Project description:The p38δ mitogen-activated protein kinase is an important signal transduction enzyme. p38δ has recently emerged as a drug target due to its tissue-specific expression patterns and its critical roles in regulation of cellular processes related to cancer and inflammatory diseases, such as cell proliferation, cell migration, apoptosis, and inflammatory responses. However, potent and specific p38δ inhibitors have not been defined so far. Moreover, in cancer disease, p38δ appears to act as a tumor suppressor or tumor promoter according to cancer and cell type studied. In this review, we outline the current understanding of p38δ roles in each cancer type, to define whether it is possible to delineate new cancer therapies based on small-molecule p38δ inhibitors. We also highlight recent advances made in the design of molecules with potential to inhibit p38 isoforms and discuss structural approaches to guide the search for p38δ inhibitors.
Project description:Bats are well-known to be natural reservoirs of various zoonotic coronaviruses, which have caused outbreaks of severe acute respiratory syndrome (SARS) and the COVID-19 pandemic in 2002 and 2019, respectively. In late 2020, two new Sarbecoviruses were found in Russia, isolated in Rhinolophus bats, i.e., Khosta-1 in R. ferrumequinum and Khosta-2 in R. hipposideros. The potential danger associated with these new species of Sarbecovirus is that Khosta-2 has been found to interact with the same entry receptor as SARS-CoV-2. Our multidisciplinary approach in this study demonstrates that Khosta-1 and -2 currently appear to be not dangerous with low risk of spillover, as confirmed by prevalence data and by phylogenomic reconstruction. In addition, the interaction between Khosta-1 and -2 with ACE2 appears weak, and furin cleavage sites are absent. While the possibility of a spillover event cannot be entirely excluded, it is currently highly unlikely. This research further emphasizes the importance of assessing the zoonotic potential of widely distributed batborne CoV in order to monitor changes in genomic composition of viruses and prevent spillover events (if any).
Project description:Limited views are often obtained in the setting of cardiac ultrasound, however, the likelihood of missing left ventricular (LV) dysfunction based on a single view is not known. We sought to determine the echo views that were least likely to miss LV systolic dysfunction in consecutive transthoracic echocardiograms (TTEs). Structured data from TTEs performed at 2 hospitals from September 25, 2017, to January 15, 2019, were screened. Studies of interest were those with reported LV dysfunction. Views evaluated were the parasternal long-axis (PLAX), parasternal-short axis at mitral (PSAX M), papillary muscle (PSAX PM), and apical (PSAX A) levels, apical 2 (AP2), apical 3 (AP3), and apical 4 (AP4) chamber views. The probability that a view contained at least 1 abnormal segment was determined and analyzed with McNemar's test for 21 adjusted pair-wise comparisons. There were 4102 TTE studies included for analysis. TTEs on males comprised 72.7% of studies with a mean LV ejection fraction of 42.8 ± 9.7%. The echo view with the greatest likelihood of encompassing an abnormal segment was the AP2 view with a prevalence of 93.4% (p < 0.001, compared to all other views). The PLAX view performed the worst with a prevalence of 82.5% (p < 0.015, compared to all other views). The best parasternal view for the detection of abnormality was the PSAX PM view at 90.4%. In conclusions, a single echo view will contain abnormal segments > 82% of the time in the setting of LV systolic dysfunction, with a prevalence of up to 93.4% in the apical windows.
Project description:The genus Calonectria with its Cylindrocladium asexual morphs has been subject to several taxonomic revisions in the past. These have resulted in the recognition of 116 species, of which all but two species (C. hederae and C. pyrochroa) are supported by ex-type cultures and supplemented with DNA barcodes. The present study is based on a large collection of unidentified Calonectria isolates that have been collected over a period of 20 years from various substrates worldwide, which has remained unstudied in the basement of the CBS-KNAW Fungal Biodiversity Centre. Employing a polyphasic approach, the identities of these isolates were resolved and shown to represent many new phylogenetic species. Of these, 24 are newly described, while C. uniseptata is reinstated at species level. We now recognise 141 species that include some of the most important plant pathogens globally.
Project description:A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an echocardiogram. Currently, there is no consensus whether the mislabeled samples can be used to create a feasible clinical prediction model of ejection fraction (EF). The aim of this study was to test two types of input methods for the classification of images, and to test the accuracy of the prediction model for EF in a learning database containing mislabeled images that were not checked by observers. We enrolled 340 patients with five standard views (long axis, short axis, 3-chamber view, 4-chamber view and 2-chamber view) and 10 images in a cycle, used for training a convolutional neural network to classify views (total 17,000 labeled images). All DICOM images were rigidly registered and rescaled into a reference image to fit the size of echocardiographic images. We employed 5-fold cross validation to examine model performance. We tested models trained by two types of data, averaged images and 10 selected images. Our best model (from 10 selected images) classified video views with 98.1% overall test accuracy in the independent cohort. In our view classification model, 1.9% of the images were mislabeled. To determine if this 98.1% accuracy was acceptable for creating the clinical prediction model using echocardiographic data, we tested the prediction model for EF using learning data with a 1.9% error rate. The accuracy of the prediction model for EF was warranted, even with training data containing 1.9% mislabeled images. The CNN algorithm can classify images into five standard views in a clinical setting. Our results suggest that this approach may provide a clinically feasible accuracy level of view classification for the analysis of echocardiographic data.
Project description:Windows provide access to daylight and outdoor views, influencing building design. Various glazing and window shade materials are used to mitigate glare, overheating and privacy issues, and they affect view clarity. Among them, we evaluated the effect of window films, electrochromic (EC) glass, and fabric shades on view clarity. We conducted an experiment with 50 participants using visual tests adapted from clinical vision tests (visual acuity, contrast sensitivity, color sensitivity) and images displayed on a computer monitor in a controlled laboratory. Window films and EC glass tints outperformed fabric shades in visual acuity, contrast sensitivity and view satisfaction with the exception of the darkest EC tint state and dark grey VLT 3% shade for color sensitivity and view satisfaction. The EC tints pose internal reflection issues and fabric shades are preferred for visual privacy. Window films and EC glass hinder participants' blue-green color discrimination while fabric shades also decrease red-yellow color discrimination. Visual acuity predicts view satisfaction and contrast sensitivity is the strongest predictor for visual privacy. Generally, higher visible light transmittance and lower solar reflectance (darker color) enhance human visual performance. The proposed workflow provides an experimental procedure, identifies the primary variables and establishes a predictive framework for assessing view clarity of fenestration.
Project description:Cardiac resynchronization therapy (CRT) is an implant-based therapy applied to patients with a specific heart failure (HF) profile. The identification of patients that may benefit from CRT is a challenging task and the application of current guidelines still induce a non-responder rate of about 30%. Several studies have shown that the assessment of left ventricular (LV) mechanics by speckle tracking echocardiography can provide useful information for CRT patient selection. A comprehensive evaluation of LV mechanics is normally performed using three different echocardioraphic views: 4, 3 or 2-chamber views. The aim of this study is to estimate the relative importance of strain-based features extracted from these three views, for the estimation of CRT response. Several features were extracted from the longitudinal strain curves of 130 patients and different methods of feature selection (out-of-bag random forest, wrapping and filtering) have been applied. Results show that more than 50% of the 20 most important features are calculated from the 4-chamber view. Although features from the 2- and 3-chamber views are less represented in the most important features, some of the former have been identified to provide complementary information. A thorough analysis and interpretation of the most informative features is also provided, as a first step towards the construction of a machine-learning chain for an improved selection of CRT candidates.
Project description:PurposeCardiac boundary segmentation of echocardiographic images is important for cardiac function assessment and disease diagnosis. However, it is challenging to segment cardiac ventricles due to the low contrast-to-noise ratio and speckle noise of the echocardiographic images. Manual segmentation is subject to interobserver variability and is too slow for real-time image-guided interventions. We aim to develop a deep learning-based method for automated multi-structure segmentation of echocardiographic images.MethodsWe developed an anchor-free mask convolutional neural network (CNN), termed Cardiac-SegNet, which consists of three subnetworks, that is, a backbone, a fully convolutional one-state object detector (FCOS) head, and a mask head. The backbone extracts multi-level and multi-scale features from endocardium image. The FOCS head utilizes these features to detect and label the region-of-interests (ROIs) of the segmentation targets. Unlike the traditional mask regional CNN (Mask R-CNN) method, the FCOS head is anchor-free and can model the spatial relationship of the targets. The mask head utilizes a spatial attention strategy, which allows the network to highlight salient features to perform segmentation on each detected ROI. For evaluation, we investigated 450 patient datasets by a five-fold cross-validation and a hold-out test. The endocardium (LVEndo ) and epicardium (LVEpi ) of the left ventricle and left atrium (LA) were segmented and compared with manual contours using the Dice similarity coefficient (DSC), Hausdorff distance (HD), mean absolute distance (MAD), and center-of-mass distance (CMD).ResultsCompared to U-Net and Mask R-CNN, our method achieved higher segmentation accuracy and fewer erroneous speckles. When our method was evaluated on a separate hold-out dataset at the end diastole (ED) and the end systole (ES) phases, the average DSC were 0.952 and 0.939 at ED and ES for the LVEndo , 0.965 and 0.959 at ED and ES for the LVEpi , and 0.924 and 0.926 at ED and ES for the LA. For patients with a typical image size of 549 × 788 pixels, the proposed method can perform the segmentation within 0.5 s.ConclusionWe proposed a fast and accurate method to segment echocardiographic images using an anchor-free mask CNN.
Project description:Cardiac amyloidosis (CA) may affect all cardiac structures, including the valves. From 423 patients undergoing a diagnostic workup for CA we selected 2 samples of 20 patients with amyloid transthyretin (ATTR-) or light-chain (AL-) CA, and age- and sex-matched controls. We chose 31 echocardiographic items related to the mitral, aortic and tricuspid valves, giving a value of 1 to each abnormal item. Patients with ATTR-CA displayed more often a shortened/hidden and restricted posterior mitral valve leaflet (PMVL), thickened mitral chordae tendineae and aortic stenosis than those with AL-CA, and less frequent PMVL calcification than matched controls. Score values were 15.8 (13.6-17.4) in ATTR-CA, 11.0 (9.3-14.9) in AL-CA, 12.8 (11.1-14.4) in ATTR-CA controls, and 11.0 (9.1-13.0) in AL-CA controls (p = 0.004 for ATTR- vs. AL-CA, 0.009 for ATTR-CA vs. their controls, and 0.461 for AL-CA vs. controls). Area under the curve values to diagnose ATTR-CA were 0.782 in patients with ATTR-CA or matched controls, and 0.773 in patients with LV hypertrophy. Patients with ATTR-CA have a prominent impairment of mitral valve structure and function, and higher score values. The valve score may help identify patients with ATTR-CA among patients with CA or unexplained hypertrophy.