Serum level of long noncoding RNA B3GALT5-AS1 as a diagnostic biomarker of colorectal cancer.

Aim: Long noncoding RNA (lncRNA) B3GALT5-AS1 has been reported as a biomarker for cancer monitoring. This research aims to identify serum long noncoding RNA B3GALT5-AS1 as a new biomarker for the diagnosis of colorectal cancer (CRC) and evaluate its clinical value. Materials & methods: Serum B3GALT5-AS1 expression levels were measured by quantitative real-time PCR. Results: The level of B3GALT5-AS1 in CRC patients was significantly lower than that of healthy patients (p < 0.0001). Further exploration validated that high serum B3GALT5-AS1 level was related to tumor node metastasis (TNM) stage (p = 0.008) and histological differentiation (p = 0.027). Compared with the healthy control group, AUCROC of serum B3GALT5-AS1 in the CRC group was 0.762 with 95% CI: 0.698-0.826 (p < 0.0001). Conclusion: B3GALT5-AS1 may be served as a diagnostic marker for distinguishing CRC patients from healthy people.

The extraction of buckwheat protein and its interaction with kappa-carrageenan: Textural, rheological, microstructural, and chemical properties.

Current plant-based foods use plant proteins as a key structuring and texturing ingredient. The use of water for extraction can replace conventional protein extraction methods. Water extraction of protein is environmentally friendly and could prevent the loss of protein functionality due to extreme pH changes. This study demonstrates an aqueous extraction method, coupled with ultrasound as pre-treatment, to obtain buckwheat protein (BWPE) and assess its gelling property and composited gel with kappa-carrageenan (k-carr). Textural and rheological analyses showed that the hardness and storage modulus of the composited gel containing 1 % w/w BWPE and 1 % w/w k-carr was 4.2-fold and 100-fold, respectively, higher than k-carr gel at 1 % w/w. Light microscopy showed a mixed bi-continuous gel system, with k-carr reinforcing the protein gel network. Besides volume exclusion effects, chemical bond and FTIR analyses revealed that adding k-carr to BWPE altered the protein's secondary structure and mediated protein denaturation during heating. This results in greater ß-sheet content, which creates a more organised gel structure. These results demonstrated that ultrasound-assisted water-extracted BWPE, together with varying concentrations of k-carr, can be used to develop composited gels of tailorable textural and rheological properties to suit different food applications.

Predicting the Textural Properties of Plant-Based Meat Analogs with Machine Learning.

Plant-based meat analogs are food products that mimic the appearance, texture, and taste of real meat. The development process requires laborious experimental iterations and expert knowledge to meet consumer expectations. To address these problems, we propose a machine learning (ML)-based framework to predict the textural properties of meat analogs. We introduce the proximate compositions of the raw materials, namely protein, fat, carbohydrate, fibre, ash, and moisture, in percentages and the "targeted moisture contents" of the meat analogs as input features of the ML models, such as Ridge, XGBoost, and MLP, adopting a build-in feature selection mechanism for predicting "Hardness" and "Chewiness". We achieved a mean absolute percentage error (MAPE) of 22.9%, root mean square error (RMSE) of 10.101 for Hardness, MAPE of 14.5%, and RMSE of 6.035 for Chewiness. In addition, carbohydrates, fat and targeted moisture content are found to be the most important factors in determining textural properties. We also investigate multicollinearity among the features, linearity of the designed model, and inconsistent food compositions for validation of the experimental design. Our results have shown that ML is an effective aid in formulating plant-based meat analogs, laying out the groundwork to expediently optimize product development cycles to reduce costs.

Mathematical optimisation of extruded mixed plant protein-based meat analogues based on amino acid compositions.

Developing meat analogues of superior amino acid (AA) profiles in the food industry is a challenge as plant proteins contain less of some essential AA than animal proteins. Mathematical optimisation models such as linear/non-linear programming models were used to overcome this challenge and create high-moisture meat analogues (HMMA) with AA profiles as close as possible to chicken breast meat. The effect on the physiochemical properties and specific mechanical energy (SME) of the HMMA was investigated. The AA content of HMMA was generally lower than chicken. Strong intermolecular bonds present in the globulin fraction could hinder protein acid hydrolysis of HMMA. Plant proteins also affect the HMMA colour as certain AA forms Maillard reaction products with higher browning intensity. Lastly, different characteristics of plant proteins resulted in different SME values under the same extrusion conditions. While mathematical programming can optimise plant protein combinations, fortification is required to match the AA profile of HMMA to an animal source.

A Novel Approach to Structure Plant-Based Yogurts Using High Pressure Processing.

Current plant-based yogurts are made by the fermentation of plant-based milks. Although this imparts fermented flavors and probiotic cultures, the process is relatively longer and often leads to textural issues. The protein content of these plant-based yogurts is also lower than their dairy counterparts. To overcome these challenges, this paper explores the high pressure processing (HPP) of plant protein ingredients as an alternative structuring strategy for plant-based yogurts. Using mung bean (MB), chickpea (CP), pea (PP), lentil (LP), and faba bean (FB) proteins as examples, this work compared the viscosity and viscoelastic properties of high pressure-structured (600 MPa, 5 min, 5 °C) 12% (w/w) plant protein gels without, and with 5% (w/w) sunflower oil (SO) to commercial plain skim and whole milk Greek yogurts and discussed the feasibility of using HPP to develop plant-based yogurts. HPP formed viscoelastic gels (G' > G'') for all plant protein samples with comparable gel strength (G'~102-103 Pa; tan δ~0.2-0.3) to commercial dairy yogurts. The plant protein gel strength decreased in the order: CP~CPSO~LP~LPSO > MBSO~PPSO~FB~FBSO > PP >> MB. Modest addition of sunflower oil led to little change in viscoelastic properties for all plant protein samples except for MB and PP, where gel strength increased with incorporated oil. The emulsion gels were also more viscous than the hydrogels. Nonetheless, the viscosity of the plant protein gels was similar to the dairy yogurts. Finally, a process involving separate biotransformation for optimized flavor production and high pressure processing for consistent texture generation was proposed. This could lead to high protein plant-based yogurt products with desirable texture, flavor, and nutrition.