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Checkpoint-mediated cell cycle arrest also prevents further telomere erosion and deprotection that would favor chromosome rearrangements, which are known to increase cancer-associated genome instability. This review summarizes recent insights into functions and regulation of Tel1/ATM and Mec1/ATR at telomeres both in the presence and in the absence of telomerase, focusing mainly on discoveries in budding yeast.
The Anatomical Therapeutic Chemical (ATC) system is an official classification system established by the World Health Organization for medicines. Correctly assigning ATC classes to given compounds is an important research problem in drug discovery, which can not only discover the possible active ingredients of the compounds, but also infer theirs therapeutic, pharmacological, and chemical properties.
In this paper, we develop an end-to-end multi-label classifier called CGATCPred to predict 14 main ATC classes for given compounds. In order to extract rich features of each compound, we use the deep Convolutional Neural Network (CNN) and shortcut connections to represent and learn the seven association scores between the given compound and others. Moreover, we construct the correlation graph of ATC classes and then apply graph convolutional network (GCN) on the graph for label embedding abstraction. We use all label embedding to guide the learning process of compound representation. As a result, by using the Jackknife test, CGATCPred obtain reliable Aiming of 81.94%, Coverage of 82.88%, Accuracy 80.81%, Absolute True 76.58% and Absolute False 2.75%, yielding significantly improvements compared to exiting multi-label classifiers.
The codes of CGATCPred are available at https//github.com/zhc940702/CGATCPred and https//zenodo.org/record/4552917.
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.Candida auris is an emerging fungal pathogen of rising concern due to global spread, the ability to cause healthcare-associated outbreaks, and antifungal resistance. Genomic analyses revealed that early contemporaneously detected cases of C. auris were geographically stratified into four major clades. While Clades I, III, and IV are responsible for ongoing outbreaks of invasive and multidrug-resistant infections, Clade II, also termed the East Asian clade, consists primarily of cases of ear infection, is often susceptible to all antifungal drugs, and has not been associated with outbreaks. Here, we generate chromosome-level assemblies of twelve isolates representing the phylogenetic breadth of these four clades and the only isolate described to date from Clade V. This Clade V genome is highly syntenic with those of Clades I, III, and IV, although the sequence is highly divergent from the other clades. Clade II genomes appear highly rearranged, with translocations occurring near GC-poor regions, and large subtelomeric deletions in most chromosomes, resulting in a substantially different karyotype. Rearrangements and deletion lengths vary across Clade II isolates, including two from a single patient, supporting ongoing genome instability. Deleted subtelomeric regions are enriched in Hyr/Iff-like cell-surface proteins, novel candidate cell wall proteins, and an ALS-like adhesin. Cell wall proteins from these families and other drug-related genes show clade-specific signatures of selection in Clades I, III, and IV. Subtelomeric dynamics and the conservation of cell surface proteins in the clades responsible for global outbreaks causing invasive infections suggest an explanation for the different phenotypes observed between clades.
Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routinely collected laboratory studies to predict surgical outcomes in U.S. Veterans with CD.
Adults with CD from a Veterans Health Administration, Veterans Integrated Service Networks (VISN) 10 cohort examined between 2001 and 2015 were used for analysis. Patient demographics, medication use, and longitudinal laboratory values were used to model future surgical outcomes within 1 year. Specifically, data at the time of prediction combined with historical laboratory data characteristics, described as slope, distribution statistics, fluctuation, and linear trend of laboratory values, were considered and principal component analysis transformations were performed to reduce the dimensionality. Lasso regularized logistic regression was used to select features and construct predictiery.Interferon α (IFNα) is a type I interferon, an essential cytokine employed by the immune system to fight viruses. Although a number of the structures of type I interferons have been reported, most of the known structures of IFNα are in complex with its receptors. CORT125134 There are only two examples of structures of free IFNα one is a dimeric X-ray structure without side-chain information; and another is an NMR structure of human IFNα. Although we have shown that Sortilin is involved in the secretion of IFNα, the details of the molecular interaction and the secretion mechanism remain unclear. Recently, we solved the X-ray structure of mouse Sortilin, but the structure of mouse IFNα remained unknown. In the present study, we determined the crystal structure of mouse IFNα2 at 2.1 Å resolution and investigated its interaction with Sortilin. Docking simulations suggested that Arg22 of mouse IFNα2 is important for the interaction with mouse Sortilin. Mutation of Arg22 to alanine facilitated IFNα2 secretion, as determined by flow cytometry, highlighting the contribution of this residue to the interaction with Sortilin. These results suggest an important role for Arg22 in mouse IFNα for Sortilin-mediated IFNα trafficking.
An interrelation between cancer and thrombosis is known, but population-based studies on the risk of both arterial thromboembolism (ATE) and venous thromboembolism (VTE) have not been performed.
International Classification of Disease 10th Revision (ICD-10) diagnosis codes of all publicly insured persons in Austria (0-90 years) were extracted from the Austrian Association of Social Security Providers dataset covering the years 2006-07 (n = 8306244). Patients with a history of cancer or active cancer were defined as having at least one ICD-10 'C' diagnosis code, and patients with ATE and/or VTE as having at least one of I21/I24 (myocardial infarction), I63/I64 (stroke), I74 (arterial embolism), and I26/I80/I82 (venous thromboembolism) diagnosis code. Among 158675 people with cancer, 8559 (5.4%) had an ATE diagnosis code and 7244 (4.6%) a VTE diagnosis code. In contrast, among 8147569 people without cancer, 69381 (0.9%) had an ATE diagnosis code and 29307 (0.4%) a VTE diagnosis code. This corresponds to age-stratified random-effects relative risks (RR) of 6.