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Exosome-Transmitted miR-224-5p Promotes Colorectal Cancer Cell Proliferation via Targeting ULK2 in p53-Dependent Manner

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Biomedical and Environmental Sciences 2024年第1期37卷 71-84页

作者： YANG Le Mei ZHENG Qi LIU Xiao Jia LI Xian Xian Veronica Lim CHEN Qi ZHAO Zhong Hua WANG Shu Yang Department of Pathology School of Basic MedicineFudan UniversityShanghai 200032China Department of Pathology Dunedin School of MedicineUniversity of OtagoDunedin 9054New Zealand Department of Pathology Huashan HospitalFudan UniversityShanghai 200040China Shanghai Cancer Institute Renji HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai 200127China Teladoc Health Inc Shanghai 200335China Shanghai Medical College Fudan UniversityShanghai 200032China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai 200032China

Objective To investigate the role and molecular mechanism of exosomal miR-224-5p in colorectal cancer(CRC).Methods The miR-224-5p expression in CRC patient tissues and cell-derived exosomes was measured by laser capture microdissection and qRT-PCR,***-luciferase reporter gene assay was used to determine the target gene of *** protein expressions of p53 and unc-51 like kinase 2(ULK2)in CRC cells were detected by western *** cytometry was used to detect cell cycle and *** proliferation was measured by CCK8 and EdU *** The miR-224-5p expression was upregulated in CRC tissues and increased progressively with the rise of CRC *** cells secreted extracellular miR-224-5p mainly in an exosome-dependent manner,and then miR-224-5p could be transferred to surrounding tumor cells to regulate cell proliferation in the form of autocrine or ***,ULK2 was characterized as a direct target of miR-224-5p and was downregulated in CRC ***,ULK2 inhibited CRC cell proliferation in a p53-dependent ***,exosome-derived miR-224-5p partially reversed the proliferation regulation of ULK2 on CRC *** Our findings demonstrate that exosome-transmitted miR-224-5p promotes p53-dependent cell proliferation by targeting ULK2 in CRC,which may offer promising targets for CRC prevention and therapy.

关键词： miR-224-5p Exosome ULK2 p53 Cell proliferation Colorectal cancer

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Di, Xinhan Dai, Xiaokun Zhang, Xinkang Chen, Xinrong Fudan Universiry Academy for Engineering&Technology China Deepearthgo Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China

ISBN: (纸本)9798350302493

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users' virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results. © 2023 IEEE.

关键词： Network architecture

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PLTN: Noisy label learning in long-tailed medical images with adaptive prototypes

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Neurocomputing 2025年 645卷

作者： He, Zhiqing Xu, Jie Huang, Peng Wang, Qin Wang, Yuanyuan Guo, Yi Department of Biomedical Engineering School of Information Science and Technology Fudan University Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

Label noise poses a significant challenge in medical image classification, particularly for minority diseases in long-tailed distribution. Most Noisy Label Learning methods rely on clean sample selection to learn representative patterns. However, the imbalanced data distribution and intra-class variability cause model bias towards head classes at the inter-class level and dominant patterns at the intra-class level. Existing methods show limited performance in handling these biases simultaneously, causing inefficient clean sample selection and hindering minority class training. Thus, we propose PLTN, named Prototype-guided Long-Tail Noisy Learning, a novel approach that selects comprehensive clean samples from each class and enhances minority class training to improve diagnostic accuracy of rare disease in long tailed medical data. First, Adaptive Multi-Prototype Learning (AMPL) module is designed to enable precise selection of clean samples, especially for minority classes. It achieves this by capturing diverse feature patterns through a dynamic prototype learning approach. The clean samples are then treated as labeled data while the noisy samples are used as unlabeled data in a semi-supervised learning paradigm. Second, we introduce Sharpness-aware Distributional Mixup (SDM) to enhance minority class training. SDM employs distributional mixup to enrich tail classes representations, simultaneously minimizing loss landscape sharpness to enhance model generalization for all classes, benefiting tail classes. Experiments on three typical medical datasets demonstrate that our method outperforms state-of-the-art methods. On HAM10000 dataset with 20 % label noise, we achieve an 80.34 % Macro-F1 score, with notable gains in tail class performance while maintaining head class performance, showing potential for real-world clinical applications. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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Shape Estimation of Flexible Probe using Spatial-Attention FlexShapeNet

Shape Estimation of Flexible Probe using Spatial-Attention F...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Xue Gao Lihong Huang Yuanyuan Wang Yi Guo Department of Electronic Engineering Fudan University Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai China

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Ultrasound imaging with flexible probes requires the knowledge of element array geometry, which is variable and unknown. Here, we propose a spatial-attention shape estimation model, named FlexShapeNet, to estimate accurate element array geometry of flexible probes from radio-frequency (RF) data. A spatial attention (SA) module is introduced to capture deformation information resulting from variations in the shape of the flexible probe, which effectively reduces the significant disparities in shape estimation accuracy among different imaging targets. The delay-and-sum (DAS) beamformer is then used to reconstruct ultrasound images with estimated shape and the raw RF data. Experimental results demonstrate that FlexShapeNet achieves the highest shape estimation accuracy for flexible probes without the need for multiple iterations of beamforming.

关键词： Radio frequency Geometry Accuracy Ultrasonic imaging Shape Deformation Estimation imaging Arrays Probes

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Chest-Diffusion: A Light-Weight Text-to-Image Model for Report-to-CXR Generation

Chest-Diffusion: A Light-Weight Text-to-Image Model for Repo...

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IEEE International Symposium on Biomedical imaging

作者： Peng Huang Xue Gao Lihong Huang Jing Jiao Xiaokang Li Yuanyuan Wang Yi Guo Department of Electronic Engineering Fudan University Shanghai China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Text-to-image generation has important implications for generation of diverse and controllable images. Several attempts have been made to adapt Stable Diffusion (SD) to the medical domain. However, the large distribution difference between medical reports and natural texts, as well as high computational complexity in common stable diffusion limit the authenticity and feasibility of the generated medical images. To solve above problems, we propose a novel light-weight transformer-based diffusion model learning framework, Chest-Diffusion, for report-to-CXR generation. Chest-Diffusion employs a domain-specific text encoder to obtain accurate and expressive text features to guide image generation, improving the authenticity of the generated images. Meanwhile, we introduce a light-weight transformer architecture as the denoising model, reducing the computational complexity of the diffusion model. Experiments demonstrate that our Chest-Diffusion achieves the lowest FID score 24.456, under the computation budget of 118.918 GFLOPs, which is nearly one-third of the computational complexity of SD.

关键词： Accuracy Computational modeling Noise reduction Text to image computer architecture Transformers Diffusion models

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CHEST-DIFFUSION: A LIGHT-WEIGHT TEXT-TO-IMAGE MODEL FOR REPORT-TO-CXR GENERATION

arXiv

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arXiv 2024年

作者： Huang, Peng Gao, Xue Huang, Lihong Jiao, Jing Li, Xiaokang Wang, Yuanyuan Guo, Yi Department of Electronic Engineering Fudan University Shanghai200433 China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai200032 China

Text-to-image generation has important implications for generation of diverse and controllable images. Several attempts have been made to adapt Stable Diffusion (SD) to the medical domain. However, the large distribution difference between medical reports and natural texts, as well as high computational complexity in common stable diffusion limit the authenticity and feasibility of the generated medical images. To solve above problems, we propose a novel lightweight transformer-based diffusion model learning framework, Chest-Diffusion, for report-to-CXR generation. Chest-Diffusion employs a domain-specific text encoder to obtain accurate and expressive text features to guide image generation, improving the authenticity of the generated images. Meanwhile, we introduce a light-weight transformer architecture as the denoising model, reducing the computational complexity of the diffusion model. Experiments demonstrate that our Chest-Diffusion achieves the lowest FID score 24.456, under the computation budget of 118.918 GFLOPs, which is nearly one-third of the computational complexity of SD. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational complexity

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A Feasible Way to Assess the Lower Extremity Motor Function of Stroke Patients - A Preliminary Study 15

A Feasible Way to Assess the Lower Extremity Motor Function ...

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15th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics, CISP-BMEI 2022

作者： Liu, Sen Xie, Hongyu Yuan, Han Tian, Jing Yang, Cuiwei Wu, Yi Center for Biomedical Engineering Fudan University Shanghai China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Accurate assessment of the mobility of stroke patients is crucial to their rehabilitation management. In order to quantify the lower extremity motor function in stroke sufferers, two tasks, toe tapping and knee flexion and extension, were designed based on the Brunnstrom approach. In this work, five hemiplegic patients in Brunnstrom stage III or IV were recruited. Acceleration signals under different tasks were recorded using tri-axial accelerometers, which were placed at the arches of the feet. Various kinematic parameters were then calculated, and a statistical test was conveyed to investigate the correlations between Brunnstrom stages and the features. The results showed that in the toe tapping task, the dominant frequency of the y-axis and the proportion of signal power in specific frequency band of the x-axis were significantly higher in stage III than in stage IV. On the contrary, the standard deviation and the skewness of the y-axis were lower in stage III. Besides, there were also significant differences in the amplitude of knee flexion between cohorts at different Brunnstrom stages. It is expected that the findings of this study would be helpful for the monitoring and rehabilitation of stroke patients. © 2022 IEEE.

关键词： Patient rehabilitation

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Distinct immune escape and microenvironment between RG-like and pri-OPC-like glioma revealed by single-cell RNA-seq analysis

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Frontiers of Medicine 2024年第1期18卷 147-168页

作者： Weiwei Xian Mohammad Asad Shuai Wu Zhixin Bai Fengjiao Li Junfeng Lu Gaoyu Zu Erin Brintnell Hong Chen Ying Mao Guomin Zhou Bo Liao Jinsong Wu Edwin Wang Linya You Department of Human Anatomy&Histoembryology School of Basic Medical SciencesFudan UniversityShanghai200032China Cumming School of Medicine University of CalgaryCalgaryAlbertaT2N 4N1Canada Glioma Surgery Division Department of NeurosurgeryHuashan HospitalFudan UniversityShanghai200040China Department of Pathology Huashan HospitalFudan UniversityShanghai200040China Department of Neurosurgery Huashan HospitalFudan UniversityShanghai200040China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Fudan UniversityShanghai200040China School of Mathematics and Statistics Hainan Normal UniversityHaikou570100China

The association of neurogenesis and gliogenesis with glioma remains *** conducting single-cell RNA-seq analyses on 26 gliomas,we reported their classification into primitive oligodendrocyte precursor cell(pri-OPC)-like and radial glia(RG)-like tumors and validated it in a public cohort and TCGA *** RG-like tumors exhibited wild-type isocitrate dehydrogenase and tended to carry EGFR mutations,and the pri-OPC-like ones were prone to carrying TP53 *** subclones only in pri-OPC-like tumors showed substantially down-regulated MHC-I genes,suggesting their distinct immune evasion ***,the two subgroups appeared to extensively modulate glioma-infiltrating lymphocytes in distinct *** specific genes not expressed in normal immune cells were found in glioma-infiltrating *** example,glial/glioma stem cell markers OLIG1/PTPRZ1 and B cell-specific receptors IGLC2/IGKC were expressed in pri-OPC-like and RG-like glioma-infiltrating lymphocytes,*** expression was positively correlated with those of immune checkpoint genes(e.g.,LGALS33)and poor survivals as validated by the increased expression of LGALS3 upon IGKC overexpression in Jurkat *** finding indicated a potential inhibitory role in tumor-infiltrating lymphocytes and could provide a new way of cancer immune evasion.

关键词： single-cell RNA-seq glioma radial glia primitive oligodendrocyte precursor cell immune escape

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3D Clothed Human Reconstruction From One In-the-Wild RGB Image

3D Clothed Human Reconstruction From One In-the-Wild RGB Ima...

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IEEE International Conference on Image Processing

作者： Liangjing Shao Benshuang Chen Xinrong Chen Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

In recent years, much achievement have been made in the field of 3D clothed human reconstruction. However, most of researches performed not well for reconstruction from in-the-wild images due to the domain gap between the synthetic images of training datasets and the in-the-wild images. In this study, a modular model, including clothes encoder, body encoder and cloth generator, is proposed to perform 3D clothed human reconstruction from one single-view in-the-wild RGB image. In particular, we introduce the adaptive aggregation of convolution and multi-head attention into the cloth encoder and apply the adjustment of the segmentation at the preprocessing stage. According to experiments on MSCOCO and 3DPW datasets, the proposed method achieves state-of-the-art performance on 3D clothed human reconstruction from in-the-wild images compared with previous works.

关键词： Training Solid modeling Image segmentation Adaptation models Three-dimensional displays Convolution Data preprocessing

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Intelligent Evaluation of the Course of Atrial Fibrillation Using Transfer Learning 15

Intelligent Evaluation of the Course of Atrial Fibrillation ...

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15th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics, CISP-BMEI 2022

作者： Sun, Chen Li, Chunpu Huo, Hongye Zhong, Gaoyan Liu, Sen Wang, Aiguo Deng, Xintao Yang, Cuiwei Fudan University Center for Biomedical Engineering Shanghai China Xinghua City People's Hospital Department of Cardiology Jiangsu China The Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Atrial fibrillation (AF) is the most common chronic cardiovascular disease. As the disease progresses, AF could have various impact on the quality of life and the health of patients. The scientific management of AF depends on the development of targeted therapeutic measures according to the disease course. In view of the noninvasiveness and convenience of electrocardiogram (ECG), this study attempted to assess the course of AF based on standard 12-lead ECG. Therefore, based on transfer learning, we used MobileNetV2 to achieve automatic grading of AF treatment. By taking 12-lead ECG images of patients with mild and severe AF as network input, the pre-trained model was retrained to recognize different courses. The results show that our proposed method exhibits excellent performance with accuracy of 84.1 %, precision of 86.5%, recall of 83.1%, specificity of 85.3%, and F1-score of 84.8%. Our study provides a feasible way of assessing the course of AF and is of great significance to promote the graded diagnosis and treatment of AF. © 2022 IEEE.

关键词： Electrocardiograms

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