ω-3 fatty acids suppress inflammatory cytokine production by macrophages and hepatocytes

Objective: Long-term total parenteral nutrition (TPN) in children is often complicated by parental nutrition-associated liver disease and may even lead to liver failure. Recently, the addition of ω-3 fatty acids to TPN has been shown to reduce the risk of parental nutrition-associated liver disease. The purpose of this study was to explore the anti-inflammatory effects of ω-3 fatty acids (eicosapentaenoic acid [EPA]) to demonstrate the protection of the liver against hepatic steatosis and damage. Materials and Methods: Lipopolysaccharide (LPS) and prostaglandin E 2 (PGE 2) were used to stimulate human macrophages and hepatocytes (THLE-3) to induce in vitro inflammatory condition. The cells were then incubated with either ω-3 (EPA) or ω-6 (arachidonic acid) fatty acids. Supernatants were collected at different time points for the measurement of tumor necrosis factor α (TNF-α), interleukin 6 (IL-6), and interleukin 10 (IL-10) using enzyme-linked immunosorbent assay. Furthermore, pretreated macrophages by LPS stimulation and after incubation with EPA were added to prestimulated hepatocytes for the subsequent measurement of cytokine response. Results: Eicosapentaenoic acid effectively reduced LPS-induced or PGE 2-induced TNF-α and IL-6 expression, and increased IL-10 expression significantly when compared with arachidonic acid. Furthermore, supernatant collected after co-culturing EPA with macrophages also suppressed the levels of TNF-α and IL-6 in hepatocytes. This would suggest that EPA not only had an anti-inflammatory effect on macrophages and hepatocytes directly, it could indirectly reduce hepatocyte inflammation through activated macrophages. Conclusions: The addition of ω-3 fatty acids in TPN suppresses the inflammatory response via direct and indirect routes. The findings may help explain the clinical benefits of EPA in pediatric patients receiving long-term TPN. © 2010 Elsevier Inc.postprin

XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging

Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. This work proposes a sequence-to-sequence sleep staging model, XSleepNet, that is capable of learning a joint representation from both raw signals and time-frequency images. Since different views may generalize or overfit at different rates, the proposed network is trained such that the learning pace on each view is adapted based on their generalization/overfitting behavior. As a result, the network is able to retain the representation power of different views in the joint features which represent the underlying distribution better than those learned by each individual view alone. Furthermore, the XSleepNet architecture is principally designed to gain robustness to the amount of training data and to increase the complementarity between the input views. Experimental results on five databases of different sizes show that XSleepNet consistently outperforms the single-view baselines and the multi-view baseline with a simple fusion strategy. Finally, XSleepNet also outperforms prior sleep staging methods and improves previous state-of-the-art results on the experimental databases

Improving GANs for Speech Enhancement

Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use of a single generator to perform one-stage enhancement mapping. In this work, we propose to use multiple generators that are chained to perform multi-stage enhancement mapping, which gradually refines the noisy input signals in a stage-wise fashion. Furthermore, we study two scenarios: (1) the generators share their parameters and (2) the generators' parameters are independent. The former constrains the generators to learn a common mapping that is iteratively applied at all enhancement stages and results in a small model footprint. On the contrary, the latter allows the generators to flexibly learn different enhancement mappings at different stages of the network at the cost of an increased model size. We demonstrate that the proposed multi-stage enhancement approach outperforms the one-stage SEGAN baseline, where the independent generators lead to more favorable results than the tied generators. The source code is available at http://github.com/pquochuy/idsegan.Comment: This letter has been accepted for publication in IEEE Signal Processing Letter

Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning.

BACKGROUND: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging. METHODS: We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks as the means for transfer learning. The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain (i.e. the small cohort) to complete knowledge transfer. We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database. The target domains are purposely adopted to cover different degrees of data mismatch to the source domains. RESULTS: Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the proposed deep transfer learning approach. CONCLUSIONS: These results suggest the efficacy of the proposed approach in addressing the above-mentioned data-variability and data-inefficiency issues. SIGNIFICANCE: As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small

Computational fluid dynamics modeling of symptomatic intracranial atherosclerosis may predict risk of stroke recurrence.

BackgroundPatients with symptomatic intracranial atherosclerosis (ICAS) of ≥ 70% luminal stenosis are at high risk of stroke recurrence. We aimed to evaluate the relationships between hemodynamics of ICAS revealed by computational fluid dynamics (CFD) models and risk of stroke recurrence in this patient subset.MethodsPatients with a symptomatic ICAS lesion of 70-99% luminal stenosis were screened and enrolled in this study. CFD models were reconstructed based on baseline computed tomographic angiography (CTA) source images, to reveal hemodynamics of the qualifying symptomatic ICAS lesions. Change of pressures across a lesion was represented by the ratio of post- and pre-stenotic pressures. Change of shear strain rates (SSR) across a lesion was represented by the ratio of SSRs at the stenotic throat and proximal normal vessel segment, similar for the change of flow velocities. Patients were followed up for 1 year.ResultsOverall, 32 patients (median age 65; 59.4% males) were recruited. The median pressure, SSR and velocity ratios for the ICAS lesions were 0.40 (-2.46-0.79), 4.5 (2.2-20.6), and 7.4 (5.2-12.5), respectively. SSR ratio (hazard ratio [HR] 1.027; 95% confidence interval [CI], 1.004-1.051; P = 0.023) and velocity ratio (HR 1.029; 95% CI, 1.002-1.056; P = 0.035) were significantly related to recurrent territorial ischemic stroke within 1 year by univariate Cox regression, respectively with the c-statistics of 0.776 (95% CI, 0.594-0.903; P = 0.014) and 0.776 (95% CI, 0.594-0.903; P = 0.002) in receiver operating characteristic analysis.ConclusionsHemodynamics of ICAS on CFD models reconstructed from routinely obtained CTA images may predict subsequent stroke recurrence in patients with a symptomatic ICAS lesion of 70-99% luminal stenosis

Performance of the new automated Abbott RealTime MTB assay for rapid detection of Mycobacterium tuberculosis complex in respiratory specimens

The automated high-throughput Abbott RealTime MTB real-time PCR assay has been recently launched for Mycobacterium tuberculosis complex (MTBC) clinical diagnosis. This study would like to evaluate its performance. We first compared its diagnostic performance with the Roche Cobas TaqMan MTB assay on 214 clinical respiratory specimens. Prospective analysis of a total 520 specimens was then performed to further evaluate the Abbott assay. The Abbott assay showed a lower limit of detection at 22.5 AFB/ml, which was more sensitive than the Cobas assay (167.5 AFB/ml). The two assays demonstrated a significant difference in diagnostic performance (McNemar’s test; P = 0.0034), in which the Abbott assay presented significantly higher area under curve (AUC) than the Cobas assay (1.000 vs 0.880; P = 0.0002). The Abbott assay demonstrated extremely low PCR inhibition on clinical respiratory specimens. The automated Abbott assay required only very short manual handling time (0.5 h), which could help to improve the laboratory management. In the prospective analysis, the overall estimates for sensitivity and specificity of the Abbott assay were both 100 % among smear-positive specimens, whereas the smear-negative specimens were 96.7 and 96.1 %, respectively. No cross-reactivity with non-tuberculosis mycobacterial species was observed. The superiority in sensitivity of the Abbott assay for detecting MTBC in smear-negative specimens could further minimize the risk in MTBC false-negative detection. The new Abbott RealTime MTB assay has good diagnostic performance which can be a useful diagnostic tool for rapid MTBC detection in clinical laboratories. © 2015, Springer-Verlag Berlin Heidelberg.postprin

Natural products triptolide, celastrol, and withaferin A inhibit the chaperone activity of peroxiredoxin I

published_or_final_versio

Strong Casimir force reduction through metallic surface nanostructuring

The Casimir force between bodies in vacuum can be understood as arising from their interaction with an infinite number of fluctuating electromagnetic quantum vacuum modes, resulting in a complex dependence on the shape and material of the interacting objects. Becoming dominant at small separations, the force plays a significant role in nanomechanics and object manipulation at the nanoscale, leading to a considerable interest in identifying structures where the Casimir interaction behaves significantly different from the well-known attractive force between parallel plates. Here we experimentally demonstrate that by nanostructuring one of the interacting metal surfaces at scales below the plasma wavelength, an unexpected regime in the Casimir force can be observed. Replacing a flat surface with a deep metallic lamellar grating with sub-100 nm features strongly suppresses the Casimir force and for large inter-surfaces separations reduces it beyond what would be expected by any existing theoretical prediction.Comment: 11 pages, 8 figure

Mitoxantrone and Analogues Bind and Stabilize i-Motif Forming DNA Sequences

YesThere are hundreds of ligands which can interact with G-quadruplex DNA, yet very few which target i-motif. To appreciate an understanding between the dynamics between these structures and how they can be affected by intervention with small molecule ligands, more i-motif binding compounds are required. Herein we describe how the drug mitoxantrone can bind, induce folding of and stabilise i-motif forming DNA sequences, even at physiological pH. Additionally, mitoxantrone was found to bind i-motif forming sequences preferentially over double helical DNA. We also describe the stabilisation properties of analogues of mitoxantrone. This offers a new family of ligands with potential for use in experiments into the structure and function of i-motif forming DNA sequences