Tumor-Derived Exosomal Protein Tyrosine Phosphatase Receptor Type O Polarizes Macrophage to Suppress Breast Tumor Cell Invasion and Migration

Tumor-derived exosomes, containing multiple nucleic acids and proteins, have been implicated to participate in the interaction between tumor cells and microenvironment. However, the functional involvement of phosphatases in tumor-derived exosomes is not fully understood. We and others previously demonstrated that protein tyrosine phosphatase receptor type O (PTPRO) acts as a tumor suppressor in multiple cancer types. In addition, its role in tumor immune microenvironment remains elusive. Bioinformatical analyses revealed that PTPRO was closely associated with immune infiltration, and positively correlated to M1-like macrophages, but negatively correlated to M2-like macrophages in breast cancer tissues. Co-cultured with PTPRO-overexpressing breast cancer cells increased the proportion of M1-like tumor-associated macrophages (TAMs) while decreased that of M2-like TAMs. Further, we observed that tumor-derived exosomal PTPRO induced M1-like macrophage polarization, and regulated the corresponding functional phenotypes. Moreover, tumor cell-derived exosomal PTPRO inhibited breast cancer cell invasion and migration, and inactivated STAT signaling in macrophages. Our data suggested that exosomal PTPRO inhibited breast cancer invasion and migration by modulating macrophage polarization. Anti-tumoral effect of exosomal PTPRO was mediated by inactivating STAT family in macrophages. These findings highlight a novel mechanism of tumor invasion regulated by tumor-derived exosomal tyrosine phosphatase, which is of translational potential for the therapeutic strategy against breast cancer

Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

3D ultrasound (US) is widely used due to its rich diagnostic information, portability and low cost. Automated standard plane (SP) localization in US volume not only improves efficiency and reduces user-dependence, but also boosts 3D US interpretation. In this study, we propose a novel Multi-Agent Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D US simultaneously. Our contribution is two-fold. First, we equip the MARL with a one-shot neural architecture search (NAS) module to obtain the optimal agent for each plane. Specifically, Gradient-based search using Differentiable Architecture Sampler (GDAS) is employed to accelerate and stabilize the training process. Second, we propose a novel collaborative strategy to strengthen agents' communication. Our strategy uses recurrent neural network (RNN) to learn the spatial relationship among SPs effectively. Extensively validated on a large dataset, our approach achieves the accuracy of 7.05 degree/2.21mm, 8.62 degree/2.36mm and 5.93 degree/0.89mm for the mid-sagittal, transverse and coronal plane localization, respectively. The proposed MARL framework can significantly increase the plane localization accuracy and reduce the computational cost and model size.Comment: Early accepted by MICCAI 202

FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound

Fetal pose estimation in 3D ultrasound (US) involves identifying a set of associated fetal anatomical landmarks. Its primary objective is to provide comprehensive information about the fetus through landmark connections, thus benefiting various critical applications, such as biometric measurements, plane localization, and fetal movement monitoring. However, accurately estimating the 3D fetal pose in US volume has several challenges, including poor image quality, limited GPU memory for tackling high dimensional data, symmetrical or ambiguous anatomical structures, and considerable variations in fetal poses. In this study, we propose a novel 3D fetal pose estimation framework (called FetusMapV2) to overcome the above challenges. Our contribution is three-fold. First, we propose a heuristic scheme that explores the complementary network structure-unconstrained and activation-unreserved GPU memory management approaches, which can enlarge the input image resolution for better results under limited GPU memory. Second, we design a novel Pair Loss to mitigate confusion caused by symmetrical and similar anatomical structures. It separates the hidden classification task from the landmark localization task and thus progressively eases model learning. Last, we propose a shape priors-based self-supervised learning by selecting the relatively stable landmarks to refine the pose online. Extensive experiments and diverse applications on a large-scale fetal US dataset including 1000 volumes with 22 landmarks per volume demonstrate that our method outperforms other strong competitors.Comment: 16 pages, 11 figures, accepted by Medical Image Analysis(2023

Residual learning diagnosis detection: an advanced residual learning diagnosis detection system for COVID-19 in industrial internet of things

Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7 s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19.This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0806802, in part by the National Natural Science Foundation of China under Grant 61876131 and Grant U1936102, and in part by the Tianjin Key Project of AI under Grant 19ZXZNGX00030