Abdikrog5573

A Gram-negative, strictly anaerobic mucin-degrading bacterium, which we designated strain E39T, was isolated from the rumen epithelium of Korean cattle. The cells were non-motile and had a coccus morphology. Growth of strain E39T was observed at 30-45°C (optimum, 39°C), pH 6.5-8.5 (optimum, pH 7.5), and in the presence of 0.0-1.0% (w/v) NaCl (optimum, 0.0-0.5%). Strain E39T contained C160, C180, C181 ω9c, iso-C150, and anteiso-C150 as the major fatty acids. The major polar lipids were phosphatidylethanolamine, unidentified aminophospholipid, and unidentified lipids. find more The major respiratory isoprenoid quinones were MK-8 and MK-9. The major fermented end-products of mucin were acetate and succinate. The G+C content of the genomic DNA was 46.4 mol%. Strain E39T was most closely related to Alloprevotella rava 81/4-12T with an 87.3% 16S rRNA gene sequence similarity. On the basis of phenotypic, chemotaxonomic, and molecular properties, strain E39T represents a novel genus of the family Prevotellaceae; as such, the name Pseudoprevotella muciniphila gen. nov., sp. nov. is proposed. A functional annotation of the whole genome sequences of P. muciniphila E39T revealed that this bacterium has a putative mucin-degrading pathway and biosynthetic pathways of extracellular polymeric substances and virulence factors which enable bacteria to adhere to the epithelial cells and avoid the host's immune responses.Dengue is a re-emerging disease, currently considered the most important mosquito-borne arbovirus infection affecting humankind, taking into account both its morbidity and mortality. Brazil is considered an endemic country for dengue, such that more than 1,544,987 confirmed cases were notified in 2019, which means an incidence rate of 735 for every 100 thousand inhabitants. Climate is an important factor in the temporal and spatial distribution of vector-borne diseases, such as dengue. Thus, rainfall and temperature are considered macro-factors determinants for dengue, since they directly influence the population density of Aedes aegypti, which is subject to seasonal fluctuations, mainly due to these variables. This study examined the incidence of dengue fever related to the climate influence by using temperature and rainfall variables data obtained from remote sensing via artificial satellites in the metropolitan region of Rio de Janeiro, Brazil. The mathematical model that best fits the data is based on an auto-regressive moving average with exogenous inputs (ARMAX). It reproduced the values of incidence rates in the study period and managed to predict with good precision in a one-year horizon. The approach described in present work may be replicated in cities around the world by the public health managers, to build auxiliary operational tools for control and prevention tasks of dengue, as well of other arbovirus diseases.

The smoking-paradox of a better outcome in ischemic stroke patients who smoke may be due to increased efficacy of thrombolysis. We investigated the effect of smoking on outcome following endovascular therapy (EVT) with mechanical thrombectomy alone versus in combination with intra-arterial (IA-) thrombolysis.

The primary endpoint was defined by three-month modified Rankin Scale (mRS). We performed a generalized linear model and reported relative risks (RR) for smoking (adjustment for age, sex, hypertension, atrial fibrillation, stroke severity, time to EVT) in patient data stemming from the Virtual International Stroke Trials Archive-Endovascular database.

Among 1,497 patients, 740(49.4%) were randomized to EVT; among EVT patients, 524(35.0%) received mechanical thrombectomy alone and 216(14.4%) received it in combination with IA-thrombolysis. Smokers (N = 396) had lower mRS scores (mean 2.9 vs. 3.2; p = 0.02) and mortality rates (10% vs. 17.3%; p<0.001) in univariate analysis. In all patients and in patients treated with mechanical thrombectomy alone, smoking had no effect on outcome in regression analyses. In patients who received IA-thrombolysis (N = 216;14%), smoking had an adjusted RR of 1.65 for an mRS≤1 (95%CI 0.77-3.55). Treatment with IA-thrombolysis itself led to reduced RR for favorable outcome (adjusted RR 0.30); interaction analysis of IA-thrombolysis and smoking revealed that non-smokers with IA-thrombolysis had mRS≤2 in 47 cases (30%, adjusted RR 0.53 [0.41-0.69]) while smokers with IA-thrombolysis had mRS≤2 in 23 cases (38%, adjusted RR 0.61 [0.42-0.87]).

Smokers had no clear clinical benefit from EVT that incorporates IA-thrombolysis.

Smokers had no clear clinical benefit from EVT that incorporates IA-thrombolysis.Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode quantitative metrics' average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors' regions more accurate than with only CNN based SS. While, in one by one image processing mode there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation's efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available https//www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).