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

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.

[Identification of osteoid and chondroid matrix mineralization in primary bone tumors using a deep learning fusion model based on CT and clinical features: a multi-center retrospective study].

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.

One-stop detection of anterior cruciate ligament injuries on magnetic resonance imaging using deep learning with multicenter validation.

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.

Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images.

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.

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning.

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).

Precise Photodynamic Therapy by Midkine Nanobody-Engineered Nanoparticles Remodels the Microenvironment of Pancreatic Ductal Adenocarcinoma and Potentiates the Immunotherapy.

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.

TCF12 regulates the TGF-β/Smad2/3 signaling pathway to accelerate the progression of osteoarthritis by targeting CXCR4.

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.

DeepKOA: a deep-learning model for predicting progression in knee osteoarthritis using multimodal magnetic resonance images from the osteoarthritis initiative.

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).

Mn O Nanoshell Coated Metal-Organic Frameworks with Microenvironment-Driven O Production and GSH Exhaustion Ability for Enhanced Chemodynamic and Photodynamic Cancer Therapies.

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.

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

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).

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

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.

Prediction model for knee osteoarthritis using magnetic resonance-based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative.

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.

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

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.

Transformer-based multilevel region and edge aggregation network for magnetic resonance image segmentation.

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.

Enhancing autophagy and energy metabolism in the meniscus can delay the occurrence of PTOA in ACLT rat.

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.

Quantitative evaluation of the characteristic of infrapatellar fat pad Fat Content and Unsaturation Index by using hydrogen proton MR spectroscopy.

Objective: To investigate the characteristics of fat content and component in IFP using hydrogen proton MR spectroscopy (H-MRS), and to explore the correlation with the severity of OA, Hoffa-synovitis, and knee pain.

Materials And Methods: 80 volunteers were enrolled. Subjects were grouped based on Kellgren-Lawrence (K-L) grading.

NIR-PTT/ROS-Scavenging/Oxygen-Enriched Synergetic Therapy for Rheumatoid Arthritis by a pH-Responsive Hybrid CeO-ZIF-8 Coated with Polydopamine.

Rheumatoid arthritis (RA) is an inflammatory type of arthritis that causes joint pain and damage. The inflammatory cell infiltration (e.g.

Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features.

Objectives: To explore the feasibility of predicting overall survival (OS) of patients with midline glioma using multi-parameter magnetic resonance imaging (MRI) features.

Methods: Data of 84 patients with midline gliomas were retrospectively collected, including 40 patients with OS > 12 months (28 cases were adults, 14 cases were H3 K27M-mutation) and 44 patients with OS < 12 months (29 cases were adults, 31 cases were H3 K27M-mutation). Features were extracted from the largest slice of tumors, which were manually segmented on T2-weighted (T2w), T2 fluid-attenuated inversion recovery (T2 FLAIR), and contrast-enhanced T1-weighted (T1c) images.

Synergistic photothermal-photodynamic-chemotherapy toward breast cancer based on a liposome-coated core-shell AuNS@NMOFs nanocomposite encapsulated with gambogic acid.

A multifunctional nanoplatform with core-shell structure was constructed in one-pot for the synergistic photothermal, photodynamic, and chemotherapy against breast cancer. In the presence of gambogic acid (GA) as the heat-shock protein 90 (HSP90) inhibitor and the gold nanostars (AuNS) as the photothermal reagent, the assembly of Zr with tetrakis (4-carboxyphenyl) porphyrin (TCPP) gave rise to the nanocomposite AuNS@ZrTCPP-GA (AZG), which in turn, further coated with PEGylated liposome (LP) to enhance the stability and biocompatibility, and consequently the antitumor effect of the particle. Upon cellular uptake, the nanoscale metal - organic framework (NMOF) of ZrTCPP in the resulted AuNS@ZrTCPP-GA@LP (AZGL) could be slowly degraded in the weak acidic tumor microenvironment to release AuNS, Zr, TCPP, and GA to exert the synergistic treatment of tumors via the combination of AuNS-mediated mild photothermal therapy (PTT) and TCPP-mediated photodynamic therapy (PDT).