Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including a computer-aided diagnosis are desired. This study aims to classify histopathological images of malignant lymphomas through deep learning, which is a computer algorithm and type of artificial intelligence (AI) technology. We prepared hematoxylin and eosin (H&E) slides of a lesion area from 388 sections, namely, 259 with diffuse large B-cell lymphoma, 89 with follicular lymphoma, and 40 with reactive lymphoid hyperplasia, and created whole slide images (WSIs) using a whole slide system. WSI was annotated in the lesion area by experienced hematopathologists. Image patches were cropped from the WSI to train and evaluate the classifiers. Image patches at magnifications of ×5, ×20, and ×40 were randomly divided into a test set and a training and evaluation set. The classifier was assessed using the test set through a cross-validation after training. The classifier achieved the highest levels of accuracy of 94.0%, 93.0%, and 92.0% for image patches with magnifications of ×5, ×20, and ×40, respectively, in comparison to diffuse large B-cell lymphoma, follicular lymphoma, and reactive lymphoid hyperplasia. Comparing the diagnostic accuracies between the proposed classifier and seven pathologists, including experienced hematopathologists, using the test set made up of image patches with magnifications of ×5, ×20, and ×40, the best accuracy demonstrated by the classifier was 97.0%, whereas the average accuracy achieved by the pathologists using WSIs was 76.0%, with the highest accuracy reaching 83.3%. In conclusion, the neural classifier can outperform pathologists in a morphological evaluation. These results suggest that the AI system can potentially support the diagnosis of malignant lymphoma.

Hypoxia downregulates farnesoid X receptor via a hypoxia-inducible factor-independent but p38 mitogen-activated protein kinase-dependent pathway.

Farnesoid X receptor (FXR), a member of the nuclear receptor superfamily, has been shown to play pivotal roles in bile acid homeostasis by regulating the biosynthesis, conjugation, secretion and absorption of bile acids. Accumulating data suggest that FXR signaling is involved in the pathogenesis of liver and metabolic disorders. Here we show that FXR expression is significantly suppressed in HepG2 cells exposed to hypoxia. Concomitantly, the expression of the bile salt export pump, known as an FXR target gene product and responsible for the excretion of bile acids from the liver, is also decreased under hypoxia. Overexpression of hypoxia-inducible factor (HIF)-1alpha does not mimic the suppressive effect of hypoxia on FXR expression. Furthermore, simultaneous knockdown of HIF-1alpha, HIF-2alpha and HIF-3alpha fails to restore the FXR expression level under hypoxia, indicating that HIF is not involved in hypoxia-evoked FXR downregulation. Instead, we demonstrate that p38 mitogen-activated protein kinase is an indispensable factor for FXR downregulation under hypoxia. Thus, we propose a novel liver disorder model in which two signaling molecules, p38 mitogen-activated protein kinase and FXR, may contribute to the linkage of two pathogenic conditions, i.e. ischemia, a condition accompanying hypoxia, and cholestasis, a condition with intrahepatic accumulation of cytotoxic bile acids.