Incident osteoarthritis associated with increased allied health services use in ‘baby boomer’ Australian women

Cartilage repair using stem cells & biomaterials: advancement from bench to bedside

Osteoarthritis (OA) involves gradual destruction of articular cartilagemanifested by pain, stiffness of joints, and impaired movement especially in knees and hips. Non-vascularity of this tissue hinders its self-regenerative capacity and thus, the application of reparative or restorative modalities becomes imperative in OA treatment. In recent years, stem cell-based therapies have been explored as potential modalities for addressing OA complications. While mesenchymal stem cells (MSCs) hold immense promise, the recapitulation of native articular cartilage usingMSCs remains elusive. In this review, we have highlighted the chondrogenic potential of MSCs, factors guiding in vitro chondrogenic differentiation, biomaterials available for cartilage repair, their current market status, and the outcomes of major clinical trials. Our search on ClinicalTrials.gov using terms "stem cell" and "osteoarthritis" yielded 83 results. An analysis of the 29 trials that have been completed revealed differences in source of MSCs (bone marrow, adipose tissue, umbilical cord etc.), cell type (autologous or allogenic), and dose administered. Moreover, only 02 out of 29 studies have reported the use of matrix for cartilage repair. From future perspective, aconsensus on choice of cells, differentiation inducers, biomaterials, and clinical settings might pave a way for concocting robust strategies to improve the clinical applicability of biomimetic neocartilage constructs.

Viewpoint on Time Series and Interrupted Time Series Optimum Modeling for Predicting Arthritic Disease Outcomes

PURPOSE OF REVIEW

The propose of this viewpoint is to improve or facilitate the clinical decision-making in the management/treatment strategies of arthritis patients through knowing, understanding, and having access to an interactive process allowing assessment of the patient disease outcome in the future.

RECENT FINDINGS

In recent years, the time series (TS) concept has become the center of attention as a predictive model for making forecast of unseen data values. TS and one of its technologies, the interrupted TS (ITS) analysis (TS with one or more interventions), predict the next period(s) value(s) of a given patient based on their past and current information. Traditional TS/ITS methods involve segmented regression-based technologies (linear and nonlinear), while stochastic (linear modeling) and artificial intelligence approaches, including machine learning (complex nonlinear relationships between variables), are also used; however, each have limitations. We will briefly describe TS/ITS, provide examples of their application in arthritic diseases; describe their methods, challenges, and limitations; and propose a combined (stochastic and artificial intelligence) procedure in post-intervention that will optimize ITS modeling. This combined method will increase the accuracy of ITS modeling by profiting from the advantages of both stochastic and nonlinear models to capture all ITS deterministic and stochastic components. In addition, this combined method will allow ITS outcomes to be predicted as continuous variables without having to consider the time lag produced between the pre- and post-intervention periods, thus minimizing the prediction error not only for the given data but also for all possible future patterns in ITS. The use of reliable prediction methodologies for arthritis patients will permit treatment of not only the disease, but also the patient with the disease, ensuring the best outcome prediction for the patient.