Double base mismatches mediated catalytic hairpin assembly for enzyme-free single-base mutation detection: integrating signal recognition and amplification in one.

Single nucleotide polymorphism (SNP) biosensors are emerging rapidly for their promising applications in human disease prevention diagnosis, treatment, and prognosis. However, it remains a bottleneck in equipping simple and stable biosensors with the traits of high sensitivity, non-enzyme, and low cost. Double base mismatches mediated chain displacement reactions have attracted fascinating advantages of tailorable thermodynamics stability, non-enzyme, and excellent assembly compliance to involvement in SNP identification. As the base mismatch position and amount in DNA sequence can be artificially adjusted, it provides plenty of selectivity and specificity for exploring perfect biosensors. Herein, a biosensor with double base mismatches mediated catalytic hairpin assembly (CHA) is designed via one base mismatch in the toehold domain and the other base mismatch in the stem sequence of hairpin 1 (H1) by triggering CHA reaction to achieve selective amplification of the mutation target (MT) and fluorescence resonance energy transfer (FRET) effect that is composed of Cy3 and Cy5 terminally attached H1 and hairpin 2 (H2). Depending on the rationally designed base mismatch position and toehold length, the fabricated biosensors show superior SNP detection performance, exhibiting a good linearity with high sensitivity of 6.6 fM detection limit and a broad detection abundance of 1%. The proposed biosensor can be used to detect the KRAS mutation gene in real samples and obtain good recoveries between 106 and 116.99%. Remarkably, these extendible designs of base mismatches can be used for more types of SNP detection, providing flexible adjustment based on base mismatch position and toehold length variations, especially for their thermodynamic model for DNA-strand displacement reactions.

Physical activity is associated with allostatic load: Evidence from the National Health and Nutrition Examination Survey.

We determined the associations between physical activity and allostatic load, and whether it is a modifiable factor related to allostatic load. We obtained data from the National Health and Nutrition Examination Survey (NHANES) database collected between 2017 and March 2020. The relationship between physical activity and allostatic load was examined using a logistic regression model. In the unadjusted model, physical activity level was associated with allostatic load index (odds ratio [OR] = 0.664, 95% confidence interval [CI]: 0.550, 0.802, P＜0.001), with this relationship being retained in the adjusted model (OR = 0.739, 95%CI: 0.603, 0.907; P = 0.004). Sedentary behaviour was also related to allostatic load index (OR = 1.236, 95%CI: 1.005, 1.520; P = 0.044). Our findings indicated that sufficient physical activity is associated with a lower allostatic load index, and sedentary behaviour is associated with a higher allostatic load index. Physical activity is a modifiable factor related to allostatic load.