Introduction: Consumers seldom pay attention to the nutritional aspects of food during purchasing. The study examined the influence of age and level of education on the nutrition knowledge and dietary choices of a sample of Chinese consumers. Methods: Probability and non-probability sampling methods were used to select four supermarkets and subjects for this cross-sectional descriptive survey conducted in Shenyang, the capital city of Liaoning Province in China. A response rate of 95.9% was obtained after retrieving 400 questionnaires out of 417 distributed. Results: About two-thirds (615%) of the respondents rated their nutrition knowledge level as "fair". Independent t-test revealed a significant age difference in knowledge levels (t (398) = 5.467, p < 0.01 (two-tailed)] with 7.0% (eta square (re) = 0.070) of the variance in nutritional knowledge explained by age groups. Significant differences (p < 0.01) in knowledge level jF (2, 397) = 7.765, p = 0.00] were also observed among the three educational groups using one-way ANOVA, with nutritional knowledge accounting for 19.1% (eta square (re) = 0.191) of the variance. However, age and level of education did not present any significant influence on the respondents' food choices. The results suggest that nutrition knowledge increased with age and level of education, but did not sufficiently affect food choices. Conclusion: Supportive nutrition education on use of label information complemented with practical use of such information may augment nutrition knowledge and assist consumers to make healthy food choices

Introduction: Consumers seldom pay attention to the nutritional aspects of food during purchasing. The study examined the influence of age and level of education on the nutrition knowledge and dietary choices of a sample of Chinese consumers. Methods: Probability and non-probability sampling methods were used to select four supermarkets and subjects for this cross-sectional descriptive survey conducted in Shenyang, the capital city of Liaoning Province in China. A response rate of 95.9% was obtained after retrieving 400 questionnaires out of 417 distributed. Results: About two-thirds (615%) of the respondents rated their nutrition knowledge level as "fair". Independent t-test revealed a significant age difference in knowledge levels (t (398) = 5.467, p < 0.01 (two-tailed)] with 7.0% (eta square (re) = 0.070) of the variance in nutritional knowledge explained by age groups. Significant differences (p < 0.01) in knowledge level jF (2, 397) = 7.765, p = 0.00] were also observed among the three educational groups using one-way ANOVA, with nutritional knowledge accounting for 19.1% (eta square (re) = 0.191) of the variance. However, age and level of education did not present any significant influence on the respondents' food choices. The results suggest that nutrition knowledge increased with age and level of education, but did not sufficiently affect food choices. Conclusion: Supportive nutrition education on use of label information complemented with practical use of such information may augment nutrition knowledge and assist consumers to make healthy food choices.

Objective To investigate the differential expression of proteins in the serum of diabetic patients quantitative proteomics technologies, and to identify specific protein biomarkers. Methods Serum samples from three diabetic patients and three healthy controls in Shenzhen in 2023 were collected. Immunoprecipitation and nano liquid chromatography-quadrupole-electrostatic orbitrap-linear ion trap mass spectrometry (easy-nanoLC-Q-orbitrap-fusion) were employed to perform fraction and no-fraction-based quantitative analysis of the serum proteome in diabetic patients and healthy controls. The mass spectrometry data were analyzed by MaxQuant1.6.1.0, and the differential proteins identified by the fraction-based method were subjected to gene ontology (GO) functional clustering and kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. Results A total of 251 reliable proteins were identified by the no-fraction-based method, among which 17 differentially expressed proteins were selected. In contrast, 494 reliable proteins were identified by the fraction-based method, and 74 differentially expressed proteins were selected. Among them, seven proteins showed consistent changes in both methods. Five proteins were downregulated, including pregnancy zone protein (PZP), carbonic anhydrase 1 (CAH1), mannose-binding protein C (MBL2), fibrinogen alpha chain (FIBA), and haptoglobin (HPT). Two proteins were upregulated, including apolipoprotein(a) (LPA) and complement factor H-related protein 4 (CFH4). GO analysis revealed that the differentially expressed proteins were mainly involved in molecular functions such as glycosaminoglycan binding and heparin binding. KEGG pathway analysis showed that the differentially expressed proteins were mainly associated with complement and coagulation cascades, cell adhesion, PI3K-Akt signaling pathway, and other pathways. Conclusions Fraction-based no-fractional quantitative proteomics analysis provides an effective approach for analyzing differential protein expression in the serum of diabetic patients, and offers a valuable technique for screening specific protein biomarkers of serum samples in large-scale cohorts of disease.