The steroid receptor (SR) complex contains FKBP51 and FKBP52, which bind to tacrolimus (TAC) and cyclophilin 40, which, in turn, bind to cyclosporine (CYA); these influence the intranuclear mobility of steroid-SR complexes. Pharmacodynamic interactions are thought to exist between steroids and calcineurin inhibitors (CNIs) on the SR complex. We examined the effect of CNIs on steroid sensitivity. Methylprednisolone (MPSL) sensitivity was estimated as the concentration inhibiting mitosis in 50% (IC50) of peripheral blood mononuclear cells and as the area under the MPSL concentration-proliferation suppressive rate curves (CPS-AUC) in 30 healthy subjects. MPSL sensitivity was compared between the additive group (AG) as the MPSL sensitivity that was a result of addition of the proliferation suppressive rate of CNIs to that of MPSL and the mixed culture group (MCG) as MPSL sensitivity of mixed culture with both MPSL and CNIs in identical patients. IC50 values of MPSL and cortisol sensitivity were examined before and 2 months after CNI administration in 23 renal transplant recipients. IC50 and CPS-AUC values of MPSL were lower in the MCG than in the AG with administration of TAC and CYA. The CPS-AUC ratio of MCG and AG was lower in the TAC group. IC50 values of MPSL and cortisol tended to be lower after administration of TAC and CYA, and a significant difference was observed in the IC50 of cortisol after TAC administration. Steroid sensitivity increased with both TAC and CYA. Furthermore, TAC had a greater effect on increasing sensitivity. Thus, concomitant administration of CNIs and steroids can increase steroid sensitivity.

Financial fraud activities have soared despite the advancement of fraud detection models empowered by machine learning (ML). To address this issue, we propose a new framework of feature engineering for ML models. The framework consists of feature creation that combines feature aggregation and feature transformation, and feature selection that accommodates a variety of ML algorithms. To illustrate the effectiveness of the framework, we conduct an experiment using an actual financial transaction dataset and show that the framework significantly improves the performance of ML fraud detection models. Specifically, all the ML models complemented by a feature set generated from our framework surpass the same models without such a feature set by nearly 40% on the F1-measure and 20% on the Area Under the Curve (AUC) value.

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The total losses through online banking in the United Kingdom have increased because fraudulent techniques have progressed and used advanced technology. Using the history transaction data is the limit for discovering various patterns of fraudsters. Autoencoder has a high possibility to discover fraudulent action without considering the unbalanced fraud class data. Although the autoencoder model uses only the majority class data, in our hypothesis, if the original data itself has various feature vectors related to transactions before inputting the data in autoencoder then the performance of the detection model is improved. A new feature engineering framework is built that can create and select effective features for deep learning in remote banking fraud detection. Based on our proposed framework [19], new features have been created using feature engineering methods that select effective features based on their importance.In the experiment, a real-life transaction dataset has been used which was provided by a private bank in Europe and built autoencoder models with three different types of datasets: With original data, with created features and with selected effective features. We also adjusted the threshold values (1 and 4) in the autoencoder and evaluated them with the different types of datasets. The result demonstrates that using the new framework the deep learning models with the selected features are significantly improved than the ones with original data.

Pain and stress alleviation after acupuncture treatment was assessed in this study. Patients responded to a questionnaire designed to determine the amount of stress they were experiencing, and data were obtained for patient salivary amylase, cortisol, secretary IgA (s-IgA), and leptin receptor (OBRb). As a part of this study on acute pain, 6 factors were extracted from the questionnaire. The second factor (pain removal) was well correlated with salivary amylase activity in patients with cervico-omo-brachial syndrome. An evaluation of cumulative acupuncture treatments showed that salivary cortisol increased and s-IgA decreased. In addition, a decreased s-IgA level signiˆcantly correlated with chronic pain removal. The questionnaire correlated well with measurements of salivary markers suggesting that they can be taken as indices of therapeutic e‹cacy in acupuncture treatment.