67 results match your criteria: "Southern Medical University (Academy of Orthopedics[Affiliation]"

LMSST-GCN: Longitudinal MRI sub-structural texture guided graph convolution network for improved progression prediction of knee osteoarthritis.

Comput Methods Programs Biomed

January 2025

School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China; Pazhou Lab, Guangzhou 510330, China. Electronic address:

Background And Objectives: Accurate prediction of progression in knee osteoarthritis (KOA) is significant for early personalized intervention. Previous methods commonly focused on quantifying features from a specific sub-structure imaged at baseline and resulted in limited performance. We proposed a longitudinal MRI sub-structural texture-guided graph convolution network (LMSST-GCN) for improved KOA progression prediction.

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Methods: We retrospectively collected CT scan data from 276 patients with pathologically confirmed primary bone tumors from 4 medical centers in Guangdong Province between January, 2010 and August, 2021. A convolutional neural network (CNN) was employed as the deep learning architecture. The optimal baseline deep learning model (R-Net) was determined through transfer learning, and an optimized model (S-Net) was obtained through algorithmic improvements.

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Article Synopsis
  • Accurately classifying primary bone tumors is essential for treatment decisions, yet many medical images are incomplete; this study created a deep learning model using incomplete multimodal images and clinical data.
  • The research involved 1305 patients with confirmed bone tumors, employing a new classification model called PBTC-TransNet, which was evaluated based on its accuracy and other performance metrics.
  • The model demonstrated strong classification performance with an average AUC of 0.847 for internal tests and shows promise across various imaging modalities, achieving a high accuracy of 84.3% with the best results in X-ray-only cases.
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  • Multi-sequence magnetic resonance imaging (MRI) is essential for detecting knee abnormalities but requires expert radiologists for accurate interpretation.
  • A new deep learning model was developed using co-plane attention to classify knee abnormalities, tested on a large dataset of 1748 subjects with 12 different abnormalities.
  • The model achieved a high accuracy of 0.78, outperforming junior radiologists and showing similar performance to senior radiologists, while also improving overall diagnostic accuracy for radiologists who used the model's output.
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  • Axial spondyloarthritis (axSpA) is often diagnosed late in HLA-B27-negative patients, leading to missed treatment opportunities, prompting the development of an AI tool called NegSpA-AI to improve diagnosis using MRI and clinical features.
  • The study analyzed data from 454 HLA-B27-negative patients, using various MRI techniques and applying deep learning for axSpA versus non-axSpA differentiation, split across training and testing groups.
  • NegSpA-AI outperformed junior rheumatologists in diagnostic accuracy, demonstrated by high areas under the curve in multiple test sets, and significantly improved the performance of radiologists when used as an assistive tool.
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  • Meniscal injuries often lead to knee pain and can be a precursor to osteoarthritis; this study seeks to enhance diagnosis using an automatic classification system for meniscal injuries based on MRI images.
  • Utilizing data from the Osteoarthritis Initiative, researchers developed a model called LGSA-UNet, which effectively segments and classifies different types of meniscus injuries with high accuracy and DICE coefficients.
  • The results showed that the deep learning models outperformed a junior radiologist in diagnosing meniscal injuries, indicating that AI could significantly improve diagnostic efficiency in this area.
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Surgery and targeted therapy are of equal importance for colorectal cancer (CRC) treatment. However, complete CRC tumor resection remains challenging, and new targeted agents are also needed for efficient CRC treatment. Cadherin 17 (CDH17) is a membrane protein that is highly expressed in CRC and, therefore, is an ideal target for imaging-guided surgery and therapeutics.

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Nanobody-Engineered Biohybrid Bacteria Targeting Gastrointestinal Cancers Induce Robust STING-Mediated Anti-Tumor Immunity.

Adv Sci (Weinh)

August 2024

Department of Geriatrics and Shenzhen Clinical Research Centre for Geriatrics, Department of Urology, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology, The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, 518020, China.

Bacteria can be utilized for cancer therapy owing to their preferential colonization at tumor sites. However, unmodified non-pathogenic bacteria carry potential risks due to their non-specific targeting effects, and their anti-tumor activity is limited when used as monotherapy. In this study, a biohybrid-engineered bacterial system comprising non-pathogenic MG1655 bacteria modified with CDH17 nanobodies on their surface and conjugated with photosensitizer croconium (CR) molecules is developed.

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  • A deep learning model was developed to classify histological types of primary bone tumors using radiographs, aiming to support radiologists in their assessments.
  • The study analyzed data from 878 patients, categorizing the tumors into five types and evaluating the model’s performance based on metrics like accuracy and sensitivity.
  • Results showed that the model improved both the accuracy and confidence of radiologists, indicating its potential clinical utility in diagnosing bone tumors more effectively than traditional methods.
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Background: Anterior cruciate ligament (ACL) injuries are closely associated with knee osteoarthritis (OA). However, diagnosing ACL injuries based on knee magnetic resonance imaging (MRI) has been subjective and time-consuming for clinical doctors. Therefore, we aimed to devise a deep learning (DL) model leveraging MRI to enable a comprehensive and automated approach for the detection of ACL injuries.

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Background: Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability.

Purpose: To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome.

Study Type: Retrospective.

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Objective: To develop a deep learning (DL) model for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI and further develop a DL model for classifying axial spondyloarthritis (axSpA) and non-axSpA.

Materials And Methods: This study retrospectively collected 706 patients with FM who underwent SIJ MRI from center 1 (462 axSpA and 186 non-axSpA) and center 2 (37 axSpA and 21 non-axSpA). Patients from center 1 were divided into the training, validation, and internal test sets (n = 455, 64, and 129).

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Pancreatic ductal adenocarcinoma (PDAC) is notorious for its resistance against chemotherapy and immunotherapy due to its dense desmoplastic and immunosuppressive tumor microenvironment (TME). Traditional photodynamic therapy (PDT) was also less effective for PDAC owing to poor selectivity, insufficient penetration, and accumulation of photosensitizers in tumor sites. Here, we designed a light-responsive novel nanoplatform targeting the TME of PDAC through tumor-specific midkine nanobodies (Nbs), which could efficiently deliver semiconducting polymeric nanoparticles (NPs) to the TME of PDAC and locally produce abundant reactive oxygen species (ROS) for precise photoimmunotherapy.

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Objective: Osteoarthritis (OA), which involves total joint damage and dysfunction, is a leading cause of disability worldwide. However, its exact pathogenesis remains unclear. Here, we identified TCF12 as an important regulator of the progression of OA.

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Article Synopsis
  • - The study aimed to create a deep learning framework for the automatic detection, segmentation, and classification of primary bone tumors (PBTs) and bone infections using MRIs from multiple sources.
  • - It used a patient dataset divided into training, internal validation, and external validation, analyzing T1 and T2-weighted images alongside clinical data to assess the model's performance in comparison to radiologists.
  • - The results showed that the framework outperformed junior radiologists in accuracy for classifying conditions, achieving high scores for detection and segmentation, indicating its potential for clinical application.
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Background: No investigations have thoroughly explored the feasibility of combining magnetic resonance (MR) images and deep-learning methods for predicting the progression of knee osteoarthritis (KOA). We thus aimed to develop a potential deep-learning model for predicting OA progression based on MR images for the clinical setting.

Methods: A longitudinal case-control study was performed using data from the Foundation for the National Institutes of Health (FNIH), composed of progressive cases [182 osteoarthritis (OA) knees with both radiographic and pain progression for 24-48 months] and matched controls (182 OA knees not meeting the case definition).

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Nanomedicine exhibits emerging potentials to deliver advanced therapeutic strategies in the fight against triple-negative breast cancer (TNBC). Nevertheless, it is still difficult to develop a precise codelivery system that integrates highly effective photosensitizers, low toxicity, and hydrophobicity. In this study, PCN-224 is selected as the carrier to enable effective cancer therapy through light-activated reactive oxygen species (ROS) formation, and the PCN-224@Mn O @HA is created in a simple one-step process by coating Mn O nanoshells on the PCN-224 template, which can then be used as an "ROS activator" to exert catalase- and glutathione peroxidase-like activities to alleviate tumor hypoxia while reducing tumor reducibility, leading to improved photodynamic therapeutic (PDT) effect of PCN-224.

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A pH-responsive metal-organic framework for the co-delivery of HIF-2α siRNA and curcumin for enhanced therapy of osteoarthritis.

J Nanobiotechnology

January 2023

Department of Medical Imaging, Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong Province), Southern Medical University, Guangzhou, 510630, Guangdong, People's Republic of China.

The occurrence of osteoarthritis (OA) is highly correlated with the reduction of joint lubrication performance, in which persistent excessive inflammation and irreversible destruction of cartilage dominate the mechanism. The inadequate response to monotherapy methods, suboptimal efficacy caused by undesirable bioavailability, short retention, and lack of stimulus-responsiveness, are few unresolved issues. Herein, we report a pH-responsive metal-organic framework (MOF), namely, MIL-101-NH, for the co-delivery of anti-inflammatory drug curcumin (CCM) and small interfering RNA (siRNA) for hypoxia inducible factor (HIF-2α).

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Remodeling the Tumor Microenvironment with Core-Shell Nanosensitizer Featuring Dual-Modal Imaging and Multimodal Therapy for Breast Cancer.

ACS Appl Mater Interfaces

January 2023

Department of Medical Imaging, Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong Province), Southern Medical University, Guangzhou, Guangdong510630, China.

To improve the efficiency of radiation therapy (RT) for breast cancer, a designable multifunctional core-shell nanocomposite of FeP@Pt is constructed using Fe(III)-polydopamine (denoted as FeP) as the core and platinum particles (Pt) as the shell. The hybrid structure is further covered with hyaluronic acid (HA) to give the final nanoplatform of FeP@Pt@HA (denoted as ). exhibits good biological stability, prolongs blood circulation time, and is simultaneously endowed with tumor-targeting ability.

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Background: The infrapatellar fat pad (IPFP) plays an important role in the incidence of knee osteoarthritis (OA). Magnetic resonance (MR) signal heterogeneity of the IPFP is related to pathologic changes. In this study, we aimed to investigate whether the IPFP radiomic features have predictive value for incident radiographic knee OA (iROA) 1 year prior to iROA diagnosis.

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Tumor Acidic Microenvironment-Responsive Promodulator Iron Oxide Nanoparticles for Photothermal-Enhanced Chemodynamic Immunotherapy of Cancer.

ACS Biomater Sci Eng

February 2023

Department of Medical Imaging, Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics Guangdong Province), Southern Medical University, Guangzhou, Guangdong 510630, P. R. China.

Cancer nanomedicine combined with immunotherapy has emerged as a promising strategy for the treatment of cancer. However, precise regulation of the activation of antitumor immunity in targeting tissues for safe and effective cancer immunotherapy remains challenging. Herein, we report a tumor acidic microenvironment-responsive promodulator iron oxide nanoparticle (termed as FGR) with pH-activated action for photothermal-enhanced chemodynamic immunotherapy of cancer.

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To improve the quality of magnetic resonance (MR) image edge segmentation, some researchers applied additional edge labels to train the network to extract edge information and aggregate it with region information. They have made significant progress. However, due to the intrinsic locality of convolution operations, the convolution neural network-based region and edge aggregation has limitations in modeling long-range information.

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Neurofibromatosis type 1 of the left lower limb: A case report.

Asian J Surg

May 2023

Department of Joint Surgery, Center for Orthopaedic Surgery, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, Guangdong, China; Orthopedic Hospital of Guangdong Province, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, Guangzhou, Guangdong, China. Electronic address:

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Osteoarthritis (OA) is a progressive degenerative joint disease characterized by the destruction of the articular cartilage, meniscus and the like. Autophagy and cellular energy metabolism are the mechanisms by which cells maintain homeostasis. However, little is known about the effects of autophagy and cellular energy metabolism on meniscus degeneration, and the pathogenesis of posttraumatic osteoarthritis (PTOA) after the meniscal injury is rarely reported.

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