Pyoderma gangrenosum (PG) is a rare, inflammatory dermatologic condition characterized by painful cutaneous ulcerations. Herein, we describe the third documented case of PG arising in an elective plastic surgery patient who had undergone an otherwise uncomplicated facelift. We describe the course of her diagnosis and management of PG involving her face and neck and then progressing to her lower extremities. Although the etiology remains unknown, PG often arises in a host with another autoimmune disease. In the case described, the patient was diagnosed with an IgA gammopathy shortly after development of PG. Following the case report, the pathogenesis, diagnosis and treatment strategy of PG is briefly reviewed.
  Marfan syndrome is one of the most common inherited disorders of connective tissue caused by fibrillin-1 mutations, characterized by enhanced transcription factor AP-1 DNA binding activity and subsequently abnormally increased expression and activity of matrix-metalloproteinases (MMPs). We aimed to establish a novel adeno-associated virus (AAV)-based strategy for long-term expression of an AP-1 neutralising RNA hairpin (hp) decoy oligonucleotide (dON) in the aorta to prevent aortic elastolysis in a murine model of Marfan syndrome.
 
  Using fibrillin-1 hypomorphic mice (mgR/mgR), aortic grafts from young (9 weeks old) donor mgR/mgR mice were transduced ex vivo with AAV vectors and implanted as infrarenal aortic interposition grafts in mgR/mgR mice. Grafts were explanted after 30 days. For in vitro studies isolated primary aortic smooth muscle cells from mgR/mgR mice were used. Elastica-van-Giesson staining visualized elastolysis, ROS production was assessed using DHE staining. RNA F.I.S.H. verified AP-1 hp dOial to prevent life-threatening elastolysis and aortic complications.
 This study provides a novel single treatment option to achieve long-term expression of a transcription factor AP-1 neutralising decoy oligonucleotide in the aorta of mgR/mgR mice with the potential to prevent life-threatening elastolysis and aortic complications.
  Two key steps in the analysis of uncultured viruses recovered from metagenomes are the taxonomic classification of the viral sequences and the identification of putative host(s). Both steps rely mainly on the assignment of viral proteins to orthologs in cultivated viruses. Viral Protein Families (VPFs) can be used for the robust identification of new viral sequences in large metagenomics datasets. Despite the importance of VPF information for viral discovery, VPFs have not yet been explored for determining viral taxonomy and host targets.
 
  In this work we classified the set of VPFs from the IMG/VR database and developed VPF-Class. VPF-Class is a tool that automates the taxonomic classification and host prediction of viral contigs based on the assignment of their proteins to a set of classified VPFs. Applying VPF-Class on 731K uncultivated virus contigs from the IMG/VR database, we were able to classify 363K contigs at the genus level and predict the host of over 461K contigs. In the RefSeq database, VPF-class reported an accuracy of nearly 100% to classify dsDNA, ssDNA and retroviruses, at the genus level, considering a membership ratio and a confidence score of 0.2. The accuracy in host prediction was 86.4%, also at the genus level, considering a membership ratio of 0.3 and a confidence score of 0.5. And, in the prophages dataset, the accuracy in host prediction was 86% considering a membership ratio of 0.6 and a confidence score of 0.8. Moreover, from the Global Ocean Virome dataset, over 817K viral contigs out of 1 million were classified.
 
  The implementation of VPF-Class can be downloaded from https//github.com/biocom-uib/vpf-tools.
 
  http//bioinfo.uib.es/~recerca/VPF-Class/.
 http//bioinfo.uib.es/~recerca/VPF-Class/.
  Automatic phenotype concept recognition from unstructured text remains a challenging task in biomedical text mining research. Previous works that address the task typically use dictionary-based matching methods, which can achieve high precision but suffer from lower recall. Recently, machine learning-based methods have been proposed to identify biomedical concepts, which can recognize more unseen concept synonyms by automatic feature learning.  https://www.selleckchem.com/products/Nutlin-3.html  However, most methods require large corpora of manually annotated data for model training, which is difficult to obtain due to the high cost of human annotation.
 
  In this paper, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text. We first use all concepts and synonyms in HPO to construct a dictionary, which is then used to automatically build a distantly supervised training dataset for machine learning. Next, a cutting-edge deep l.
  Ligand-receptor (L-R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L-R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L-R interactions in order to study the functional effects of cell-cell communications would be of great value.
 
  In this study, we developed Cellinker, a manually curated resource of literature-supported L-R interactions that play roles in cell-cell communication. We aimed to provide a useful platform for studies on cell-cell communication mediated by L-R interactions. The current version of Cellinker documents over 3,700 human and 3,200 mouse L-R protein-protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L-R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16 L-R PPIs involved in CoV-human interactions (including 12 L-R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L-R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies.
 
  Cellinker is available at http//www.rna-society.org/cellinker/.
 
  Supplementary data are available at Bioinformatics online.
 Supplementary data are available at Bioinformatics online.