Refine
Document Type
- Article (12)
- Doctoral Thesis (2)
- Conference Proceeding (1)
Has Fulltext
- yes (15)
Is part of the Bibliography
- no (15)
Keywords
- Amblyopia (3)
- Dose response (3)
- Eccentric fixation (3)
- Efficiency (3)
- Occlusion treatment (3)
- inflammation (2)
- 5-lipoxygenase (1)
- ABC transporter (1)
- ARDS (1)
- COVID-19 (1)
Institute
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
Leukotrienes (LTs) are inflammatory mediators that play a pivotal role in many diseases like asthma bronchiale, atherosclerosis and in various types of cancer. The key enzyme for generation of LTs is the 5-lipoxygenase (5-LO). Here, we present a novel putative protein isoform of human 5-LO that lacks exon 4, termed 5-LOΔ4, identified in cells of lymphoid origin, namely the Burkitt lymphoma cell lines Raji and BL41 as well as primary B and T cells. Deletion of exon 4 does not shift the reading frame and therefore the mRNA is not subjected to non-mediated mRNA decay (NMD). By eliminating exon 4, the amino acids Trp144 until Ala184 are omitted in the corresponding protein. Transfection of HEK293T cells with a 5-LOΔ4 expression plasmid led to expression of the corresponding protein which suggests that the 5-LOΔ4 isoform is a stable protein in eukaryotic cells. We were also able to obtain soluble protein after expression in E. coli and purification. The isoform itself lacks canonical enzymatic activity as it misses the non-heme iron but it still retains ATP-binding affinity. Differential scanning fluorimetric analysis shows two transitions, corresponding to the two domains of 5-LO. Whilst the catalytic domain of 5-LO WT is destabilized by calcium, addition of calcium has no influence on the catalytic domain of 5-LOΔ4. Furthermore, we investigated the influence of 5-LOΔ4 on the activity of 5-LO WT and proved that it stimulates 5-LO product formation at low protein concentrations. Therefore regulation of 5-LO by its isoform 5-LOΔ4 might represent a novel mechanism of controlling the biosynthesis of lipid mediators.