Anti-inflammatory and antioxidant properties of HDLs are impaired in type 2 diabetes.

ObjectiveIn mice, 4F, an apolipoprotein A-I mimetic peptide that restores HDL function, prevents diabetes-induced atherosclerosis. We sought to determine whether HDL function is impaired in type 2 diabetic (T2D) patients and whether 4F treatment improves HDL function in T2D patient plasma in vitro.Research design and methodsHDL anti-inflammatory function was determined in 93 T2D patients and 31 control subjects as the ability of test HDLs to inhibit LDL-induced monocyte chemotactic activity in human aortic endothelial cell monolayers. The HDL antioxidant properties were measured using a cell-free assay that uses dichlorofluorescein diacetate. Oxidized fatty acids in HDLs were measured by liquid chromatography-tandem mass spectrometry. In subgroups of patients and control subjects, the HDL inflammatory index was repeated after incubation with L-4F.ResultsThe HDL inflammatory index was 1.42 ± 0.29 in T2D patients and 0.70 ± 0.19 in control subjects (P < 0.001). The cell-free assay was impaired in T2D patients compared with control subjects (2.03 ± 1.35 vs. 1.60 ± 0.80, P < 0.05), and also HDL intrinsic oxidation (cell-free assay without LDL) was higher in T2D patients (1,708 ± 739 vs. 1,233 ± 601 relative fluorescence units, P < 0.001). All measured oxidized fatty acids were significantly higher in the HDLs of T2D patients. There was a significant correlation between the cell-free assay values and the content of oxidized fatty acids in HDL fractions. L-4F treatment restored the HDL inflammatory index in diabetic plasma samples (from 1.26 ± 0.17 to 0.71 ± 0.11, P < 0.001) and marginally affected it in healthy subjects (from 0.81 ± 0.16 to 0.66 ± 0.10, P < 0.05).ConclusionsIn patients with T2D, the content of oxidized fatty acids is increased and the anti-inflammatory and antioxidant activities of HDLs are impaired

Conditional Generative Data Augmentation for Clinical Audio Datasets

In this work, we propose a novel data augmentation method for clinical audio datasets based on a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP), operating on log-mel spectrograms. To validate our method, we created a clinical audio dataset which was recorded in a real-world operating room during Total Hip Arthroplasty (THA) procedures and contains typical sounds which resemble the different phases of the intervention. We demonstrate the capability of the proposed method to generate realistic class-conditioned samples from the dataset distribution and show that training with the generated augmented samples outperforms classical audio augmentation methods in terms of classification performance. The performance was evaluated using a ResNet-18 classifier which shows a mean Macro F1-score improvement of 1.70% in a 5-fold cross validation experiment using the proposed augmentation method. Because clinical data is often expensive to acquire, the development of realistic and high-quality data augmentation methods is crucial to improve the robustness and generalization capabilities of learning-based algorithms which is especially important for safety-critical medical applications. Therefore, the proposed data augmentation method is an important step towards improving the data bottleneck for clinical audio-based machine learning systems

Oral Apolipoprotein A-I Mimetic D-4F Lowers HDL-Inflammatory Index in High-Risk Patients: A First-in-Human Multiple-Dose, Randomized Controlled Trial.

A single dose of the apolipoprotein (apo)A-I mimetic peptide D-4F rendered high-density lipoprotein (HDL) less inflammatory, motivating the first multiple-dose study. We aimed to assess safety/tolerability, pharmacokinetics, and pharmacodynamics of daily, orally administered D-4F. High-risk coronary heart disease (CHD) subjects added double-blinded placebo or D-4F to statin for 13 days, randomly assigned 1:3 to ascending cohorts of 100, 300, then 500 mg (n = 62; 46 men/16 women). D-4F was safe and well-tolerated. Mean ± SD plasma D-4F area under the curve (AUC, 0-8h) was 6.9 ± 5.7 ng/mL*h (100 mg), 22.7 ± 19.6 ng/mL*h (300 mg), and 104.0 ± 60.9 ng/mL*h (500 mg) among men, higher among women. Whereas placebo dropped HDL inflammatory index (HII) 28% 8 h postdose (range, 1.25-0.86), 300-500 mg D-4F effectively halved HII: 1.35-0.57 and 1.22-0.63, respectively (P \u3c 0.03 vs. placebo). Oral D-4F peptide dose predicted HII suppression, whereas plasma D-4F exposure was dissociated, suggesting plasma penetration is unnecessary. In conclusion, oral D-4F dosing rendered HDL less inflammatory, affirming oral D-4F as a potential therapy to improve HDL function

Differentiable Graph Module (DGM) for Graph Convolutional Networks

Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. One of the limitations of the majority of current graph neural network architectures is that they are often restricted to the transductive setting and rely on the assumption that the underlying graph is known and fixed. Often, this assumption is not true since the graph may be noisy, or partially and even completely unknown. In such cases, it would be helpful to infer the graph directly from the data, especially in inductive settings where some nodes were not present in the graph at training time. Furthermore, learning a graph may become an end in itself, as the inferred structure may provide complementary insights next to the downstream task. In this paper, we introduce Differentiable Graph Module (DGM), a learnable function that predicts edge probabilities in the graph which are optimal for the downstream task. DGM can be combined with convolutional graph neural network layers and trained in an end-to-end fashion. We provide an extensive evaluation on applications in healthcare, brain imaging, computer graphics, and computer vision showing a significant improvement over baselines both in transductive and inductive settings

Improved Techniques for the Conditional Generative Augmentation of Clinical Audio Data

Data augmentation is a valuable tool for the design of deep learning systems to overcome data limitations and stabilize the training process. Especially in the medical domain, where the collection of large-scale data sets is challenging and expensive due to limited access to patient data, relevant environments, as well as strict regulations, community-curated large-scale public datasets, pretrained models, and advanced data augmentation methods are the main factors for developing reliable systems to improve patient care. However, for the development of medical acoustic sensing systems, an emerging field of research, the community lacks large-scale publicly available data sets and pretrained models. To address the problem of limited data, we propose a conditional generative adversarial neural network-based augmentation method which is able to synthesize mel spectrograms from a learned data distribution of a source data set. In contrast to previously proposed fully convolutional models, the proposed model implements residual Squeeze and Excitation modules in the generator architecture. We show that our method outperforms all classical audio augmentation techniques and previously published generative methods in terms of generated sample quality and a performance improvement of 2.84% of Macro F1-Score for a classifier trained on the augmented data set, an enhancement of 1.14% in relation to previous work. By analyzing the correlation of intermediate feature spaces, we show that the residual Squeeze and Excitation modules help the model to reduce redundancy in the latent features. Therefore, the proposed model advances the state-of-the-art in the augmentation of clinical audio data and improves the data bottleneck for the design of clinical acoustic sensing systems

Characterization of designed, synthetically accessible bryostatin analog HIV latency reversing agents.

HIV latency in resting CD4+ T cell represents a key barrier preventing cure of the infection with antiretroviral drugs alone. Latency reversing agents (LRAs) can activate HIV expression in latently infected cells, potentially leading to their elimination through virus-mediated cytopathic effects, host immune responses, and/or therapeutic strategies targeting cells actively expressing virus. We have recently described several structurally simplified analogs of the PKC modulator LRA bryostatin (termed bryologs) designed to improve synthetic accessibility, tolerability in vivo, and efficacy in inducing HIV latency reversal. Here we report the comparative performance of lead bryologs, including their effects in reducing cell surface expression of HIV entry receptors, inducing proinflammatory cytokines, inhibiting short-term HIV replication, and synergizing with histone deacetylase inhibitors to reverse HIV latency. These data provide unique insights into structure-function relationships between A- and B-ring bryolog modifications and activities in primary cells, and suggest that bryologs represent promising leads for preclinical advancement

Apolipoprotein A-I Mimetic Peptides Prevent Atherosclerosis Development and Reduce Plaque Inflammation in a Murine Model of Diabetes

ObjectiveTo determine the effect of the apolipoprotein A-I (ApoA-I) mimetic peptide, D-4F, on atherosclerosis development in a pre-existing diabetic condition.Research design and methodsWe induced hyperglycemia in 6-week-old apoE(-/-) female mice using streptozotocin. Half of the diabetic apoE(-/-) mice received D-4F in drinking water. Ten weeks later, plasma lipids, glucose, insulin levels, atherosclerotic lesions, and lesion macrophage content were measured.ResultsDiabetic apoE(-/-) mice developed ∼300% more lesion area, marked dyslipidemia, increased glucose levels, and reduced plasma insulin levels when compared with nondiabetic apoE(-/-) mice. Atherosclerotic lesions were significantly reduced in the D-4F-treated diabetic apoE(-/-) mice in whole aorta (1.11 ± 0.73 vs. 0.58 ± 0.44, percentage of whole aorta, P < 0.01) and in aortic roots (36,038 ± 18,467 μm²/section vs. 17,998 ± 12,491 μm²/section, P < 0.01) when compared with diabetic apoE(-/-) mice that did not receive D-4F. Macrophage content in atherosclerotic lesions from D-4F-treated diabetic apoE(-/-) mice was significantly reduced when compared with nontreated animals (78.03 ± 26.1 vs. 29.6 ± 15.2 P < 0.001, percentage of whole plaque). There were no differences in glucose, insulin, total cholesterol, HDL cholesterol, and triglyceride levels between the two groups. Arachidonic acid, PGE₂, PGD₂, 15-HETE, 12-HETE, and 13-HODE concentrations were significantly increased in the liver tissue of diabetic apoE(-/-) mice compared with nondiabetic apoE(-/-) mice and significantly reduced by D-4F treatment.ConclusionsOur results suggest that oral D-4F can prevent atherosclerosis development in pre-existing diabetic mice and this is associated with a reduction in hepatic arachidonic acid and oxidized fatty acid levels

Sonification as a Reliable Alternative to Conventional Visual Surgical Navigation

Despite the undeniable advantages of image-guided surgical assistance systems in terms of accuracy, such systems have not yet fully met surgeons' needs or expectations regarding usability, time efficiency, and their integration into the surgical workflow. On the other hand, perceptual studies have shown that presenting independent but causally correlated information via multimodal feedback involving different sensory modalities can improve task performance. This article investigates an alternative method for computer-assisted surgical navigation, introduces a novel sonification methodology for navigated pedicle screw placement, and discusses advanced solutions based on multisensory feedback. The proposed method comprises a novel sonification solution for alignment tasks in four degrees of freedom based on frequency modulation (FM) synthesis. We compared the resulting accuracy and execution time of the proposed sonification method with visual navigation, which is currently considered the state of the art. We conducted a phantom study in which 17 surgeons executed the pedicle screw placement task in the lumbar spine, guided by either the proposed sonification-based or the traditional visual navigation method. The results demonstrated that the proposed method is as accurate as the state of the art while decreasing the surgeon's need to focus on visual navigation displays instead of the natural focus on surgical tools and targeted anatomy during task execution