Dataset Information

Matrix metalloproteinases in rheumatoid arthritis and osteoarthritis: a state of the art review.

ABSTRACT: Although the pathological mechanisms involved in osteoarthritis (OA) and rheumatoid arthritis (RA) are different, the onset and progression of both diseases are associated with several analogous clinical manifestations, inflammation, and immune mechanisms. In both diseases, cartilage destruction is mediated by matrix metalloproteinases (MMPs) synthesized by chondrocytes and synovium fibroblasts. This review aims to summarize recent articles regarding the role of MMPs in OA and RA, as well as the possible methods of targeting MMPs to alleviate the degradation processes taking part in OA and RA. The novel experimental MMP-targeted treatments in OA and RA are MMP inhibitors eg. 3-B2, taraxasterol, and naringin, while other treatments aim to silence miRNAs, lncRNAs, or transcription factors. Additionally, other recent MMP-related developments include gene polymorphism of MMPs, which have been linked to OA susceptibility, and the MMP-generated neoepitope of CRP, which could serve as a biomarker of OA progression.

SUBMITTER: Pulik L

PROVIDER: S-EPMC10373173 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Matrix metalloproteinases in rheumatoid arthritis and osteoarthritis: a state of the art review.

Pulik Łukasz Ł Łęgosz Paweł P Motyl Gabriela G

Reumatologia 20230702 3

Although the pathological mechanisms involved in osteoarthritis (OA) and rheumatoid arthritis (RA) are different, the onset and progression of both diseases are associated with several analogous clinical manifestations, inflammation, and immune mechanisms. In both diseases, cartilage destruction is mediated by matrix metalloproteinases (MMPs) synthesized by chondrocytes and synovium fibroblasts. This review aims to summarize recent articles regarding the role of MMPs in OA and RA, as well as the ...[more]

PMID: 37522140

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Project description:Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Dataset Information

Matrix metalloproteinases in rheumatoid arthritis and osteoarthritis: a state of the art review.

Publications

Matrix metalloproteinases in rheumatoid arthritis and osteoarthritis: a state of the art review.

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