Inhibition of polyphenols on Maillard reaction products and their induction of related diseases: A comprehensive review.

BACKGROUND: Food products undergo a pronounced Maillard reaction (MR) during the cooking process, leading to the generation of substantial quantities of Maillard reaction products (MRPs). Within this category, advanced glycation end products (AGEs), acrylamide (AA), and heterocyclic amines (HAs) have been implicated as potential risk factors associated with the development of diseases. PURPOSE: To explore the effects of polyphenols, a class of bioactive compounds found in plants, on the inhibition of MRPs and related diseases. Previous research has mainly focused on their interactions with proteins and their effects on the gastrointestinal tract and other diseases, while fewer studies have examined their inhibitory effects on MRPs. The aim is to offer a scientific reference for future research investigating the inhibitory role of polyphenols in the MR. METHODS: The databases PubMed, Embase, Web of Science and The Cochrane Library were searched for appropriate research. RESULTS: Polyphenols have the potential to inhibit the formation of harmful MRPs and prevent related diseases. The inhibition of MRPs by polyphenols primarily occurs through the following mechanisms: trapping α-dicarbonyl compounds, scavenging free radicals, chelating metal ions, and preserving protein structure. Simultaneously, polyphenols exhibit the ability to impede the onset and progression of related diseases such as diabetes, atherosclerosis, cancer, and Alzheimer's disease through diverse pathways. CONCLUSION: This review presents that inhibition of polyphenols on Maillard reaction products and their induction of related diseases. Further research is imperative to enhance our comprehension of additional pathways affected by polyphenols and to fully uncover their potential application value in inhibiting MRPs.

Reinforcing alginate matrixes by tea polysaccharide conjugates or their stabilized nanoemulsion for probiotics encapsulation: Characterization, survival after gastrointestinal digestion and ambient storage.

Tea polysaccharide conjugates (TPC) were used as fillers in the form of biopolymer or colloidal particles (TPC stabilized nanoemulsion, NE) for reinforcing alginate (ALG) beads to improve the probiotic viability. Results demonstrated that adding TPC or NE to ALG beads significantly enhanced the gastrointestinal viability of encapsulated probiotics when compared to free cells. Moreover, the survivability of free and ALG encapsulated probiotics markedly decreased to 2.03 ± 0.05 and 2.26 ± 0.24 log CFU/g, respectively, after 2 weeks ambient storage, indicating pure ALG encapsulation had no effective storage protective capability. However, adding TPC or NE could greatly enhance the ambient storage viability of probiotics, with ALG + NE beads possessing the best protection (8.93 ± 0.06 log CFU/g) due to their lower water activity and reduced porosity. These results suggest that TPC and NE reinforced ALG beads have the potential to encapsulate, protect and colonic delivery of probiotics.

Effects of pile fermentation on the physicochemical, functional, and biological properties of tea polysaccharides.

This study investigated the influence of pile fermentation on the physicochemical, functional, and biological properties of tea polysaccharides (TPS). Results indicated that the extraction yield, uronic acid content, and polyphenol content of TPS greatly increased from 1.8, 13.1 and 6.3 % to 4.1, 27.9, and 7.8 %, respectively, but the molecular weight markedly decreased from 153.7 to 76.0 kDa after pile fermentation. Additionally, the interfacial, emulsion formation, and emulsion stabilization properties of TPS were significantly improved after pile fermentation. For instance, 1.0 wt% TPS isolated from dark tea (D-TPS) can fabricate 8.0 wt% MCT oil-in-water nanoemulsion (d32 ≈ 159 nm) with potent storage stability. Moreover, the antioxidant and α-glucosidase inhibitory activities of D-TPS was higher than that of TPS isolated from sun-dried raw tea (R-TPS). Overall, this study indicated that pile fermentation markedly affected the physicochemical and structural characteristics of TPS, thereby improving their functional and biological properties.

Identification of Gene Regulatory Networks Using Variational Bayesian Inference in the Presence of Missing Data.

The identification of gene regulatory networks (GRN) from gene expression time series data is a challenge and open problem in system biology. This paper considers the structure inference of GRN from the incomplete and noisy gene expression data, which is a not well-studied issue for GRN inference. In this paper, the dynamical behavior of the gene expression process is described by a stochastic nonlinear state-space model with unknown noise information. A variational Bayesian (VB) framework are proposed to estimate the parameters and gene expression levels simultaneously. One of the advantages of this method is that it can easily handle the missing observations by generating the prediction values. Considering the sparsity of GRN, the smoothed gene data are modeled by the extreme gradient boosting tree, and the regulatory interactions among genes are identified by the importance scores based on the tree model. The proposed method is tested on the artificial DREAM4 datasets and one real gene expression dataset of yeast. The comparative results show that the proposed method can effectively recover the regulatory interactions of GRN in the presence of missing observations and outperforms the existing methods for GRN identification.

Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis.

Tumor-derived circulating cell-free DNA (cfDNA) provides critical clues for cancer early diagnosis, yet it often suffers from low sensitivity. Here, we present a cancer early diagnosis approach using tumor fractions deciphered from circulating cfDNA methylation signatures. We show that the estimated fractions of tumor-derived cfDNA from cancer patients increase significantly as cancer progresses in two independent datasets. Employing the predicted tumor fractions, we establish a Bayesian diagnostic model in which training samples are only derived from late-stage patients and healthy individuals. When validated on early-stage patients and healthy individuals, this model exhibits a sensitivity of 86.1% for cancer early detection and an average accuracy of 76.9% for tumor localization at a specificity of 94.7%. By highlighting the potential of tumor fractions on cancer early diagnosis, our approach can be further applied to cancer screening and tumor progression monitoring.

PyMiner: A method for metabolic pathway design based on the uniform similarity of substrate-product pairs and conditional search.

Metabolic pathway design is an essential step in the course of constructing an efficient microbial cell factory to produce high value-added chemicals. Meanwhile, the computational design of biologically meaningful metabolic pathways has been attracting much attention to produce natural and non-natural products. However, there has been a lack of effective methods to perform metabolic network reduction automatically. In addition, comprehensive evaluation indexes for metabolic pathway are still relatively scarce. Here, we define a novel uniform similarity to calculate the main substrate-product pairs of known biochemical reactions, and develop further an efficient metabolic pathway design tool named PyMiner. As a result, the redundant information of general metabolic network (GMN) is eliminated, and the number of substrate-product pairs is shown to decrease by 81.62% on average. Considering that the nodes in the extracted metabolic network (EMN) constructed in this work is large in scale but imbalanced in distribution, we establish a conditional search strategy (CSS) that cuts search time in 90.6% cases. Compared with state-of-the-art methods, PyMiner shows obvious advantages and demonstrates equivalent or better performance on 95% cases of experimentally verified pathways. Consequently, PyMiner is a practical and effective tool for metabolic pathway design.

Research on the Design of and Preference for Collection Modes of Reusable Takeaway Containers to Promote Sustainable Consumption.

The development of the online to offline business has accelerated the growth of the online food ordering market in China. The widespread use of disposable takeaway containers has resulted in a large amount of waste, which seriously affects the ecological environment. This paper studied the collection modes of reusable takeaway containers and the preferences of consumers and merchants. First, after two rounds of discussion and revision, four takeaway container collection modes were designed. Second, based on the survey results of consumers and merchants, a binary logistic regression model was applied to analyze the preferences of consumers and merchants. The results showed that the consumers' delivery requirements and the current disposal of takeaway containers had a significant impact on consumers' preferences. Consumers were more concerned about the hygienic status of the containers, food health and safety, while the merchants were more concerned about the increased costs. The promotion of collection modes requires the special consideration of the locations of dishwashing facilities and increased costs. Finally, according to the preferences and concerns of consumers and merchants, several suggestions on promoting the collection mode, such as the use of different promotion strategies for different people, the short distance of dishwashing facilities, reward systems, and food safeguard measures were proposed. This research provides guidance for decision making regarding the sustainable consumption and the promotion of reusable takeaway containers, which will contribute to resource conservation, ecological environmental improvement and sustainability.

Two Efficient Twin ELM Methods With Prediction Interval.

In the operational optimization and scheduling problems of actual industrial processes, such as iron and steel, and microelectronics, the operational indices and process parameters usually need to be predicted. However, for some input and output variables of these prediction models, there may exist a lot of uncertainties coming from themselves, the measurement error, the rough representation, and so on. In such cases, constructing a prediction interval (PI) for the output of the corresponding prediction model is very necessary. In this paper, two twin extreme learning machine (TELM) models for constructing PIs are proposed. First, we propose a regularized asymmetric least squares extreme learning machine (RALS-ELM) method, in which different weights of its squared error loss function are set according to whether the error of the model output is positive or negative in order that the above error can be differentiated in the parameter learning process, and Tikhonov regularization is introduced to reduce overfitting. Then, we propose an asymmetric Bayesian extreme learning machine (AB-ELM) method based on the Bayesian framework with the asymmetric Gaussian distribution (AB-ELM), in which the weights of its likelihood function are determined as the same method in RALS-ELM, and the type II maximum likelihood algorithm is derived to learn the parameters of AB-ELM. Based on RALS-ELM and AB-ELM, we use a pair of weights following the reciprocal relationship to obtain two nonparallel regressors, including a lower-bound regressor and an upper-bound regressor, respectively, which can be used for calculating the PIs. Finally, some discussions are given, about how to adjust the weights adaptively to meet the desired PI, how to use the proposed TELMs for nonlinear quantile regression, and so on. Results of numerical comparison on data from one synthetic regression problem, three University of California Irvine benchmark regression problems, and two actual industrial regression problems show the effectiveness of the proposed models.