The significant 6-thioguanine nucleotide level-response relationship may support metabolite monitoring to improve thiopurine efficacy in pediatric IBD. The reported response predictors may be helpful for treatment optimization in AZA-treated children with IBD, but should be proved in prospective studies.

Few data on azathioprine (AZA) therapy for inflammatory bowel disease (IBD) exist for children. We evaluated whether the 6-thioguanine nucleotides (6-TGN) level predicts AZA refractoriness in children with IBD and whether children benefit an AZA dose escalation. Seventy-eight children with IBD initially treated with an AZA dose of 1.5-2.5 mg/kg/day were retrospectively included. The dose was adjusted based on the clinical status. The receiver operating characteristic curve and logistic regression were used to determine predictors for AZA resistance. Initially, 18 of 40 (45%) patients receiving a dose of <2 mg/kg/day and 11 of 38 (28.9%) patients receiving a dose of 2-2.5 mg/kg/day achieved remission. The 6-TGN level above 250 pmol/8.10(8) RBCs was associated with a higher remission rate, though non-significant. Among 35 patients with a dose escalation due to treatment failure, 12 (34.3%) achieved remission (the median 6-TGN level increased from 260 to 394 pmol/8.10(8) RBCs [P = .002]), 23 (67.6%) were AZA refractory. A 6-TGN level above 405 pmol/8.10(8) RBCs was the only predictor for AZA resistance (sensitivity 78.3%, specificity 75%, OR 10.8 [95% CI: 2.1-55.7, P = .004]). Serial metabolite monitoring is useful to identify children with IBD resistant to AZA. Children who cannot achieve remission despite a 6-TGN level above 405 pmol/8.10(8) RBCs should receive alternative therapies than dose increase.

The Takagi-Sugeno (T-S) fuzzy model is a versatile approach widely used in system control, often in combination with other strategies. This paper addresses key control challenges linked to the T-S system and presents important considerations to ensure its successful application using the Lyapunov theorem. One crucial aspect is determining the optimal number of premise variables and selecting accurate fuzzy rules for the T-S model. Additionally, the theorem based on Linear Matrix Inequality (LMI) is developed to enable effective disturbance rejection. To enhance stability control, constraints are imposed on the output angle and control input of a rotary inverted pendulum (RIP). By integrating T-S fuzzy control, disturbance rejection, and input/output constraints, robust stability in controlling the RIP is achieved. Extensive simulations are performed to showcase the efficiency of the suggested method, and the simulation results are thoroughly discussed and analyzed to verify the efficacy of the control method.