Effects of different remifentanil doses on the stress reaction and BIS value of video laryngoscope-guided tracheal intubation

Purpose: To explore the affinity of different remifentanil doses for  intravenous anesthesia in video laryngoscope-guided tracheal intubation.Methods: Eighty patients who required anesthesia for elective non-ophthalmic surgery were included. They were divided into four groups (A, B, C and D) and received a different dose of either 1, 1.5, or 2 μg/kg remifentanil or a dose of 2 μg/kg fentanyl, respectively. An anesthetic state was achieved and maintained by administration of 3 - 5 mg/kg propofolum and 0.1 - 0.3 mg/kg remifentanil. The mean value of the various indices, including arterial pressure (MAP), bispectral index and heart rate (HR) wererecorded prior to anesthesia induction (T0), prior to intubation (T1),  instantly before intubation (T2), and at 1 (T3), 3 (T4) and 5 (T5) after the intubation. Cortisol concentration was measured at T0, T1 and T5.Results: Remifentanil (1 μg/kg) induced a moderate increase in HR and MAP at T3 compared with fentanyl. HR and MAP in the lower dose group were significantly higher than those in groups B and C at T3. Compared to T1, the concentrations of cortisol decreased after anesthesia and then significantly increased during tracheal intubation. Cortisol concentration in group B was the lowest at T5.Conclusion: The most effective concentrations of remifentanil are 1 and 1.5 μg/kg for anesthesia induction and tracheal intubation, respectively.Keywords: Remifentanil, Stress reaction, Bispectral index, Video laryngoscope, Tracheal intubatio

HybridNet: Dual-Branch Fusion of Geometrical and Topological Views for VLSI Congestion Prediction

Accurate early congestion prediction can prevent unpleasant surprises at the routing stage, playing a crucial character in assisting designers to iterate faster in VLSI design cycles. In this paper, we introduce a novel strategy to fully incorporate topological and geometrical features of circuits by making several key designs in our network architecture. To be more specific, we construct two individual graphs (geometry-graph, topology-graph) with distinct edge construction schemes according to their unique properties. We then propose a dual-branch network with different encoder layers in each pathway and aggregate representations with a sophisticated fusion strategy. Our network, named HybridNet, not only provides a simple yet effective way to capture the geometric interactions of cells, but also preserves the original topological relationships in the netlist. Experimental results on the ISPD2015 benchmarks show that we achieve an improvement of 10.9% compared to previous methods