Discrimination and quantification of volatile compounds in beer by FTIR combined with machine learning approaches.

The composition of volatile compounds in beer is crucial to the quality of beer. Herein, we identified 23 volatile compounds, namely, 12 esters, 4 alcohols, 5 acids, and 2 phenols, in nine different beer types using GC-MS. By performing PCA of the data of the flavor compounds, the different beer types were well discriminated. Ethyl caproate, ethyl caprylate, and phenylethyl alcohol were identified as the crucial volatile compounds to discriminate different beers. PLS regression analysis was performed to model and predict the contents of six crucial volatile compounds in the beer samples based on the characteristic wavelength of the FTIR spectrum. The R2 value of each sample in the prediction model was 0.9398-0.9994, and RMSEP was 0.0122-0.7011. The method proposed in this paper has been applied to determine flavor compounds in beer samples with good consistency compared with GC-MS.

A novel approach has been developed to produce pure plant-based gel soy yogurt by combining soy proteins (7S/11S), high pressure homogenization, and glycation reaction.

This research sought to examine how the physicochemical characteristics of soy globulins and different processing techniques influence the gel properties of soy yogurt. The goal was to improve these gel properties and rectify any texture issues in soy yogurt, ultimately aiming to produce premium-quality plant-based soy yogurt. In this research study, the investigation focused on examining the impact of 7S/11S, homogenization pressure, and glycation modified with glucose on the gel properties of soy yogurt. A plant-based soy yogurt with superior gel and texture properties was successfully developed using a 7S/11S globulin-glucose conjugate at a 1:3 ratio and a homogenization pressure of 110 MPa. Compared to soy yogurt supplemented with pectin or gelatin, this yogurt demonstrated enhanced characteristics. These findings provide valuable insights into advancing plant protein gels and serve as a reference for cultivating new soybean varieties by soybean breeding experts.

Study on potential antigenicity and functional properties of whey protein treated by high hydrostatic pressure based on structural analysis.

High hydrostatic pressure (HHP) is extensively utilized in the field of food processing due to its remarkable ability to preserve the freshness of food. The potential antigenicity of ß-lactoglobulin (ß-LG) in whey protein isolate (WPI, 3%) treated by HHP was detected by enzyme linked immunosorbent assay (ELISA) using monoclonal antibodies. Furthermore, the impact of pressure-induced structural alterations on the emulsification properties and antioxidant activity of WPI was investigated. The findings revealed that pressures exceeding 300 MPa resulted in molecular aggregation, the formation of inter-molecular disulfide bonds, and an increase in surface hydrophobicity (H0). The percentage of ß-sheet decreased along with the pressure. The results showed the increment of α-helix and ß-turn with pressure. ELISA demonstrated a significant reduction in the antigenicity of ß-LG following HHP treatment (100-600 MPa), with a slight recovery observed at 300 MPa. These spatial structural modifications led to the unfolding of the ß-LG molecule, thereby enhancing its digestibility. Moreover, HHP treatment substantially improved the antioxidant properties, with the exposure to hydrophobic amino acids contributing to increased antioxidant properties and emulsion stability.

Preparation of fat substitute based on maize starch hydrolysates and application in reduced-fat acidified milk gel.

Reduced-fat food has become a popular choice among contemporary consumers. This study aims to develop a starch-based fat substitute and incorporate it into reduced-fat milk gel acidified with glucono-δ-lactone (GDL) to achieve similar rheological properties as a full-fat gel. The gel properties of the fat substitute were assessed. The study examined the rheological properties, syneresis, textural properties and microstructure of acidified milk gels while also monitoring acidification process. Starch hydrolysates with low dextrose equivalent (DE) (<5.1 %) can serve as an effective fat substitute due to their excellent gelling properties The rheological and textural properties of the reduced-fat acidified milk gel with DE at 3.1 % of starch hydrolysate and 30 % fat substitution are similar to those of the full-fat milk gel. The syneresis and confocal laser scanning microscopy (CLSM) results indicated that the microstructure of the reduced-fat acidified milk gel was similar to the full-fat version. Moreover, the sensory properties of the reduced-fat acidified milk gel were acceptable when the DE was 3.1 %, and 30 % fat was replaced. In our study, we utilized hydrolyzed starch to produce reduced-fat acidified milk gels, which could potentially be used in the development of reduced-fat yogurt formulations.

Isolation, Purification, and Structural Characterization of Polysaccharides from Codonopsis pilosula and Their Anti-Tumor Bioactivity by Immunomodulation.

The activity of polysaccharides is usually related to molecular weight. The molecular weight of polysaccharides is critical to their immunological effect in cancer therapy. Herein, the Codonopsis polysaccharides of different molecular weights were isolated using ultrafiltration membranes of 60- and 100-wDa molecular weight cut-off to determine the relationship between molecular weight and antitumor activities. First, three water-soluble polysaccharides CPPS-I (<60 wDa), CPPS-II (60-100 wDa), and CPPS-III (>100 wDa) from Codonopsis were isolated and purified using a combination of macroporous adsorption resin chromatography and ultrafiltration. Their structural characteristics were determined through chemical derivatization, GPC, HPLC, FT-IR, and NMR techniques. In vitro experiments indicated that all Codonopsis polysaccharides exhibited significant antitumor activities, with the tumor inhibition rate in the following order: CPPS-II > CPPS-I > CPPS-III. The treatment of CPPS-II exhibited the highest inhibition rate at a high concentration among all groups, which was almost as efficient as that of the DOX·HCL (10 µg/mL) group at 125 µg/mL concentration. Notably, CPPS-II demonstrated the ability to enhance NO secretion and the antitumor ability of macrophages relative to the other two groups of polysaccharides. Finally, in vivo experiments revealed that CPPS-II increased the M1/M2 ratio in immune system regulation and that the tumor inhibition effect of CPPS-II + DOX was superior to that of DOX monotherapy, implying that CPPS-II + DOX played a synergistic role in regulating the immune system function and the direct tumor-killing ability of DOX. Therefore, CPPS-II is expected to be applied as an effective cancer treatment or adjuvant therapy.

N-acetylcysteine improves autism-like behavior by recovering autophagic deficiency and decreasing Notch-1/Hes-1 pathway activity.

N-acetylcysteine (NAC) has been reported to improve social interaction behavior, irritability, self-injury, and anxiety-like behavior in autism. However, the molecular mechanism underlying the therapeutic roles of NAC in autism remains unknown. This study mainly aimed to investigate the therapeutic effect of NAC on valproic acid (VPA)-induced autism model and the underlying mechanisms. Our results showed that NAC ameliorated the deficits in sociability and the anxiety- and repetitive-like behaviors displayed by VPA-exposed rats. In addition, VPA exposure induced autophagic deficiency and enhanced Notch-1/Hes-1 pathway activity based on lowered Beclin-1 and LC3B levels, while increased expression of p62, Notch-1, and Hes-1 expression at the protein level. However, NAC recovered VPA-induced autophagic deficiency and reduced Notch-1/Hes-1 pathway activity in a VPA-exposed autism rat model and SH-SY5Y neural cells. The present results demonstrated that NAC improves autism-like behavioral abnormalities by inactivating Notch-1/Hes-1 signaling pathway and recovering autophagic deficiency. Taken together, this study helps to elucidate a novel molecular mechanism that underlies the therapeutic actions of NAC in autism and suggests its potential to ameliorate behavioral abnormalities in neurodevelopmental disorders.

Purification and identification of xanthine oxidase inhibitory peptides from enzymatic hydrolysate of α-lactalbumin and bovine colostrum casein.

The development of food-derived Xanthine Oxidase (XO) inhibitors is critical to the treatment of hyperuricemia and oxidative stress-related disease. Few studies report on milk protein hydrolysates' XO inhibitory activity, with the mechanism of their interaction remaining elusive. Here, different commercial enzymes were used to hydrolyze α-lactalbumin and bovine colostrum casein. The two proteins hydrolyzed by alkaline protease exhibited the most potent XO inhibitory activity (bovine casein: IC50 = 0.13 mg mL-1; α-lactalbumin: IC50 = 0.28 mg mL-1). Eight potential XO inhibitory peptides including VYPFPGPI, GPVRGPFPIIV, VYPFPGPIPN, VYPFPGPIHN, QLKRFSFRSFIWR, LVYPFPGPIHN, AVFPSIVGR, and GFININSLR (IC50 of 4.67-8.02 mM) were purified and identified from alkaline protease hydrolysates by using gel filtration, LC-MS/MS and PeptideRanker. The most important role of inhibiting activity of peptides is linked to hydrophobic interactions and hydrogen bonding based on the results of molecular docking and molecular dynamics simulation. The enzymatic hydrolysate of α-lactalbumin and bovine colostrum casein could be a competitive candidates for hyperuricemia-resisting functional food.

Low-fat ice cream model system: impact of incorporation of alcalase hydrolyzed zein.

Research on fat substitutes with low calories and good flavor is important to reduce the fat content in food. In this paper, the selection of fat substitutes and the preparation of low-fat ice cream were carried out through looking at the emulsion properties of the enzymatic hydrolysis of zein. The results showed that the emulsifying activity of zein after enzymatic hydrolysis for 10 min was 66.76 m2 g-1, and the emulsifying stability was 78.51 min, showing the best emulsifying properties. Enzymatic hydrolysis of zein can effectively reduce the degree of lipid oxidation. The protein digestibility in intestinal juice was also significantly improved, and the release rate of free fatty acids in the emulsion reached more than 80%. The viscosity, shear stress, elastic modulus, electronic nose and electronic tongue of ice cream with 10% oil substitute were close to those of full-fat ice cream. It is expected to provide a basis for the development of functional foods.

Nanoemulsion: A novel delivery approach for thermosensitive IgG on inhibiting milk fat oxidation.

IgG, a biologically active substance in bovine colostrum, is easily inactivated during heat treatment and edible process to lose its biological activity. Nanoemulsion can effectively protect IgG to maintain its biological activity from injurious treatment. In this study, a food-grade nanoemulsion system was developed to protect IgG from heat and acid damage. It can be found that the residual rate of nanoemulsion-protected IgG reaches 87.1 % after 10 min at 72 °C. After 5 min at 82 °C, the residual rate of IgG in nanoemulsion was 18.7 % higher than that in PBS. In the simulated gastric fluid at pH 2.0, the residual rate of IgG in the nanoemulsion reacted for 4 h was 21.5 % higher than that in PBS. It indicated that nanoemulsion system can improve the heat and acid resistance of IgG compared with others, which is attributed to the lowest water activity of nanoemulsion. The contents of hydroperoxide and malondialdehyde in the milk after storage for 72 h with nanoemulsion-protected IgG were 0.12 meq/kg and 0.04 mg/kg, respectively, less than that of PBS-protected IgG. IgG is protected by nanoemulsion can effectively protect its activity during processing, which provides a theoretical basis for its direct application in liquid milk.

Incidence, predictors and prognosis of acute kidney injury in acute ST-segment elevation myocardial infarction patients undergoing emergent coronary angiography/primary percutaneous coronary intervention.

BACKGROUND: Acute kidney injury (AKI) after coronary angiography (CAG) and primary percutaneous coronary intervention (PPCI) is frequently observed, and often interpreted as contrast induced-AKI. This study aimed to investigate the incidence, predictors and outcomes of AKI in acute ST-segment elevation myocardial infarction (STEMI) patients undergoing emergent CAG/PPCI using the control group of STEMI patients who were not exposed to contrast agents within the first 72 h. METHODS: We performed a retrospective analysis of 1670 STEMI patients. Of them, 673 patients underwent emergent CAG/PPCI, and 997 patients treated with thrombolysis or no reperfusion therapy who were not exposed to contrast material during the first 72 h. AKI was defined as an increase of serum creatinine ≥ 44.2 mmol/L or ≥ 25% from baseline within 72 h. Patents were then followed up for the occurrence of all-cause mortality for 40 months (interquartile range: 24-55 months). RESULTS: After propensity score matching, 505 pairs of patients were matched. Overall, the incidence of AKI was 27.4%, and AKI rates were not significantly different in patients with and without emergent CAG/PPCI procedure (27.5% vs. 27.3%, P = 0.944). Multivariate logistic regression analysis identified that the independent predictors of AKI were female, elevated interleukin-6 level, decreased lymphocyte count, left ventricular ejection fraction < 50% and use of diuretics in patients with emergent CAG/PPCI. Patients with AKI than those without AKI experienced higher incidence of acute heart failure with Killip class III (9.4% vs. 3.3%, P = 0.005; 15.2% vs. 6.8%, P = 0.003, respectively) and mortality (5.8% vs. 1.4%, P = 0.014; 12.3% vs. 4.6%, P = 0.002, respectively) in patients with and without emergent CAG/PPCI. Multivariate Cox regression analysis confirmed that AKI was independently associated with long-term mortality rate at 40 months follow-up in patients with and without emergent CAG/PPCI (HR = 1.867, 95% CI: 1.086-3.210, P = 0.024; HR = 1.700, 95% CI: 1.219-2.370, P = 0.002, respectively). CONCLUSIONS: Approximately 27.0% of STEMI patients experience AKI, which is strongly associated with an increased short- and long-term mortality regardless of emergent CAG/PPCI procedure. The development of AKI is mainly related to female gender, inflammation reaction, heart failure and use of diuretics in STEMI patients undergoing emergent CAG/PPCI.

Rhodiola rosea polysaccharides-based nanoparticles loaded with DOX boosts chemo-immunotherapy for triple-negative breast cancer by re-educating Tumor-associated macrophages.

Hydrophobic drug delivery vectors suffer significant challenges in cancer therapy, including efficient encapsulation and tumor targeting ability. In the present study, Rhodiola rosea polysaccharides (RHPs), which have the ability to modulate Tumor-associated macrophages and typical structural characteristics, were employed as an immunoactive vector for drug delivery. Folic acid (FA) and stearic acid (SA) were chemically modified to the backbone of RHPs to obtain the self-assembly and tumor-targeting behavior. Further, the hydrophobic drug, doxorubicin (DOX), was encapsulated in the RHPs derivatives (FA-RHPs-SA) with high efficiency. Additionally, the optimally formed DOX@FA-RHPs-SA had a uniform size distribution of approximately 196 nm and a pH-sensitive release capacity in different acidic conditions. In vitro experiments demonstrated that tumor cells could efficiently uptake DOX@FA-RHPs-SA. Furthermore, the modulatory function of the FA-RHPs-SA on RAW264.7 macrophages was also demonstrated in the transition from M0 to M1 phenotypes, and the M2 differentiated into the M1. Finally, the in vivo antitumor study revealed that the inhibitory effect of DOX@FA-RHPs-SA was superior to the DOX monotherapy treatment, and the new preparation functioned synergistically by inducing tumor cell apoptosis and modulating immune cell function. In conclusion, this study described an RHPs-based hydrophobic delivery vector and achieved an additional helpful antitumor effect by modulating Tumor-associated macrophages.

Qualitative and quantitative prediction of food allergen epitopes based on machine learning combined with in vitro experimental validation.

An allergen epitope is a part of molecules that can specifically bind to immunoglobulin E (IgE), causing an allergic reactions. To predict protein epitopes and their binding ability to IgE, quantitative structure-activity relationship (QSAR) models were established using four algorithms combined with the selected chemical descriptors. The model predicted the binding capabilities of the epitopes to IgE with the R2 and root mean squared error (RMSE) as 0.7494 and 0.2375, respectively. The model's performance was validated using an enzyme-linked immunosorbent assay (ELISA). The results showed that the established QSAR model could efficiently and accurately predict the allergic reaction of food protein epitopes. The prediction results of the model and the experimental results were consistent, with a Pearson correlation coefficient of 0.8956. The results from both the QSAR model and in vitro experiments indicated that amino acid sequence 116-130 was a novel IgE-binding epitope of ß-LG.

Effect of ferulic acid covalent conjugation on the functional properties and antigenicity of ß-lactoglobulin.

Binding to phenolics can improve the functional properties of proteins. Changes in structure, functional properties, and antigenicity of ß-lactoglobulin (ß-LG) after covalent conjugation with ferulic acid (FA) at different mass ratios were reported here. The results of SDS-PAGE and gel exclusion chromatography confirmed that covalent complexes were formed. When the mass ratio of ß-LG and FA was 10:6, the binding content of FA was the highest. Fluorescence spectroscopy, UV-visible absorption spectrometry, and FTIR analysis showed that the structure of the complexes was more stretched compared to native ß-LG. The addition of FA significantly improved the emulsifying property of ß-LG. When the mass ratio was 10:6, the radical scavenging activities of DPPH and ABTS reached 65.06% and 88.22%, respectively, and the antigenicity of ß-LG reduced by about 35%. This study provides novel ß-LG-FA complexes in food systems to reduce the antigenicity of ß-LG and improve functional properties.

Roles of the Notch signaling pathway and microglia in autism.

The Notch signaling pathway is mainly involved in the regulation of neural stem cell proliferation, survival and differentiation during the development of the central nervous system. As a neurodevelopmental disorder, autism is associated with an abnormal increase in the number of microglia in several brain regions. These findings suggest that the pathogenesis of autism may be related to the Notch signaling pathway and microglia. In this review, we discuss how Notch pathway activity leads to behavioral abnormalities such as learning and memory impairment by influencing neuronal biological activities. An increase in microglial protein synthesis and abnormal autophagy can affect synaptic development and lead to behavioral abnormalities, and all of these changes can lead to autism. Furthermore, the Notch signaling pathway regulates the activation and differentiation of microglia and promotes inflammatory responses, leading to the occurrence of autism. When excessive reactive oxygen species (ROS) secreted by microglia cannot be cleared by autophagy in a timely manner, Notch signaling pathway activity is affected, possibly further increasing susceptibility to autism. This review reveals the mechanism underlying the role of the Notch signaling pathway, microglia and their interaction in the pathogenesis of autism and provides a theoretical reference for targeted clinical therapies for autism.

Structure-guided discovery of food-derived GABA-T inhibitors as hunters for anti-anxiety compounds.

With the acceleration of the pace of life, people may face all kinds of pressure, and anxiety has become a common mental issue that is seriously affecting human life. Safe and effective food-derived compounds may be used as anti-anxiety compounds. In this study, anti-anxiety compounds were collected and curated for database construction. Quantitative structure-activity relationship (QSAR) models were developed using a combination of various machine-learning approaches and chemical descriptors to predict natural compounds in food with anti-anxiety effects. High-throughput molecular docking was used to screen out compounds that could function as anti-anxiety molecules by inhibiting γ-aminobutyrate transaminase (GABA-T) enzyme, and 7 compounds were screened for in vitro activity verification. Pharmacokinetic analysis revealed three compounds (quercetin, lithocholic acid, and ferulic acid) that met Lipinski's Rule of Five and inhibited the GABA-T enzyme to alleviate anxiety in vitro. The established QSAR model combined with molecular docking and molecular dynamics was proved by the synthesis and discovery of novel food-derived anti-anxiety compounds.

Structure-guided discovery of antioxidant peptides bounded to the Keap1 receptor as hunter for potential dietary antioxidants.

Human health can be damaged by free radicals, and antioxidant peptides are excellent radical scavengers. Antioxidant tripeptides data set based on 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulofnic acid) (ABTS) assay was created, 9 types of descriptors were integrated and 4 quantitative structure-activity relationship (QSAR) models were constructed in this study. Several structural factors influencing the activity of antioxidant tripeptides and the dominant amino acids at each position of tripeptides were revealed by the optimal model. Ten food-derived tripeptides with higher activity were selected for synthesis and activity determination. Molecular docking results demonstrated that these tripeptides were stably bound to the Keap1 receptor, further elucidating the antioxidant mechanism. It was known from the simulation of gastrointestinal digestion experiments that the model results possessed a guiding effect on the selection of proteins with high antioxidant activity. The performance of the model was proved to be robust after validation.

Combining experimental techniques with molecular dynamics to investigate the impact of different enzymatic hydrolysis of ß-lactoglobulin on the antigenicity reduction.

ß-Lactoglobulin (ß-LG) is one of the major food allergens. Enzymatic hydrolysis is a promising strategy to reduce the antigenicity of ß-LG in industrial production. The relationship between the cleavage sites of ß-LG by protease and its antigenic active sites were explored in this study. Molecular docking and molecular dynamics (MD) were used to analyze the active sites and interaction force of ß-LG and IgG antibody. Whey protein was hydrolyzed by four specific enzymes and the antigenicity of the hydrolysates were determined by ELISA. The results of MD showed that the amino acid residue Gln155 (-4.48 kcal mol-1) played the most important roles in the process of binding. Hydrolysates produced by AY-10, which was the only one with specificity towards cleavage sites next to a Gln, had the lowest antigenicity at the same hydrolysis degree. Antigenicity decrease was related to the energy contribution of the cleavage site in the active sites.

Various machine learning approaches coupled with molecule simulation in the screening of natural compounds with xanthine oxidase inhibitory activity.

Gout is a common inflammatory arthritis associated with various comorbidities, such as cardiovascular disease and metabolic syndrome. Xanthine oxidase inhibitors (XOIs) have emerged as effective substances to control gout. Much attention has been given to the search for natural XOIs. In this study, a molecular database of natural XOIs was created for modeling purposes. Quantitative structure-activity relationship models were developed by combining various machine learning approaches and three descriptor pools. The models revealed several features of XOIs, including hydrophobicity and steric molecular structures. Experimental results showed the xanthine oxidase (XO) inhibitory activity of predicted compounds. Vanillic acid was identified as a promising new XOI candidate, with an IC50 of 0.593 µg mL-1. The functions of hydrogen bonds and hydrophobic interactions in XO activity inhibition were confirmed by molecular docking. This study fills knowledge gaps pertaining to the discovery of natural XOIs and to the interaction mechanisms between XOIs and XO.

Prediction of flavor and retention index for compounds in beer depending on molecular structure using a machine learning method.

In order to make a preliminary prediction of flavor and retention index (RI) for compounds in beer, this work applied the machine learning method to modeling depending on molecular structure. Towards this goal, the flavor compounds in beer from existing literature were collected. The database was classified into four groups as aromatic, bitter, sulfury, and others. The RI values on a non-polar SE-30 column and a polar Carbowax 20M column from the National Institute of Standards Technology (NIST) were investigated. The structures were converted to molecular descriptors calculated by molecular operating environment (MOE), ChemoPy and Mordred, respectively. By combining the pretreatment of the descriptors, machine learning models, including support vector machine (SVM), random forest (RF) and k-nearest neighbour (kNN) were utilized for beer flavor models. Principal component regression (PCR), random forest regression (RFR) and partial least squares (PLS) regression were employed to predict the RI. The accuracy of the test set was obtained by SVM, RF, and kNN. Among them, the combination of descriptors calculated by Mordred and RF model afforded the highest accuracy of 0.686. R 2 of the optimal regression model achieved 0.96. The results indicated that the models can be used to predict the flavor of a specific compound in beer and its RI value.

Predictive Modeling of Angiotensin I-Converting Enzyme Inhibitory Peptides Using Various Machine Learning Approaches.

Food-derived angiotensin I-converting enzyme (ACE) inhibitory peptides could potentially be used as safe supportive therapeutic products for high blood pressure. Theoretical approaches are promising methods with the advantage through exploring the relationships between peptide structures and their bioactivities. In this study, peptides with ACE inhibitory activity were collected and curated. Quantitative structure-activity relationship (QSAR) models were developed by using the combination of various machine learning approaches and chemical descriptors. The resultant models have revealed several structure features accounting for the ACE inhibitions. 14 new dipeptides predicted to lower blood pressure by inhibiting ACE were selected. Molecular docking indicated that these dipeptides formed hydrogen bonds with ACE. Five of these dipeptides were synthesized for experimental testing. The QSAR models developed were proofed to design and propose novel ACE inhibitory peptides. Machine learning algorithms and properly selected chemical descriptors can be promising modeling approaches for rational design of natural functional food components.