Selenium May Be Involved in Esophageal Squamous Cancer Prevention by Affecting GPx3 and FABP1 Expression: A Case-Control Study Based on Bioinformatic Analysis.

The role of selenium in the developmental process of esophageal cancer (EC) requires further investigation. To explore the relationship between selenium-related factors and EC through bioinformatic analysis, a case-control study was conducted to verify the results. Utilizing the GEPIA and TCGA databases, we delineated the differential expression of glutathione peroxidase 3 (GPx3) in EC and normal tissues, identified differentially expressed genes (DEGs), and a performed visualization analysis. Additionally, 100 pairs of dietary and plasma samples from esophageal precancerous lesions (EPLs) of esophageal squamous cancer (ESCC) cases and healthy controls from Huai'an district, Jiangsu, were screened. The levels of dietary selenium, plasma selenium, and related enzymes were analyzed using inductively coupled plasma mass spectrometry (ICP-MS) or ELISA kits. The results showed lower GPx3 expression in tumor tissues compared to normal tissues. Further analysis revealed that DEGs were mainly involved in the fat digestion and absorption pathway, and the core protein fatty acid binding protein 1 (FABP1) was significantly upregulated and negatively correlated with GPx3 expression. Our case-control study found that selenium itself was not associated with EPLs risk. However, both the decreased concentration of GPx3 and the increase in FABP1 were positively correlated with the EPLs risk (p for trend = 0.035 and 0.046, respectively). The different expressions of GPx3 and FABP1 reflect the potential of selenium for preventing ESCC at the EPLs stage. GPx3 may affect myocardial infarction through FABP1, which remains to be further studied.

Study on alleviation effect of stachyose on food allergy through TLR2/NF-κB signal pathway in a mouse model.

AIMS: To explore the effect on food allergy of stachyose. MAIN METHODS: The egg allergen ovalbumin (OVA) was used to induce a food allergy model of BALB/c mice, and different doses of stachyose were given in process. Using enzyme-linked immunosorbent assay (ELISA) methods to detect the levels of IgE, IgG1, histamine and cytokines. And flow cytometry was used to analyze TH1/TH2 balance further. Besides, Hematoxylin-eosin (HE) staining was used to observe changes of intestinal morphology. Lastly, Reverse Transcription-Polymerase Chain Reaction (RT-qPCR) and western Blot was conducted to explore the possible mechanism. KEY FINDINGS: Compared with OVA group, serum IgE and IgG1 levels in the low-dose (1mg/mouse) group and high-dose (5mg/mouse) group of stachyose were significantly reduced (P < 0.05); the level of plasma histamine was also decreased significantly (P < 0.05) and the body temperature were decreased. In all, allergic symptoms were alleviated after stachyose treatment. Furthermore, TH1/TH2 balance was improved after stachyose treatment. Lastly, the expression of TLR2 and NF-κB were increased significantly (P < 0.05) in both mRNA and protein levels after stachyose treatment. SIGNIFICANCE: Food allergy was alleviated through improving TH1/TH2 balance by activating TLR2/NF-κB signal by stachyose.

Learning impurity spectral functions from density of states.

Using numerical renormalization group calculation, we construct a dataset with 100 K samples, and train six different neural networks for the prediction of spectral functions from density of states (DOS) of the host material. We find that a combination of gated recurrent unit (GRU) network and bidirectional GRU (BiGRU) performances the best among all the six neural networks. The mean absolute error of the GRU + BiGRU network can reach 0.052 and 0.043 when this network is evaluated on the original dataset and two other independent datasets. The average time of spectral function predictions from machine learning is on the scale of 10-5-10-6that of traditional impurity solvers for Anderson impurity model. This investigation pave the way for the application of recurrent neural network and convolutional neural network in the prediction of spectral functions from DOSs in machine learning solvers of magnetic impurity problems.