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Objectives Create and evaluate the effectiveness of a structured education program in children and young people (CYP) with type 1 diabetes using continuous glucose monitoring (CGM). Design and methods Step 1: CGM devices were evaluated for predetermined criteria using a composite score. Step 2: The education program was developed following review of international structured education guidance, dynamic glucose management (DynamicGM) literature, award‐winning diabetes educators' websites, and CGM user feedback. Step 3: Program effectiveness was assessed at six months by change in time below range (TBR) (<3.9mmol/L), time in range (TIR) (3.9‐10.0mmol/L), time above range level 2 (TAR2) (>13.9mmol/L), severe hypoglycemia and HbA1c using a paired T‐test. A DynamicGM score was developed to assess proactive glucose management. Factors predicting TBR and TIR were assessed using regression analysis. Results Dexcom G6 was chosen for integrated CGM (iCGM) status and highest composite score (29/30). Progressive DynamicGM strategies were taught through five sessions delivered over two months. Fifty CYP (23 male) with a mean (±SD) age and diabetes duration of 10.2 (±4.8) and 5.2 (±3.7) years respectively, who completed the education program were prospectively evaluated. Evaluation at six months showed a significant reduction in TBR (10.4% to 2.1%, p<.001), TAR2 (14.1% to 7.3%, p<.001), HbA1c [7.4 to 7.1% (57.7 to 53.8 mmol/mol), p<.001] and severe hypoglycemic episodes (10 to 1, p<.05); TIR increased (47.4% to 57.0%, p<.001). Number of Dexcom followers (p<.05) predicted reduction in TBR and DynamicGM score (p<.001) predicted increased TIR. Conclusion Teaching DynamicGM strategies successfully improves TIR and reduces hypoglycemia.

High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent inhouse HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp 3 -hybridized carbons (Fsp3), and fraction of molecular framework (f MF ). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened. Beta-binomial statistical models were built to model the molecular hit rate as a function of these descriptors. The advantage of the beta-binomial statistical models is that the correlation between the descriptors is taken into account. Higher degree polynomial terms of the descriptors were also added into the beta-binomial statistic model to improve the model quality. The relative influence of different molecular descriptors on molecular hit rate has been estimated, taking into account that the descriptors are correlated to each other through applying beta-binomial statistical modeling. The results show that ClogP has the largest influence on the molecular hit rate, followed by Fsp3 and HEV. f MF has only a minor influence besides its correlation with the other molecular descriptors.

We outline the procedure for identification and estimation of self exiting threshold autoregressive—SETAR—models, based on cusum tests. Forecasting for a general nonlinear autoregressive—NLAR—model is then discussed and a recurrence relation for quantities related to the forecast distribution is given. Some preliminary results from fitting and forecasting SETAR models are then summarised and discussed.