The Elecsys® AMH assay demonstrated good precision under routine conditions, and is suitable for determining AMH levels in serum and lithium-heparin plasma.

Vaccines have been seen as the most important solution for ending the coronavirus disease 2019 (COVID‐19) pandemic. The aim of this study is to evaluate the antibody levels after inactivated virus vaccination. We included 148 healthcare workers (74 with prior COVID‐19 infection and 74 with not). They received two doses of inactivated virus vaccine (CoronaVac). Serum samples were prospectively collected three times (Days 0, 28, 56). We measured SARS‐CoV‐2 IgGsp antibodies quantitatively and neutralizing antibodies. After the first dose, antibody responses did not develop in 64.8% of the participants without prior COVID‐19 infection. All participants had developed antibody responses after the second dose. We observed that IgGsp antibody titers elicited by a single vaccine dose in participants with prior COVID‐19 infection were higher than after two doses of vaccine in participants without prior infection (geometric mean titer: 898 and 607 AU/ml). IgGsp antibodies, participants with prior COVID‐19 infection had higher antibody levels as geometric mean titers at all time points (p < 0.001). We also found a positive correlation between IgGsp antibody titers and neutralizing capacity (rs = 0.697, p < 0.001). Although people without prior COVID‐19 infection should complete their vaccination protocol, the adequacy of a single dose of vaccine is still in question for individuals with prior COVID‐19. New methods are needed to measure the duration of protection of vaccines and their effectiveness against variants as the world is vaccinated. We believe quantitative IgGsp values may reflect the neutralization capacity of some vaccines.

Objectives The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. Methods We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription–polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. Results The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study’s data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). Conclusions ML models presented in this study can be used as clinical decision support tools to contribute to physicians’ clinical judgment for COVID-19 diagnoses.

Adiponectin has multiple protective effects on vascular endothelium through anti-inflammatory and anti-atherogenic properties. Recent data suggested that endothelial activation and inflammation may contribute to the pathogenesis of slow coronary flow (SCF). Therefore, we investigated whether adiponectin plasma concentrations were decreased in patients with SCF compared to subjects with normal coronary flow. The study population consisted of 35 patients with angiographically documented SCF in all three coronary arteries and 35 sex- and age-matched cases with normal coronary flow. Coronary flow rates of all participants were determined by Thrombolysis in Myocardial Infarction (TIMI) frame count. Plasma adiponectin concentrations were measured by an enzyme-linked immunosorbent assay method using commercially available adiponectin kits. There were no statistically significant differences between the patients with SCF and the subjects with normal coronary flow in terms of demographic characteristics and cardiovascular risk factors (P>0.05). Plasma adiponectin concentrations of patients with SCF were found to be significantly lower than those with normal coronary flow (4.77+/-3.86 mg/ml vs 10.8+/-6.60 mg/ml, P=0.001, respectively). Plasma adiponectin levels were correlated significantly and inversely with mean TIMI frame count in patients with SCF (r= -0.441, P=0.008). Furthermore, the Receiver Operator Characteristics curve of adiponectin concentrations showed that an adiponectin <4.6 mg/ml is associated with SCF with a sensitivity of 68.6%, specificity of 82.9%, positive predictive value of 80.0%, and negative predictive value of 72.5%. Our findings suggest that endothelial inflammation may play a role in the pathogenesis of SCF phenomenon.

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