OBJECTIVE Basal insulin Fc (BIF) (insulin efsitora alfa; LY3209590), a fusion protein combining a novel single-chain insulin variant with a human IgG Fc domain, is designed for once-weekly basal insulin administration. This phase 2 study assessed the safety and efficacy of BIF versus degludec in insulin-naive patients with type 2 diabetes (T2D) previously treated with oral antihyperglycemic medications. RESEARCH DESIGN AND METHODS During this randomized, parallel, open-label study, 278 insulin-naive patients with T2D were randomly assigned (1:1) to receive BIF once weekly or degludec once daily over the 26-week treatment period. Both groups were titrated to fasting glucose of 80–100 mg/dL (4.4 to <5.6 mmol/L). The primary end point was HbA1c change from baseline to week 26 (noninferiority margin 0.4%). Secondary end points included fasting blood glucose (FBG), six-point glucose profiles, and rate of hypoglycemia. RESULTS After 26 weeks of treatment, BIF demonstrated a noninferior HbA1c change from baseline versus degludec, with a treatment difference of 0.06% (90% CI −0.11, 0.24; P = 0.56). Both BIF and degludec treatment led to significant reductions in FBG from baseline. At week 26, the between-treatment difference for BIF versus degludec was 4.7 mg/dL (90% CI 0.1, 9.3; P = 0.09). The rate of level 2 hypoglycemia was low and not significantly different between treatment groups (BIF 0.22 events/patient/year, degludec 0.15 events/patient/year; P = 0.64); there was no severe hypoglycemia. The occurrence of treatment-emergent adverse events was also similar between BIF and degludec. CONCLUSIONS Once-weekly BIF achieved excellent glycemic control similar to degludec, with no concerning hypoglycemia or other safety findings.

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Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people's livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

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Intravenous thrombolysis (IVT) improves functional outcome after acute ischemic stroke (AIS) and is the standard first-line treatment; however, it is associated with many complications, including cerebral hemorrhage. Cancer patients are susceptible to thrombotic events-collectively referred to as Trousseau syndrome (TS)-owing to their hypercoagulable state. Here, we describe the case of a 55-year-old male with a history of hypertension for over 10 years who underwent surgery for removal of a cancer of lower esophagus, with no subsequent treatment. Three months later, he was admitted to the emergency department of our hospital with sudden dizziness and incoherent speech. Brain computed tomography revealed multiple cerebral infarctions. The patient was treated by IVT with tissue plasminogen activator (rtPA) after the onset of symptoms, which improved by the end of the treatment. However, a few months later, he experienced a recurrence of cerebral infarction and hemorrhage, which has rarely been reported. The clinical course of this case suggests that the suitability of thrombolysis with rtPA in the acute phase of cerebral infarction complicated with TS should be carefully considered.