Richmondmcintosh4211

On the molecular level, ticagrelor enhanced renal Epac-1 mRNA expression, Rap-1 activation (Rap-1-GTP) and SOCS-3 level. On the contrary, it inhibited the protein expression of JAK-2/STAT-3 hub, TNF-α and MDA contents, as well as caspase-3 activity. Additionally, ticagrelor enhanced the protein expression/content of AKT/Nrf-2/HO-1 axis. All these beneficial effects were obviously antagonized upon using R-CE3F4. In conclusion, ticagrelor reno-therapeutic effect is partly mediated through modulating the Epac-1/Rap-1-GTP, AKT/Nrf-2/HO-1 and JAK-2/STAT-3/SOCS-3 trajectories, pathways that integrate to afford novel explanations to its anti-inflammatory, anti-oxidant, and anti-apoptotic potentials.Previous studies have suggested that Lycium barbarum (L. barbarum) has a radioprotective function, although more in-depth investigation is still required. We investigated the radioprotective efficacy of extract of the fruits of L. barbarum (LBE) and its radioprotective mechanisms. Mice were exposed to 8.5 Gy, 5.5 Gy, or 6.0 Gy total body irradiation (TBI), and the survival rate, lymphocyte percentage, amount of cytokines, and viability of the irradiated cells, as well as the gut microbiome and fecal metabolomics were studied. LBE enhanced the survival of the mice exposed to 8.5 Gy γ-ray TBI or 5.5 Gy X-ray TBI. After 6.0 Gy γ-ray TBI, LBE exhibited good immunomodulatory properties, mainly characterized by the accelerated recovery of lymphocyte percentages, and the enhanced expression of immune-related cytokines. LBE reconstituted the gut microbiota of irradiated mice, increased the relative abundance of potentially beneficial genera (e.g., Turicibacter, Akkermansia), and decreased the relative abundance of potentially harmful bacterial genera (e.g., Rikenellaceae_RC9_gut_group). Beneficial regulatory effects of LBE on the host metabolites were also noted, and the major upregulated metabolites induced by LBE, such as Tetrahydrofolic acid and N-ornithyl-L-taurine, were positively correlated with the immune factor interleukin (IL)-6. In vitro, LBE also increased the vitality of rat small intestinal epithelial cells (IEC-6) after 4.0 Gy γ-ray irradiation and promoted the growth of Akkermansia muciniphila. These results confirmed a radioprotective function of LBE and indicated that the radioprotective mechanism may be due to immunomodulation and the synergistically modulating effect on the gut microbiota and related metabolites.Leishmaniasis, a neglected parasitic disease caused by a unicellular protozoan of the genus Leishmania, is transmitted through the bite of a female sandfly. The disease remains a major public health problem and is linked to tropical and subtropical regions, with an endemic picture in several regions, including East Africa, the Mediterranean basin and South America. The different causative species display a diversity of clinical presentations; therefore, the immunological data on leishmaniasis are both scarce and controversial for the different forms and infecting species of the parasite. The present review highlights the main immune parameters associated with leishmaniasis that might contribute to a better understanding of the pathogenicity of the parasite and the clinical outcomes of the disease. Our aim was to provide a concise overview of the immunobiology of the disease and the factors that influence it, as this knowledge may be helpful in developing novel chemotherapeutic and vaccine strategies.

Dysregulation of the endogenous acid-base balance can contribute to inflammation and cancer development if metabolic acidosis is sustained. The epidemiologic evidence on the association between diet-dependent acid load and cancer risk is scarce and inconsistent. We aim to explore the possible role of dietary acid load in lung cancer (LC) risk.

A case-control study was performed on 843 LC cases and 1466 controls by using a multi-topic questionnaire, including a food frequency questionnaire. Controls were matched to cases by age-frequency, urban/rural residence, and region. Food-derived nutrients were calculated from available databases. The dietary acid load was calculated using validated measures as potential renal acid load (PRAL) score and net endogenous acid production (NEAP) score. Odds ratios (ORs) were estimated by logistic regression.

We found direct associations between dietary acid load and LC risk. The highest quartile of the NEAP score was significantly associated (OR=2.22, p

<0.001). Thet the first one to be published on this issue, further studies are needed to confirm these findings.Cardiac myocyte aggregate orientation has a strong impact on cardiac electrophysiology and mechanics. Studying the link between structural characteristics, strain, and stresses over the cardiac cycle and cardiac function requires a full volumetric representation of the microstructure. FK506 In this work, we exploit the structural similarity across hearts to extract a low-rank representation of predominant myocyte orientation in the left ventricle from high-resolution magnetic resonance ex-vivo cardiac diffusion tensor imaging (cDTI) in porcine hearts. We compared two reduction methods, Proper Generalized Decomposition combined with Singular Value Decomposition and Proper Orthogonal Decomposition. We demonstrate the existence of a general set of basis functions of aggregated myocyte orientation which defines a data-driven, personalizable, parametric model featuring higher flexibility than existing atlas and rule-based approaches. A more detailed representation of microstructure matching the available patient data can improve the accuracy of personalized computational models. Additionally, we approximate the myocyte orientation of one ex-vivo human heart and demonstrate the feasibility of transferring the basis functions to humans.In this paper, we propose a framework for functional connectivity network (FCN) analysis, which conducts the brain disease diagnosis on the resting state functional magnetic resonance imaging (rs-fMRI) data, aiming at reducing the influence of the noise, the inter-subject variability, and the heterogeneity across subjects. To this end, our proposed framework investigates a multi-graph fusion method to explore both the common and the complementary information between two FCNs, i.e., a fully-connected FCN and a 1 nearest neighbor (1NN) FCN, whereas previous methods only focus on conducting FCN analysis from a single FCN. Specifically, our framework first conducts the graph fusion to produce the representation of the rs-fMRI data with high discriminative ability, and then employs the L1SVM to jointly conduct brain region selection and disease diagnosis. We further evaluate the effectiveness of the proposed framework on various data sets of the neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimers Disease (AD). The experimental results demonstrate that the proposed framework achieves the best diagnosis performance via selecting reasonable brain regions for the classification tasks, compared to state-of-the-art FCN analysis methods.The dearth of annotated data is a major hurdle in building reliable image segmentation models. Manual annotation of medical images is tedious, time-consuming, and significantly variable across imaging modalities. The need for annotation can be ameliorated by leveraging an annotation-rich source modality in learning a segmentation model for an annotation-poor target modality. In this paper, we introduce a diverse data augmentation generative adversarial network (DDA-GAN) to train a segmentation model for an unannotated target image domain by borrowing information from an annotated source image domain. This is achieved by generating diverse augmented data for the target domain by one-to-many source-to-target translation. The DDA-GAN uses unpaired images from the source and target domains and is an end-to-end convolutional neural network that (i) explicitly disentangles domain-invariant structural features related to segmentation from domain-specific appearance features, (ii) combines structural features from the source domain with appearance features randomly sampled from the target domain for data augmentation, and (iii) train the segmentation model with the augmented data in the target domain and the annotations from the source domain. The effectiveness of our method is demonstrated both qualitatively and quantitatively in comparison with the state of the art for segmentation of craniomaxillofacial bony structures via MRI and cardiac substructures via CT.Unsupervised domain adaptation (UDA) generally learns a mapping to align the distribution of the source domain and target domain. The learned mapping can boost the performance of the model on the target data, of which the labels are unavailable for model training. Previous UDA methods mainly focus on domain-invariant features (DIFs) without considering the domain-specific features (DSFs), which could be used as complementary information to constrain the model. In this work, we propose a new UDA framework for cross-modality image segmentation. The framework first disentangles each domain into the DIFs and DSFs. To enhance the representation of DIFs, self-attention modules are used in the encoder which allows attention-driven, long-range dependency modeling for image generation tasks. Furthermore, a zero loss is minimized to enforce the information of target (source) DSFs, contained in the source (target) images, to be as close to zero as possible. These features are then iteratively decoded and encoded twice to maintain the consistency of the anatomical structure. To improve the quality of the generated images and segmentation results, several discriminators are introduced for adversarial learning. Finally, with the source data and their DIFs, we train a segmentation network, which can be applicable to target images. We validated the proposed framework for cross-modality cardiac segmentation using two public datasets, and the results showed our method delivered promising performance and compared favorably to state-of-the-art approaches in terms of segmentation accuracies. The source code of this work will be released via https//zmiclab.github.io/projects.html, once this manuscript is accepted for publication.Parahydrogen-induced polarization (PHIP) is a source of nuclear spin hyperpolarization, and this technique allows for the preparation of biomolecules for in vivo metabolic imaging. PHIP delivers hyperpolarization in the form of proton singlet order to a molecule, but most applications require that a heteronuclear (e.g. 13C or 15N) spin in the molecule is hyperpolarized. Here we present high field pulse methods to manipulate proton singlet order in the [1-13C]fumarate, and in particular to transfer the proton singlet order into 13C magnetization. We exploit adiabatic pulses, i.e., pulses with slowly ramped amplitude, and use constant-adiabaticity variants the spin Hamiltonian is varied in such a way that the generalized adiabaticity parameter is time-independent. This allows for faster polarization transfer, and we achieve 96.2% transfer efficiency in thermal equilibrium experiments. We demonstrate this in experiments using hyperpolarization, and obtain 6.8% 13C polarization. This work paves the way for efficient hyperpolarization of nuclear spins in a variety of biomolecules, since the high-field pulse sequences allow individual spins to be addressed.