Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Animal Sciences
Title: Levetiracetam as an adjunct to phenobarbital treatment in cats with suspected idiopathic epilepsy
Abstract: OBJECTIVE To assess pharmacokinetics, efficacy, and tolerability of oral levetiracetam administered as an adjunct to phenobarbital treatment in cats with poorly controlled suspected idiopathic epilepsy. DESIGN-Open-label, noncomparative clinical trial. ANIMALS 12 cats suspected to have idiopathic epilepsy that was poorly controlled with phenobarbital or that had unacceptable adverse effects when treated with phenobarbital. PROCEDURES Cats were treated with levetiracetam (20 mg/kg [9.1 mg/lb], PO, q 8 h). After a minimum of 1 week of treatment, serum levetiracetam concentrations were measured before and 2, 4, and 6 hours after drug administration, and maximum and minimum serum concentrations and elimination half-life were calculated. Seizure frequencies before and after initiation of levetiracetam treatment were compared, and adverse effects were recorded. RESULTS Median maximum serum levetiracetam concentration was 25.5 microg/mL, median minimum serum levetiracetam concentration was 8.3 microg/mL, and median elimination half-life was 2.9 hours. Median seizure frequency prior to treatment with levetiracetam (2.1 seizures/mo) was significantly higher than median seizure frequency after initiation of levetiracetam treatment (0.42 seizures/mo), and 7 of 10 cats were classified as having responded to levetiracetam treatment (ie, reduction in seizure frequency of >or=50%). Two cats had transient lethargy and inappetence. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that levetiracetam is well tolerated in cats and may be useful as an adjunct to phenobarbital treatment in cats with idiopathic epilepsy.
Extracted statement — Selective reporting: high
Research field: Animal Sciences
Title: Pharmacokinetics of phenobarbital in the cat following intravenous and oral administration
Abstract: Phenobarbital was administered to eight healthy cats as a single intravenous dose of 10 mg/kg. Serum phenobarbital concentrations were determined using an immunoassay technique. The intravenous data were fitted to one-, two- and three-compartment models. After statistical comparison of the three models, a two-compartment model was selected. Following intravenous administration, the drug was rapidly distributed (distribution half-life = 0.046 +/- 0.007 h) with a large apparent volume of distribution (931 +/- 44.8 mL/kg). Subsequent elimination of phenobarbital from the body was slow (elimination half-life = 58.8 +/- 4.21 h). Three weeks later, a single oral dose of phenobarbital (10 mg/kg) was administered to the same group of cats. A one-compartment model with an input component was used to describe the results. After oral administration, the initial rapid absorption phase (absorption half-life = 0.382 +/- 0.099 h) was followed by a plateau in the serum concentration (13.5 +/- 0.148 micrograms/mL) for approximately 10 h. The half-life of the terminal elimination phase (76.1 +/- 6.96 h) was not significantly different from the half-life determined for the intravenous route. Bioavailability of the oral drug was high (F = 1.20 +/- 0.120). Based on the pharmacokinetic parameters determined in this study, phenobarbital appears to be a suitable drug for use as an anticonvulsant in the cat.
Extracted statement — Selective reporting: high
Research field: Animal Sciences
Title: Pharmacokinetics and toxicity of zonisamide in cats
Abstract: With the eventual goal of making zonisamide (ZNS), a relatively new antiepileptic drug, available for the treatment of epilepsy in cats, the pharmacokinetics after a single oral administration at 10 mg/kg and the toxicity after 9-week daily administration of 20 mg/kg/day of ZNS were studied in healthy cats. Pharmacokinetic parameters obtained with a single administration of ZNS at 10 mg/day were as follows: C max =13.1 μg/ml; T max =4.0 h; T 1/2 =33.0 h; areas under the curves (AUCs)=720.3 μg/mlh (values represent the medians). The study with daily administrations revealed that the toxicity of ZNS was comparatively low in cats, suggesting that it may be an available drug for cats. However, half of the cats that were administered 20 mg/kg/day daily showed adverse reactions such as anorexia, diarrhoea, vomiting, somnolence and locomotor ataxia.
Extracted statement — Incomplete outcome   data: low
Research field: Animal Sciences
Title: Pharmacokinetics of phenobarbital in the cat following intravenous and oral administration
Abstract: Phenobarbital was administered to eight healthy cats as a single intravenous dose of 10 mg/kg. Serum phenobarbital concentrations were determined using an immunoassay technique. The intravenous data were fitted to one-, two- and three-compartment models. After statistical comparison of the three models, a two-compartment model was selected. Following intravenous administration, the drug was rapidly distributed (distribution half-life = 0.046 +/- 0.007 h) with a large apparent volume of distribution (931 +/- 44.8 mL/kg). Subsequent elimination of phenobarbital from the body was slow (elimination half-life = 58.8 +/- 4.21 h). Three weeks later, a single oral dose of phenobarbital (10 mg/kg) was administered to the same group of cats. A one-compartment model with an input component was used to describe the results. After oral administration, the initial rapid absorption phase (absorption half-life = 0.382 +/- 0.099 h) was followed by a plateau in the serum concentration (13.5 +/- 0.148 micrograms/mL) for approximately 10 h. The half-life of the terminal elimination phase (76.1 +/- 6.96 h) was not significantly different from the half-life determined for the intravenous route. Bioavailability of the oral drug was high (F = 1.20 +/- 0.120). Based on the pharmacokinetic parameters determined in this study, phenobarbital appears to be a suitable drug for use as an anticonvulsant in the cat.
Extracted statement — Incomplete outcome   data: low
Research field: Animal Sciences
Title: Pharmacokinetics of phenobarbital in the cat following multiple oral administration
Abstract: Phenobarbital was administered orally to seven healthy cats at a dose of 5 mg/kg once a day for 21 days. Serum phenobarbital concentrations were determined using a commercial immunoassay technique. A one-compartment model was used to describe the final elimination curve. The elimination half-life (t1/2 b) after the final day of treatment was 43.3 +/- 2.92 h. The large apparent volume of distribution of 695.0 +/- 43.9 mL/kg suggests that the drug was widely distributed within the body. The t1/2 b following multiple oral administration was significantly shorter than previously reported for a single oral dose of phenobarbital in the cat. Analysis of pharmacokinetic results after days 1 and 21 of treatment suggested that the elimination kinetics of phenobarbital did not change significantly with multiple oral administration. It appears that differences in elimination kinetics can exist between populations of cats. These differences emphasize the need for individual monitoring of cats receiving phenobarbital.
Extracted statement — Incomplete outcome   data: low
Research field: Animal Sciences
Title: Levetiracetam in the management of feline audiogenic reflex seizures: a randomised, controlled, open-label study
Abstract: <jats:sec><jats:title>Objectives</jats:title><jats:p> Currently, there are no published randomised, controlled veterinary trials evaluating the efficacy of antiepileptic medication in the treatment of myoclonic seizures. Myoclonic seizures are a hallmark of feline audiogenic seizures (FARS). </jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p> This prospective, randomised, open-label trial compared the efficacy and tolerability of levetiracetam (20–25 mg/kg q8h) with phenobarbital (3–5 mg/kg q12h) in cats with suspected FARS that experienced myoclonic seizures. Cats were included that had ⩾12 myoclonic seizure days during a prospective 12 week baseline period. This was followed by a 4 week titration phase (until a therapeutic serum concentration of phenobarbital was achieved) and a 12 week treatment phase. </jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p> Fifty-seven cats completed the study: 28 in the levetiracetam group and 29 in the phenobarbital group. A reduction of ⩾50% in the number of myoclonic seizure days was seen in 100% of patients in the levetiracetam group and in 3% of patients in the phenobarbital group ( P &lt;0.001) during the treatment period. Levetiracetam-treated cats had higher freedom from myoclonic seizures (50.0% vs 0%; P &lt;0.001) during the treatment period. The most common adverse events were lethargy, inappetence and ataxia, with no difference in incidence between levetiracetam and phenobarbital. Adverse events were mild and transient with levetiracetam but persistent with phenobarbital. </jats:p></jats:sec><jats:sec><jats:title>Conclusions and relevance</jats:title><jats:p> These results suggest that levetiracetam is an effective and well tolerated treatment for cats with myoclonic seizures and is more effective than phenobarbital. Whether it will prevent the occurrence of generalised tonic–clonic seizures and other forebrain signs if used early in the course of FARS is not yet clear. </jats:p></jats:sec>
Extracted statement — Randomization: low
Research field: Animal Sciences
Title: Levetiracetam in the management of feline audiogenic reflex seizures: a randomised, controlled, open-label study
Abstract: <jats:sec><jats:title>Objectives</jats:title><jats:p> Currently, there are no published randomised, controlled veterinary trials evaluating the efficacy of antiepileptic medication in the treatment of myoclonic seizures. Myoclonic seizures are a hallmark of feline audiogenic seizures (FARS). </jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p> This prospective, randomised, open-label trial compared the efficacy and tolerability of levetiracetam (20–25 mg/kg q8h) with phenobarbital (3–5 mg/kg q12h) in cats with suspected FARS that experienced myoclonic seizures. Cats were included that had ⩾12 myoclonic seizure days during a prospective 12 week baseline period. This was followed by a 4 week titration phase (until a therapeutic serum concentration of phenobarbital was achieved) and a 12 week treatment phase. </jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p> Fifty-seven cats completed the study: 28 in the levetiracetam group and 29 in the phenobarbital group. A reduction of ⩾50% in the number of myoclonic seizure days was seen in 100% of patients in the levetiracetam group and in 3% of patients in the phenobarbital group ( P &lt;0.001) during the treatment period. Levetiracetam-treated cats had higher freedom from myoclonic seizures (50.0% vs 0%; P &lt;0.001) during the treatment period. The most common adverse events were lethargy, inappetence and ataxia, with no difference in incidence between levetiracetam and phenobarbital. Adverse events were mild and transient with levetiracetam but persistent with phenobarbital. </jats:p></jats:sec><jats:sec><jats:title>Conclusions and relevance</jats:title><jats:p> These results suggest that levetiracetam is an effective and well tolerated treatment for cats with myoclonic seizures and is more effective than phenobarbital. Whether it will prevent the occurrence of generalised tonic–clonic seizures and other forebrain signs if used early in the course of FARS is not yet clear. </jats:p></jats:sec>
Extracted statement — Selective reporting: low
Research field: Animal Sciences
Title: Treatment and long-term follow-up of cats with suspected primary epilepsy
Abstract: We report an evaluation of the treatment and outcome of cats with suspected primary epilepsy. Phenobarbital therapy was used alone or in combination with other anti-epileptic drugs. Outcome after treatment was evaluated mainly on the basis of number of seizures per year and categorised into four groups: seizure-free, good control (1–5 seizures per year), moderate control (6–10 seizures per year) and poor control (more than 10 seizures per year). About 40–50% of cases became seizure-free, 20–30% were considered good-to-moderately controlled and about 30% were poorly controlled depending on the year of treatment considered. The duration of seizure events after treatment decreased in 26/36 cats and was unchanged in eight cats. The subjective severity of seizure also decreased in 25 cats and was unchanged in nine cats. Twenty-six cats had a good quality of life, nine cats an impaired quality of life and one cat a bad quality of life. Despite being free of seizures for years, cessation of treatment may lead to recurrence of seizures in most cats.
Extracted statement — Study groups: moderate
Research field: Animal Sciences
Title: Clinical characterization of epilepsy of unknown cause in cats
Abstract: Background The diagnosis of feline epilepsy of unknown cause (EUC) requires a thorough diagnostic evaluation, otherwise the prevalence of EUC could be overestimated. Hypothesis Feline EUC is a clinically defined disease entity, which differs from feline hippocampal necrosis by the absence of magnetic resonance imaging (MRI) signal alteration of the hippocampus. The objectives of this study were (1) to evaluate the prevalence of EUC in a hospital population of cats by applying well‐defined inclusion criteria, and (2) to describe the clinical course of EUC. Animals Eighty‐one cats with recurrent seizures. Methods Retrospective study—medical records were reviewed for cats presented for evaluation of recurrent seizures (2005–2010). Inclusion criteria were a defined diagnosis based on laboratory data, and either MRI or histopathology. Final outcome was confirmed by telephone interview with the owner. Magnetic resonance images were reviewed to evaluate hippocampal morphology and signal alterations. Results Epilepsy of unknown cause was diagnosed in 22% of cats with epilepsy. Physical, neurologic, and laboratory examinations, and either 1.5 T MRI and cerebrospinal fluid analysis or postmortem examination failed to identify an underlying cause. Cats with EUC had a higher survival rate (P < .05) and seizure remission occurred frequently (44.4%). Conclusion and Clinical Importance A detailed clinical evaluation and diagnostic imaging with MRI is recommended in any cat with recurrent seizures. The prognosis of cats with normal MRI findings and a clinical diagnosis of EUC are good. Standardized imaging guidelines should be established to assess the hippocampus in cats.
Extracted statement — Disease definitions (characterization): well
Research field: Artificial Intelligence
Title: Building ontologies from XML data sources
Abstract: In this paper, we present a tool called X2OWL that aims at building an OWL ontology from an XML datasource. This method is based on XML schema to automatically generate the ontology structure, as well as, a set of mapping bridges. The presented method also includes a refinement step that allows to clean the mapping bridges and possibly to restructure the generated ontology.
Extracted statement — rule: Automatic
Research field: Artificial Intelligence
Title: Transforming XML schema to OWL using patterns
Abstract: One of the promises of the Semantic Web is to support applications that easily and seamlessly deal with heterogeneous data. Most data on the Web, however, is in the Extensible Markup Language (XML) format, but using XML requires applications to understand the format of each data source that they access. To achieve the benefits of the Semantic Web involves transforming XML into the Semantic Web language, OWL (Ontology Web Language), a process that generally has manual or only semi-automatic components. In this paper we present a set of patterns that enable the direct, automatic transformation from XML Schema into OWL allowing the integration of much XML data in the Semantic Web. We focus on an advanced logical representation of XML Schema components and present an implementation, including a comparison with related work.
Extracted statement — rule: Automatic
Research field: Artificial Intelligence
Title: An efficient XML to OWL converter
Abstract: XML has become the de-facto standard of data exchange format in E-businesses. Although XML can support syntactic inter-operability, problems arise when data sources represented as XML documents are needed to be integrated. The reason is that XML lacks support for efficient sharing of conceptualization. The Web Ontology Language (OWL) can play an important role here as it can enable semantic inter-operability, and it supports the representation of domain knowledge using classes, properties and instances for applications. In many applications it is required to convert huge XML documents automatically to OWL ontologies, which is receiving a lot of attention. There are some existing converters for this job. Unfortunately they have serious shortcomings, e. g., they do not address the handling of characteristics like internal references, (transitive) import(s), include etc. which are commonly used in XML Schemas. To alleviate these drawbacks, we propose a new framework for mapping XML to OWL automatically. We illustrate our technique on examples to show the efficacy of our approach. We also provide the performance measures of our approach on some standard datasets. We also check the correctness of the conversion process.
Extracted statement — rule: Automatic
Research field: Artificial Intelligence
Title: Automatic generation of OWL ontology from XML data source
Abstract: The eXtensible Markup Language (XML) can be used as data exchange format in different domains. It allows different parties to exchange data by providing common understanding of the basic concepts in the domain. XML covers the syntactic level, but lacks support for reasoning. Ontology can provide a semantic representation of domain knowledge which supports efficient reasoning and expressive power. One of the most popular ontology languages is the Web Ontology Language (OWL). It can represent domain knowledge using classes, properties, axioms and instances for the use in a distributed environment such as the World Wide Web. This paper presents a new method for automatic generation of OWL ontology from XML data sources.
Extracted statement — rule: Automatic
Research field: Artificial Intelligence
Title: Automatic transforming XML documents into OWL Ontology
Abstract: DTD and its instance have been considered the standard for data representation and information exchange format on the current web. However, when coming to the next generation of web, the Semantic Web, the drawbacks of XML and its schema are appeared. They mainly focus on the structure level and lack support for data representation. Meanwhile, some Semantic Web applications such as intelligent information services and semantic search engines require not only the syntactic format of the data, but also the semantic content. These requirements are supported by the Web Ontology Language (OWL), which is one of the recent W3C recommendation. But nowadays the amount of data presented in OWL is small in compare with XML data. Therefore, finding a way to utilize the available XML documents for the Semantic Web is a current challenge research. In this work we present an effective solution for transforming XML document into OWL domain knowledge. While keeping the original structure, our work also adds more semantics for the XML document. Moreover, whole of the transformation processes are done automatically without any outside intervention. Further, unlike previous approaches which focus on the schema level, we also extend our methodology for the data level by transforming specific XML instances into OWL individuals. The results in existing OWL syntaxes help them to be loaded immediately by the Semantic Web applications.
Extracted statement — rule: Automatic
Research field: Artificial Intelligence
Title: A framework for explainable deep neural models using external knowledge graphs
Abstract: Deep neural networks (DNNs) have become the gold standard for solving challenging classification problems, especially given complex sensor inputs (e.g., images and video). While DNNs are powerful, they are also brittle, and their inner workings are not fully understood by humans, leading to their use as “black-box” models. DNNs often generalize poorly when provided new data sampled from slightly shifted distributions; DNNs are easily manipulated by adversarial examples; and the decision-making process of DNNs can be difficult for humans to interpret. To address these challenges, we propose integrating DNNs with external sources of semantic knowledge. Large quantities of meaningful, formalized knowledge are available in knowledge graphs and other databases, many of which are publicly obtainable. But at present, these sources are inaccessible to deep neural methods, which can only exploit patterns in the signals they are given to classify. In this work, we conduct experiments on the ADE20K dataset, using scene classification as an example task where combining DNNs with external knowledge graphs can result in more robust and explainable models. We align the atomic concepts present in ADE20K (i.e., objects) to WordNet, a hierarchically-organized lexical database. Using this knowledge graph, we expand the concept categories which can be identified in ADE20K and relate these concepts in a hierarchical manner. The neural architecture we present performs scene classification using these concepts, illuminating a path toward DNNs which can efficiently exploit high-level knowledge in place of excessive quantities of direct sensory input. We hypothesize and experimentally validate that incorporating background knowledge via an external knowledge graph into a deep learning-based model should improve the explainability and robustness of the model.
Extracted statement — Machine Learning Model Integration: external
Research field: Artificial Intelligence
Title: Fvqa: Fact-based visual question answering
Abstract: Visual Question Answering (VQA) has attracted much attention in both computer vision and natural language processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited. It excludes questions which require common sense, or basic factual knowledge to answer, for example. Here we introduce FVQA (Fact-based VQA), a VQA dataset which requires, and supports, much deeper reasoning. FVQA primarily contains questions that require external information to answer. We thus extend a conventional visual question answering dataset, which contains image-question-answer triplets, through additional image-question-answer-supporting fact tuples. Each supporting-fact is represented as a structural triplet, such as <Cat,CapableOf,ClimbingTrees>. We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting-facts.
Extracted statement — Machine Learning Model Integration: external