Quantitative profiling and mapping of small molecules by laser desorption/ionization mass spectrometry: combinations of carbon-based nano-matrices and sample preparation protocols.

The choices of matrices and protocols for sample deposition are critical factors, which impact each other in the matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). Previous reports on MALDI MS matrices have only compared their performances in terms of their MS signal intensities and provided optical microphotos or MALDI MS images of sample spots but typically lacked quantitative evaluation. Therefore, there is an urgent need to develop a multivariate model to evaluate the performance of different combinations of matrices and sample protocols.

Foldable, Stretchable, Superelastic, and Tunable Wettability Aramid Nanofiber Aerogels for Efficient Thermal Insulation and Oil-Water Separation.

Aerogels hold great potential in thermal insulation, catalytic supports, adsorption, and separation, due to their low density, high porosity, and low thermal conductivity. However, their inherent mechanical fragility and limited control functionality pose substantial challenges that hinder their practical use. In this study, a strategy is developed for the fabrication of cross-linked aramid nanofiber aerogels (cANFAs) by combining internanofiber surface cross-linking with ice-templating techniques.

Estimating the impacts of calorie labels in fast-food settings using a novel comparison: Comparing California drive-through and in-store purchases.

Prior studies assessing the impact of calorie labels in fast-food settings have relied on comparisons across local and state jurisdictions with and without labeling mandates; several well-designed studies indicate a small reduction of calories purchased as a result of the labels. This study exploits a staggered roll-out of calorie labels in California to study the same issue using a novel comparison of in-store purchases with calorie information and drive-through purchases without calorie information at the same locations. With this design, consumers in both the treatment and comparison groups have been subject to the same social signals associated with the policy change and may have been exposed to calorie information during prior purchases, narrowing the intervention under study to the impact of posted menu labels at the point of purchase.

Prospective Multi-Site Validation of AI to Detect Tuberculosis and Chest X-Ray Abnormalities.

Background: Using artificial intelligence (AI) to interpret chest X-rays (CXRs) could support accessible triage tests for active pulmonary tuberculosis (TB) in resource-constrained settings.

Methods: The performance of two cloud-based CXR AI systems - one to detect TB and the other to detect CXR abnormalities - in a population with a high TB and human immunodeficiency virus (HIV) burden was evaluated. We recruited 1978 adults who had TB symptoms, were close contacts of known TB patients, or were newly diagnosed with HIV at three clinical sites.

Relationship between community characteristics and impact of calorie labeling on fast-food purchases.

Objective: The objective of this study was to evaluate potential sources of heterogeneity in the effect of calorie labeling on fast-food purchases among restaurants located in areas with different neighborhood characteristics.

Methods: In a quasi-experimental design, using transaction data from 2329 Taco Bell restaurants across the United States between 2008 and 2014, we estimated the relationships of census tract-level income, racial and ethnic composition, and urbanicity with the impacts of calorie labeling on calories purchased per transaction.

Results: Calorie labeling led to small, absolute reductions in calories purchased across all population subgroups, ranging between -9.