Laparoscopically-assisted vaginal hysterectomy for enlarged uterus: operative outcomes and the learning curve

Objectives: The aim of the study was to compare the effects of uterine size and surgeon experience on the surgical out­comes of laparoscopically-assisted vaginal hysterectomy (LAVH) for benign gynecological conditions. Material and methods: This was a retrospective analysis of 184 LAVH cases. All hysterectomies were performed by the same surgeon and divided into two groups, with uterine weight of < 280 g (group 1) and uterine weight of > 280 g (group 2). The groups were compared in terms of the effects of the uterine size and surgeon experience vs. the operative outcomes (operative time, change in hemoglobin levels, hospital stay, and perioperative complications). Results: No significant differences in mean age, parity, history of chronic systemic diseases and previous surgery history were observed between the two groups. However, operative time was significantly greater in group 2 as compared to group 1 (132.1 ± 42.7 minutes vs. 111.5 ± 30.4 minutes, p < 0.05). There were no differences in the hospital stay and perioperative complications between the two groups. One case of bladder injury occurred in each group and one patient underwent a second laparoscopic surgery for postoperative bleeding in group 2. Greater surgeon experience was demonstrated to be associated with decreased operative bleeding and, consequently, smaller differences between preoperative and postop­erative hemoglobin levels. Operative time was also reduced as the surgeon was getting more experienced but the effect did not reach statistical significance. Conclusions: Our study supports the thesis that LAVH is a safe and effective procedure for managing benign gynecologi­cal conditions. Despite increased operative time, LAVH can be safely performed for enlarged uterus in conjunction with increased surgeon experience

EDAM: an ontology of bioinformatics operations, types of data and identifiers, topics and formats

Motivation: Advancing the search, publication and integration of bioinformatics tools and resources demands consistent machine-understandable descriptions. A comprehensive ontology allowing such descriptions is therefore required. Results: EDAM is an ontology of bioinformatics operations (tool or workflow functions), types of data and identifiers, application domains and data formats. EDAM supports semantic annotation of diverse entities such as Web services, databases, programmatic libraries, standalone tools, interactive applications, data schemas, datasets and publications within bioinformatics. EDAM applies to organizing and finding suitable tools and data and to automating their integration into complex applications or workflows. It includes over 2200 defined concepts and has successfully been used for annotations and implementations. Availability: The latest stable version of EDAM is available in OWL format from http://edamontology.org/EDAM.owl and in OBO format from http://edamontology.org/EDAM.obo. It can be viewed online at the NCBO BioPortal and the EBI Ontology Lookup Service. For documentation and license please refer to http://edamontology.org. This article describes version 1.2 available at http://edamontology.org/EDAM_1.2.owl.publishedVersio

DES-mutation : system for exploring links of mutations and diseases

During cellular division DNA replicates and this process is the basis for passing genetic information to the next generation. However, the DNA copy process sometimes produces a copy that is not perfect, that is, one with mutations. The collection of all such mutations in the DNA copy of an organism makes it unique and determines the organism's phenotype. However, mutations are often the cause of diseases. Thus, it is useful to have the capability to explore links between mutations and disease. We approached this problem by analyzing a vast amount of published information linking mutations to disease states. Based on such information, we developed the DES-Mutation knowledgebase which allows for exploration of not only mutation-disease links, but also links between mutations and concepts from 27 topic-specific dictionaries such as human genes/proteins, toxins, pathogens, etc. This allows for a more detailed insight into mutation-disease links and context. On a sample of 600 mutation-disease associations predicted and curated, our system achieves precision of 72.83%. To demonstrate the utility of DES-Mutation, we provide case studies related to known or potentially novel information involving disease mutations. To our knowledge, this is the first mutation-disease knowledgebase dedicated to the exploration of this topic through text-mining and data-mining of different mutation types and their associations with terms from multiple thematic dictionaries

Literature-Based Enrichment Insights into Redox Control of Vascular Biology

In cellular physiology and signaling, reactive oxygen species (ROS) play one of the most critical roles. ROS overproduction leads to cellular oxidative stress. This may lead to an irrecoverable imbalance of redox (oxidation-reduction reaction) function that deregulates redox homeostasis, which itself could lead to several diseases including neurodegenerative disease, cardiovascular disease, and cancers. In this study, we focus on the redox effects related to vascular systems in mammals. To support research in this domain, we developed an online knowledge base, DES-RedoxVasc, which enables exploration of information contained in the biomedical scientific literature. The DES-RedoxVasc system analyzed 233399 documents consisting of PubMed abstracts and PubMed Central full-text articles related to different aspects of redox biology in vascular systems. It allows researchers to explore enriched concepts from 28 curated thematic dictionaries, as well as literature-derived potential associations of pairs of such enriched concepts, where associations themselves are statistically enriched. For example, the system allows exploration of associations of pathways, diseases, mutations, genes/proteins, miRNAs, long ncRNAs, toxins, drugs, biological processes, molecular functions, etc. that allow for insights about different aspects of redox effects and control of processes related to the vascular system. Moreover, we deliver case studies about some existing or possibly novel knowledge regarding redox of vascular biology demonstrating the usefulness of DES-RedoxVasc. DES-RedoxVasc is the first compiled knowledge base using text mining for the exploration of this topic

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features

Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer

Fundus topographical distribution patterns of ocular toxoplasmosis

BACKGROUND: To establish topographic maps and determine fundus distribution patterns of ocular toxoplasmosis (OT) lesions. METHODS: In this retrospective study, patients who presented with OT to ophthalmology clinics from four countries (Argentina, Turkey, UK, USA) were included. Size, shape and location of primary (1°)/recurrent (2°) and active/inactive lesions were converted into a two-dimensional retinal chart by a retinal drawing software. A final contour map of the merged image charts was then created using a custom Matlab programme. Descriptive analyses were performed. RESULTS: 984 lesions in 514 eyes of 464 subjects (53% women) were included. Mean area of all 1° and 2° lesions was 5.96±12.26 and 5.21±12.77 mm2, respectively. For the subset group lesions (eyes with both 1° and 2° lesions), 1° lesions were significantly larger than 2° lesions (5.52±6.04 mm2 vs 4.09±8.90 mm2, p=0.038). Mean distances from foveola to 1° and 2° lesion centres were 6336±4267 and 5763±3491 µm, respectively. The majority of lesions were found in temporal quadrant (p<0.001). Maximum overlap of all lesions was at 278 µm inferotemporal to foveola. CONCLUSION: The 1° lesions were larger than 2° lesions. The 2° lesions were not significantly closer to fovea than 1° lesions. Temporal quadrant and macular region were found to be densely affected underlining the vision threatening nature of the disease

The EMBRACE web service collection

The EMBRACE (European Model for Bioinformatics Research and Community Education) web service collection is the culmination of a 5-year project that set out to investigate issues involved in developing and deploying web services for use in the life sciences. The project concluded that in order for web services to achieve widespread adoption, standards must be defined for the choice of web service technology, for semantically annotating both service function and the data exchanged, and a mechanism for discovering services must be provided. Building on this, the project developed: EDAM, an ontology for describing life science web services; BioXSD, a schema for exchanging data between services; and a centralized registry (http://www.embraceregistry.net) that collects together around 1000 services developed by the consortium partners. This article presents the current status of the collection and its associated recommendations and standards definition

Sequenceserver: A Modern Graphical User Interface for Custom BLAST Databases

Comparing newly obtained and previously known nucleotide and amino-acid sequences underpins modern biological research. BLAST is a well-established tool for such comparisons but is challenging to use on new data sets. We combined a user-centric design philosophy with sustainable software development approaches to create Sequenceserver, a tool for running BLAST and visually inspecting BLAST results for biological interpretation. Sequenceserver uses simple algorithms to prevent potential analysis errors and provides flexible text-based and visual outputs to support researcher productivity. Our software can be rapidly installed for use by individuals or on shared servers

ILA-2: An Inductive Learning Algorithm over uncertain data

In this paper we describe the ILA-2 rule induction algorithm from the machine learning domain. ILA2 is the improved version of a novel inductive learning algorithm, namely ILA. We first describe the basic algorithm ILA, then present how the algorithm was improved. We also compare ILA-2 to a range of induction algorithms, including ILA. According to the empirical comparisons, ILA-2 appears to be comparable to CN2 and C4.5 algorithms in terms of output classifiers&apos; accuracy and size