RESUMO
We present here an assessment of a 'real-life' value of automated machine learning algorithm (AI) for examination of immunohistochemistry for fibroblast growth factor receptor-2 (FGFR2) in breast cancer (BC). Expression of FGFR2 in BC (n = 315) measured using a certified 3DHistech CaseViewer/QuantCenter software 2.3.0. was compared to the manual pathologic assessment in digital slides (PA). Results revealed: (i) substantial interrater agreement between AI and PA for dichotomized evaluation (Cohen's kappa = 0.61); (ii) strong correlation between AI and PA H-scores (Spearman r = 0.85, p < 0.001); (iii) a small constant error and a significant proportional error (Passing-Bablok regression y = 0.51 × X + 29.9, p < 0.001); (iv) discrepancies in H-score in cases of extreme (strongest/weakest) or heterogeneous FGFR2 expression and poor tissue quality. The time of AI was significantly longer (568 h) than that of the pathologist (32 h). This study shows that the described commercial machine learning algorithm can reliably execute a routine pathologic assessment, however, in some instances, human expertise is essential.
RESUMO
Blood coagulation consists of a series of zymogens that can be converted by limited proteolysis to active enzymes leading to the generation of thrombin. Fresh plasma and human thrombin was incubated with extracts from berries of Aronia melanocarpa or seeds of Vitis vinifera (0.5; 5; 50 µg/ml). The in vitro experiments showed that both extracts prolonged clotting time and decreased the maximal velocity of fibrin polymerization in human plasma. Moreover thrombin incubation with both extracts results in the inhibition of amidolytic activity of this enzyme. It gives hopes for development of diet supplements, which may be preventing thrombosis in pathological states.