Advances in osteoarthritis imaging

Identification and Construction of a Disulfidptosis-Mediated Diagnostic Model and Associated Immune Microenvironment of Osteoarthritis from the Perspective of PPPM

Background

Osteoarthritis (OA) is a major cause of human disability. Despite receiving treatment, patients with the middle and late stage of OA have poor survival outcomes. Therefore, within the framework of predictive, preventive, and personalized medicine (PPPM/3PM), early personalized diagnosis of OA is particularly prominent. PPPM aims to accurately identify disease by integrating multiple omic techniques; however, the efficiency of currently available methods and biomarkers in predicting and diagnosing OA should be improved. Disulfidptosis, a novel programmed cell death mechanism and appeared in particular metabolic status, plays a mysterious characteristic in the occurrence and development of OA, which warrants further investigation.

Methods

In this study, we integrated three public datasets from the Gene Expression Omnibus (GEO) database, including 26 OA samples and 20 normal samples. Via a series of bioinformatic analysis and machine learning, we identified the diagnostic biomarkers and several subtypes of OA. Moreover, the expression of these biomarkers were verified in our in-house cohort and the single cell dataset.

Results

Three significant regulators of disulfidptosis (NCKAP1, OXSM, and SLC3A2) were identified through differential expression analysis and machine learning. And a nomogram constructed based on these three regulators exhibited ideal efficiency in predicting early- and late-stage OA. Furthermore, based on the expression of three regulators, we identified two disulfidptosis-related subtypes of OA with different infiltration of immune cells and personalized expression level of immune checkpoints. Notably, the expression of the three regulators was demonstrated in a single-cell RNA profile and verified in the synovial tissue in our in-house cohort including 6 OA patients and 6 normal people. Finally, an efficient disulfidptosis-mediated diagnostic model was constructed for OA, with the AUC value of 97.6923% in the training set and 93.3333% and 100% in two validation sets.

Conclusion

Overall, with regard to PPPM, this study provided novel insights into the role of disulfidptosis regulators in the personalized diagnosis and treatment of OA.

S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality

The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.

Epidemiology of osteoarthritis: literature update 2022-2023

PURPOSE OF REVIEW

This review highlights recently published studies on osteoarthritis (OA) epidemiology, including topics related to understudied populations and joints, imaging, and advancements in artificial intelligence (AI) methods.

RECENT FINDINGS

Contemporary research has improved our understanding of the burden of OA in typically understudied regions, including ethnic and racial minorities in high-income countries, the Middle East and North Africa (MENA) and Latin America. Efforts have also been made to explore the burden and risk factors in OA in previously understudied joints, such as the hand, foot, and ankle. Advancements in OA imaging techniques have occurred alongside the developments of AI methods aiming to predict disease phenotypes, progression, and outcomes.

SUMMARY

Continuing efforts to expand our knowledge around OA in understudied populations will allow for the creation of targeted and specific interventions and inform policy changes aimed at reducing disease burden in these groups. The burden and disability associated with OA is notable in understudied joints, warranting further research efforts that may lead to effective therapeutic options. AI methods show promising results of predicting OA phenotypes and progression, which also may encourage the creation of targeted disease modifying OA drugs (DMOADs).

The emerging role of lncRNAs in osteoarthritis development and potential therapy

Osteoarthritis impairs the functions of various joints, such as knees, hips, hands and spine, which causes pain, swelling, stiffness and reduced mobility in joints. Multiple factors, including age, joint injuries, obesity, and mechanical stress, could contribute to osteoarthritis development and progression. Evidence has demonstrated that genetics and epigenetics play a critical role in osteoarthritis initiation and progression. Noncoding RNAs (ncRNAs) have been revealed to participate in osteoarthritis development. In this review, we describe the pivotal functions and molecular mechanisms of numerous lncRNAs in osteoarthritis progression. We mention that long noncoding RNAs (lncRNAs) could be biomarkers for osteoarthritis diagnosis, prognosis and therapeutic targets. Moreover, we highlight the several compounds that alleviate osteoarthritis progression in part via targeting lncRNAs. Furthermore, we provide the future perspectives regarding the potential application of lncRNAs in diagnosis, treatment and prognosis of osteoarthritis.

The role of imaging in osteoarthritis

Osteoarthritis is a complex whole-organ disorder that involves molecular, anatomic, and physiologic derangement. Advances in imaging techniques have expanded the role of imaging in evaluating osteoarthritis and functional changes. Radiography, magnetic resonance imaging, computed tomography (CT), and ultrasonography are commonly used imaging modalities, each with advantages and limitations in evaluating osteoarthritis. Radiography comprehensively analyses alignment and osseous features, while MRI provides detailed information about cartilage damage, bone marrow edema, synovitis, and soft tissue abnormalities. Compositional imaging derives quantitative data for detecting cartilage and tendon degeneration before structural damage occurs. Ultrasonography permits real-time scanning and dynamic joint evaluation, whereas CT is useful for assessing final osseous detail. Imaging plays an essential role in the diagnosis, management, and research of osteoarthritis. The use of imaging can help differentiate osteoarthritis from other diseases with similar symptoms, and recent advances in deep learning have made the acquisition, management, and interpretation of imaging data more efficient and accurate. Imaging is useful in monitoring and predicting the prognosis of osteoarthritis, expanding our understanding of its pathophysiology. Ultimately, this enables early detection and personalized medicine for patients with osteoarthritis. This article reviews the current state of imaging in osteoarthritis, focusing on the strengths and limitations of various imaging modalities, and introduces advanced techniques, including deep learning, applied in clinical practice.

Protease-Activatable Porphyrin Molecular Beacon for Osteoarthritis Management

Despite the substantial burden posed by osteoarthritis (OA) globally, difficult challenges remain in achieving early OA diagnosis and adopting effective disease-modifying treatments. In this study, we use a biomolecular approach to address these limitations by creating an inherently theranostic molecular beacon whose imaging and therapeutic capabilities are activated by early pathological changes in OA. This platform comprised (1) a peptide linker substrate for metalloproteinase-13 (MMP-13), a pathological protease upregulated in OA, which was conjugated to (2) a porphyrin moiety with inherent multimodal imaging, photodynamic therapy, and drug delivery capabilities, and (3) a quencher that silences the porphyrin's endogenous fluorescence and photoreactivity when the beacon is intact. In diseased OA tissue with upregulated MMP-13 expression, this porphyrin molecular beacon (PP_MMP13B) was expected to undergo sequence-specific cleavage, yielding porphyrin fragments with restored fluorescence and photoreactivity that could, respectively, be used as a readout of MMP-13 activity within the joint for early OA imaging and disease-targeted photodynamic therapy. This study focused on the synthesis and characterization of PP_MMP13B, followed by a proof-of-concept evaluation of its OA imaging and drug delivery potential. In solution, PP_MMP13B demonstrated 90% photoactivity quenching in its intact form and robust MMP-13 activation, yielding a 13-fold increase in fluorescence post-cleavage. In vitro, PP_MMP13B was readily uptaken and activated in an MMP-13 cell expression-dependent manner in primary OA synoviocytes without exuding significant cytotoxicity. This translated into effective intra-articular cartilage (to a 50 μm depth) and synovial uptake and activation of PP_MMP13B in a destabilization of the medial meniscus OA mouse model, yielding strong fluorescence contrast (7-fold higher signal than background) at the diseased joint site. These results provide the foundation for further exploration of porphyrin molecular beacons for image-guided OA disease stratification, effective articular delivery of disease-modify agents, and OA photodynamic therapy.

Artificial-Intelligence-Aided Radiographic Diagnostic of Knee Osteoarthritis Leads to a Higher Association of Clinical Findings with Diagnostic Ratings

BACKGROUND

Radiographic knee osteoarthritis (OA) severity and clinical severity are often dissociated. Artificial intelligence (AI) aid was shown to increase inter-rater reliability in radiographic OA diagnosis. Thus, AI-aided radiographic diagnoses were compared against AI-unaided diagnoses with regard to their correlations with clinical severity.

METHODS

Seventy-one DICOMs (m/f = 27:42, mean age: 27.86 ± 6.5) (X-ray format) were used for AI analysis (KOALA software, IB Lab GmbH). Subjects were recruited from a physiotherapy trial (MLKOA). At baseline, each subject received (i) a knee X-ray and (ii) an assessment of five main scores (Tegner Scale (TAS); Knee Injury and Osteoarthritis Outcome Score (KOOS); International Physical Activity Questionnaire; Star Excursion Balance Test; Six-Minute Walk Test). Clinical assessments were repeated three times (weeks 6, 12 and 24). Three physicians analyzed the presented X-rays both with and without AI via KL grading. Analyses of the (i) inter-rater reliability (IRR) and (ii) Spearman's Correlation Test for the overall KL score for each individual rater with clinical score were performed.

RESULTS

We found that AI-aided diagnostic ratings had a higher association with the overall KL score and the KOOS. The amount of improvement due to AI depended on the individual rater.

CONCLUSION

AI-guided systems can improve the ratings of knee radiographs and show a stronger association with clinical severity. These results were shown to be influenced by individual readers. Thus, AI training amongst physicians might need to be increased. KL might be insufficient as a single tool for knee OA diagnosis.