The evaluation of catechins reducing heterocyclic aromatic amine formation: Structure-activity relationship and mechanism speculation.

The favorable inhibitory effect of tea polyphenols on heterocyclic aromatic amines (HAAs) has been confirmed in many past studies. The objective of this study was to investigate the structure-activity relationship of catechins that act as inhibitors of HAA formation in chemical models. Two kinds of quantitative structure-activity relationship models for catechin-inhibiting-HAA were established. We chose two kinds of HAAs including 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) and 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx), and five catechins including epigallocatechin gallate (EGCG), epicatechin gallate (ECG), epigallocatechin (EGC), epicatechin (EC), and catechin (C). The inhibitory effect of five catechins were in the following order: EGCG > ECG > EGC > C > EC. Thereinto, EGCG and ECG showed dramatically better inhibition on the formation of PhIP and MeIQx, especially EGCG. Further, the mechanisms of catechin-inhibiting-HAA were speculated by correlation analysis. The free radical-scavenging ability was predicted to be the most relevant to the inhibitory effect of ECG, EGC, EC and C on HAAs. Differently, the phenylacetaldehyde-trapping ability might be the more important mechanism of EGCG inhibiting PhIP in chemical model system. This study may bring a broader idea for controlling the formation of HAAs according to the structure of catechins.

A systematic study on the chemical model of polycyclic aromatic hydrocarbons formation from nutrients (glucose, amino acids, fatty acids) in food.

Polycyclic aromatic hydrocarbons (PAHs), prominent carcinogens formed during food processing, pose health risks through long-term consumption. This study focuses on 16 priority PAHs in the European Union, investigating their formation during pyrolysis. Glucose, amino acids and fatty acids are important food nutrients. To further explore whether these nutrients in food form PAHs during heating, a single chemical model method was used to heat these nutrients respectively, and GC-MS/MS was used to identify and quantify the obtained components. Glucose is the most basic nutrient in food, so the influence of water, pH, temperature and other factors on the formation of PAHs was studied in the glucose model. At the same time, the models of amino acids and fatty acids were used to assist in improving the entire nutrient research system. According to our results, some previously reported mechanisms of PAHs formation by fatty acids heating were confirmed. In addition, glucose and amino acids could also produce many PAHs after heating, and some conclusions were improved by comparing the intermediates of PAHs from three types of nutrients.

Exploring the roles of excess amino acids, creatine, creatinine, and glucose in the formation of heterocyclic aromatic amines by UPLC-MS/MS.

The prevention and control of heterocyclic aromatic amines (HAA) formation to mitigate of potential risks to humans, can be achieved by targeting their precursors. In this study, the detailed roles of individual and excess component (20 common α-amino acids, creatine, creatinine, and glucose) on HAA formation in roasted beef patties were examined using UPLC-MS/MS. The results confirmed the reported classical precursors of HAAs. Some components regulated the competitive production of Norharman and Harman. Glycine (Gly) and glucose favored Norharman formation, while cysteine (Cys) and phenylalanine (Phe) for Harman. Serine (Ser) and threonine (Thr) were identified as potential precursors for IQx-type HAAs. Interestingly, methionine (Met), Gly, Thr, Cys, alanine (Ala), and Ser were revealed as more targeted underlying precursors for 1,6-DMIP and 1,5,6-TMIP, and the formation mechanism was inferred. Furthermore, Pro, Leu, His, Ile, Lys and Asp were considered as great inhibitors for HAAs.

Target area distillation and section attention segmentation network for accurate 3D medical image segmentation.

3D medical image segmentation has an essential role in medical image analysis, while attention mechanism has improved the performance by a large margin. However, existing methods obtained the attention coefficient in a small receptive field, resulting in possible performance limitations. Radiologists usually scan all the slices first to have an overall idea of the target, and then analyze regions of interest in multiple 2D views in clinic practice. We simulate radiologists' recognition process and propose to exploit the 3D context information in a deeper manner for accurate 3D medical images segmentation. Due to the similarity of human body structure, medical images of different populations have highly similar shape and location information, so we use target region distillation to extract the common segmented region information. Particularly, we proposed two optimizations including Target Area Distillation and Section Attention. Target Area Distillation adds positions information to the original input to let the network has an initial attention of the target, while section attention performs attention extraction in three 2D sections thus with large range of receptive field. We compare our method against several popular networks in two public datasets including ImageCHD and COVID-19. Experimental results show that our proposed method improves the segmentation Dice score by 2-4% over the state-of-the-art methods. Our code has been released to the public (Anonymous link).