Aims: To review 25 cases of breast hamartoma and discuss the pathological criteria, and the usefulness of imaging modalities, fine needle aspiration cytology (FNAC), and needle core biopsy in the diagnosis. Methods: The hamartomas were assessed for interlobular fibrotic stroma, stromal adipose tissue content, pseudo-angiomatous stromal hyperplasia, and epithelial changes (hyperplasia, adenosis or apocrine metaplasia, and cyst formation). All imagings, previous FNACs, and biopsies were also reviewed. Results: Imaging (mammography, ultrasound, and magnetic resonance imaging) was performed in 18 cases, and mostly showed encapsulated masses with a heterogeneous appearance. Microscopically, all hamartomas demonstrated good demarcation with fibrous tissue condensation. Adipose tissue was noted in all cases (5-90%; mean, 31%), and interlobular fibrosis in 21 cases. Benign epithelial hyperplasia occurred in 10 cases, and pseudo-angiomatous stromal hyperplasia or cystic ducts in eight cases each. Apocrine metaplasia, calcification, stromal giant cells, and adenosis occurred in four cases or less. Two cases showed coexisting ductal carcinoma in situ limited to within the hamartoma. Needle core biopsies (four cases) and FNAC (14 cases) were largely insufficient, inconclusive, or nonspecific. Conclusions: Hamartomas do not possess specific diagnostic histological features. The role of FNAC and needle core biopsy in making the diagnosis is limited, and requires clinical and radiological correlation to avoid underdiagnosis.

BackgroundInfertility is a significant disability, yet there are no reliable estimates of its global prevalence. Studies on infertility prevalence define the condition inconsistently, rendering the comparison of studies or quantitative summaries of the literature difficult. This study analyzed key components of infertility to develop a definition that can be consistently applied to globally available household survey data.MethodsWe proposed a standard definition of infertility and used it to generate prevalence estimates using 53 Demographic and Health Surveys (DHS). The analysis was restricted to the subset of DHS that contained detailed fertility information collected through the reproductive health calendar. We performed sensitivity analyses for key components of the definition and used these to inform our recommendations for each element of the definition.ResultsExposure type (couple status, contraceptive use, and intent), exposure time, and outcomes were key elements of the definition that we proposed. Our definition produced estimates that ranged from 0.6% to 3.4% for primary infertility and 8.7% to 32.6% for secondary infertility. Our sensitivity analyses showed that using an exposure measure of five years is less likely to misclassify fertile unions as infertile. Additionally, using a current, rather than continuous, measure of contraceptive use over five years resulted in a median relative error in secondary infertility of 20.7% (interquartile range of relative error [IQR]: 12.6%-26.9%), while not incorporating intent produced a corresponding error in secondary infertility of 58.2% (IQR: 44.3%-67.9%).ConclusionsIn order to estimate the global burden of infertility, prevalence estimates using a consistent definition need to be generated. Our analysis provided a recommended definition that could be applied to widely available global household data. We also summarized potential biases that should be considered when making estimates of infertility prevalence using household survey data.

Obesity is associated with large employer costs from direct health care and insurance claims and indirect costs from lost productivity owing to workdays lost because of illness and disability.

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Genomic medicine is transforming patient care. However, the speed of development has left a knowledge gap between discovery and effective implementation into clinical practice. Since 2010, the Training Residents in Genomics (TRIG) Working Group has found success in building a rigorous genomics curriculum with implementation tools aimed at pathology residents in postgraduate training years 1–4. Based on the TRIG model, the interprofessional Undergraduate Training in Genomics (UTRIG) Working Group was formed. Under the aegis of the Undergraduate Medical Educators Section of the Association of Pathology Chairs and representation from nine additional professional societies, UTRIG’s collaborative goal is building medical student genomic literacy through development of a ready-to-use genomics curriculum. Key elements to the UTRIG curriculum are expert consensus-driven objectives, active learning methods, rigorous assessment and integration.