SummaryBackground Pituitary adenomas (PAs) are associated with increased morbidity and mortality. The optimal delivery of services and the provision of care for patients with PAs require distribution of the resources proportionate to the impact of these conditions on the community. Currently, the resource allocation for PAs in the health care system is lacking a reliable and an up-to-date epidemiological background that would reflect the recent advances in the diagnostic technologies, leading to the earlier recognition of these tumours. Objectives To determine the prevalence, the diagnostic delay and the characteristics of patients with PA in a well-defined geographical area of the UK (Banbury, Oxfordshire). Patients and methods Sixteen general practitioner (GP) surgeries covering the area of Banbury and a total population of 89 334 inhabitants were asked to participate in the study (data confirmed on 31 July 2006). Fourteen surgeries with a total of 81,449 inhabitants (91% of the study population) agreed to take part. All cases of PAs were found following an exhaustive computer database search of agreed terms by the staff of each Practice and data on age, gender, presenting manifestations and their duration, imaging features at diagnosis, history of multiple endocrine neoplasia type 1 and family history of PA were collected. Results A total of 63 patients with PA were identified amongst the study population of 81,149, with a prevalence of 77AE6 PA cases/ 100,000 inhabitants (prolactinomas; PRLoma: 44AE4, nonfunctioning PAs: 22AE2, acromegaly; ACRO: 8AE6, corticotroph adenoma: 1AE2 and unknown functional status; UFS: 1AE2/100,000 inhabitants). The distribution of each PA subtype was for PRLoma 57%, nonfunctioning PAs 28%, ACRO 11%, corticotroph adenoma 2% and UFS 2%. The median age at diagnosis and the duration of symptoms until diagnosis (in years) were for PRLoma 32AE0 and 1AE5, nonfunctioning PAs 51AE5 and 0AE8, ACRO 47 and 4AE5 and corticotroph adenoma 57 and 7, respectively. PRLoma was the most frequent PA diagnosed up to the age of 60 years (0-20 years: 75% and 20-60 years: 61% of PAs) and nonfunctioning PA after the age of 60 years (60% of PAs). Nonfunctioning PAs dominated in men (57% of all men with PA) and PRLoma in women (76% of all women with PA). Five patients (7AE9%) presented with classical pituitary apoplexy, with a prevalence of 6AE2 cases/100,000 inhabitants. Conclusions Based on a well-defined population in Banbury (Oxfordshire, UK), we have shown that PAs have a fourfold increased prevalence than previously thought; our data confirm that PAs have a higher burden on the Health Care System and optimal resource distribution for both clinical care and research activities aiming to improve the outcome of these patients are needed.

Despite patients being treated early and frequently with immunomodulators and biological therapy in Western Europe, 5-year outcomes including surgery and phenotype progression in this cohort were comparable across Western and Eastern Europe. Differences in treatment strategies between Western and Eastern European centres did not affect the disease course. Treatment with immunomodulators reduced the risk of surgery and hospitalisation.

This article explores the use of principal component analysis (PCA) and T2 and Q-statistic measures to detect and distinguish damages in structures. For this study, two structures are used for experimental assessment: a steel sheet and a turbine blade of an aircraft. The analysis has been performed in two ways: (i) by exciting the structure with low-frequency vibrations using a shaker and using several piezoelectric (PZT) sensors attached on the surface, and (ii) by exciting at high-frequency vibrations using a single PZT as actuator and several PZTs as sensors. A known vibration signal is applied and the dynamical responses are analyzed. A PCA model is built using data from the undamaged structure as a reference base line. The defects in the turbine blade are simulated by attaching a mass on the surface at different positions. Instead, a progressive crack is produced to the steel sheet. Data from sets of experiments for undamaged and damaged scenarios are projected into the PCA model. The first two projections, and the Q-statistic and T2-statistic indices are analyzed. Q-statistic indicates how well each sample conforms to the PCA model. It is a measure of the difference or residual between a sample and its projection into the principal components retained in the model. T2-statistic index is a measure of the variation of each sample within the PCA model. Results of each scenario are presented and discussed demonstrating the feasibility and potential of using this formulation in structural health monitoring.

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