OBJECTIVEMonogenic diabetes is rare but is an important diagnosis in pediatric diabetes clinics. These patients are often not identified as this relies on the recognition of key clinical features by an alert clinician. Biomarkers (islet autoantibodies and C-peptide) can assist in the exclusion of patients with type 1 diabetes and allow systematic testing that does not rely on clinical recognition. Our study aimed to establish the prevalence of monogenic diabetes in U.K. pediatric clinics using a systematic approach of biomarker screening and targeted genetic testing. RESEARCH DESIGN AND METHODSWe studied 808 patients (79.5% of the eligible population) <20 years of age with diabetes who were attending six pediatric clinics in South West England and Tayside, Scotland. Endogenous insulin production was measured using the urinary C-peptide creatinine ratio (UCPCR). C-peptide-positive patients (UCPCR ‡0.2 nmol/mmol) underwent islet autoantibody (GAD and IA2) testing, with patients who were autoantibody negative undergoing genetic testing for all 29 identified causes of monogenic diabetes. RESULTSA total of 2.5% of patients (20 of 808 patients) (95% CI 1.6-3.9%) had monogenic diabetes (8 GCK, 5 HNF1A, 4 HNF4A, 1 HNF1B, 1 ABCC8, 1 INSR). The majority (17 of 20 patients) were managed without insulin treatment. A similar proportion of the population had type 2 diabetes (3.3%, 27 of 808 patients). CONCLUSIONSThis large systematic study confirms a prevalence of 2.5% of patients with monogenic diabetes who were <20 years of age in six U.K. clinics. This figure suggests that ∼50% of the estimated 875 U.K. pediatric patients with monogenic diabetes have still not received a genetic diagnosis. This biomarker screening pathway is a practical approach that can be used to identify pediatric patients who are most appropriate for genetic testing.

The age of black bream (Acanthopagrus butcheri) in the Gippsland Lakes of south-eastern Australia was estimated with high precision from sectioned otoliths of fish sampled from 1993 to 1996. Ageing techniques were validated by following the progression of age classes over 4 years. Correct identification of the first increment was aided by reference to the position of the subcupular meshwork fibre zone, and age assignment was confirmed by linear regression analyses of otolith weight against fish age. The growth of black bream was found to be slower, and their natural life span longer, than previously estimated from length–frequency distributions and scale readings. The maximum age recorded was 29 years, with most black bream 4–9 years old and few fish more than 10 years old. There were significant differences between the growth rates of males and females. The von Bertalanffy growth parameters were: L∞ 54.5 cm FL, t0 –5.21 years, K 0.042 year-1 for females and L∞ 38.2 cm FL, t0 –3.70 years, K 0.077 year-1 for males. The current age structure suggests that recruitment has been episodic since 1981 and low for three recent years in succession.

There is an increasing demand for aging data to provide inputs to stock assessment models for management of exploited fish populations. Image analysis software and computer hardware allow more rapid processing of samples and data. This paper describes a fully integrated system that has been in operation for 5 years and has been used to provide age estimates for more than 150 species. The system combines the requirements of high‐quality “production” aging with the benefits of a customized image analysis system. The system improves the work environment, increases efficiency, aids data collection, and improves quality control. All aging studies require unbiased and precise age estimates; however, the ongoing process of production aging has particular requirements for quality assurance. A classification of aging studies is proposed based on objectives of the study, and the features and key procedural requirements of each study type are described.

Ages of orange roughy (Hoplostethus atlanticus) determined by two methods (counting annuli on the surface of whole and in longitudinally sectioned otoliths) were similar up to maturity. Beyond maturity, age estimates from sectioned otoliths exceeded those from whole otoliths. Maximum recorded age was 125 years for an individual 41 cm standard length (SL), and age at maturity was estimated to be 25 years (30–32 cm SL). These are consistent with ages estimated previously by radiometric methods. Results demonstrated a two-stage linear relationship between otolith weight and age that confirmed the two-stage otolith mass growth model previously used in radiometric ageing. However, in the radiometric analyses the reduction in otolith growth was arbitrarily estimated at 45% of the immature rate whereas annuli data demonstrated a reduction after maturity to 62% of the immature rate. The new estimates of otolith mass growth rate were incorporated into the radiometric data and ages recalculated, which reduced age estimates for 38–40 cm SL fish from 77–149 to 59–101 years. The radiometric data were also recalculated using only the percentage reduction in otolith growth after maturity, giving the radiometric age of 125 ± 9 years for the oldest fish.