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Background:
Recently, we gained evidence that impairment of rOat1 and rOat3 expression induced by ischemic acute kidney injury (AKI) is mediated by COX metabolites and this suppression might be critically involved in renal damage.
Methods:
(i) Basolateral organic anion uptake into proximal tubular cells after model ischemia and reperfusion (I/R) was investigated by fluorescein uptake. The putative promoter sequences from hOAT1 (SLC22A6) and hOAT3 (SCL22A8) were cloned into a reporter plasmid, transfected into HEK cells and (ii) transcriptional activity was determined after model ischemia and reperfusion as a SEAP reporter gen assay. Inhibitors or antagonists were applied with the beginning of reperfusion.
Results:
By using inhibitors of PKA (H89) and PLC (U73122), antagonists of E prostanoid receptor type 2 (AH6809) and type 4 (L161,982), we gained evidence that I/R induced down regulation of organic anion transport is mediated by COX1 metabolites via E prostanoid receptor type 4. The latter signaling was confirmed by application of butaprost (EP2 agonist) or TCS2510 (EP4 agonist) to control cells. In brief, the latter signaling was verified for the transcriptional activity in the reporter gen assay established. Therein, selective inhibitors for COX1 (SC58125) and COX2 (SC560) were also applied.
Conclusion:
Our data show (a) that COX1 metabolites are involved in the regulation of renal organic anion transport(ers) after I/R via the EP4 receptor and (b) that this is due to transcriptional regulation of the respective transporters. As the promoter sequences cloned were of human origin and expressed in a human renal epithelial cell line we (c) hypothesize that the regulatory mechanisms described after I/R is meaningful for humans as well.
Context:
Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2–11% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values.
Objective:
Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy.
Design:
Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19–84 yr) and 45 ACC patients (20–80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26–201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids.
Results:
Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88% (area under the curve = 0.96) when using only the nine most differentiating markers.
Conclusions:
Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.
Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.