No abstract

Recently, meta-analysis studies reported that hyperuricaemia is associated with higher incidence of type 2 diabetes mellitus (T2DM), however, there are limited data on the Asian population. The aim of this observational study is to estimate the long-term impact of hyperuricaemia on the new-onset T2DM and cardiovascular events. This study is based on a single-centre, all-comers, and large retrospective cohort. Subjects that visited from January 2004 to February 2014 were enrolled using the electronic database of Korea University Guro Hospital. A total of 10 505 patients without a history of T2DM were analyzed for uric acid, fasting glucose and haemoglobin (Hb) A1c level. Inclusion criteria included both Hb A1c <5.7% and fasting glucose level <100 mg/dL without T2DM. Hyperuricaemia was defined as a uric acid level ≥7.0 mg/dL in men, and ≥6.5 mg/dL in women. To adjust baseline confounders, a propensity score matching (PSM) analysis was performed. The impact of hyperuricaemia on the new-onset T2DM and cardiovascular events were compared with the non-hyperuricaemia during the 5-year clinical follow-up. After PSM, baseline characteristics of both groups were balanced. In a 5-year follow-up, the hyperuricaemia itself was a strong independent predictor of the incidence of new-onset T2DM (HR, 1.78; 95% CI, 1.12 to 2.8). Hyperuricaemia was a strong independent predictor of new-onset T2DM, which suggests a substantial implication for a correlation between uric acid concentration and insulin resistance (or insulin sensitivity). Also, hyperuricaemia is substantially implicated in cardiovascular risks and the further long-term cardiovascular events in the crude population, but it is not an independent predictor of long-term cardiovascular mortality in the matched population.

Background Glioblastoma multiforme (GBM) is an aggressive malignant primary brain tumor. Wnt/β-catenin is known to be related to GBM stemness. Cancer stem cells induce immunosuppressive and treatment resistance in GBM. We hypothesized that Wnt/β-catenin-related genes with immunosuppression could be related to the prognosis in patients with GBM. Methods We obtained the clinicopathological data of 525 patients with GBM from the brain cancer gene database. The fraction of tumor-infiltrating immune cells was evaluated using in silico flow cytometry. Among gene sets of Wnt/β-catenin pathway, Dickkopf-3 (DKK3) gene related to the immunosuppressive response was found using machine learning. We performed gene set enrichment analysis (GSEA), network-based analysis, survival analysis and in vitro drug screening assays based on Dickkopf-3 (DKK3) expression. Results In analyses of 31 genes related to Wnt/β-catenin signaling, high DKK3 expression was negatively correlated with increased antitumoral immunity, especially CD8 + and CD4 + T cells, in patients with GBM. High DKK3 expression was correlated with poor survival and disease progression in patients with GBM. In pathway-based network analysis, DKK3 was directly linked to the THY1 gene, a tumor suppressor gene. Through in vitro drug screening, we identified navitoclax as an agent with potent activity against GBM cell lines with high DKK3 expression. Conclusions These results suggest that high DKK3 expression could be a therapeutic target in GBM. The results of the present study could contribute to the design of future experimental research and drug development programs for GBM. Graphical abstract

This study investigated the usefulness of deep learning-based automatic detection of anterior disc displacement (ADD) from magnetic resonance imaging (MRI) of patients with temporomandibular joint disorder (TMD). Sagittal MRI images of 2520 TMJs were collected from 861 men and 399 women (average age 37.33 ± 18.83 years). A deep learning algorithm with a convolutional neural network was developed. Data augmentation and the Adam optimizer were applied to reduce the risk of overfitting the deep-learning model. The prediction performances were compared between the models and human experts based on areas under the curve (AUCs). The fine-tuning model showed excellent prediction performance (AUC = 0.8775) and acceptable accuracy (approximately 77%). Comparing the AUC values of the from-scratch (0.8269) and freeze models (0.5858) showed lower performances of the other models compared to the fine-tuning model. In Grad-CAM visualizations, the fine-tuning scheme focused more on the TMJ disc when judging ADD, and the sparsity was higher than that of the from-scratch scheme (84.69% vs. 55.61%, p < 0.05). The three fine-tuned ensemble models using different data augmentation techniques showed a prediction accuracy of 83%. Moreover, the AUC values of ADD were higher when patients with TMD were divided by age (0.8549–0.9275) and sex (male: 0.8483, female: 0.9276). While the accuracy of the ensemble model was higher than that of human experts, the difference was not significant (p = 0.1987–0.0671). Learning from pre-trained weights allowed the fine-tuning model to outperform the from-scratch model. Another benefit of the fine-tuning model for diagnosing ADD of TMJ in Grad-CAM analysis was the deactivation of unwanted gradient values to provide clearer visualizations compared to the from-scratch model. The Grad-CAM visualizations also agreed with the model learned through important features in the joint disc area. The accuracy was further improved by an ensemble of three fine-tuning models using diversified data. The main benefits of this model were the higher specificity compared to human experts, which may be useful for preventing true negative cases, and the maintenance of its prediction accuracy across sexes and ages, suggesting a generalized prediction.

Nearest-neighbor estimators for the Kullback-Leiber (KL) divergence that are asymptotically unbiased have recently been proposed and demonstrated in a number of applications. However, with a small number of samples, nonparametric methods typically suffer from large estimation bias due to the nonlocality of information derived from nearest-neighbor statistics. In this letter, we show that this estimation bias can be mitigated by modifying the metric function, and we propose a novel method for learning a locally optimal Mahalanobis distance function from parametric generative models of the underlying density distributions. Using both simulations and experiments on a variety of data sets, we demonstrate that this interplay between approximate generative models and nonparametric techniques can significantly improve the accuracy of nearest-neighbor-based estimation of the KL divergence.

We consider the problem of learning a local metric to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic error arising from finite sampling effects, and find an appropriate local metric that maximally reduces the bias based upon knowledge from generative models. As a byproduct, the asymptotic theoretical analysis in this work relates metric learning with dimensionality reduction, which was not understood from previous discriminative approaches. Empirical experiments show that this learned local metric enhances the discriminative nearest neighbor performance on various datasets using simple class conditional generative models.

Cancer-associated fibroblasts (CAFs) participate in critical processes in the tumor microenvironment, such as extracellular matrix remodeling, reciprocal signaling interactions with cancer cells and crosstalk with infiltrating inflammatory cells. However, the relationships between CAFs and survival are not well known in lung cancer. The aim of this study was to reveal the correlations of CAFs with survival rates, genetic alterations and immune activities. This study reviewed the histological features of 517 patients with lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database. We performed gene set enrichment analysis (GSEA), network-based analysis and survival analysis based on CAFs in four histological types of lung adenocarcinoma: acinar, papillary, micropapillary and solid. We found four hallmark gene sets, the epithelial-mesenchymal transition, angiogenesis, hypoxia, and inflammatory response gene sets, that were associated with the presence of CAFs. CAFs were associated with tumor proliferation, elevated memory CD4+T cells and high CD274 (encoding PD-L1) expression. In the pathway analyses, CAFs were related to blood vessel remodeling, matrix organization, negative regulation of apoptosis and transforming growth factor-β signaling. In the survival analysis of each histological type, CAFs were associated with poor prognosis in the solid type. These results may contribute to the development of therapeutic strategies against lung adenocarcinoma cases in which CAFs are present.

We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant’s visual cortex uniformly with equal probability, the participant’s intention groups the strokes and thus perceives a ‘letter Gestalt’. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multi-class intentional control of EEG-BMIs without using gaze-shifting.