A Practical Guide to Injection Therapy in Hand Tendinopathies: A Systematic Review of Randomized Controlled Trials.

Hand tendinopathies represent a pathological condition associated with significant disability. However, due to this high heterogeneity of the treatments and their efficacy, there is still a lack of consensus on the infiltrative therapy of the hand. This systematic review aimed to investigate the efficacy of injection techniques in the treatment of pain related to the main hand tendinopathies. We searched online medical databases (PubMed, Pedro, Cochrane Library, Scopus, and WoS). Only RCTs published in the last 10 years (up to 5 August 2024), written in English, and related to infiltrative treatment in wrist and hand tendinopathies were evaluated. The risk of bias in RCTs was assessed with Version 2 of the Cochrane Risk of Bias tool for randomized trials (RoB 2). Out of 641 articles identified, 23 were included in the final synthesis: 14 RCTs on trigger finger, and 9 RCTs on de Quervain's tenosynovitis. The present systematic review showed that infiltrative therapy of trigger finger and de Quervain's tenosynovitis constitutes a fundamental element in the treatment of these pathological conditions, in terms of pain reduction and improvement in the functionality of the hand.

Clinical validation of a new control-oriented model of insulin and glucose dynamics in subjects with type 1 diabetes.

BACKGROUND: The development of an artificial pancreas requires an accurate representation of diabetes pathophysiology to create effective and safe control systems for automatic insulin infusion regulation. The aim of the present study is the assessment of a previously developed mathematical model of insulin and glucose metabolism in type 1 diabetes and the evaluation of its effectiveness for the development and testing of control algorithms. METHODS: Based on the already existing "minimal model" a new mathematical model was developed composed of glucose and insulin submodels. The glucose model includes the representation of peripheral uptake, hepatic uptake and release, and renal clearance. The insulin model describes the kinetics of exogenous insulin injected either subcutaneously or intravenously. The estimation of insulin sensitivity allows the model to personalize parameters to each subject. Data sets from two different clinical trials were used here for model validation through simulation studies. The first set had subcutaneous insulin injection, while the second set had intravenous insulin injection. The root mean square error between simulated and real blood glucose profiles (G(rms)) and the Clarke error grid analysis were used to evaluate the system efficacy. RESULTS: Results from our study demonstrated the model's capability in identifying individual characteristics even under different experimental conditions. This was reflected by an effective simulation as indicated by G(rms), and clinical acceptability by the Clarke error grid analysis, in both clinical data series. CONCLUSIONS: Simulation results confirmed the capacity of the model to faithfully represent the glucose-insulin relationship in type 1 diabetes in different circumstances.

Control oriented model of insulin and glucose dynamics in type 1 diabetics.

The aim of the study was to realize a mathematical model of insulin-glucose relationship in type I diabetes and test its effectiveness for the design of control algorithms in external artificial pancreas. A new mathematical model, divided into glucose and insulin sub-models, was developed from the so-called "minimal model". The key feature is the representation of insulin sensitivity so as to permit the personalisation of the parameters. Real-time applications are based on an insulin standardised model. Clinical data were used to estimate model parameters. Root mean square error between simulated and real blood glucose profiles (G(rms)) was used to evaluate system efficacy. Results from parameter estimation and insulin standardisation showed a good capability of the model to identify individual characteristics. Simulation results with a G(rms) 1.30 mmol/l in the worst case testified the capacity of the model to accurately represent glucose-insulin relationship in type 1 diabetes allowing self tuning in real time.