SiO 2 Induces Iron Overload and Ferroptosis in Cardiomyocytes in a Silicosis Mouse Model.

Objective: The aim of this study was to explore the role and mechanism of ferroptosis in SiO 2-induced cardiac injury using a mouse model. Methods: Male C57BL/6 mice were intratracheally instilled with SiO 2 to create a silicosis model. Ferrostatin-1 (Fer-1) and deferoxamine (DFO) were used to suppress ferroptosis. Serum biomarkers, oxidative stress markers, histopathology, iron content, and the expression of ferroptosis-related proteins were assessed. Results: SiO 2 altered serum cardiac injury biomarkers, oxidative stress, iron accumulation, and ferroptosis markers in myocardial tissue. Fer-1 and DFO reduced lipid peroxidation and iron overload, and alleviated SiO 2-induced mitochondrial damage and myocardial injury. SiO 2 inhibited Nuclear factor erythroid 2-related factor 2 (Nrf2) and its downstream antioxidant genes, while Fer-1 more potently reactivated Nrf2 compared to DFO. Conclusion: Iron overload-induced ferroptosis contributes to SiO 2-induced cardiac injury. Targeting ferroptosis by reducing iron accumulation or inhibiting lipid peroxidation protects against SiO 2 cardiotoxicity, potentially via modulation of the Nrf2 pathway.

Adaptive Huber Regression.

Big data can easily be contaminated by outliers or contain variables with heavy-tailed distributions, which makes many conventional methods inadequate. To address this challenge, we propose the adaptive Huber regression for robust estimation and inference. The key observation is that the robustification parameter should adapt to the sample size, dimension and moments for optimal tradeoff between bias and robustness. Our theoretical framework deals with heavy-tailed distributions with bounded (1 + δ)-th moment for any δ > 0. We establish a sharp phase transition for robust estimation of regression parameters in both low and high dimensions: when δ ≥ 1, the estimator admits a sub-Gaussian-type deviation bound without sub-Gaussian assumptions on the data, while only a slower rate is available in the regime 0 < δ < 1 and the transition is smooth and optimal. In addition, we extend the methodology to allow both heavy-tailed predictors and observation noise. Simulation studies lend further support to the theory. In a genetic study of cancer cell lines that exhibit heavy-tailedness, the proposed methods are shown to be more robust and predictive.

Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity.

In this article, we study the problem of testing the mean vectors of high dimensional data in both one-sample and two-sample cases. The proposed testing procedures employ maximum-type statistics and the parametric bootstrap techniques to compute the critical values. Different from the existing tests that heavily rely on the structural conditions on the unknown covariance matrices, the proposed tests allow general covariance structures of the data and therefore enjoy wide scope of applicability in practice. To enhance powers of the tests against sparse alternatives, we further propose two-step procedures with a preliminary feature screening step. Theoretical properties of the proposed tests are investigated. Through extensive numerical experiments on synthetic data sets and an human acute lymphoblastic leukemia gene expression data set, we illustrate the performance of the new tests and how they may provide assistance on detecting disease-associated gene-sets. The proposed methods have been implemented in an R-package HDtest and are available on CRAN.

Guarding against Spurious Discoveries in High Dimensions.

Many data-mining and statistical machine learning algorithms have been developed to select a subset of covariates to associate with a response variable. Spurious discoveries can easily arise in high-dimensional data analysis due to enormous possibilities of such selections. How can we know statistically our discoveries better than those by chance? In this paper, we define a measure of goodness of spurious fit, which shows how good a response variable can be fitted by an optimally selected subset of covariates under the null model, and propose a simple and effective LAMM algorithm to compute it. It coincides with the maximum spurious correlation for linear models and can be regarded as a generalized maximum spurious correlation. We derive the asymptotic distribution of such goodness of spurious fit for generalized linear models and L1-regression. Such an asymptotic distribution depends on the sample size, ambient dimension, the number of variables used in the fit, and the covariance information. It can be consistently estimated by multiplier bootstrapping and used as a benchmark to guard against spurious discoveries. It can also be applied to model selection, which considers only candidate models with goodness of fits better than those by spurious fits. The theory and method are convincingly illustrated by simulated examples and an application to the binary outcomes from German Neuroblastoma Trials.

[Effect of noise on the auditory system and the intelligence development of premature infants treated in the neonatal intensive care unit].

OBJECTIVE: High noise levels (>70 dB) in the neonatal intensive care unit (NICU) are common in some primary hospitals. This study aimed to investigate the noise in the NICU on auditory system and intelligence development in premature infants. METHODS: One hundred premature infants with respiratory distress syndrome who needed mechanical ventilation therapy were randomly divided into observation and control groups according to the use of earmuffs. The duration of mechanical ventilation therapy lasted for 2 to 15 days in the two groups. After weaning from mechanical ventilator, the auditory brainstem response, cranial B-ultrasonography, and the intelligence development assessment were performed. RESULTS: The percentage of total (23% vs 47%) and mild hearing loss (15% vs 35%) in the observation group was significantly lower than that in the control group (p<0.05) 2 to 3 days after weaning from mechanical ventilator. The incidence of periventricular hemorrhage intraventricular hemorrhage (PVH-IVH) or periventricular leukomalacia (PVL) in the observation group was significantly lower than that in the control group (21% vs 42%; p<0.05). The intelligence development assessment performed in the first 6 and 12 months of life showed that the mental development index and the psychomotor development index in the observation group were much higher than those in the control group (p<0.05). CONCLUSIONS: The noise in the NICU can result in mild hearing loss and retardation of intelligence development and increase the incidence of PVH-IVH and PVL in premature infants. The use of earmuff may reduce the adverse events.