21,223 research outputs found
Roles of reconstituted high-density lipoprotein nanoparticles in cardiovascular disease: A new paradigm for drug discovery
Epidemiological results revealed that there is an inverse correlation between high-density lipoprotein (HDL) cholesterol levels and risks of atherosclerotic cardiovascular disease (ASCVD). Mounting evidence supports that HDLs are atheroprotective, therefore, many therapeutic approaches have been developed to increase HDL cholesterol (HDL-C) levels. Nevertheless, HDL-raising therapies, such as cholesteryl ester transfer protein (CETP) inhibitors, failed to ameliorate cardiovascular outcomes in clinical trials, thereby casting doubt on the treatment of cardiovascular disease (CVD) by increasing HDL-C levels. Therefore, HDL-targeted interventional studies were shifted to increasing the number of HDL particles capable of promoting ATP-binding cassette transporter A1 (ABCA1)-mediated cholesterol efflux. One such approach was the development of reconstituted HDL (rHDL) particles that promote ABCA1-mediated cholesterol efflux from lipid-enriched macrophages. Here, we explore the manipulation of rHDL nanoparticles as a strategy for the treatment of CVD. In addition, we discuss technological capabilities and the challenge of relating preclinical in vivo mice research to clinical studies. Finally, by drawing lessons from developing rHDL nanoparticles, we also incorporate the viabilities and advantages of the development of a molecular imaging probe with HDL nanoparticles when applied to ASCVD, as well as gaps in technology and knowledge required for putting the HDL-targeted therapeutics into full gear
Comfort-Centered Design of a Lightweight and Backdrivable Knee Exoskeleton
This paper presents design principles for comfort-centered wearable robots
and their application in a lightweight and backdrivable knee exoskeleton. The
mitigation of discomfort is treated as mechanical design and control issues and
three solutions are proposed in this paper: 1) a new wearable structure
optimizes the strap attachment configuration and suit layout to ameliorate
excessive shear forces of conventional wearable structure design; 2) rolling
knee joint and double-hinge mechanisms reduce the misalignment in the sagittal
and frontal plane, without increasing the mechanical complexity and inertia,
respectively; 3) a low impedance mechanical transmission reduces the reflected
inertia and damping of the actuator to human, thus the exoskeleton is
highly-backdrivable. Kinematic simulations demonstrate that misalignment
between the robot joint and knee joint can be reduced by 74% at maximum knee
flexion. In experiments, the exoskeleton in the unpowered mode exhibits 1.03 Nm
root mean square (RMS) low resistive torque. The torque control experiments
demonstrate 0.31 Nm RMS torque tracking error in three human subjects.Comment: 8 pages, 16figures, Journa
Multiple Instance Curriculum Learning for Weakly Supervised Object Detection
When supervising an object detector with weakly labeled data, most existing
approaches are prone to trapping in the discriminative object parts, e.g.,
finding the face of a cat instead of the full body, due to lacking the
supervision on the extent of full objects. To address this challenge, we
incorporate object segmentation into the detector training, which guides the
model to correctly localize the full objects. We propose the multiple instance
curriculum learning (MICL) method, which injects curriculum learning (CL) into
the multiple instance learning (MIL) framework. The MICL method starts by
automatically picking the easy training examples, where the extent of the
segmentation masks agree with detection bounding boxes. The training set is
gradually expanded to include harder examples to train strong detectors that
handle complex images. The proposed MICL method with segmentation in the loop
outperforms the state-of-the-art weakly supervised object detectors by a
substantial margin on the PASCAL VOC datasets.Comment: Published in BMVC 201
- …