Background: Homologous and heterologous SARS‐CoV‐2 vaccinations yield different spike protein‐directed humoral and cellular immune responses. This study aimed to explore their currently unknown interdependencies.Methods: COV‐ADAPT is a prospective, observational cohort study of 417 healthcare workers who received vaccination with homologous ChAdOx1 nCoV‐19, homologous BNT162b2 or with heterologous ChAdOx1 nCoV‐19/BNT162b2. We assessed humoral (anti‐spike‐RBD‐IgG, neutralizing antibodies, and avidity) and cellular (spike‐induced T‐cell interferon‐γ release) immune responses in blood samples up to 2 weeks before (T1) and 2–12 weeks following secondary immunization (T2).Results: Initial vaccination with ChAdOx1 nCoV‐19 resulted in lower anti‐spike‐RBD‐IgG compared with BNT162b2 (70 ± 114 vs. 226 ± 279 BAU/ml, p < .01) at T1. Booster vaccination with BNT162b2 proved superior to ChAdOx1 nCoV‐19 at T2 (anti‐spike‐RBD‐IgG: ChAdOx1 nCoV‐19/BNT162b2 2387 ± 1627 and homologous BNT162b2 3202 ± 2184 vs. homologous ChAdOx1 nCoV‐19 413 ± 461 BAU/ml, both p < .001; spike‐induced T‐cell interferon‐γ release: ChAdOx1 nCoV‐19/BNT162b2 5069 ± 6733 and homologous BNT162b2 4880 ± 7570 vs. homologous ChAdOx1 nCoV‐19 1152 ± 2243 mIU/ml, both p < .001). No significant differences were detected between BNT162b2‐boostered groups at T2. For ChAdOx1 nCoV‐19, no booster effect on T‐cell activation could be observed. We found associations between anti‐spike‐RBD‐IgG levels (ChAdOx1 nCoV‐19/BNT162b2 and homologous BNT162b2) and T‐cell responses (homologous ChAdOx1 nCoV‐19 and ChAdOx1 nCoV‐19/BNT162b2) from T1 to T2. Additionally, anti‐spike‐RBD‐IgG and T‐cell response were linked at both time points (all groups combined). All regimes yielded neutralizing antibodies and increased antibody avidity at T2.Conclusions: Interdependencies between humoral and cellular immune responses differ between common SARS‐CoV‐2 vaccination regimes. T‐cell activation is unlikely to compensate for poor humoral responses.

Article Differences in procalcitonin measurements between three BRAHMS-partnered immunoassays (Liaison, Elecsys and Architect) was published on September 1, 2019 in the journal Clinical Chemistry and Laboratory Medicine (CCLM) (volume 57, issue 9).

Anti-SARS-CoV-2-specific serological responses are a topic of ongoing evaluation studies. In the study presented here, the anti-SARS-CoV-2 surrogate neutralization assays by TECOmedical and DiaPROPH -Med were assessed in a head-to-head comparison with serum samples of individuals after vaccination as well as after previous infection with SARS-CoV-2. In case of discordant results, a cell culture-based neutralization assay was applied as a reference standard. The TECOmedical assay showed sensitivity and specificity of 100% and 61.3%, respectively, the DiaPROPH-Med assay 95.0% and 48.4%, respectively. As a side finding of the study, differences in the likelihood of expressing neutralizing antibodies could be shown for different exposition types. So, 60 of 81 (74.07%) of the samples with only one vaccination showed an expression of neutralizing antibodies in contrast to 85.71% (60 of 70 samples) of the samples with two vaccinations and 100% (40 of 40) of the samples from previously infected individuals. In conclusion, the both assays showed results similar to previous assessments. While the measured diagnostic accuracy of both assays requires further technical improvement of this diagnostic approach, as the calculated specificity values of 61.3% and 48.4%, respectively, appear acceptable for diagnostic use only in populations with a high percentage of positive subjects, but not at expectedly low positivity rates.

Severe aortic stenosis (AS) is a common pathological condition in an ageing population imposing significant morbidity and mortality. Based on distinct hemodynamic features, i.e., ejection fraction (EF), transvalvular gradient and stroke volume, four different AS subtypes can be distinguished: (i) normal EF and high gradient, (ii) reduced EF and high gradient, (iii) reduced EF and low gradient, and (iv) normal EF and low gradient. These subtypes differ with respect to pathophysiological mechanisms, cardiac remodeling, and prognosis. However, little is known about metabolic changes in these different hemodynamic conditions of AS. Thus, we carried out metabolomic analyses in serum samples of 40 AS patients (n = 10 per subtype) and 10 healthy blood donors (controls) using ultrahigh-performance liquid chromatography–tandem mass spectroscopy. A total of 1293 biochemicals could be identified. Principal component analysis revealed different metabolic profiles in all of the subgroups of AS (All-AS) vs. controls. Out of the determined biochemicals, 48% (n = 620) were altered in All-AS vs. controls (p < 0.05). In this regard, levels of various acylcarnitines (e.g., myristoylcarnitine, fold-change 1.85, p < 0.05), ketone bodies (e.g., 3-hydroxybutyrate, fold-change 11.14, p < 0.05) as well as sugar metabolites (e.g., glucose, fold-change 1.22, p < 0.05) were predominantly increased, whereas amino acids (e.g., leucine, fold-change 0.8, p < 0.05) were mainly reduced in All-AS. Interestingly, these changes appeared to be consistent amongst all AS subtypes. Distinct differences between AS subtypes were found for metabolites belonging to hemoglobin metabolism, diacylglycerols, and dihydrosphingomyelins. These findings indicate that relevant changes in substrate utilization appear to be consistent for different hemodynamic subtypes of AS and may therefore reflect common mechanisms during AS-induced heart failure. Additionally, distinct metabolites could be identified to significantly differ between certain AS subtypes. Future studies need to define their pathophysiological implications.

The pathophysiology of heart failure with preserved ejection fraction (HFpEF) is poorly understood and therapeutic strategies are lacking. This study aimed to identify plasma proteins with pathophysiological relevance in HFpEF and with respect to spironolactone-induced effects. We assessed 92 biomarkers in plasma samples from 386 HFpEF patients—belonging to the Aldo-DHF trial—before (baseline, BL) and after one-year treatment (follow up, FU) with spironolactone (verum) or a placebo. At BL, various biomarkers showed significant associations with the two Aldo-DHF primary end point parameters: 33 with E/e’ and 20 with peak VO₂. Ten proteins including adrenomedullin, FGF23 and inflammatory peptides (e.g., TNFRSF11A, TRAILR2) were significantly associated with both parameters, suggesting a role in the clinical HFpEF presentation. For 13 proteins, expression changes from BL to FU were significantly different between verum and placebo. Among them were renin, growth hormone, adrenomedullin and inflammatory proteins (e.g., TNFRSF11A, IL18 and IL4RA), indicating distinct spironolactone-mediated effects. BL levels of five proteins, e.g., inflammatory markers such as CCL17, IL4RA and IL1ra, showed significantly different effects on the instantaneous risk for hospitalization between verum and placebo. This study identified plasma proteins with different implications in HFpEF and following spironolactone treatment. Future studies need to define their precise mechanistic involvement.

In SARS-CoV-2 diagnostics, cycle threshold (Ct) values from qRT-PCRs semi-quantitatively estimate a patient’s viral load. However, relevant analytical differences between qRT-PCR assays are often neglected. This study was designed (i) to identify such differences between five commonly used assays and (ii) to demonstrate a straightforward strategy to harmonize them. QRT-PCRs for SARS-CoV-2 were carried out in 85 oropharyngeal swab samples using three fully automated (Alinity m, cobas^®6800 and GeneXpert) and two semi-automated (genesig^® and RIDA^®GENE) assays. Qualitative results (positive/negative) showed excellent comparability between the fully automated assays, but not between the Alinity m and semi-automated methods. Ct values significantly varied between all the methods, with the median values ranging from 22.76 (Alinity m) to 30.89 (RIDA^®GENE) and 31.50 (genesig^®), indicating the lowest sensitivity for semi-automated methods. Passing–Bablok analysis further revealed systemic biases. Assay-specific viral load concentration calculations—based on generated individual standard curves—resulted in much better comparability between the assays. Applying these calculations, significant differences were no longer detectable. This study highlights relevant analytical differences between SARS-CoV-2 qRT-PCR assays, leading to divergent decisions about the mandatory isolation of infected individuals. Secondly, we propose a strategy to harmonize qRT-PCR assays to achieve better comparability. Our findings are of particular interest for laboratories utilizing different assays.