The Role of T-box Transcription Factor in a Pituitary Adenoma Diagnostic Algorithm.

CONTEXT.­: We previously examined pituitary adenomas with immunohistochemical (IHC) stains for steroidogenic factor 1, Pit-1, anterior pituitary hormones, cytokeratin CAM 5.2, and the α-subunit of human chorionic gonadotropin and found that a screening panel comprising stains for steroidogenic factor 1, Pit-1, and adrenocorticotropic hormone successfully classified most cases and reduced the overall number of stains required. OBJECTIVES.­: To examine the potential role of IHC stain for T-box transcription factor (Tpit) in the classification of our series of pituitary adenomas and to update our screening panel as necessary. DESIGN.­: We collected 157 pituitary adenomas from 2 institutions and included these in tissue microarrays. Immunostains for Tpit were scored in a blinded fashion using the Allred system. Adenomas were assigned to a gold standard class based on IHC pattern followed by application of available clinical and serologic information. Test characteristics were calculated. Correlation analyses, cluster analyses, and classification tree analyses were used to see whether IHC staining patterns reliably reflected adenoma class. RESULTS.­: Of the cases collected, 147 (93.6%) had sufficient material for Tpit analysis. IHC stain for Tpit identified 8 null cell adenomas (all nonfunctioning clinically) as silent corticotrophs; Tpit stains showed better sensitivity, specificity, positive predictive value, and negative predictive value than IHC for adrenocorticotropic hormone and cytokeratin CAM 5.2. Correlation analyses continued to show the expected relationships among IHC stains. Cluster analyses showed grouping of adenomas into clinically consistent groups. Classification tree analysis underscored the central role of transcription factor IHC stains, including Tpit, in adenoma classification. CONCLUSIONS.­: Substitution of Tpit stain for the adrenocorticotropic hormone stain improves our prior algorithm by reducing the number of false-negatives and false-positives. As a result, fewer adenomas are classified as null cell adenoma, and more adenomas are classified as silent corticotroph adenoma.

Residential proximity to greenhouse agriculture and neurobehavioral performance in Ecuadorian children.

BACKGROUND: Children living near greenhouse agriculture may have an increased risk of pesticide exposure due to drift or direct contact with pesticide-treated areas. However, little is known about whether this increased potential for chronic exposure may impair their neurodevelopment. METHODS: We examined 307 children aged 4-9 years, living in agricultural communities in Ecuador (ESPINA study). The two exposures calculated were residential distance from the nearest flower plantation perimeter and flower plantation surface area within 100 m of homes. Five neurobehavioral domains were assessed: Attention/Inhibitory Control, Memory/Learning, Visuospatial processing and Sensorimotor (higher values reflect better performance). Low scores were defined according to the test's cut-offs. Models were adjusted for demographic, socio-economic and growth variables. RESULTS: The mean (SD) residential distance to the nearest flower plantation was 446 m (344). Living 100 m closer to crops was associated with increased odds (OR [95% CI]) of low scores in the domains of Memory/Learning (1.24 [1.05, 1.46]) and Language (1.09 [1.00, 1.19]). Associations were strongest among children living within 50 m, having significantly lower scores in Language (-1.28 which is ~50% of a SD [-2.50, -0.06]), Attention/Inhibitory Control (-1.24 units, [-2.45, -0.04]), and Memory/Learning (-0.91, [-1.99, 0.17]), compared to children living farther than 500 m. Analyses of areas of flower crops near homes concurred with these findings. CONCLUSIONS: Close residential proximity to greenhouse floricultural crops was associated with adverse neurobehavioral performance in Attention/Inhibitory Control, Language and Memory/Learning among children. This highlights the importance of reducing pesticide drift from plantations to nearby homes.

Home proximity to flower plantations and higher systolic blood pressure among children.

BACKGROUND: Pesticide drift from agricultural plantations increases the chemical exposure potential of people living nearby. Some studies have described positive associations between pesticide exposures and blood pressure (BP) in adults, whereas limited evidence in children suggests negative associations. This study characterized the association between home proximity to plantations and BP among children living in a flower-growing county in Ecuador. METHODS: We included 310 4-9-year-old children living in Pedro Moncayo County, Ecuador as part of The ESPINA study. We calculated age, gender and height-specific BP z-scores. Geographic coordinates of homes and flower plantations were collected using GPS receivers and satellite imagery. Exposure-outcome associations were analyzed using linear regression. RESULTS: The mean home distance to the nearest flower plantation was 449 m (SD: 347) and the median plantation area within 150 m of participants' homes was 989 m2 (25th-75Th percentile: 492-3164) among those with non-zero values. Children living closer to plantations had lower AChE activity. Systolic BP z-score increased with greater residential proximity to plantations (0.24 SD per 1000 m [95% CI: 0.01, 0.47]) and with greater areas of flower plantations within 150 m of homes (0.03 SD per 1000 m2 [0.00, 0.06]), after adjusting for socio-economic, anthropometric and other factors. Further adjustment for acetylcholinesterase and hemoglobin strengthened these associations. CONCLUSIONS: Proximity of homes to flower plantations and greater plantation areas within 150 m from homes were associated with higher systolic BP, independent of cholinesterase activity. This suggests that non-cholinesterase inhibitor pesticide drift from agricultural plantations may be sufficient to induce physiologic changes on children living nearby.

Steroidogenic Factor 1, Pit-1, and Adrenocorticotropic Hormone: A Rational Starting Place for the Immunohistochemical Characterization of Pituitary Adenoma.

CONTEXT: -Pituitary adenoma classification is complex, and diagnostic strategies vary greatly from laboratory to laboratory. No optimal diagnostic algorithm has been defined. OBJECTIVE: -To develop a panel of immunohistochemical (IHC) stains that provides the optimal combination of cost, accuracy, and ease of use. DESIGN: -We examined 136 pituitary adenomas with stains of steroidogenic factor 1 (SF-1), Pit-1, anterior pituitary hormones, cytokeratin CAM5.2, and α subunit of human chorionic gonadotropin. Immunohistochemical staining was scored using the Allred system. Adenomas were assigned to a gold standard class based on IHC results and available clinical and serologic information. Correlation and cluster analyses were used to develop an algorithm for parsimoniously classifying adenomas. RESULTS: -The algorithm entailed a 1- or 2-step process: (1) a screening step consisting of IHC stains for SF-1, Pit-1, and adrenocorticotropic hormone; and (2) when screening IHC pattern and clinical history were not clearly gonadotrophic (SF-1 positive only), corticotrophic (adrenocorticotropic hormone positive only), or IHC null cell (negative-screening IHC), we subsequently used IHC for prolactin, growth hormone, thyroid-stimulating hormone, and cytokeratin CAM5.2. CONCLUSIONS: -Comparison between diagnoses generated by our algorithm and the gold standard diagnoses showed excellent agreement. When compared with a commonly used panel using 6 IHC for anterior pituitary hormones plus IHC for a low-molecular-weight cytokeratin in certain tumors, our algorithm uses approximately one-third fewer IHC stains and detects gonadotroph adenomas with greater sensitivity.

Effect of street connectivity and density on adult BMI: results from the Twin Cities Walking Study.

BACKGROUND: The prevalence of overweight and obesity in the US population has risen dramatically in recent years. To try to explain this, some studies have examined the association between the built environment and obesity (measured using the body mass index (BMI)). Most of these studies have not sought to identify causal effects, but rather correlations. METHODS: Data from the Twin Cities Walking Study were used to examine the effect of population density and block size on BMI. Although the Twin Cities Walking Study is a cross-sectional observational study, the matched-sampling design is novel in that it maximises environmental variance while minimising person variance to enhance exchangeability of subjects and more closely mimic an experimental study. RESULTS: Contrary to expectations, the hypothesised most walkable neighbourhood (high density, small block stratum) had the greatest mean and median BMI. After adjusting for demographic covariates, physical activity and clustering due to neighbourhood, no conclusive effect of population density by block size on BMI was found (ß=-1.024, 95% CI -2.408 to 0.359). CONCLUSION: There is no evidence of an effect of population density by block size on BMI.