摘要：【中文】本发明总体上涉及监测体内施用糖皮质激素介导的药效学反应的方法。更具体地说，本发明涉及利用基因签名的变化作为糖皮质激素暴露的药效学标志物的方法。 【EN】The present invention relates generally to methods of monitoring pharmacodynamic responses mediated by glucocorticoid administration in vivo. More specifically, the invention relates to methods of using changes in gene signatures as pharmacodynamic markers of glucocorticoid exposure.

摘要：【中文】本发明涉及在患有或易患急性心力衰竭的患者中控制、保持或降低血压和/或治疗、预防或减轻例如呼吸困难的症状的方法。所述方法涉及给药有效量的包含例如氯维地平的短效二氢吡啶化合物的药物组合物。所述药物组合物可以初始剂量给药，并且如果血压未被控制或保持在目标血压范围内或者降低到目标血压范围内，则可滴定所述初始剂量以达到在所述目标血压范围内的血压。所述患者的收缩压可为约120mmHg或更高。 【EN】The present invention relates to methods of controlling, maintaining or reducing blood pressure and/or treating, preventing or alleviating symptoms such as dyspnea in a patient suffering from or susceptible to acute heart failure. The methods involve administering an effective amount of a pharmaceutical composition comprising a short-acting dihydropyridine compound such as clevidipine. The pharmaceutical composition may be administered at an initial dose, and if the blood pressure is not controlled or maintained within or reduced to within a target blood pressure range, the initial dose may be titrated to achieve a blood pressure within the target blood pressure range. The patient's systolic blood pressure may be about 120mmHg or higher.

摘要：【中文】用于机动车辆预测性电荷规划和动力系控制的智能车辆系统和逻辑、制造/操作此系统的方法及具有智能电荷规划和动力系控制能力的电驱动车辆。智能电动车辆的基于AI的预测性电荷规划的系统和方法使用机器学习(ML)驾驶员模型，其利用可用交通、位置和道路地图信息估计车辆速度和推进扭矩需求以导出给定行程的总能耗。智能混合动力车辆的基于AI的预测性动力系控制的系统和方法将ML驾驶员模型与深度学习技术一起使用导出具有可用交通、地理位置、地理空间和地图数据的由预览路线限定的驾驶循环简档。利用收集的数据发展ML生成的驾驶员模型来复制驾驶员行为并预测预览路线的驾驶循环简档，包括预测的车辆速度、推进扭矩和加速器/制动踏板位置。 【EN】An intelligent vehicle system and logic for predictive charge planning and powertrain control of a motor vehicle, a method of manufacturing/operating such a system, and an electrically driven vehicle having intelligent charge planning and powertrain control capabilities. Systems and methods for AI-based predictive charge planning for intelligent electric vehicles use a Machine Learning (ML) driver model that estimates vehicle speed and propulsion torque requirements using available traffic, location, and road map information to derive total energy consumption for a given trip. Systems and methods for AI-based predictive powertrain control of intelligent hybrid vehicles use an ML driver model with deep learning techniques to derive a driving cycle profile defined by a preview route with available traffic, geographic location, geospatial, and map data. The ML generated driver model is developed using the collected data to replicate the driver behavior and predict a driving cycle profile for the preview route, including predicted vehicle speed, propulsion torque, and accelerator/brake pedal position.