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Abstract: Certain maternal immune responses against the fetus at the maternal–fetal interface may contribute to recurrent miscarriage (RM). Trophoblast cells involve in forming of maternal–fetal interface and interact with many immune cells, including NK cells. NK cells accumulate at the maternal–fetal interface and play a critical role during pregnancy. NKG2D ligands can activate NK cells through engaging with corresponding receptors. The 5′-end flanking regions of DNA sequence of some NKG2D ligands contain heat shock elements. It is very possible that oxidative stress, produced in pathological process of RM, induces abnormal NKG2D ligand expression in the trophoblast cells, which stimulate cytotoxicity of NK cells. Moreover, in normal pregnancy, soluble NKG2D ligands are secreted into sera by syncytiotrophoblast cells, which disturb NKG2D-mediated maternal anti-fetus immunity. Reduction of soluble NKG2D ligand levels in association with upregulation of NKG2D on immune cells may also contribute to pathogenesis of RM. [Copyright &y& Elsevier]

To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors. Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO). The efficacy of each model was assessed using the subject operating characteristic (ROC) curve, The DeLong test was used to assess the differences in the AUC values of the different models, and the clinical benefit of each model was assessed using the decision curve (DCA), and the predictive accuracy of the model was assessed using the calibration curve (CCA). Among the constructed models for classification of breast phyllodes tumors, the fusion model (AUC: 0.97) had the best diagnostic efficacy and highest clinical benefit. The traditional radiomics model (AUC: 0.81) had better diagnostic efficacy compared with subregion radiomics model (AUC: 0.70). De-Long test, there is a statistical difference between the fusion model traditional radiomics model, and subregion radiomics model in the training group. Among the models constructed to distinguish phyllodes tumors from fibroadenomas in the breast, the TDT_CIDL model (AUC: 0.974) had the best predictive efficacy and the highest clinical benefit. De-Long test, the TDT_CI combination model was statistically different from the remaining five models in the training group. Traditional radiomics models, subregion radiomics models and deep learning models based on MRI sequences can help to differentiate benign from junctional phyllodes tumors, phyllodes tumors from fibroadenomas, and provide personalized treatment for patients. [ABSTRACT FROM AUTHOR]