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Urol Clin North Am ; 49(1): 23-38, 2022 Feb.
Article En | MEDLINE | ID: mdl-34776052

Among the various robotic devices that exist for urologic surgery, the most common are synergistic telemanipulator systems. Several have achieved clinical feasibility and have been licensed for use in humans: the standard da Vinci, Avatera, Hinotori, Revo-i, Senhance, Versius, and Surgenius. Handheld and hands-on synergistic systems are also clinically relevant for use in urologic surgeries, including minimally invasive and endoscopic approaches. Future trends of robotic innovation include an exploration of more robust haptic systems that offer kinesthetic and tactile feedback; miniaturization and microrobotics; enhanced visual feedback with greater magnification and higher fidelity detail; and autonomous robots.

Robotic Surgical Procedures/instrumentation , Robotics/history , Urologic Surgical Procedures/instrumentation , Feedback , History, 20th Century , History, 21st Century , Humans , Laparoscopy , Robotic Surgical Procedures/history , Terminology as Topic , Urologic Surgical Procedures/history , Urologic Surgical Procedures/methods
Stud Health Technol Inform ; 285: 153-158, 2021 Oct 27.
Article En | MEDLINE | ID: mdl-34734867

According to the "Istituto Superiore di Sanita'" (ISS), hospital infections are the most frequent and serious complication of health care. This constitutes a real health emergency which requires incisive and joint action at all levels of the local and national health organization. Most of the valuable information related to the presence of a specific microorganism in the blood are written into the notes field of the laboratory exams results. The main objective of this work is to build a Natural Language Processing (NLP) pipeline for the automatic extraction of the names of microorganisms present in the clinical texts. A sample of 499 microbiological notes have been analysed with the developed system and all the microorganisms names have been extracted correctly, according to the labels given by the expert.

Natural Language Processing , Terminology as Topic , Bacteria/classification , Delivery of Health Care , Electronic Health Records , Fungi/classification , Viruses/classification
Nat Commun ; 12(1): 5961, 2021 10 13.
Article En | MEDLINE | ID: mdl-34645806

Mutations play a fundamental role in the development of cancer, and many create targetable vulnerabilities. There are both public health and basic science benefits from the determination of the proportion of all cancer cases within a population that include a mutant form of a gene. Here, we provide the first such estimates by combining genomic and epidemiological data. We estimate KRAS is mutated in only 11% of all cancers, which is less than PIK3CA (13%) and marginally higher than BRAF (8%). TP53 is the most commonly mutated gene (35%), and KMT2C, KMT2D, and ARID1A are among the ten most commonly mutated driver genes, highlighting the role of epigenetic dysregulation in cancer. Analysis of major cancer subclassifications highlighted varying dependencies upon individual cancer drivers. Overall, we find that cancer genetics is less dominated by high-frequency, high-profile cancer driver genes than studies limited to a subset of cancer types have suggested.

Epigenesis, Genetic , Mutation Rate , Neoplasm Proteins/genetics , Neoplasms/epidemiology , Neoplasms/genetics , Computational Biology/methods , DNA-Binding Proteins/genetics , DNA-Binding Proteins/metabolism , Gene Expression Regulation, Neoplastic , Genetics, Population , Humans , Incidence , Neoplasm Proteins/classification , Neoplasm Proteins/metabolism , Neoplasms/classification , Neoplasms/pathology , Phosphatidylinositol 3-Kinases/genetics , Phosphatidylinositol 3-Kinases/metabolism , Proto-Oncogene Proteins B-raf/genetics , Proto-Oncogene Proteins B-raf/metabolism , Proto-Oncogene Proteins p21(ras)/genetics , Proto-Oncogene Proteins p21(ras)/metabolism , Terminology as Topic , Transcription Factors/genetics , Transcription Factors/metabolism , Tumor Suppressor Protein p53/genetics , Tumor Suppressor Protein p53/metabolism , United States/epidemiology
Arch Pathol Lab Med ; 145(11): 1367-1378, 2021 11 01.
Article En | MEDLINE | ID: mdl-34673912

CONTEXT.­: Endometrial carcinoma is the most common gynecologic malignancy in the United States and has been traditionally classified based on histology. However, the distinction of certain histologic subtypes based on morphology is not uncommonly problematic, and as such, immunohistochemical study is often needed. Advances in comprehensive tumor sequencing have provided novel molecular profiles of endometrial carcinomas. Four distinct molecular subtypes with different prognostic values have been proposed by The Cancer Genome Atlas program: polymerase epsilon ultramutated, microsatellite instability hypermutated, copy number low (microsatellite stable or no specific molecular profile), and copy number high (serouslike, p53 mutant). OBJECTIVE.­: To discuss the utilities of commonly used immunohistochemical markers for the classification of endometrial carcinomas and to review the recent advancements of The Cancer Genome Atlas molecular reclassification and their potential impact on treatment strategies. DATA SOURCES.­: Literature review and authors' personal practice experience. CONCLUSIONS.­: The current practice of classifying endometrial cancers is predominantly based on morphology. The use of ancillary testing, including immunohistochemistry, is helpful in the identification, differential diagnosis, and classification of these cancers. New developments such as molecular subtyping have provided insightful prognostic values for endometrial carcinomas. The proposed The Cancer Genome Atlas classification is poised to gain further prominence in guiding the prognostic evaluation for tailored treatment strategies in the near future.

Biomarkers, Tumor , Carcinoma/diagnosis , Endometrial Neoplasms/diagnosis , Immunohistochemistry , Molecular Diagnostic Techniques , Biomarkers, Tumor/analysis , Biomarkers, Tumor/genetics , Carcinoma/chemistry , Carcinoma/genetics , Carcinoma/pathology , DNA Copy Number Variations , DNA Polymerase II/genetics , Endometrial Neoplasms/chemistry , Endometrial Neoplasms/genetics , Endometrial Neoplasms/pathology , Female , Gene Dosage , Humans , Microsatellite Instability , Mutation , Poly-ADP-Ribose Binding Proteins/genetics , Predictive Value of Tests , Prognosis , Terminology as Topic , Tumor Suppressor Protein p53/genetics
Am J Hum Genet ; 108(10): 1813-1816, 2021 10 07.
Article En | MEDLINE | ID: mdl-34626580

The use of approved nomenclature in publications is vital to enable effective scientific communication and is particularly crucial when discussing genes of clinical relevance. Here, we discuss several examples of cases where the failure of researchers to use a HUGO Gene Nomenclature Committee (HGNC)-approved symbol in publications has led to confusion between unrelated human genes in the literature. We also inform authors of the steps they can take to ensure that they use approved nomenclature in their manuscripts and discuss how referencing HGNC IDs can remove ambiguity when referring to genes that have previously been published with confusing alias symbols.

Databases, Genetic/standards , Genes/genetics , Genome, Human , Research Personnel/standards , Terminology as Topic , Genomics , Humans
OMICS ; 25(11): 681-692, 2021 11.
Article En | MEDLINE | ID: mdl-34678084

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Computational Biology/trends , Genomics/trends , Metabolomics/trends , Proteomics/trends , Animals , COVID-19 , Computer Graphics , Epigenomics/trends , Gene Expression Profiling/trends , Humans , Pharmacogenetics/trends , Publications , SARS-CoV-2 , Terminology as Topic
Nat Commun ; 12(1): 5319, 2021 09 07.
Article En | MEDLINE | ID: mdl-34493718

Modern machine learning (ML) technologies have great promise for automating diverse clinical and research workflows; however, training them requires extensive hand-labelled datasets. Disambiguating abbreviations is important for automated clinical note processing; however, broad deployment of ML for this task is restricted by the scarcity and imbalance of labeled training data. In this work we present a method that improves a model's ability to generalize through novel data augmentation techniques that utilizes information from biomedical ontologies in the form of related medical concepts, as well as global context information within the medical note. We train our model on a public dataset (MIMIC III) and test its performance on automatically generated and hand-labelled datasets from different sources (MIMIC III, CASI, i2b2). Together, these techniques boost the accuracy of abbreviation disambiguation by up to 17% on hand-labeled data, without sacrificing performance on a held-out test set from MIMIC III.

Data Mining/methods , Deep Learning , Terminology as Topic , Biomedical Research , Datasets as Topic , Humans
Nat Commun ; 12(1): 5556, 2021 09 21.
Article En | MEDLINE | ID: mdl-34548483

Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.

Cell Lineage/genetics , Eukaryotic Cells/classification , Software , Terminology as Topic , Vocabulary, Controlled , Algorithms , Animals , Biomarkers/metabolism , Datasets as Topic , Gene Expression , Humans