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Bone metabolism appears to influence insulin secretion and sensitivity, and insulin promotes bone formation in animals, but similar evidence in humans is limited. The objectives of this study are to explore if bone turnover markers were associated with insulin secretion and sensitivity and to determine if bone turnover markers predict changes in insulin secretion and sensitivity. The study population encompassed 576 non-diabetic adult men with normal glucose tolerance (NGT; n = 503) or impaired glucose regulation (IGR; n = 73). Baseline markers of bone resorption (CTX) and formation (P1NP) were determined in the fasting state and after a 2-h hyperinsulinaemic, euglycaemic clamp. An intravenous glucose tolerance test (IVGTT) and a 2-h oral glucose tolerance test (OGTT) were performed at baseline, and the OGTT was repeated after 3 years. There were no differences in bone turnover marker levels between NGT and IGR. CTX and P1NP levels decreased by 8.0% (p < 0.001) and 1.9% (p < 0.01) between baseline and steady-state during the clamp. Fasting plasma glucose was inversely associated with CTX and P1NP both before and after adjustment for recruitment centre, age, BMI, smoking and physical activity. However, baseline bone turnover markers were neither associated with insulin sensitivity (assessed using hyperinsulinaemic euglycaemic clamp and OGTT) nor with insulin secretion capacity (based on IVGTT and OGTT) at baseline or at follow-up. Although inverse associations between fasting glucose and markers of bone turnover were identified, this study cannot support an association between insulin secretion and sensitivity in healthy, non-diabetic men.
Background: Testing for COVID-19 with quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) may result in delayed detection of disease. Antigen detection via lateral flow testing (LFT) is faster and amenable to population-wide testing strategies. Our study assesses the diagnostic accuracy of LFT compared to RT-PCR on the same primarycare patients in Austria. Methods: Patients with mild to moderate flu-like symptoms attending a general practice network in an Austrian district (October 22 to November 30, 2020) received clinical assessment including LFT. All suspected COVID-19 cases obtained additional RT-PCR and were divided into two groups: Group 1 (true reactive): suspected cases with reactive LFT and positive RT-PCR; and Group 2 (false non-reactive): suspected cases with a non-reactive LFT but positive RT-PCR. Findings: Of the 2,562 symptomatic patients, 1,037 were suspected of COVID-19 and 826 (79.7%) patients tested RT-PCR positive. Among patients with positive RT-PCR, 788/826 tested LFT reactive (Group 1) and 38 (4.6%) non-reactive (Group 2). Overall sensitivity was 95.4% (95%CI: [94%,96.8%]), specificity 89.1% (95%CI: [86.3%, 91.9%]), positive predictive value 97.3% (95%CI:[95.9%, 98.7%]) and negative predictive value 82.5% (95%CI:[79.8%, 85.2%]). Reactive LFT and positive RT-PCR were positively correlated (r = 0.968,95CI=[0.952,0.985] and κ=0.823, 95%CI=[0.773,0.866]). Reactive LFT was negatively correlated with Ct-value (r = -0.2999,p < 0.001) and pre-test symptom duration (r = -0.1299,p = 0.0043) while Ct-value was positively correlated with pre-test symptom duration (r = 0.3733),p < 0.001). Interpretation: We show that LFT is an accurate alternative to RT-PCR testing in primary care. We note the importance of administering LFT properly, here combined with clinical assessment in symptomatic patients.
Quantitative models have several advantages compared to qualitative methods for pest risk assessments (PRA). Quantitative models do not require the definition of categorical ratings and can be used to compute numerical probabilities of entry and establishment, and to quantify spread and impact. These models are powerful tools, but they include several sources of uncertainty that need to be taken into account by risk assessors and communicated to decision makers. Uncertainty analysis (UA) and sensitivity analysis (SA) are useful for analyzing uncertainty in models used in PRA, and are becoming more popular. However, these techniques should be applied with caution because several factors may influence their results. In this paper, a brief overview of methods of UA and SA are given. As well, a series of practical rules are defined that can be followed by risk assessors to improve the reliability of UA and SA results. These rules are illustrated in a case study based on the infection model of Magarey et al. (2005) where the results of UA and SA are shown to be highly dependent on the assumptions made on the probability distribution of the model inputs.
Die vorliegende Übersicht über die Biomarkern TIMP‑2 („tissue inhibitor of metalloprokinase 2“) und IGFBP7 („insulin-like growth factor binding protein 7“) wird im Rahmen der Serie „Biomarker“ des Zentralblatts für Arbeitsmedizin, Arbeitsschutz und Ergonomie publiziert. Die Marker TIMP‑2 und IGFBP7 eignen sich zur Abschätzung der Nierenschädigung und zur frühen Diagnostik der akuten Niereninsuffizienz. Hier zeigen diese eine hohe Sensitivität und Spezifität.
Background: Antibody detection of SARS-CoV-2 requires an understanding of its variation, course, and duration.
Methods: Antibody response to SARS-CoV-2 was evaluated over 5–430 days on 828 samples across COVID-19 severity levels, for total antibody (TAb), IgG, IgA, IgM, neutralizing antibody (NAb), antibody avidity, and for receptor-binding-domain (RBD), spike (S), or nucleoprotein (N). Specificity was determined on 676 pre-pandemic samples.
Results: Sensitivity at 30–60 days post symptom onset (pso) for TAb-S/RBD, TAb-N, IgG-S, IgG-N, IgA-S, IgM-RBD, and NAb was 96.6%, 99.5%, 89.7%, 94.3%, 80.9%, 76.9% and 92.8%, respectively. Follow-up 430 days pso revealed: TAb-S/RBD increased slightly (100.0%); TAb-N decreased slightly (97.1%); IgG-S and IgA-S decreased moderately (81.4%, 65.7%); NAb remained positive (94.3%), slightly decreasing in activity after 300 days; there was correlation with IgG-S (Rs = 0.88) and IgA-S (Rs = 0.71); IgG-N decreased significantly from day 120 (15.7%); IgM-RBD dropped after 30–60 days (22.9%). High antibody avidity developed against S/RBD steadily with time in 94.3% of patients after 430 days. This correlated with persistent antibody detection depending on antibody-binding efficiency of the test design. Severe COVID-19 correlated with earlier and higher antibody response, mild COVID-19 was heterogeneous with a wide range of antibody reactivities. Specificity of the tests was ≥99%, except for IgA (96%).
Conclusion: Sensitivity of anti-SARS-CoV-2 assays was determined by test design, target antigen, antibody avidity, and COVID-19 severity. Sustained antibody detection was mainly determined by avidity progression for RBD and S. Testing by TAb and for S/RBD provided the highest sensitivity and longest detection duration of 14 months so far.
Background: We evaluated the sensitivity of the D-statistic, a parsimony-like method widely used to detect gene flow between closely related species. This method has been applied to a variety of taxa with a wide range of divergence times. However, its parameter space and thus its applicability to a wide taxonomic range has not been systematically studied. Divergence time, population size, time of gene flow, distance of outgroup and number of loci were examined in a sensitivity analysis.
Result: The sensitivity study shows that the primary determinant of the D-statistic is the relative population size, i.e. the population size scaled by the number of generations since divergence. This is consistent with the fact that the main confounding factor in gene flow detection is incomplete lineage sorting by diluting the signal. The sensitivity of the D-statistic is also affected by the direction of gene flow, size and number of loci. In addition, we examined the ability of the f-statistics, fˆGf^G and fˆhomf^hom, to estimate the fraction of a genome affected by gene flow; while these statistics are difficult to implement to practical questions in biology due to lack of knowledge of when the gene flow happened, they can be used to compare datasets with identical or similar demographic background.
Conclusions: The D-statistic, as a method to detect gene flow, is robust against a wide range of genetic distances (divergence times) but it is sensitive to population size. The D-statistic should only be applied with critical reservation to taxa where population sizes are large relative to branch lengths in generations.