Žiberna, Klemen medicina
Works: | 13 works in 14 publications in 2 languages and 29 library holdings |
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Roles: | Author, Editor |
1 edition published in 2009 in Slovenian and held by 6 WorldCat member libraries worldwide
2 editions published in 2003 in Slovenian and held by 4 WorldCat member libraries worldwide
1 edition published in 2009 in Slovenian and held by 3 WorldCat member libraries worldwide
1 edition published in 2002 in Slovenian and held by 3 WorldCat member libraries worldwide
1 edition published in 2004 in Slovenian and held by 2 WorldCat member libraries worldwide
1 edition published in 2008 in Slovenian and held by 2 WorldCat member libraries worldwide
1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide
Background: Adverse systemic reactions (SRs) are more common during the honeybee then the wasp venom immunotherapy (VIT). Our aim was to evaluate risk factors for SRs during the build-up phase of honeybee venom immunotherapy
1 edition published in 2014 in English and held by 2 WorldCat member libraries worldwide
Background: Honeybee venom-allergic patients are at a greater risk of a systemic reaction for subsequent insect sting than those with Vespula venom allergy. Our aim was to evaluate risk factors which could contribute to the severity of honeybee field sting anaphylactic reaction
1 edition published in 2017 in English and held by 1 WorldCat member library worldwide
Transcriptionally activated monocytes are recruited to the heart after acute myocardial infarction (AMI). After AMI in mice and humans, the number of extracellular vesicles (EVs) increased acutely. In humans, EV number correlated closely with the extent of myocardial injury. We hypothesized that EVs mediate splenic monocyte mobilization and program transcription following AMI. Some plasma EVs bear endothelial cell (EC) integrins, and both proinflammatory stimulation of ECs and AMI significantly increased VCAM-1-positive EV release. Injected EC-EVs localized to the spleen and interacted with, and mobilized, splenic monocytes in otherwise naive, healthy animals. Analysis of human plasma EV-associated miRNA showed 12 markedly enriched miRNAs after AMI; functional enrichment analyses identified 1,869 putative mRNA targets, which regulate relevant cellular functions (e.g., proliferation and cell movement). Furthermore, gene ontology termed positive chemotaxis as the most enriched pathway for the miRNA-mRNA targets. Among the identified EV miRNAs, EC-associated miRNA-126-3p and -5p were highly regulated after AMI. miRNA-126-3p and -5p regulate cell adhesion- and chemotaxis-associated genes, including the negative regulator of cell motility, plexin-B2. EC-EV exposure significantly downregulated plexin-B2 mRNA in monocytes and upregulated motility integrin ITGB2. These findings identify EVs as a possible novel signaling pathway by linking ischemic myocardium with monocyte mobilization and transcriptional activation following AMI
1 edition published in 2020 in English and held by 1 WorldCat member library worldwide
1 edition published in 2004 in Slovenian and held by 1 WorldCat member library worldwide
1 edition published in 2020 in English and held by 1 WorldCat member library worldwide
Objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia, with an estimated prevalence of around 1.6% in the adult population. The analysis of the electrocardiogram (ECG) data acquired in the UK Biobank represents an opportunity to screen for AF in a large sub-population in the UK. The main objective of this paper is to assess ten machine-learning methods for automated detection of subjects with AF in the UK Biobank dataset. Approach: Six classical machine-learning methods based on support vector machines are proposed and compared with state-of-the-art techniques (including a deep-learning algorithm), and finally a combination of a classical machine-learning and deep learning approaches. Evaluation is carried out on a subset of the UK Biobank dataset, manually annotated by human experts. Main results: The combined classical machine-learning and deep learning method achieved an F1 score of 84.8% on the test subset, and a Cohen's kappa coefficient of 0.83, which is similar to the inter-observer agreement of two human experts. Significance: The level of performance indicates that the automated detection of AF in patients whose data have been stored in a large database, such as the UK Biobank, is possible. Such automated identification of AF patients would enable further investigations aimed at identifying the different phenotypes associated with AF
1 edition published in 2015 in English and held by 1 WorldCat member library worldwide


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- Kodba, Stane Thesis advisor
- Klanjšček, Jure Editor
- Godec, Darjan Editor
- Kos, Jure medicina Editor
- Orel, Jure Editor
- Korać, Tina Editor
- Trdan, Dejan Editor
- Gantar, Melita Editor
- Triglav, Jure 1986- Editor
- Vrečar, Mojca