 May 20页

作者： Lin, Yu-Han Chen, Chia-Wei Chen, Mei-Yi Xu, Li Tian, Xuejiao Cheung, Siu-Hung Wu, Yen-Ling Siriwon, Natnaree Wu, Si-Han Mou, Kurt Yun Taiwan International Graduate Program in Molecular Medicine National Yang Ming Chiao Tung University and Academia Sinica Taipei 11529 Taiwan Graduate Institute of Nanomedicine and Medical Engineering Taipei Medical University Taipei 11031 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei 11529 Taiwan Research Center for Applied Sciences Academia Sinica Taipei 11529 Taiwan Chakri Naruebodindra Medical Institute Faculty of Medicine Ramathibodi Hospital Mahidol University Samutprakarn 10540 Thailand International Ph.D. Program in Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica Taipei 11529 Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu 30013 Taiwan

Bacterial outer membrane vesicles (OMVs) represent powerful immunoadjuvant nanocarriers with the capacity to reprogram the tumor microenvironment (TME) and activate immune responses. Here, we investigate a nanotherapeutic platform integrating immunostimulatory cytosine-phosphate-guanine oligodeoxynucleotides (CpG-ODNs, hereafter termed CpG) into mesoporous silica nanoparticles cloaked with OMVs (CpG@MSN-PEG/PEI@OMVs) for cancer immunotherapy. Systemic administration of these nanohybrids facilitates precise tumor targeting, induces antitumor cytokines such as IFNγ, and suppresses immunosuppressive cytokine TGF-β, reshaping the TME. Additionally, CpG@MSN-PEG/PEI@OMVs promote M1 macrophage polarization, dendritic cell maturation, and the generation of durable tumor-specific immune memory, resulting in pronounced tumor regression with minimal systemic toxicity. The platform demonstrates efficacy against metastatic and solid tumor models including 4T1 breast and MC38 colorectal cancers. Transcriptomic analyses reveal that CpG@MSN-PEG/PEI@OMVs enhance mitochondrial oxidative phosphorylation in T cells within tumor-draining lymph nodes, mitigating T cell exhaustion and restoring metabolic fitness. These results support the potential of CpG@MSN-PEG/PEI@OMVs as a modular nanoplatform to modulate innate and adaptive immunity in cancer immunotherapy. © 2025 The Authors. Published by American Chemical Society.

关键词： bacterial outer membrane vesicles mesoporous silica nanoparticles mitochondrial activity T cell exhaustion phenotypes tumor microenvironment

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Improving energy conversion efficiency of ion-driven artificial muscles based on carbon nanotube yarn

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Journal of Power Sources 2025年 646卷

作者： Hyeon, Jae Sang Wang, Qiong Tawfick, Sameh Lee, JeongA Smith, Kyle C. Zhang, Mengmeng Park, Jong Woo Song, Gyu Hyeon Wang, Zhong Fang, Shaoli Baughman, Ray H. Kim, Seon Jeong Department of Biomedical Engineering and Electronic Engineering Hanyang University Seoul04763 Korea Republic of Department of Mechanical Science and Engineering Grainger College of Engineering University of Illinois at Urbana-Champaign Urbana61801 United States Department of Materials Science and Engineering Grainger College of Engineering University of Illinois at Urbana-Champaign Urbana61801 United States Beckman Institute for Advanced Science and Technology Grainger College of Engineering University of Illinois at Urbana-Champaign Urbana61801 United States Computational Science and Engineering Program Grainger College of Engineering University of Illinois at Urbana-Champaign Urbana61801 United States Alan G. MacDiarmid NanoTech Institute University of Texas at Dallas Richardson75080 United States

While artificial muscles provide giant work and power densities compared to natural muscles, their reported energy conversion efficiencies have so far been low. We here demonstrate a tension optimization process (TOP) for fabricating coiled carbon nanotube artificial muscles having record efficiencies. These TOP muscles were made by applying about 20 times higher tensile stress during pre-coiling twist insertion than the tensile stress applied during coiling, resulting in high twist density and high spring index. The TOP muscles driven by the tetrabutylammonium cation provide 6.1 J/g contractile work, which is ∼152 times the maximum capability of human skeletal muscles, and 13.1 % contractile energy efficiency. In addition, the contractile energy efficiency of the TOP muscles driven by the bis(trifluoromethanesulfonyl)imide anion is maximized to 38.8 % by minimizing side redox reactions. In the case of full-cycle actuation, which considers the whole cycle of contraction and relaxation, we increased the full-cycle energy conversion efficiency of TOP muscles to 6.7 %, which is 4.5 times that previously reported for ion-driven artificial muscles. © 2025 Elsevier B.V.

关键词： Energy conversion efficiency

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A Rapid Manual Solid Phase Peptide Synthesis Method for High-Throughput Peptide Production

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Journal of biomedical Materials Research - Part A 2025年第5期113卷 e37922页

作者： Overby, Clyde Abraham, Brittany Adjei-Sowah, Emmanuella March, Alyson Ling, Kevin Basu, Sayantani Benoit, Danielle S. W. Department of Biomedical Engineering University of Rochester RochesterNY United States Center for Musculoskeletal Research University of Rochester RochesterNY United States Department of Chemical Engineering University of Rochester RochesterNY United States Materials Science Program University of Rochester RochesterNY United States Department of Bioengineering Knight Campus for Accelerating Scientific Impact University of Oregon EugeneOR United States

Solid phase peptide synthesis (SPPS) techniques are critical for developing and using peptides in various biomedical applications. However, typical synthesis routes used in SPPS are either resource-intensive (e.g., with automated synthesis or commercial services) or time-consuming (e.g., with manual benchtop synthesis). Here, a rapid manual synthesis method was developed to produce up to 8 peptides with fast cycle times simultaneously. Peptides synthesized manually were of equivalent or superior quality to those produced by in-house microwave-assisted automated peptide synthesis, with higher average crude purity of 70% compared to 50%. The method significantly reduced synthesis time, enabling the parallel coupling of up to 8 amino acids simultaneously in 15–20 min, as opposed to traditional benchtop peptide synthesis, which requires 80–150 min per amino acid. This approach offers an intermediate throughput between milligram-scale libraries and gram-scale single peptide synthesis, enabling rapid iteration for novel peptide designs without the need for expensive automated systems. As a result, peptide modifications, including incorporation of unnatural amino acids, can be explored, accelerating the development of peptides for a wide range of applications. © 2025 The Author(s). Journal of biomedical Materials Research Part A published by Wiley Periodicals LLC.

关键词： Medical applications

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Enhancing biomedical engineering Education Through Curricular Innovation: Developing Industry-Ready Engineers

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biomedical engineering Education 2025年 1-7页

作者： Boyd, Lawrence Bradley, Joe Gray, Marissa Keeling, Novella M. Ochia, Ruth Reuther, Katherine E. Department of Bioengineering Clemson University Clemson USA Department of Bioengineering | Carle Illinois College of Medicine University of Illinois Urbana-Champaign Urbana USA School of Engineering Brown University Providence USA Biomedical Engineering Program University of Colorado Boulder Boulder USA Department of Bioengineering Temple University Philadelphia USA Department of Bioengineering University of Pennsylvania Philadelphia USA Center for Health Devices and Technology University of Pennsylvania Philadelphia USA

biomedical engineering (BME) curricula are necessarily broad and it has been reported that BME students can experience substantial barriers to their career attainment compared to other engineering majors. Challenges and curricular innovations for addressing industry needs were discussed in workshops held at the BME Education Summit. This event was attended by educators and BME industry representatives. Skills needed by employers included verbal and written communication, mechanical design, engineering competency, teamwork, initiative, and self-motivation. Areas reported to be not well addressed in the BME curriculum included manufacturing, design controls, drafting, regulatory affairs, and quality systems. Specialized master’s programs are one approach to meeting industry needs. Micro-credentialing and certificate programs are another proposed innovation for appropriately training biomedical engineers for careers in industry. Another approach is via formal entrepreneurship and commercialization training. This may include having specific course offerings, assignments, and case studies focused on entrepreneurship and commercialization; having guest speakers and representatives from industry integrated in the classroom; and teaching and mentoring from faculty with expertise in these areas. Specific next steps to align academic program offerings with industry needs include directly developing specialized programs with industry partners, assessing specialty programs by both academic and industrial entities, aligning specialty program outcomes with ABET student outcomes or encouraging graduate accreditation bodies to develop a framework that directly assesses specialty programs. There is a need among the BME community to establish a broad standard for collecting informative data regarding BME-graduate industry readiness from courses, programs, employers, and recent alumni.

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Joint Source Decomposition of Concurrent EEG-FMRI Data in Epilepsy and Control Groups

Joint Source Decomposition of Concurrent EEG-FMRI Data in Ep...

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IEEE International Symposium on biomedical Imaging

作者： J. Mason Harding Haatef Pourmotabbed Yamin Li Kimberly Rogge-Obando Kate Wang Sarah E. Goodale Shiyu Wang Camden Bibro Bergen Allee Caroline Martin Victoria L. Morgan Dario J. Englot Catie Chang Department of Electrical and Computer Engineering Vanderbilt University Department of Biomedical Engineering Vanderbilt University Department of Computer Science Vanderbilt University Neuroscience Graduate Program Vanderbilt University Vanderbilt Memory and Alzheimer's Center Vanderbilt University Medical Center Department of Neurological Surgery Vanderbilt University Medical Center Department of Radiology Vanderbilt University Medical Center

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

EEG and fMRI are complementary, noninvasive technologies for investigating human brain function. These modalities have been used to uncover large-scale functional networks and their disruptions in clinical populations. Given the high temporal resolution of EEG and high spatial resolution of fMRI, integrating these modalities can provide a more holistic understanding of brain activity. This work explores a multimodal source decomposition technique for extracting shared modes of temporal variation between fMRI BOLD signals and EEG spectral power fluctuations in the resting state. The resulting components are then compared between patients with focal epilepsy and controls, revealing multimodal network differences between groups.

关键词： Neuroimaging Brain Fluctuations Epilepsy Functional magnetic resonance imaging Electroencephalography Pattern recognition Hemodynamics Spatial resolution Signal resolution

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ATLAS: a rationally designed anterograde transsynaptic tracer

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Nature Methods 2025年第5期22卷 1101-1111页

作者： Rivera, Jacqueline F. Huang, Haoyang Weng, Weiguang Sohn, Heesung Girasole, Allison E. Li, Shun Albanese, Madeline A. Qin, Melissa Tao, Can Klug, Molly E. Rao, Sadhna Paletzki, Ronald Herring, Bruce E. Kanoski, Scott E. Zhang, Li I. Gerfen, Charles R. Sabatini, Bernardo L. Arnold, Don B. Department of Biological Sciences Dornsife College University of Southern California Los Angeles CA United States Neuroscience Graduate Program University of Southern California Los Angeles CA United States Howard Hughes Medical Institute Department of Neurobiology Harvard Medical School Boston MA United States Center for Neural Circuits and Sensory Processing Disorders Zilkha Neurogenetic Institute Department of Physiology and Neuroscience Keck School of Medicine University of Southern California Los Angeles CA United States Section on Neuroanatomy National Institute of Mental Health Bethesda MD United States Department of Biomedical Engineering Viterbi School of Engineering University of Southern California Los Angeles CA United States

Genetically modified rabies virus can map neural circuits retrogradely from genetically determined cells. However, similar tools for anterograde tracing are not available. Here, we describe a method for anterograde transsynaptic tracing from genetically determined neurons based on a rationally designed protein, ATLAS. Expression of ATLAS in neurons causes presynaptic release of a payload composed of an antibody-like protein, ***, which binds to the N terminus of GluA1, and a recombinase. In the synaptic cleft, *** binds to GluA1, causing the payload to be endocytosed into postsynaptic cells and delivered to the nucleus, where it triggers expression of a recombinase-dependent reporter. In mice, ATLAS mediates monosynaptic transneuronal tracing from random or genetically determined cells that is strictly anterograde, synaptic and nontoxic. Moreover, ATLAS-mediated tracing shows activity dependence, suggesting that it can label active circuits underlying specific behaviors. Finally, ATLAS is composed of modular components that can be independently replaced or modified. © The Author(s) 2025.

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UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences

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PeerJ Computer Science 2025年 11卷 e2636-e2636页

作者： van der Zee, Tim J. Tecchio, Paolo Hahn, Daniel Raiteri, Brent J. Biomedical Engineering Graduate Program University of Calgary Calgary Canada Faculty of Kinesiology University of Calgary Calgary Canada Department of Human Movement Science Ruhr University Bochum Bochum Germany Department of Movement Sciences Katholieke Universiteit Leuven Leuven Belgium School of Human Movement and Nutrition Sciences University of Queensland Brisbane Australia

Background. Brightness-mode (B-mode) ultrasound is a valuable tool to non- invasively image skeletal muscle architectural changes during movement, but auto- matically tracking muscle fascicles remains a major challenge. Existing fascicle tracking algorithms either require time-consuming drift corrections or yield noisy estimates that require post-processing. We therefore aimed to develop an algorithm that tracks fascicles without drift and with low noise across a range of experimental conditions and image acquisition settings. Methods. We applied a Kalman filter to combine fascicle length and fascicle angle estimates from existing and openly-available UltraTrack and TimTrack algorithms into a hybrid algorithm called UltraTimTrack. We applied the hybrid algorithm to ultrasound image sequences collected from the human medial gastrocnemius of healthy individuals (N D8, four women), who performed cyclical submaximal plantar flexion contractions or remained at rest during passive ankle joint rotations at given frequencies and amplitudes whilst seated in a dynamometer chair. We quantified the algorithm's tracking accuracy, noise, and drift as the respective mean, cycle-to-cycle variability, and accumulated between-contraction variability in fascicle length and fascicle angle. We expected UltraTimTrack's estimates to be less noisy than TimTrack's estimates and to drift less than UltraTrack's estimates across a range of conditions and image acquisition settings. Results. The proposed algorithm yielded low-noise estimates like UltraTrack and was drift-free like TimTrack across the broad range of conditions we tested. Over 120 cyclical contractions, fascicle length and fascicle angle deviations of UltraTimTrack accumulated to 2.1 ± 1.3 mm (mean ± sd) and 0.8 ± 0.7 deg, respectively. This was considerably less than UltraTrack (67.0 ± 59.3 mm, 9.3 ± 8.6 deg) and similar to TimTrack (1.9 ± 2.2 mm, 0.9 ± 1.0 deg). Average cycle-to-cycle variability of UltraTimTrack was 1.4 ± 0.4

关键词： Medial gastrocnemius Muscle architecture Fascicle length Ultrasound imaging Ankle dynamometry B-mode ultrasonography Optical flow Hough transform Pennation angle Muscle contraction

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PanNet: A Feature-based Attention Aggregation Model for Segmenting Pancreatic Ductal Adenocarcinoma on Contrast-enhanced CT Images of the Abdomen

PanNet: A Feature-based Attention Aggregation Model for Segm...

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Medical Imaging 2025: Computer-Aided Diagnosis

作者： Pal, Debojyoti Dutta, Kaushik Ludwig, Daniel R. Shoghi, Kooresh Imaging Science Program Washington University in St Louis St LouisMO United States Department of Radiology Washington University School of Medicine St LouisMO United States Department of Biomedical Engineering Washington University in St Louis St LouisMO United States

ISBN: (纸本)9781510685925

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal forms of cancer with a 5-year survival rate of only 8%. One of the primary reasons for the low survival rate is the late detection of the PDAC cancer. This work focuses on the segmentation of PDAC for improved detection of pancreatic masses which will ultimately promote diagnosis during earlier stages of the disease. We propose a novel automatic segmentation model, PanNet which uses multi-level skip connection along with novel feature-based attention aggregation (FAA) block to improve the accuracy of the PDAC detection. The FAA block improves the interpolation power of the model in the decoder blocks, thereby reducing false positive pixels in the predicted tumor masks. The pixel-wise attention algorithm in the FAA block is applied across all channels in the 3D feature vectors obtained from individual decoder blocks. This leads to a substantial improvement of up to 7.3% in Dice Score (DSC) score on two datasets, each containing a test set of patients with early onset of PDAC. This aggregates to an improvement in the pixels of tumor volume prediction by at most 60.2% in comparison to state-of-the-art (SOTA) pancreas segmentation models across both the datasets. The proposed PanNet can be utilized for early detection of PDAC cancer, given its consistent and enhanced segmentation performance demonstrated across multiple datasets in this paper. © 2025 SPIE.

关键词： Image segmentation

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A machine learning tool for prediction of vertebral compression fracture following stereotactic body radiation therapy for spinal metastases

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Radiotherapy and Oncology 2025年 208卷 110912-110912页

作者： Burgess, Laura Rezkalla, Matthew Klein, Geoffrey Karagoz, Batuhan Santos, Gonzalo Martinez Malmirian, Mobin Whyne, Cari Sahgal, Arjun Hardisty, Michael Department of Radiation Oncology Odette Cancer Centre Sunnybrook Health Sciences Centre Toronto ON Canada Holland Bone and Joint Program Sunnybrook Research Institute Toronto ON Canada Department of Medical Biophysics University of Toronto Toronto ON Canada Engineering Science Faculty of Engineering University of Toronto ON Canada Department of Surgery and Institute of Biomedical Engineering University of Toronto Canada

Background and Purpose: The most common adverse event following spine stereotactic body radiotherapy (SBRT) is vertebral compression fracture (VCF). There is interest in the development of patient-specific tools that can predict those at high risk of developing VCF. This study aimed to develop a machine learning tool able to predict the development of VCF following spine SBRT using clinical, dosimetric and tumor risk factors. Methods and Materials: A retrospective review of a prospectively maintained database of spinal segments treated with SBRT for spinal metastases was utilized. Machine learning models were applied to this dataset and their ability to predict for VCF was evaluated. Data was split into training and validation sets. Spinal Instability Neoplastic Score (SINS) is the current clinical standard for predicting spine instability in the setting of metastatic disease and served as the baseline model for comparison. Results: Between 2008 and 2021, 1406 spinal segments were contained within the database. Logistic regression, neural network/multi-layer perceptron, support vector machine and random forest were the machine learning models applied to the dataset. Their accuracy, precision, F1-score, sensitivity and specificity was determined, together with that of SINS. Across performance metrics, the machine learning models outperformed SINS with random forest model having the best performance. Important factors that increase the risk of VCF were identified and include age, pain, extent of pre-existing VCF, location and spinal alignment. Conclusions: A machine learning model predicting for VCF following spine SBRT has been developed. This model outperformed the current clinical standard of SINS in the prediction of VCF. © 2025 Elsevier B.V.

关键词： Machine Learning Spinal metastases Spine SBRT Vertebral Compression Fracture

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Prediction tools for assessing functional impacts of gene mutations in Clear Cell Renal Cell Carcinoma: A comparative study

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Computers in Biology and Medicine 2025年第Pt A期192卷 110187-110187页

作者： Yang, Cheng-Hong Lin, Guan-Cheng Wu, Chih-Hsien Chen, Jin-Bor Chuang, Li-Yeh Department of Information Management Tainan University of Technology Tainan Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Drug Development and Value Creation Research Center Kaohsiung Taiwan Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung Taiwan Division of Nephrology Department of Internal Medicine Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Taiwan Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering I-Shou University Kaohsiung Taiwan Taiwan

Renal cell carcinoma (RCC) is a well-known malignancy characterized by specific gene mutations that elevate its occurrence. It can be categorized into clear cell RCC (ccRCC), papillary RCC, and chromophobe RCC, which account for approximately 85 % of all primary RCC cases. The mutations typically involve single-nucleotide variants (SNVs) that lead to amino acid substitutions, which influence various biological functions, including gene expression. Therefore, predicting the functional consequences of RCC-related single-nucleotide polymorphisms (SNPs), including substitutions, insertions, deletions, and duplications, is crucial for the effective clinical management of RCC. In recent years, the accessibility and popularity of tools for predicting SNP functional variations have grown, especially in research concerning the potential risks associated with key gene mutations in cancer. Accordingly, this study focused on commonly mutated genes in ccRCC, namely VHL, BAP1, PBRM1, and SETD2. Public data from sources such as The Cancer Genome Atlas and the National Center for Biotechnology Information were used to identify 61 gene mutation positions. Nine nonsynonymous mutation prediction tools were used for analysis, namely SIFT, Polyphen-2, Mutation Assessor, Fathmm, MutPred, CHASM, Revel, Provean, and SNP&GO. The tools were evaluated by their statistical performance, prediction distribution, and receiver operating characteristic curves. The three tools with the highest accuracy for predicting the functional consequences of mutations in the four frequently mutated genes associated with ccRCC were SIFT (accuracy of 0.75), Provean (0.7), and Polyphen-2 (0.69). These findings offer valuable insights into predicting ccRCC-related gene mutation effects, but further research and validation are essential to support their clinical applications. © 2025

关键词： Transcription

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