Introduction: Fertility treatment with frozen thawed embryo transfer (FET) is widely used. Women treated in artificial cycles (AC-FET) receive high doses of estrogen in contrast to natural cycles (NC-FET), where no estrogen is administered. Estrogen substitution may be associated with increased risk of thromboembolism. Our aim is therefore to characterize changes in blood coagulation parameters defined as surrogate thrombotic risk markers in women undergoing estrogen substitution during AC-FET. Materials: In our prospective cohort study, we enrolled 34 women in either: AC-FET (n = 19) or NC-FET (n = 15). Women were recruited at the Department of Obstetrics and Gynaecology, Horsens Fertility Clinic, Denmark, from August 2019 -November 2020. Blood samples were obtained at four timepoints. Thrombin generation, platelet aggregation and fibrinolysis were evaluated as thrombotic risk markers. Results: Within the AC-FET group, we found a significantly shorter lagtime (p < 0.05) and time to peak (TTP) (p < 0.001) after hormone substitution compared to baseline. Furthermore, a significantly higher mean peak (p < 0.0001) and larger endogenous thrombin potential (ETP) (p < 0.0001) was observed. When compared to the NC-FET group, women receiving AC-FET had a significantly shorter mean TTP (p < 0.005), higher mean peak (p < 0.0001) and larger ETP (p < 0.05). Additionally, we demonstrated a significantly prolonged lysis time within the AC-FET group (p < 0.001).
Conclusion:Our results indicate that women receiving AC-FET have a significantly increased thrombin generation which may increase the thromboembolic risk in women being estrogen substituted.
Machine Learning applications provide a promising method to support clinical practitioners in Breast Cancer (BC) detection. Currently, Fine Needle Aspiration (FNA) is a commonly applied diagnostic method for BC tumors, which, however, is associated with ominous false negative misclassifications. For this purpose, the present study explores Artificial Neural Networks (ANNs) with the aim of outperforming clinical practices via FNA in classifying benign or malignant BC cases with regard to an improved accuracy and reduced False Negative Rate (FNR) using the Breast Cancer Wisconsin (Diagnostic) Dataset (WDBC). The findings reveal that a dense ANN with a single hidden layer including 15 neurons can reach a testing accuracy of 98.60% and a FNR of 0% on a scaled dataset. In combination with several introduced improvement measures, a high degree of generalizability is associated with the model under the consideration of the relatively small dataset. As a result, this model outperforms not only clinical practitioners but also 72 classifiers from the recent literature.
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