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Sequence-controlled dynamic covalent units enable decoupling of mechanical and self-healing performance of polymers

Science China Chemistry 2025年第2期68卷 745-753页

作者： Luzhi Zhang Hongfei Huang Lijie Sun Xiaopeng Ma Hui Tan Zhengwei You Respiratory Department Shenzhen Children’s Hospital Affiliated to Shantou University Medical CollegeShenzhen 518038China State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and EngineeringInstitute of Functional MaterialsResearch Base of Textile Materials for Flexible Electronics and Biomedical Applications(China Textile Engineering Society)Shanghai Key Laboratory of Lightweight CompositeShanghai Engineering Research Center of Nano-Biomaterials and Regenerative MedicineDonghua UniversityShanghai 201620China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China

Sequence regulation provides an effective approach to controlling the properties of polymer ***,this approach remains an open question in the field of dynamic polymers,which emerge as more and more important new generation ***,we systematically investigate the effect of sequence control of dynamic covalent units in tuning the properties of *** sequence-controlled poly(oxime-urethanes)are *** dynamic oxime-urethane groups are relatively dispersed(SCP-1)or concentrated(SCP-2)distributed in their molecular *** sequence control strategy provides an efficient way to decouple the mechanical and self-healing performance of polymers,which is one of the most pressing challenges in the *** relatively dispersed oxime-urethane groups in SCP-1 not only facilitate the reorganization of the dynamic covalent bonds but also increase the probability of the reformation of hydrogen *** reversible dissociation/reassociation of dynamic bonds is conducive to dissipating energy to enhance mechanical performance and promote self-healing *** a result,SCP-1 exhibits much faster self-healing than SCP-2,and its tensile strength is nearly twice that of *** addition,energy dissipation capacity and degradation behavior also show significant sequence ***,this work reveals a new molecular structure-property relationship and provides a powerful strategy to construct high-performance polymers.

关键词： sequence control dynamic polymer poly(oxime-urethane) strength polyurethane

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Generative Inpainting-Based Anomaly Detection for CT Liver Tumor Detection

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IEEE Transactions on Radiation and Plasma Medical Sciences 2025年

作者： Shi, Yongyi Niu, Chuang Simpson, Amber L. Man, Bruno De Do, Richard Wang, Ge Rensselaer Polytechnic Institute Biomedical Imaging Center Department of Biomedical Engineering School of Engineering TroyNY12180 United States Queen's University Biomedical Computing and Informatics ONK7L 3N6 Canada Ge HealthCare Technology and Innovation Center NiskayunaNY12309 United States Memorial Sloan Kettering Cancer Center Department of Radiology New YorkNY10065 United States

CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify pathological structures. However, these methods may produce suboptimal results, overlooking subtle differences among various tissue types. To address this challenge, here we employ generative diffusion prior to inpaint the liver as the reference facilitating anomaly detection. Specifically, we use an adaptive threshold to extract a mask of abnormal regions, which are then inpainted using a diffusion prior to calculating an anomaly score based on the discrepancy between the original CT image and the inpainted counterpart. Our methodology has been tested on two liver CT datasets, demonstrating a significant improvement in detection accuracy, with a 7.9% boost in the area under the curve (AUC) compared to the state-of-the-art. This performance gain underscores the potential of our approach to refine the radiological assessment of liver diseases. © 2025 IEEE.

关键词： Computerized tomography

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Using paired-agent principles and widefield fluorescence optical projection tomography to rapidly identify and localize lymph node micro metastases 11

Using paired-agent principles and widefield fluorescence opt...

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Molecular-Guided Surgery: Molecules, Devices, and Applications XI 2025

作者： Rounds, Cody C. Sharma, Anjalika Parschat, Simon Sarathchandra, A.S. Chalmika Goyal, Rishi Torres, Veronica C. Li, Chengyue Nijboer, T.S. Voskuil, Floris Witjes, Max J.H. Tichauer, Kenneth M. Department of Biomedical Engineering Illinois Institute of Technology ChicagoIL60616 United States Department of Oral & Maxillofacial Surgery University Medical Center Groningen Groningen Netherlands Department of Biomedical Engineering Oregon Health and Science University PortlandOR97239 United States

ISBN: (纸本)9781510683501

The proof-of-concept presented here utilizes a micro metastatic lymph node mode, where porcine lymph nodes are first implanted with a 200-micron human cancer cell spheroid. The lymph nodes are then infused with 40 nM ABY-029 and IRDye 700Dx-Affibody isotype conjugate, followed by widefield multi-channel imaging of 700 nm, 800 nm fluorescence and transmission images across 12 projections, followed by tomographic reconstruction of each channel. Absorption and fluorescence sensitivity matrices needed for reconstructions are created through monte-carlo simulations of photon propagation through the imaging system. Each channel is reconstructed separately at a resolution of 87x87x65 voxels, followed by calculation of binding potential (BP) maps as the ratio of the two fluorescence reconstructions, minus 1. The BP reconstruction is then overlaid onto the absorption reconstruction and the location of the EGFR+ tumor spheroid is isolated in the 3D reconstruction through inspection. These procedures are proposed for intraoperative use. © 2025 SPIE.

关键词： Lymph nodes Fluorescence Fluorescence tomography Optical tomography Projection systems Voxels Medical image reconstruction Simulations Tomography Tumors

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Convergent effects of peptides on the initiation of feeding motor programs in the mollusk Aplysia

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Journal of Neurophysiology 2025年第5期133卷 1368-1379页

作者： Evans, Colin G. Barry, Michael A. Reaver, Carrie N. Patel, Paras R. Chestek, Cynthia A. Perkins, Matthew H. Jing, Jian Cropper, Elizabeth C. Department of Neuroscience Friedman Brain Institute Icahn School of Medicine at Mount Sinai New York NY United States Department of Biomedical Engineering Biointerfaces Institute University of Michigan Ann Arbor MI United States Department of Electrical Engineering and Computer Science Neurosciences Program Robotics Program University of Michigan Ann Arbor MI United States State Key Laboratory of Pharmaceutical Biotechnology Institute for Brain Sciences Chemistry and Biomedicine Innovation Center Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology Advanced Institute for Life Sciences School of Life Sciences Nanjing University Nanjing China

Neuropeptides configure the feeding network of Aplysia. For example, egestive activity is promoted by small cardioactive peptide (SCP), and ingestive activity is promoted by a combination of feeding circuit activating peptide (FCAP) and cerebral peptide 2 (CP-2). In addition, SCP and FCAP/CP-2 have a common network effect that does not contribute to motor program specification. They increase the excitability of an interneuron, B63. In this report, we further characterized the effects of peptides on B63. We performed voltage-clamp experiments and used a step protocol to look at steady-state currents. We found that SCP and FCAP/CP-2 both induced an inward current that was virtually absent in low-sodium saline. Previous work has established that B63 is unusual in the feeding circuit in that subthreshold depolarizations are autonomously generated that can trigger motor programs. Here, we show that this autonomous activity is more frequent in the presence of peptides. Previous studies have also shown that activity of the feeding central pattern generator (CPG) can be initiated by neurons that excite B63, e.g., by cerebral buccal interneuron 2 (CBI-2), a projection neuron that triggers biting-like motor programs. Here, we show that the latency of CBI-2-induced activity is decreased by stimulation of the esophageal nerve (EN) (which releases endogenous SCP). These results, taken together with previous results, indicate that peptides that act divergently to configure network activity additionally act convergently to promote motor program induction. We present data that suggest that this arrangement facilitates brief switches between ingestive and egestive motor activity. © 2025 The Authors.

关键词： central pattern generator invertebrate modulation motor program neuropeptide

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UNIVERSAL IMAGE RESTORATION PRE-TRAINING VIA DEGRADATION CLASSIFICATION

arXiv

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arXiv 2025年

作者： Hu, JiaKui Jin, Lujia Yao, Zhengjian Lu, Yanye Institute of Medical Technology Peking University Health Science Center Peking University China Biomedical Engineering Department College of Future Technology Peking University China National Biomedical Imaging Center Peking University China China Mobile Research Institute China

This paper proposes the Degradation Classification Pre-Training (DCPT), which enables models to learn how to classify the degradation type of input images for universal image restoration pre-training. Unlike the existing self-supervised pretraining methods, DCPT utilizes the degradation type of the input image as an extremely weak supervision, which can be effortlessly obtained, even intrinsic in all image restoration datasets. DCPT comprises two primary stages. Initially, image features are extracted from the encoder. Subsequently, a lightweight decoder, such as ResNet18, is leveraged to classify the degradation type of the input image solely based on the features extracted in the first stage, without utilizing the input image. The encoder is pre-trained with a straightforward yet potent DCPT, which is used to address universal image restoration and achieve outstanding performance. Following DCPT, both convolutional neural networks (CNNs) and transformers demonstrate performance improvements, with gains of up to 2.55 dB in the 10D all-in-one restoration task and 6.53 dB in the mixed degradation scenarios. Moreover, previous self-supervised pretraining methods, such as masked image modeling, discard the decoder after pre-training, while our DCPT utilizes the pre-trained parameters more effectively. This superiority arises from the degradation classifier acquired during DCPT, which facilitates transfer learning between models of identical architecture trained on diverse degradation types. Source code and models are available at https://***/MILab-PKU/dcpt. © 2025, CC BY.

关键词： Restoration

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Generation,interrogation,and future applications of microglia-containing brain organoids

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Neural Regeneration Research 2025年第12期20卷 3448-3460页

作者： Julia Di Stefano Federica Di Marco Ilaria Cicalini Una FitzGerald Damiana Pieragostino Marleen Verhoye Peter Ponsaerts Elise Van Breedam Laboratory of Experimental Hematology Vaccine and Infectious Disease Institute(Vaxinfectio)University of AntwerpWilrijkBelgium Bio-Imaging Lab University of AntwerpWilrijkBelgium Center for Advanced Studies and Technology(CAST) G.d’Annunzio University of Chieti-PescaraChietiItaly Department of Innovative Technologies in Medicine and Dentistry University“G.d’Annunzio”of Chieti-PescaraChietiItaly CÚRAM Center for Research in Medical DevicesBiomedical EngineeringUniversity of GalwayIreland Galway Neuroscience Center University of GalwayIreland μNEURO Research Center of Excellence University of AntwerpWilrijkBelgium

Brain organoids encompass a large collection of in vitro stem cell–derived 3D culture systems that aim to recapitulate multiple aspects of in vivo brain development and ***,this review provides a brief introduction to the current state-of-the-art for neuroectoderm brain organoid development,emphasizing their biggest advantages in comparison with classical two-dimensional cell cultures and animal ***,despite their usefulness for developmental studies,a major limitation for most brain organoid models is the absence of contributing cell types from endodermal and mesodermal *** such,current research is highly investing towards the incorporation of a functional vasculature and the microglial immune *** this review,we will specifically focus on the development of immune-competent brain *** summarizing the different approaches applied to incorporate microglia,it is highlighted that immune-competent brain organoids are not only important for studying neuronal network formation,but also offer a clear future as a new tool to study inflammatory responses in vitro in 3D in a brainlike ***,our main focus here is to provide a comprehensive overview of assays to measure microglial phenotype and function within brain organoids,with an outlook on how these findings could better understand neuronal network development or restoration,as well as the influence of physical stress on microglia-containing brain ***,we would like to stress that even though the development of immune-competent brain organoids has largely evolved over the past decade,their full potential as a pre-clinical tool to study novel therapeutic approaches to halt or reduce inflammation-mediated neurodegeneration still needs to be explored and validated.

关键词： 3D cell culture brain organoids immune response immunocompetent model in vitro model microglia neural organoids neuroimmunology neuroinflammation

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Corrigendum to "Effect of cell imprinting on viability and drug susceptibility of breast cancer cells to doxorubicin" [Acta Biomaterialia, 2020, 113, 119-129] (Acta Biomaterialia (2020) 113 (119–129), (S1742706120303287), (10.1016/***.2020.06.007))

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Acta Biomaterialia 2025年 198卷 571-572页

作者： Shahriyari, Fatemeh Janmaleki, Mohsen Sharifi, Shahriar Hesar, Milad Eyvazi Hoshian, Sasha Taghiabadi, Reza Razaghian, Ahmad Ghadiri, Majid Peirovi, Afshin Mahmoudi, Morteza Nezhad, Amir Sanati Khademhosseini, Ali Division of Engineering in Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School CambridgeMA United States Division of Health Sciences & Technology Harvard-Massachusetts Institute of Technology Massachusetts Institute of Technology CambridgeMA United States Department of Materials Engineering Faculty of Engineering Imam Khomeini International University Qazvin Iran Biomedical Engineering Graduate Program University of Calgary Canada Precision Health Program and Department of Radiology Michigan State University East LansingMI48824 United States RWTH Aachen University Templergraben 55 Aachen52062 Germany Department of Chemistry and Materials Science Aalto University School of Chemical Engineering Espoo Finland Department of Mechanical Engineering Faculty of Engineering Imam Khomeini International University Qazvin Iran Nano-Biotechnology Department Pasteur Institute of Iran Tehran Iran Departments of Bioengineering Chemical and Biomolecular Engineering and Radiological Sciences Center for Minimally Invasive Therapeutics California NanoSystems Institute University of California – Los Angeles Los AngelesCA United States Terasaki Institute for Biomedical Innovation Los AngelesCA United States

The authors regret to report that the images for panels h (Plain PDMS) and I (MCF7-imprinted PDMS) in Fig. 3 were mistakenly taken from the same dataset, resulting in the omission of the correct image in panel h for the Plain PDMS day 7 image. We sincerely apologize for this error. For an accurate representation, please see the corrected Fig. 3 provided here along with its original figure caption. This does not affect the validity of the statistical analyses or the overall conclusions drawn from the study. © 2025

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Multi-Objective Representation based Dynamic Prototype Learning for Unsupervised DCE-MRI Breast Tumor Segmentation

Multi-Objective Representation based Dynamic Prototype Learn...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Li Wang, Lihui Tang, Lei Kuai, Zixiang Zhang, Jian Wei, Hongjiang College of Computer Science and Technology Guizhou University Guiyang China Radiology Department Guizhou Provincial People's Hospital Guiyang China Imaging Center Harbin Medical University Cancer Hospital Harbin China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

Unsupervised segmentation is a potential means to detect the breast tumors from DCE-MRI without using any annotated images, which can provide a coarse prior for several downstream tasks. However, the existing unsupervised segmentation methods are prone to collapse due to the presence of large background regions in breast DCE-MRI and the absence of effective constraints. To address these issues, we proposed a dynamic prototype learning network (DyPNet) based on nearest moving average (NMA) strategy and multi-cluster reconstruction (MCR) constraints for segmenting breast tumors from DCE-MRI unsupervisedly. Specifically, MCR is used to constraint the feature embeddings, ensuring the completeness and specificity of features for each cluster, and NMA is used to adaptively update the cluster prototypes according to the intensity of top-L intra-cluster samples. Using the Cosine similarity between the prototypes and the pixel embedding features, the image pixels can be clustered. By comparing the proposed method with several unsupervised segmentation models on different datasets, we demonstrated that the averaged DSC, HD95, and VM of the proposed method can be improved by 73.7%, 20.7% and 111.3% respectively. In addition, through the comparisons on downstream tasks, including one-shot, generalized and zero-shot segmentations, we further verified the effectiveness and superiority of the proposed method. © 2025 IEEE.

关键词： Breast tumor Multi-cluster reconstruction Nearest moving average Unsupervised segmentation

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Detection of Hand Movement Using Time, Frequency and Time-Frequency Features of the Electromyogram Signal in Order to Create a Human-Machine Interface

Detection of Hand Movement Using Time, Frequency and Time-Fr...

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Applied Research in Electrical engineering (AREE), National and International Conference on

作者： Seyed Mohammad Yasoubi Mahnaz Ghasemi Seyed Amirhossein Mousavi Shima Abedian Department of Mechanical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Department of Mechatronics Engineering Shahrood University of Technology Shahrood Iran Department of Biomedical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Biomedical Engineering Research Center Imamreza International University Mashhad Iran

ISBN: (数字)9798350377170

ISBN: (纸本)9798350377187

This study presents an electromyography (EMG)-based hand gesture recognition system that leverages machine learning techniques to accurately classify hand gestures. A total of 33 participants (21 men and 12 women, average age 24.8 years) performed six hand movements under controlled conditions. EMG signals were recorded at a sampling rate of 1024 Hz and filtered using a band-pass Butterworth filter (20–450 Hz) and a notch filter to remove power-line interference. Signal normalization ensured consistency across trials, and extensive feature extraction was conducted, capturing time-domain, frequency-domain, time-frequency, and nonlinear characteristics. To optimize classification, three feature selection methods were employed: Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and Mutual Information (MI). Results indicate that MI-based feature selection, coupled with a Radial Basis Function (RBF) Support Vector Machine (SVM) classifier, provided the highest accuracy ($\mathbf{9 1 . 7 \%}$) across key metrics, outperforming linear SVM. This study highlights the importance of signal preprocessing, feature extraction, and appropriate classifier selection for enhancing the robustness of EMG-based gesture recognition. The findings have potential applications in prosthetics, assistive technology, and human-machine interfaces (HMIs), offering a reliable approach for interpreting complex EMG signals.

关键词： Hands Support vector machines Band-pass filters Time-frequency analysis Accuracy Gesture recognition Feature extraction Electromyography Principal component analysis Prosthetics

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Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis

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Computers in Biology and Medicine 2025年 184卷 109438-109438页

作者： Abdelaziz, Mohammed Wang, Tianfu Anwaar, Waqas Elazab, Ahmed National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China Lab School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China Mansoura35516 Egypt Computer Science Department Misr Higher Institute of Commerce and Computers Mansoura35516 Egypt

Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information about the brain that can aid in Alzheimer's disease (AD) diagnosis. However, most existing deep learning methods still rely on patch-based extraction from neuroimaging data, which typically yields suboptimal performance due to its isolation from the subsequent network and does not effectively capture the varying scales of structural changes in the cerebrum. Moreover, these methods often simply concatenate multimodal data, ignoring the interactions between them that can highlight discriminative regions and thereby improve the diagnosis of AD. To tackle these issues, we develop a multimodal and multi-scale deep learning model that effectively leverages the interaction between the multimodal and multiscale of the neuroimaging data. First, we employ a convolutional neural network to embed each scale of the multimodal images. Second, we propose multimodal scale fusion mechanisms that utilize both multi-head self-attention and multi-head cross-attention, which capture global relations among the embedded features and weigh each modality's contribution to another, and hence enhancing feature extraction and interaction between each scale of MRI and PET images. Third, we introduce a cross-modality fusion module that includes a multi-head cross-attention to fuse MRI and PET data at different scales and promote global features from the previous attention layers. Finally, all the features from every scale are fused to discriminate between the different stages of AD. We evaluated our proposed method on the ADNI dataset, and the results show that our model achieves better performance than the state-of-the-art methods. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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