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- COVID‐19 vaccination (1)
- SARS‐CoV‐2 infection (1)
- adrenal cortex hormones (1)
- adrenal cortex neoplasms (1)
- anti‐SARS‐CoV‐2‐spike IgG (1)
- healthcare workers (1)
- mass spectrometry (1)
- metabolomics (1)
- seroprevalence (1)
- urine (1)
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EU-Project number / Contract (GA) number
- 259735 (1)
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.
Against the background of the current COVID-19 infection dynamics with its rapid spread of SARS-CoV-2 variants of concern (VOC), the immunity and the vaccine prevention of healthcare workers (HCWs) against SARS-CoV-2 continues to be of high importance. This observational cross-section study assesses factors influencing the level of anti-SARS-CoV-2-spike IgG after SARS-CoV-2 infection or vaccination. One thousand seven hundred and fifty HCWs were recruited meeting the following inclusion criteria: age ≥18 years, PCR-confirmed SARS-CoV-2 infection convalescence and/or at least one dose of COVID-19 vaccination. anti-SARS-CoV-2-spike IgG titers were determined by SERION ELISA agile SARS-CoV-2 IgG. Mean anti-SARS-CoV-2-spike IgG levels increased significantly by number of COVID-19 vaccinations (92.2 BAU/ml for single, 140.9 BAU/ml for twice and 1144.3 BAU/ml for threefold vaccination). Hybrid COVID-19 immunized respondents (after infection and vaccination) had significantly higher antibody titers compared with convalescent only HCWs. Anti-SARS-CoV-2-spike IgG titers declined significantly with time after the second vaccination. Smoking and high age were associated with lower titers. Both recovered and vaccinated HCWs presented a predominantly good humoral immune response. Smoking and higher age limited the humoral SARS-CoV-2 immunity, adding to the risk of severe infections within this already health impaired collective.