This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV) plant. The model is called HIstorical SImilar MIning (HISIMI) model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model. OPEN ACCESSEnergies 2013, 6 2625

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Purpose: The levothyroxine absorption test (LT4AT) is an important tool for distinguishing hypothyroidism due to malabsorption from “pseudomalabsorption” conditions. Our aim was to review our institution’s LT4AT results and assess its role in the management of patients with refractory hypothyroidism. Methods: We performed a retrospective study of all patients evaluated for refractory hypothyroidism who underwent LT4AT in our tertiary center between 2014 to 2020. Its results and the impact on thyroid function management during follow-up were assessed. Results: Ten female patients were included with a mean age of 40 years (min-max: 26-62). Mean weight was 72kg (min-max: 43-88) and baseline LT4 dosage ranged from 2.5 to 5.3µg/kg per day. Most common cause of hypothyroidism were postsurgical in 50% (n=5) and autoimmune in 20% (n=2). During LT4AT, normal LT4 absorption was found in all but one individual (mean FT4 increase of 231%, min-max: 85-668). The only patient with objective LT4 absorption impairment (maximal increase of 48% by hour 5) presented also Helicobacter pylori gastritis and prior history of “intestinal surgery” during childhood. No adverse events were reported during any of the LT4ATs. During follow-up [median 11.5 months (IQR 23)], 3 patients obtained euthyroidism and 6 had improved their hypothyroidism state. Conclusions: The LT4AT is an effective and safe way to assess refractory hypothyroidism and provides valuable information to distinguish LT4 malabsorption from “pseudomalabsorption”. Our data suggest that most patients with suspicious LT4 malabsorption perform normally during LT4AT. This test provides relevant information for better management of patients with refractory hypothyroidism.

Introduction MODY probability calculator (MPC) represents an easy‐to‐use tool developed by Exeter University to help clinicians prioritize which individuals should be oriented to genetic testing. We aimed to assess the utility of MPC in a Portuguese cohort with early‐onset monogenic diabetes. Methods This single‐centre retrospective study enrolled 132 participants submitted to genetic testing between 2015 and 2020. Automatic sequencing and, in case of initial negative results, generation sequencing were performed. MODY probability was calculated using the probability calculator available online. Positive and negative predictive values (PPV and NPV, respectively), accuracy, sensitivity and specificity of the calculator were determined for this cohort. Results Seventy‐three individuals were included according to inclusion criteria: 20 glucokinase (GCK‐MODY); 16 hepatocyte nuclear factor 1A (HNF1A‐MODY); 2 hepatocyte nuclear factor 4A (HNF4A‐MODY) and 35 DM individuals with no monogenic mutations found. The median probability score of MODY was significantly higher in monogenic diabetes‐positive subgroup (75.5% vs. 24.2%, p < .001). The discriminative accuracy of the calculator, as expressed by area under the curve, was 75% (95% CI: 64%–85%). In our cohort, the best cut‐off value for the MODY calculator was found to be 36%, with a PPV of 74.4%, NPV of 73.5% and corresponding sensitivity and specificity of 76.2% and 71.4%, respectively. Conclusions In a highly pre‐selected group of probands qualified for genetic testing, the Exeter MODY probability calculator provided a useful tool in individuals' selection for genetic testing, with good discrimination ability under an optimal probability cut‐off of 36%. Further geographical and population adjustments are warranted for general use.

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