Ziyu Han

The association between heavy metal exposure and diabetes is controversial and vitamin E (VE) may reduce diabetes risk. We aimed to examine the associations between blood heavy metals (BHMs) and diabetes risk and VE’s role in the relationship. From the 2007–2018 NHANES, 10,721 participants aged ≥ 18 were included for multiple statistical analyses, which revealed that BHMs and dietary VE intake were negatively associated with diabetes and fasting plasma glucose (FPG). The diabetes prevalence in each quartile (Q) of heavy metal exposure increased with age, but within age Q4, it generally decreased with exposure quartiles. Moreover, BHMs were positively associated with all-cause and diabetes-related mortalities with aging, which induced an age breakpoint of 65 years for age-stratified analyses on the associations between BHMs and diabetes risk. In those aged > 65, BHMs were negatively correlated with diabetes risk and its biomarkers; however, in adults aged 18–65, the correlation was positive. At higher VE intake levels, blood lead was associated with a lower diabetes risk and all three BHMs demonstrated lower FPG levels than those at lower VE intake levels. In conclusion, consuming sufficient VE and avoiding heavy metal exposure are highly recommended to reduce diabetes risk.

Background/Objectives: As a hyperaccumulator of selenium (Se), Cardamine violifolia (Cv) and its peptide extract could ameliorate the negative effects of a high-fat diet (HFD). However, the effects of the coaccumulation of cadmium (Cd) in Se-enriched Cv (Cv2) and the potential confounding effect on the roles of enriched Se remain unknown. We aimed to investigate whether Cv2 could alleviate HFD-induced lipid disorder and liver damage. Methods: Three groups of 31-week-old female mice were fed for 41 weeks (n = 10–12) with a control Cv-supplemented diet (Cv1D, 0.15 mg Se/kg, 30 µg Cd/kg, and 10% fat calories), a control Cv-supplemented HFD (Cv1HFD, 45% fat calories), and a Cv2-supplemented HFD (Cv2HFD, 1.5 mg Se/kg, 0.29 mg Cd/kg, and 45% fat calories). Liver and serum were collected to determine the element concentrations, markers of liver injury and lipid disorder, and mRNA and/or protein expression of lipid metabolism factors, heavy metal detoxification factors, and selenoproteins. Results: Both Cv1HFD and Cv2HFD induced obesity, and Cv2HFD downregulated Selenoi and upregulated Dio3 compared with Cv1D. When comparing Cv2HFD against Cv1HFD, Cv2 increased the liver Se and Cd, the protein abundance of Selenoh, and the mRNA abundance of 10 selenoproteins; reduced the serum TG, TC, and AST; reduced the liver TG, lipid droplets, malondialdehyde, and mRNA abundance of Mtf1 and Mt2; and differentially regulated the mRNA levels of lipid metabolism factors. Conclusions: Cv2 alleviated HFD-induced lipid dysregulation and liver damage, which was probably associated with its unique Se speciation. However, further research is needed to explore the interaction of plant-coenriched Se and Cd and its effects on health.

In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination platform as an image capturing system to obtain photographs of laboratory cultures. Consequently, a dataset was introduced consisting of 390 photos of agar plate cultures, which included 8 microorganisms. Secondly, we implemented a new algorithm for image preprocessing based on light intensity correction, which facilitated clearer differentiation between colony and media areas. Thirdly, a U²-Net was used to predict the probability distribution of the edge of the Petri dish in images to locate region of interest (ROI), and then threshold segmentation was applied to separate it. This U²-Net achieved an F1 score of 99.5% and a mean absolute error (MAE) of 0.0033 on the validation set. Then, another U²-Net was used to separate the colony region within the ROI. This U²-Net achieved an F1 score of 96.5% and an MAE of 0.005 on the validation set. After that, the colony area was segmented into multiple components containing single or adhesive colonies. Finally, the colony components (CC) were innovatively rotated and the image crops were resized as the input (with 14,921 image crops in the training set and 4281 image crops in the validation set) for the ResNet50 network to automatically count the number of colonies. Our method achieved an overall recovery of 97.82% for colony counting and exhibited excellent performance in adhesion classification. To the best of our knowledge, the proposed “light intensity correction-based image preprocessing→U²-Net segmentation for Petri dish edge→U²-Net segmentation for colony region→ResNet50-based counting” scheme represents a new attempt and demonstrates a high degree of automation and accuracy in recognizing and counting single-colony and multi-colony targets.