No abstract

No abstract

IntroductionEarly intervention in type 2 diabetes can prevent exacerbation of insulin resistance. More effective interventions can be implemented by early and precise prediction of the change in glycated haemoglobin A1c (HbA1c). Artificial intelligence (AI), which has been introduced into various medical fields, may be useful in predicting changes in HbA1c. However, the inability to explain the predictive factors has been a problem in the use of deep learning, the leading AI technology. Therefore, we applied a highly interpretable AI method, random forest (RF), to large-scale health check-up data and examined whether there was an advantage over a conventional prediction model.Research design and methodsThis study included a cumulative total of 42 908 subjects not receiving treatment for diabetes with an HbA1c <6.5%. The objective variable was the change in HbA1c in the next year. Each prediction model was created with 51 health-check items and part of their change values from the previous year. We used two analytical methods to compare the predictive powers: RF as a new model and multivariate logistic regression (MLR) as a conventional model. We also created models excluding the change values to determine whether it positively affected the predictions. In addition, variable importance was calculated in the RF analysis, and standard regression coefficients were calculated in the MLR analysis to identify the predictors.ResultsThe RF model showed a higher predictive power for the change in HbA1c than MLR in all models. The RF model including change values showed the highest predictive power. In the RF prediction model, HbA1c, fasting blood glucose, body weight, alkaline phosphatase and platelet count were factors with high predictive power.ConclusionsCorrect use of the RF method may enable highly accurate risk prediction for the change in HbA1c and may allow the identification of new diabetes risk predictors.

No abstract

Malignant cells generally acquire some immune escape mechanisms for clonal expansion. Immune escape mechanisms also contribute to the failure of graft-versus-leukemia (GVL) effect after allogeneic hematopoietic stem cell transplantation (allo-SCT). Infant leukemias with mixed-lineage leukemia (MLL) rearrangement have a remarkably short latency, and GVL effect after allo-SCT has not been clearly evidenced in these leukemias. Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-and FasL-mediated cytotoxic pathways play important roles in cytotoxic T-lymphocyte-and natural killer cell-mediated antitumor immunity and optimal GVL activity. We investigated the in vitro sensitivity of MLL-rearranged acute lymphoblastic leukemia (ALL) and acute myeloblastic leukemia (AML) cells to TRAIL-and FasL-mediated cytotoxicity. Most of cell lines and primary leukemia cells were highly resistant to TRAIL primarily owing to low cell-surface expression of death receptors in ALL and simultaneous expression of decoy receptors in AML. Nearly half of cell lines and majority of primary leukemia cells showed low sensitivity to FasL. These results suggest that resistance to death-inducing ligands, particularly to TRAIL, could be one of the mechanisms for a rapid clonal expansion and a poor sensitivity to the GVL effect in infant leukemias with MLL rearrangement.