DFUSNN: zero-shot dual-domain fusion unsupervised neural network for parallel MRI reconstruction.

Objective. Recently, deep learning models have been used to reconstruct parallel magnetic resonance (MR) images from undersampled k-space data. However, most existing approaches depend on large databases of fully sampled MR data for training, which can be challenging or sometimes infeasible to acquire in certain scenarios. The goal is to develop an effective alternative for improved reconstruction quality that does not rely on external training datasets.Approach. We introduce a novel zero-shot dual-domain fusion unsupervised neural network (DFUSNN) for parallel MR imaging reconstruction without any external training datasets. We employ the Noise2Noise (N2N) network for the reconstruction in the k-space domain, integrate phase and coil sensitivity smoothness priors into the k-space N2N network, and use an early stopping criterion to prevent overfitting. Additionally, we propose a dual-domain fusion method based on Bayesian optimization to enhance reconstruction quality efficiently.Results. Simulation experiments conducted on three datasets with different undersampling patterns showed that the DFUSNN outperforms all other competing unsupervised methods and the one-shot Hankel-k-space generative model (HKGM). The DFUSNN also achieves comparable results to the supervised Deep-SLR method.Significance. The novel DFUSNN model offers a viable solution for reconstructing high-quality MR images without the need for external training datasets, thereby overcoming a major hurdle in scenarios where acquiring fully sampled MR data is difficult.

Nucleolar protein NOC4L inhibits tumorigenesis and progression by attenuating SIRT1-mediated p53 deacetylation.

SIRT1 is an NAD+-dependent deacetylase and plays an important role in the deacetylation of both histone and non-histone proteins. Many studies revealed that SIRT1 is upregulated in a variety of tumors and tightly associated with tumorigenesis and cancer progression, but the detailed underlying mechanism of the biological processes remains unclarified. In the present study, we found a nucleolar protein NOC4L, human ortholog of yeast Noc4p, which is essential for the nuclear export of the ribosomal 40S subunit and could bind to SIRT1 to inhibit SIRT1 mediated deacetylation of p53. NOC4L interacts with SIRT1 in variety of cells under nucleolar stress and directly interacts with SIRT1 in vitro. Furthermore, we determined the C-terminal of NOC4L and the catalytic domain of SIRT1 were required for their interaction. Overexpression of NOC4L did not change the protein levels of SIRT1 or p53, but increased the acetylation of p53 and promoted cell apoptosis. Additionally, NOC4L inhibited tumor cell proliferation in a p53-dependent manner and restrained tumor growth in a nude mice xenograft model. Clinically, colorectal cancer patients with the high expression of NOC4L had a better prognosis as TP53 was normally expressed, but no significant difference was observed in survival with mutant TP53. Taken together, our results identified a novel SIRT1 regulatory protein and broaden our understanding of the molecular mechanism of how nucleolar protein NOC4L regulates p53 under nucleolar stress. This research provides an insight into tumorigenesis and cell self-protection in the early stage of DNA damage.

Macrophage deletion of Noc4l triggers endosomal TLR4/TRIF signal and leads to insulin resistance.

In obesity, macrophages drive a low-grade systemic inflammation (LSI) and insulin resistance (IR). The ribosome biosynthesis protein NOC4 (NOC4) mediates 40 S ribosomal subunits synthesis in yeast. Hereby, we reported an unexpected location and function of NOC4L, which was preferentially expressed in human and mouse macrophages. NOC4L was decreased in both obese human and mice. The macrophage-specific deletion of Noc4l in mice displayed IR and LSI. Conversely, Noc4l overexpression by lentivirus treatment and transgenic mouse model improved glucose metabolism in mice. Importantly, we found that Noc4l can interact with TLR4 to inhibit its endocytosis and block the TRIF pathway, thereafter ameliorated LSI and IR in mice.

Advances in Research on Diabetes by Human Nutriomics.

The incidence and prevalence of diabetes mellitus (DM) have increased rapidly worldwide over the last two decades. Because the pathogenic factors of DM are heterogeneous, determining clinically effective treatments for DM patients is difficult. Applying various nutrient analyses has yielded new insight and potential treatments for DM patients. In this review, we summarized the omics analysis methods, including nutrigenomics, nutritional-metabolomics, and foodomics. The list of the new targets of SNPs, genes, proteins, and gut microbiota associated with DM has been obtained by the analysis of nutrigenomics and microbiomics within last few years, which provides a reference for the diagnosis of DM. The use of nutrient metabolomics analysis can obtain new targets of amino acids, lipids, and metal elements, which provides a reference for the treatment of DM. Foodomics analysis can provide targeted dietary strategies for DM patients. This review summarizes the DM-associated molecular biomarkers in current applied omics analyses and may provide guidance for diagnosing and treating DM.

Ambient air pollution, H19/DMR methylation in cord blood and newborn size: A pilot study in Zhengzhou City, China.

Prenatal exposure to air pollutants is believed to be associated with adverse birth outcomes. However, the potential mechanisms, especially the epigenetic modified effects, still remain unclear. This study was designed to explore the association of air pollution, H19/DMR methylation levels, and birth weight and length. A total of 527 mother-infant pairs were recruited from Houzhai Center Hospital, Zhengzhou. Air pollution data during the study period was collected. The methylation at H19 promoter region and H19 DMR in maternal and cord bloods were determined using real-time PCR analysis. Ridge regression was used to analyze the association of air pollutants exposure during gestation with H19/DMR methylation and birth weight and length respectively. Results showed that prenatal exposure to NO2 was associated with higher H19 methylation in cord blood. Whereas SO2 and PM10 exposure were associated with lower H19 and H19 DMR methylation respectively. After stratification by pregnancy trimesters, the association of H19 methylation in cord blood with PM10 exposure also was found. Furthermore, prenatal exposures to air pollutants also were associated with birth weight and length. Specifically, with the increase of maternal SO2 exposure during the entire pregnancy, birth weight and length significantly decreased. While birth weight and birth length were significantly increased with NO2 exposure. The stratified analysis also found the associations between PM10 exposure and birth sizes in different trimesters. In conclusion, the gene methylation level in cord blood might be associated with prenatal environmental exposures. Birth weight and length were associated with both prenatal environmental exposures and genetic factors.