SummaryAprotinin has been used widely in surgery as an anti-bleeding agent but is associated with a number of side effects. We report that textilinin-1, a serine protease inhibitor from Pseudonaja textilis venom with sequence relatedness to aprotinin, is a potent but reversible plasmin inhibitor and has a narrower range of protease inhibition compared to aprotinin. Like aprotinin, textilinin-1 at 5 lmol/l gave almost complete inhibition of tissue plasminogen activator-induced fibrinolysis of whole blood clots. The activated partial thromboplastin time for plasma was markedly increased by aprotinin but unaffected by textilinin-1. In a mouse tail-vein bleeding model, intravenous textilinin-1 and aprotinin caused similar decreases in blood loss but time to haemostasis in the textilinin-treated animals was significantly shorter than in aprotinin-treated mice. Based on these data, textilinin-1 merits further investigation as a therapeutic alternative to aprotinin.

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Power system dispatch benefits from accurate wind power predictions. To increase the prediction precision for wind power, this paper proposes a combined model for predicting short-term wind power based on the autoregressive moving average-gated recurrent unit (ARMA-GRU). Firstly, we build the ARMA model and GRU model respectively to predict wind power. Then we optimize the combined model’s weights by quantum particle swarm algorithm (QPSO). Finally, we build an error correction model for the prediction errors to acquire the final results for the wind power predictions. Our experimental results prove the model’s reliability and the model’s high predictability is verified by comparing different prediction models.