Vaccination with recent seasonal nonadjuvanted or adjuvanted influenza vaccines induced little or no cross-reactive antibody response to 2009 H1N1 in any age group. Persons under the age of 30 years had little evidence of cross-reactive antibodies to the pandemic virus. However, a proportion of older adults had preexisting cross-reactive antibodies.

No abstract

RNA-based, multi-gene molecular assays are available and widely used for patients with ER-positive/HER2-negative breast cancers. However, RNA-based genomic tests can be costly and are not available in many countries. Methods for inferring molecular subtype from histologic images may identify patients most likely to benefit from further genomic testing. To identify patients who could benefit from molecular testing based on H&E stained histologic images, we developed an image analysis approach using deep learning. A training set of 571 breast tumors was used to create image-based classifiers for tumor grade, ER status, PAM50 intrinsic subtype, histologic subtype, and risk of recurrence score (ROR-PT). The resulting classifiers were applied to an independent test set (n = 288), and accuracy, sensitivity, and specificity of each was assessed on the test set. Histologic image analysis with deep learning distinguished low-intermediate vs. high tumor grade (82% accuracy), ER status (84% accuracy), Basal-like vs. non-Basal-like (77% accuracy), Ductal vs. Lobular (94% accuracy), and high vs. low-medium ROR-PT score (75% accuracy). Sampling considerations in the training set minimized bias in the test set. Incorrect classification of ER status was significantly more common for Luminal B tumors. These data provide proof of principle that molecular marker status, including a critical clinical biomarker (i.e., ER status), can be predicted with accuracy >75% based on H&E features. Image-based methods could be promising for identifying patients with a greater need for further genomic testing, or in place of classically scored variables typically accomplished using human-based scoring.

Context The existing evidence on food environments and diet is inconsistent, potentially due in part to heterogeneity in measures used to assess diet. The objective of this review, conducted in 2012–2013, was to examine measures of dietary intake utilized in food environment research. Evidence acquisition Included studies were published from January 2007 through June 2012 and assessed relationships between at least one food environment exposure and at least one dietary outcome. Fifty-one articles were identified using PubMed, Scopus, Web of Knowledge, and PsycINFO; references listed in the papers reviewed and relevant review articles; and the National Cancer Institute's Measures of the Food Environment website. The frequency of the use of dietary intake measures and assessment of specific dietary outcomes was examined, as were patterns of results among studies using different dietary measures. Evidence synthesis The majority of studies used brief instruments, such as screeners or one or two questions, to assess intake. Food frequency questionnaires were used in about a third of studies, one in ten used 24-hour recalls, and fewer than one in twenty used diaries. Little consideration of dietary measurement error was evident. Associations between the food environment and diet were more consistently in the expected direction in studies using less error-prone measures. Conclusions There is a tendency toward the use of brief dietary assessment instruments with low cost and burden rather than more detailed instruments that capture intake with less bias. Use of error-prone dietary measures may lead to spurious findings and reduced power to detect associations.