RESUMO
BACKGROUND: Temporomandibular joint osteoarthritis (TMJ OA) is a chronic disease that is a consequence of undue occlusal forces and is characterized by irreversible damage to the articular surfaces. Symptomatic slow-acting so-called nutraceutical drugs have been proposed as a treatment for osteoarthritis in comparison to non-steroidal anti-inflammatory drugs (NSAIDs). Oral glucosamine and chondroitin, slow-acting drugs, have been found to reduce pain and in- crease mouth opening in patients with TMJ OA. However, there is limited scientific evidence to confirm their clinical effectiveness. AIM: This systematic review was conducted to bolster the evidence supporting the assessment of the efficacy of glucosamine in the context of temporomandibular joint osteoarthritis (TMJ OA). METHODOLOGY: This review identified four review articles from databases like Medline (via PubMed), Web of Science, Scopus, and EMBASE till September 2023 after screening at the title, abstract, and full-text level. They were assessed for risk of bias with the JBI risk of bias assess- ment tool. RESULT: This review with meta-analysis focused on pooled estimate mean differences, revealing non-significant but discernible effects of glucosamine on maximum mouth opening (SMD = 0.288, p = 0.15) and pain reduction (SMD = 0.217, p = 0.476) in TMJ-related disorders. CONCLUSION: Compared to control groups with ibuprofen and tramadol, glucosamine showed slightly more favourable outcomes. However, the variability in methodology and study characteristics warrants further longitudinal studies to confirm its efficacy.
RESUMO
This correspondence proposes two novel control schemes with variable state-feedback gain to stabilize a Takagi-Sugeno (T-S) fuzzy system. The T-S fuzzy model is expressed as a linear plant with nonlinear disturbance terms in both schemes. In controller I, the T-S fuzzy model is expressed as a linear plant around a nominal plant arbitrarily selected from the set of linear subsystems that the T-S fuzzy model consists of. The variable gain then becomes a function of a gain parameter that is computed to neutralize the effect of disturbance term, which is, in essence, the deviation of the actual system dynamics from the nominal plant as the system traverses a specific trajectory. This controller is shown to stabilize the T-S fuzzy model. In controller II, individual linear subsystems are locally stabilized. Fuzzy blending of individual control actions is shown to make the T-S fuzzy system Lyapunov stable. Although applicability of both control schemes depends on the norm bound of unmatched state disturbance, this constraint is relaxed further in controller II. The efficacy of controllers I and II has been tested on two nonlinear systems.
