156 research outputs found

    Brief periods of transcutaneous auricular vagus nerve stimulation improve autonomic balance and alter circulating monocytes and endothelial cells in patients with metabolic syndrome: a pilot study

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    Abstract Background There is emerging evidence that the nervous system regulates immune and metabolic alterations mediating Metabolic syndrome (MetS) pathogenesis via the vagus nerve. This study evaluated the effects of transcutaneous auricular vagus nerve stimulation (TAVNS) on key cardiovascular and inflammatory components of MetS. Methods We conducted an open label, randomized (2:1), two-arm, parallel-group controlled trial in MetS patients. Subjects in the treatment group (n‚ÄČ=‚ÄČ20) received 30¬†min of TAVNS with a NEMOS¬ģ device placed on the¬†cymba conchae¬†of the left ear, once weekly. Patients in the control group (n‚ÄČ=‚ÄČ10) received no stimulation. Hemodynamic, heart rate variability (HRV), biochemical parameters, and monocytes, progenitor endothelial cells, circulating endothelial cells, and endothelial micro particles were evaluated at randomization, after the first TAVNS treatment, and again after 8¬†weeks of follow-up. Results An improvement in sympathovagal balance (HRV analysis) was observed after the first TAVNS session. Only patients treated with TAVNS for 8¬†weeks had a significant decrease in office BP and HR, a further improvement in sympathovagal balance, with a shift of circulating monocytes towards an anti-inflammatory phenotype and endothelial cells to a reparative vascular profile. Conclusion These results are of interest for further study of TAVNS as treatment of MetS

    Pervasive gaps in Amazonian ecological research

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    Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5‚Äď7 vast areas of the tropics remain understudied.8‚Äď11 In the American tropics, Amazonia stands out as the world‚Äôs most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13‚Äď15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon‚Äôs biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region‚Äôs vulnerability to environmental change. 15%‚Äď18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

    Pervasive gaps in Amazonian ecological research

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    Experimental yellow fever in the Squirrel Monkey (Saimiri spp.): hematological, biochemical, and immunological findings

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    National Council for Scientific and Technological Development‚ÄĒCNPq to PFCV (process 401,558/2013-4 and process 303.999/2016-0). RBT was a visiting scientist at the IEC during the experimental study with financial support of CNPqMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Ananindeua, PA, Brasil / Federal University of Par√°. Postgraduate Program in Biology of Infectious and Parasitic Agents. Bel√©m, PA, Brazil.Minist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Ananindeua, PA, BrasilMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Ananindeua, PA, BrasilMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Centro Nacional de Primatas. Ananindeua, PA, BrasilMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Centro Nacional de Primatas. Ananindeua, PA, BrasilMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Centro Nacional de Primatas. Ananindeua, PA, BrasilMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Ananindeua, PA, BrasilMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Ananindeua, PA, BrasilMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Ananindeua, PA, BrasilMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Ananindeua, PA, BrasilMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Ananindeua, PA, BrasilMinist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Ananindeua, PA, BrasilUniversity of Texas Medical Branch. Department of Pathology. Galveston, TX, USA.Minist√©rio da Sa√ļde. Secretaria de Vigil√Ęncia em Sa√ļde e Ambiente. Instituto Evandro Chagas. Ananindeua, PA, Brasil / Par√° State University. Department of Pathology. Bel√©m, PA, Brazil.Between 2016 and 2018, Brazil experienced the largest sylvatic epidemic of yellow fever virus (YFV). Despite to the magnitude and rapid spread of the epidemic, little is known about YFV dispersion. The study evaluated whether the squirrel monkey is a good model for yellow fever (YF) studies. Methods: Ten animals were infected with 1 √ó 106 PFU/mL of YFV, with one negative control. Blood samples were collected daily during the first 7 days and at 10, 20 and 30 days post infection (dpi) for detection of viral load and cytokines by RT-qPCR; measurements of AST, ALT, urea and creatinine were taken; IgM/IgG antibodies were detected by ELISA, and hemagglutination inhibition and neutralization tests were performed. The animals exhibited fever, flushed appearance, vomiting and petechiae, and one animal died. Viremia was detected between 1 and 10 dpi, and IgM/IgG antibodies appeared between 4 and 30 dpi. The levels of AST, ALT and urea increased. The immune responses were characterized by expression of S100 and CD11b cells; endothelial markers (VCAM-1, ICAM-1 and VLA-4), cell death and stress (Lysozyme and iNOS); and pro-inflammatory cytokines (IL-8, TNF-őĪ, and IFN-ő≥) and anti-inflammatory cytokines (IL-10 and TGF-ő≤). The squirrel monkeys showed changes similar to those described in humans with YF, and are a good experimental model for the study of YF

    Pervasive gaps in Amazonian ecological research

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    Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%‚Äď18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

    A Phakopsora pachyrhizi Effector Suppresses PAMP-Triggered Immunity and Interacts with a Soybean Glucan Endo-1,3-ő≤-Glucosidase to Promote Virulence

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    Asian soybean rust, caused by the fungus Phakopsora pachyrhizi, is one of the most important diseases affecting soybean production in tropical areas. During infection, P. pachyrhizi secretes proteins from haustoria that are transferred into plant cells to promote virulence. To date, only one candidate P. pachyrhizi effector protein has been characterized in detail to understand the mechanism by which it suppresses plant defenses to enhance infection. Here, we aimed to extend understanding of the pathogenic mechanisms of P. pachyrhizi based on the discovery of host proteins that interact with the effector candidate Phapa-7431740. We demonstrated that Phapa-7431740 suppresses pathogen-associated molecular pattern-triggered immunity (PTI) and that it interacts with a soybean glucan endo-1,3-ő≤-glucosidase (Gmő≤GLU), a pathogenesis-related (PR) protein belonging to the PR-2 family. Structural and phylogenetic characterization of the PR-2 protein family predicted in the soybean genome and comparison to PR-2 family members in Arabidopsis thaliana and cotton, demonstrated that Gmő≤GLU is a type IV ő≤-1,3-glucanase. Transcriptional profiling during an infection time course showed that the Gmő≤GLU mRNA is highly induced during the initial hours after infection, coinciding with peak of expression of Phapa-7431740. The effector was able to interfere with the activity of Gmő≤GLU in vitro, with a dose-dependent inhibition. Our results suggest that Phapa-7431740 may suppress PTI by interfering with glucan endo-1,3-ő≤-glucosidase activity.This article is published as Bueno, Thays Vieira, Patricia Pereira Fontes, Valeria Yukari Abe, Alice Utiyama Saito, Renato Lima Senra, Liliane Santana Oliveira, Adriana Brombini Dos Santos et al. "A Phakopsora pachyrhizi effector suppresses PAMP-triggered immunity and interacts with a soybean glucan endo-1, 3-ő≤-glucosidase to promote virulence." Molecular Plant-Microbe Interactions (MPMI) 35, no. 9 (2022): 779‚Äď790. DOI: 10.1094/MPMI-12-21-0301-R. The author(s) have dedicated the work to the public domain under the Creative Commons CC0 ‚ÄúNo Rights Reserved‚ÄĚ license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2022

    Seedless Cu Electroplating on Ru-W Thin Films for Metallisation of Advanced Interconnects

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    For decades, Ta/TaN has been the industry standard for a diffusion barrier against Cu in interconnect metallisation. The continuous miniaturisation of transistors and interconnects into the nanoscale are pushing conventional materials to their physical limits and creating the need to replace them. Binary metallic systems, such as Ru-W, have attracted considerable attention as possible replacements due to a combination of electrical and diffusion barrier properties and the capability of direct Cu electroplating. The process of Cu electrodeposition on Ru-W is of fundamental importance in order to create thin, continuous, and adherent films for advanced interconnect metallisation. This work investigates the effects of the current density and application method on the electro-crystallisation behaviour of Cu. The film structure, morphology, and chemical composition were assessed by digital microscopy, atomic force microscopy, scanning and transmission electron microscopies, energy-dispersive X-ray spectroscopy, and X-ray diffraction. The results show that it was possible to form a thin Cu film on Ru-W with interfacial continuity for current densities higher than 5 mA¬∑cm‚ąí2; however, the substrate regions around large Cu particles remained uncovered. Pulse-reverse current application appears to be more beneficial than direct current as it decreased the average Cu particle size

    Deep Learning for Identification of Acute Illness and Facial Cues of Illness

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    Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3‚Äď33.1% for the skin model) to 89.4% (66.9‚Äď98.7%, for the nose model). Specificity ranged from 42.1% (20.3‚Äď66.5%) for the nose model and 94.7% (73.9‚Äď99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62‚Äď0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35‚Äď100.00%) and specificity of 42.11% (20.25‚Äď66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness

    ABC<sub>2</sub>-SPH risk score for in-hospital mortality in COVID-19 patients

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    Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Methods: Consecutive patients (‚Č• 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March‚ÄďJuly, 2020. The model was validated in the 1054 patients admitted during August‚ÄďSeptember, as well as in an external cohort of 474 Spanish patients. Results: Median (25‚Äď75th percentile) age of the model-derivation cohort was 60 (48‚Äď72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829‚Äď0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833‚Äď0.885]) and Spanish (0.894 [95% CI 0.870‚Äď0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.</p

    Discovery of Staphylococcus aureus Adhesion Inhibitors by Automated Imaging and Their Characterization in a Mouse Model of Persistent Nasal Colonization.

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    Due to increasing mupirocin resistance, alternatives for Staphylococcus aureus nasal decolonization are urgently needed. Adhesion inhibitors are promising new preventive agents that may be less prone to induce resistance, as they do not interfere with the viability of S. aureus and therefore exert less selection pressure. We identified promising adhesion inhibitors by screening a library of 4208 compounds for their capacity to inhibit S. aureus adhesion to A-549 epithelial cells in vitro in a novel automated, imaging-based assay. The assay quantified DAPI-stained nuclei of the host cell; attached bacteria were stained with an anti-teichoic acid antibody. The most promising candidate, aurintricarboxylic acid (ATA), was evaluated in a novel persistent S. aureus nasal colonization model using a mouse-adapted S. aureus strain. Colonized mice were treated intranasally over 7 days with ATA using a wide dose range (0.5-10%). Mupirocin completely eliminated the bacteria from the nose within three days of treatment. In contrast, even high concentrations of ATA failed to eradicate the bacteria. To conclude, our imaging-based assay and the persistent colonization model provide excellent tools to identify and validate new drug candidates against S. aureus nasal colonization. However, our first tested candidate ATA failed to induce S. aureus decolonization
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