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Importance: Policymakers and researchers have proposed a variety of interpretative front-of-package food labeling systems, but it remains unclear which is most effective at encouraging people to choose healthier foods and beverages, including among people with less education.
Objective: To test the effects of 4 interpretative front-of-package food labeling systems on the healthfulness of beverage and snack selections, overall and by education level.
Design, Setting, And Participants: This randomized clinical trial of a national sample of US adults 18 years and older was conducted online from November 16 to December 3, 2022.
Intervention: Participants were randomized to view products with 1 of 5 food labeling systems, including control (calorie labels only) or 1 of 4 interpretative labeling systems: green ("choose often") labels added to healthy foods; single traffic light labels added to healthy, moderately healthy, and unhealthy foods; physical activity calorie equivalent labels added to all products; and nutrient warning labels added to products high in calories, sugar, saturated fat, or sodium. All conditions had calorie labels on all products.
Main Outcomes And Measures: Participants selected 1 of 16 beverages and 1 of 16 snacks that they wanted to hypothetically purchase. The primary outcomes were calories selected from beverages and from snacks. Secondary outcomes included label reactions and perceptions.
Results: A total of 7945 participants completed the experiment and were included in analyses (4078 [51%] female, 3779 [48%] male, and 88 [1%] nonbinary or another gender; mean [SD] age, 47.5 [17.9 years]). Compared with the control arm, exposure to the green (average differential effect [ADE], -34.2; 95% CI, -42.2 to -26.1), traffic light (ADE, -31.5; 95% CI, -39.5 to -23.4), physical activity (ADE, -39.0; 95% CI, -47.0 to -31.1), or nutrient warning labels (ADE, -28.2; 95% CI, -36.2 to -20.2) led participants to select fewer calories from beverages (all P < .001). Similarly, compared with the control label, exposure to the green (ADE, -12.7; 95% CI, -17.3 to -8.2), traffic light (ADE, -13.7; 95% CI, -18.2 to -9.1), physical activity (ADE, -18.5; 95% CI, -23.1 to -13.9), or nutrient warning labels (ADE, -14.2; 95% CI, -18.8 to -9.6) led participants to select fewer calories from snacks (all P < .001). These effects did not differ by education level. The green labels were rated as less stigmatizing than the other interpretative systems but otherwise generally received the least favorable label reactions and perceptions (eg, elicited less attention, were perceived as less trustworthy), while the nutrient warnings and physical activity labels received the most favorable ratings.
Conclusions And Relevance: In this randomized clinical trial of front-of-package food labeling systems, all 4 interpretative labeling systems reduced calories selected from beverages and from snacks compared with calorie labels, with no differences by education level.
Trial Registration: ClinicalTrials.gov Identifier: NCT05432271.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500374 | PMC |
http://dx.doi.org/10.1001/jamanetworkopen.2023.33515 | DOI Listing |
J R Soc Interface
September 2025
Institute of Intelligent Systems and Robotics, Sorbonne Université, Paris, Île-de-France, France.
A number of techniques have been developed to measure the three-dimensional trajectories of protists, which require special experimental set-ups, such as a pair of orthogonal cameras. On the other hand, machine learning techniques have been used to estimate the vertical position of spherical particles from the defocus pattern, but they require the acquisition of a labelled dataset with finely spaced vertical positions. Here, we describe a simple way to make a dataset of images labelled with vertical position from a single 5 min movie, based on a tilted slide set-up.
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September 2025
Gynaecology and Obstetrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Heilongjiang 150081, PR China. Electronic address:
Single-cell surface-enhanced Raman scattering (SERS) has emerged as a powerful tool for precision medicine owing to its label-free detection, ultrasensitivity, and unique molecular fingerprinting. Unlike conventional bulk analysis, it enables detailed characterization of cellular heterogeneity, with particular promise in circulating tumor cell (CTC) identification, tumor microenvironment (TME) metabolic profiling, subcellular imaging, and drug sensitivity assessment. Coupled with microfluidic droplet systems, SERS supports high-throughput single-cell analysis and multiparametric screening, while integration with complementary modalities such as fluorescence microscopy and mass spectrometry enhances temporal and spatial resolution for monitoring live cells.
View Article and Find Full Text PDFCell Rep Methods
September 2025
Lingang Laboratory, Shanghai 201306, China. Electronic address:
While affinity purification-mass spectrometry (AP-MS) has significantly advanced protein-protein interaction (PPI) studies, its limitations in detecting weak, transient, and membrane-associated interactions remain. To address these challenges, we introduced a proteomic method termed affinity purification coupled proximity labeling-mass spectrometry (APPLE-MS), which combines the high specificity of Twin-Strep tag enrichment with PafA-mediated proximity labeling. This method achieves improved sensitivity while maintaining high specificity (4.
View Article and Find Full Text PDFStructure
September 2025
Institute of Anatomy, University of Bern, 3012 Bern, Switzerland. Electronic address:
Cryo-electron tomography (cryoET) provides 3D datasets of organelles and proteins at nanometer and sub-nanometer resolution. However, locating target proteins in live cells remains a significant challenge. Conventional labeling methods, such as fluorescent protein tagging and immunogold labeling, are unsuitable for small structures in vitrified samples at molecular resolution.
View Article and Find Full Text PDFIEEE Trans Med Imaging
September 2025
Mammography is a primary method for early screening, and developing deep learning-based computer-aided systems is of great significance. However, current deep learning models typically treat each image as an independent entity for diagnosis, rather than integrating images from multiple views to diagnose the patient. These methods do not fully consider and address the complex interactions between different views, resulting in poor diagnostic performance and interpretability.
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