Osteoarthritis year in review 2021: imaging

Oei, Edwin H.G.; Hirvasniemi, Jukka; Zadelhoff, Tijmen A. van; Heijden, Rianne A. van der

doi:10.1016/j.joca.2021.11.012

Cited by 16 publications

(5 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, a more comprehensive assessment of the knee joint samples of each group was performed using Micro‐CT at 8 weeks after surgery (Figure 5b). [ 21 ] The subchondral bone of the knee was significantly thickened, and different degrees of subchondral osteosclerosis occurred in the PBS and MHS groups compared to that in the sham‐operation group, but this was not evident in the MHS@PPKHF group (Figure S7, Supporting Information). In addition, as shown in Figure 5e, the osteophyte volumes of the samples from each group were measured using a computer.…”

Section: Resultsmentioning

confidence: 99%

Visualizable and Lubricating Hydrogel Microspheres Via NanoPOSS for Cartilage Regeneration

et al. 2023

View full text Add to dashboard Cite

The monitoring of tissue regeneration is particularly important. However, most materials do not allow direct observation of the regeneration process in the cartilage layer. Here, using sulfhydryl polyhedral oligomeric silsesquioxane (POSS‐SH) as a nano‐construction platform, poly(ethylene glycol) (PEG), Kartogenin (KGN), hydrogenated soya phosphatidylcholine (HSPC), and fluorescein are linked through the “click chemistry” method to construct nanomaterial with fluorescence visualization for cartilage repair: POSS linked with PEG, KGN, HSPC, and fluorescein (PPKHF). PPKHF nanoparticles are encapsulated with hyaluronic acid methacryloyl to prepare PPKHF‐loaded microfluidic hyaluronic acid methacrylate spheres (MHS@PPKHF) for in situ injection into the joint cavity using microfluidic technology. MHS@PPKHF forms a buffer lubricant layer in the joint space to reduce friction between articular cartilages, while releasing encapsulated positively charged PPKHF to the deep cartilage through electromagnetic force, facilitating visualization of the location of the drug via fluorescence. Moreover, PPKHF facilitates differentiation of bone marrow mesenchymal stem cells into chondrocytes, which are located in the subchondral bone. In animal experiment, the material accelerates cartilage regeneration while allowing monitoring of cartilage layer repair progression via fluorescence signals. Thus, these POSS‐based micro–nano hydrogel microspheres can be used for cartilage regeneration and monitoring and potentially for clinical osteoarthritis therapy.

show abstract

Section: Resultsmentioning

confidence: 99%

Visualizable and Lubricating Hydrogel Microspheres Via NanoPOSS for Cartilage Regeneration

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Imaging examinations like radiography can be used to diagnose OA, grade OA severity, assess OA progression, and monitor treatment responses [ 9 ]. Radiography is the first choice in the clinical setting [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

Li,

Xie

et al. 2024

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.

show abstract

“…Up till now, OA management primarily focuses on symptom alleviation rather than curing OA [10] . Currently, the diagnosis of OA relies on medical radio image observation of joint degeneration, typically detected at advanced stages of the disease [11] . Therefore, identifying new, reliable, early biomarkers is necessary for diagnosing early OA.…”

Section: Introductionmentioning

confidence: 99%

iTRAQ proteomic analysis of exosomes derived from synovial fluid reveals disease patterns and potential biomarkers of Osteoarthritis

Wu,

Li,

Zou

et al. 2024

Preprint

View full text Add to dashboard Cite

The exosomes derived from synovial fluid (SF-exo) are indicative of the condition of the originating cell. The proteomics profiles of SF-exo are of great interest for the diagnosis of Osteoarthritis (OA). To delineate the proteomic differences between SF-exo from OA patients and those from healthy individuals, an iTRAQ quantitative proteomics was performed. A total of 439 proteins were identified, with 20 proteins exhibiting increased expression and 5 showing decreased expression in OA group. Bioinformatic analysis showed these differentially expressed proteins (DEPs) were involved in various immunity-related process terms, including complement activation and antigen binding. For further screening, we download a publicly dataset of synovial fluid and compared with our dataset. This comparison identified 5 DEPs were overlapping in two datasets, Protein-protein interaction showed C3, C4B and APOM were part of a tight interaction network. Receiver operating characteristic curve analysis and enzyme-linked immunosorbent assay implied that the 5 DEPs (C3, C4B, APOM, MMP3, DPYSL2) derived from SF-exo were potential diagnostic biomarkers for OA. In conclusion, our study first described the proteomic landscape of SF-exo in OA and identified several potential biomarkers. These findings may provide valuable insights for diagnosis and treatment of OA.

show abstract

Osteoarthritis year in review 2021: imaging

Cited by 16 publications

References 76 publications

Visualizable and Lubricating Hydrogel Microspheres Via NanoPOSS for Cartilage Regeneration

Visualizable and Lubricating Hydrogel Microspheres Via NanoPOSS for Cartilage Regeneration

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

iTRAQ proteomic analysis of exosomes derived from synovial fluid reveals disease patterns and potential biomarkers of Osteoarthritis

Contact Info

Product

Resources

About