Η σκιαγράφηση των στενών συνεργατών του Ιουστινιανού (Τριβωνιανός, Ιωάννης Καππαδόκης, Βελισάριος, Ναρσής, Μούνδος) στο έργο των Ιωάννη Λυδού, Ιωάννη Μαλάλα, Προκοπίου Καισαρείας και Αγαθία Σχολαστικού

Περιγραφή προσωπικότητος του Τριβωνιανού Ιωάννη Καππαδόκη, Βελισαρίου, Ναρσή και Μούνδου σύμφωνα με το έργο των Ιωάννη Λυδού, Ιωάννη Μαλάλα, Προκοπίου Καισαρείας και Αγαθία Σχολαστικού.Description of the personality of Trivonianos, Ioannis Kappadokis, Βelisarios, Narsis and Moundos according to the work of Ioannis Lydos, Ioannis Malalas, Prokopios Kaisareias and Agathias Scholastikos

Boosting Drug Named Entity Recognition using an Aggregate Classifier

AbstractObjectiveDrug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations.MethodsWe perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition.MaterialsOur approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers.ResultsAggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names.ConclusionWe conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations

Immune responses of patients on maintenance hemodialysis after infection by SARS-CoV-2: a prospective observational cohort study

Abstract Background Immune dysregulation in patients with acute COVID-19 under chronic hemodialysis (CHD) is fully not elucidated. The changes of mononuclear counts and mediators before and after HD and associations with final outcome were studied. Method In this prospective study, hospitalized patients with moderate-to-severe COVID-19 under CHD and matched comparators under HD were analyzed for their absolute counts of lymphoid cells and circulating inflammatory mediators. Blood samples were collected before start and at the end of the first HD session; dialysate samples were also collected. Result Fifty-nine patients with acute COVID-19 under CHD and 20 uninfected comparators under CHD were enrolled. Circulating concentrations of tumor necrosis factor-alpha (TNFα), interleukin (IL)-10, interferon-γ and platelet-derived growth factor-A were increased in patients. Concentrations of mediators did not differ before and after HD. Significant decreases of CD4-lymphocytes and CD19-lymphocytes were found in patients. The decrease of the expression of HLA-DR on CD14-monocytes was associated with unfavorable outcome (defined as WHO-CPS 6 or more by day 28); increased counts of CD19-lymphocytes were associated with better outcomes. Conclusion Patients under CHD develop an inflammatory reaction to SARS-CoV-2 characterized by increase of inflammatory mediators, decrease of circulating T-lymphocytes and decrease of the expression of HLA-DR on CD14-monocytes