Biosensors based on surface techniques, combined with the advantage of physical/chemical mechanisms, demonstrate great potential in detecting Covid-19/SARS-CoV-2.

There is limited evidence regarding the factors correlated with dietary diversity (DD) and dietary pattern (DP) in rural residents of China. This study aims to identify the DD and DP of rural residents and their association with socio-demographic factors. A cross-sectional survey was conducted in Pingnan, China. The Food Frequency Questionnaire (FFQ) was applied to evaluate dietary intake. Latent class analysis (LCA) was used to identify patterns of six food varieties, including vegetables–fruits, red meat, aquatic products, eggs, milk, and beans–nuts. Generalized linear models and multiple logistic regression models were used to determine factors associated with the DD and DP. Three DPs were detected by LCA, namely “healthy” DP (47.94%), “traditional” DP (33.94%), and “meat/animal protein” DP (18.11%). Females exhibited lower DD (β = −0.23, p = 0.003) and were more likely to adhere to “traditional” DP (OR = 1.46, p = 0.039) and “meat/animal protein” DP (OR = 2.02, p < 0.001). Higher educational levels and annual household income (AHI) were positively associated with higher DD (p < 0.05) and less likely to have “traditional” DP and “meat/animal protein” DP (p < 0.05). Non-obese people exhibited higher DD (β = 0.15, p = 0.020) and were less likely to have “meat/animal protein” DP (OR = 0.59, p = 0.001). Our study reveals that females, those with lower educational levels and AHI, and obese people are more likely to have a lower DD and are more likely to adhere to “traditional” DP and “meat/animal protein” DP. The local, regional, and even national performance of specific diet-related health promotion measures and interventions must target these vulnerable populations to improve a healthier DD and DP.

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