Recent studies have reported the prevalence of pituitary tumors to be ~1/1000 population. Many are prolactin-producing tumors that are managed medically, however, the epidemiology of surgically resected pituitary adenohypophysial neuroendocrine tumors has not been reported in a large series with detailed characterization. We reviewed 1055 adenohypophysial tumors from 1169 transsphenoidal resections from the pathology files of University Health Network, Toronto, 2001-2016. Tumors were characterized by immunohistochemical localization of transcription factors (Pit-1, ERα, SF-1, Tpit), hormones (adrenocorticotropin, growth hormone, prolactin, β-thyrotropin, β-folliculotropin, β-luteotropin, α-subunit), and other biomarkers (keratins, Ki67, p27, FGFR4). Electron microscopy was used only for unusual lesions. In this cohort, 51.3% of patients were female; the average age was 51 years. Gonadotroph tumors represented 42.5%. Pit-1-lineage-tumors represented 29.9%; these were subclassified as growth-hormone-predominant (somatotroph/mammosomatotroph/mixed; 53%), prolactin-predominant (lactotroph/acidophil-stem-cell; 28%), thyrotrophs (2%), plurihormonal (14%), and not-otherwise-specified (3%). Corticotroph tumors represented 17.1%. Only 4.5% were null cell tumors and 0.5% were unusual plurihormonal tumors. In 5.5% the tumor was not characterized for technical reasons (sample size, fixation, necrosis or other artifact). All corticotroph and plurihormonal tumors were positive for keratins; others tumors showed variable negativity with highest rates in gonadotroph (37.1%) and null cell tumors (28.2%). Tumors with a Ki67 ≥ 3% comprised 60% of this cohort. Global loss of p27 was most frequent in corticotroph neoplasms, specifically those associated with elevated glucocorticoid levels. Corticotroph and lactotroph tumors were more common among females; gonadotroph tumors were more common among males. Younger patients had mainly corticotroph and Pit-1-lineage neoplasms, whereas older patients harbored mainly gonadotroph tumors. This represents one of the largest surgical series of morphologically characterized pituitary tumors reported to date and the first to include the routine use of transcription factors for tumor classification. The data provide the basis for clinicopathologic correlations that are helpful for prognostic and predictive patient management.

In an earlier paper, outlines of footprints of persons walking normally were studied to determine whether different people make verifiably distinct footprints. Our basic null hypothesis is: given a footprint outline trace made by Subject A (Alice), then Subject B (Bob), a distinct person, cannot produce a footprint outline trace indistinguishable from that of Alice. We showed in the previous work that the probability of a chance match is less than 10−8. In this paper we report two new advances in our research. First, we establish a rigorous mathematical framework for calculating worstcase and average chance-match probabilities. Second, we repeat the previous experiment to substantiate the earlier results, but with an expanded population sample size and a more representative and significantly bigger repeated sample. These improvements and a new automated tracing procedure for extracting all numerical measures lead to a sharpened accuracy with average chance match probabilities of 7.88 × 10−10 for a general population. In other words, the odds of a chance match are one in 1.27 billion.

Comparison of the shapes of barefoot impressions from an individual with footprints or shoes linked to a crime may be useful as a means of including or excluding that individual as possibly being at the scene of a crime. The question of the distinguishability of a person’s barefoot print arises frequently. This study indicates that measurements taken from the outlines of inked footprint impressions show a great degree of variability between donors and a great degree of similarity for multiple impressions taken from the same donor. The normality of the set of measurements on footprint outlines that we have selected for this study is confirmed. A statistical justification for the use of the product rule on individual statistical precisions is developed.

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