Adsorption Layer Properties and Foam Behavior of Aqueous Solutions of Whey Protein Isolate (WPI) Modified by Vacuum Cold Plasma (VCP)

For years, cold plasma processing has been used as a non-thermal technology in industries such as food. As interfacial properties of protein play a remarkable role in many processes, this study investigates the effect of cold plasma on the foaming and interfacial behavior of WPI. The objective of this study is to evaluate the effect of different gases (air, 1:1 argon–air mixture, and sulfur hexafluoride (SF6)) used in low-pressure cold plasma (VCP) treatments of whey protein isolate (WPI) on the surface and foaming behavior of aqueous WPI solutions. Dynamic surface dilational elasticity, surface tension isotherms, surface layer thickness, and the foamability and foam stability were investigated in this study. VCP treatment did not significantly affect the adsorption layer thickness. However, an increase in induction time, surface pressure equilibrium value, and aggregated size is observed after SF6VCP treatment, which can be attributed to the reaction of WPI with the reactive SF6 species of the cold plasma. The surface dilational elastic modulus increased after VCP treatment, which can be related to the increased mechanical strength of the protein layer via sulfonation and aggregate formation. VCP treatment of WPI increases the foam stability, while the average diameter of foam bubbles and liquid drainage in the foam depends on the gas used for the cold plasma

Effects of Various Types of Vacuum Cold Plasma Treatment on the Chemical and Functional Properties of Whey Protein Isolate with a Focus on Interfacial Properties

Vacuum cold plasma (VCP), a novel non-thermal processing technology used to modify the physicochemical properties and functionalities of food materials, was applied to whey protein isolate (WPI). The treatment affects the protein chemistry and, as a result, leads to differences in the behavior in solution and at interfaces. To minimize the undesirable effects of high oxidation and to increase the effectiveness of reactive species, the VCP treatment was applied at low pressure using different types of gases (air, combination of argon and air, and sulfur hexafluoride (SF₆)). The treatment led to a decrease in the sulfur content and an increase in the carbonyl content, evidenced by oxidation reactions and enhanced disulfide bond formation, as well as cross-linking of protein molecules. Fluorescence-based indicators suggest that the hydrophobicity of the proteins as well as their aggregation increase after VCP treatment with an argon–air gas mixture; however, it decreases after VCP treatments with air and SF₆. The chemical modifications further lead to changes in the pH of aqueous WPI solutions, as well as the average size and ζ-potential of WPI aggregates. Moreover, the dynamic surface tension, surface dilational elasticity, and the thickness of the WPI adsorption layers at the air/water interface depend on the VCP type. SF₆ plasma treatment leads to a significant decrease in pH and an increase in the ζ-potential, and consequently to a significant increase in the aggregate size. The dynamic surface tension as well as the adsorption rates increase after SF₆VCP treatment, but decrease after air–VCP and argon–air–VCP treatments. The adsorbed WPI aggregates form strong viscoelastic interfacial layers, the thickness of which depends on the type of VCP treatment

Effects of Various Types of Vacuum Cold Plasma Treatment on the Chemical and Functional Properties of Whey Protein Isolate with a Focus on Interfacial Properties

Vacuum cold plasma (VCP), a novel non-thermal processing technology used to modify the physicochemical properties and functionalities of food materials, was applied to whey protein isolate (WPI). The treatment affects the protein chemistry and, as a result, leads to differences in the behavior in solution and at interfaces. To minimize the undesirable effects of high oxidation and to increase the effectiveness of reactive species, the VCP treatment was applied at low pressure using different types of gases (air, combination of argon and air, and sulfur hexafluoride (SF6)). The treatment led to a decrease in the sulfur content and an increase in the carbonyl content, evidenced by oxidation reactions and enhanced disulfide bond formation, as well as cross-linking of protein molecules. Fluorescence-based indicators suggest that the hydrophobicity of the proteins as well as their aggregation increase after VCP treatment with an argon–air gas mixture; however, it decreases after VCP treatments with air and SF6. The chemical modifications further lead to changes in the pH of aqueous WPI solutions, as well as the average size and ζ-potential of WPI aggregates. Moreover, the dynamic surface tension, surface dilational elasticity, and the thickness of the WPI adsorption layers at the air/water interface depend on the VCP type. SF6 plasma treatment leads to a significant decrease in pH and an increase in the ζ-potential, and consequently to a significant increase in the aggregate size. The dynamic surface tension as well as the adsorption rates increase after SF6VCP treatment, but decrease after air–VCP and argon–air–VCP treatments. The adsorbed WPI aggregates form strong viscoelastic interfacial layers, the thickness of which depends on the type of VCP treatment

Noninvasive detection of any-stage cancer using free glycosaminoglycans

Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine (Nurine = 220 cancer vs. 360 healthy) and plasma (Nplasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83-0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers