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Seafood fraud - often involving substitution of one species by another - has attracted much attention as it is prevalent worldwide. Whilst DNA analysis has helped to combat this type of fraud some of the methods currently in use are time-consuming and require sophisticated equipment or highly-trained personnel. This work describes the development of a new, real-time PCR TaqMan assay for the detection of ling (Molva molva) in seafood products. For this purpose, specific primers and a minor groove binding (MGB) TaqMan probe were designed to amplify the 81bp region on the cyt b gene. Efficiency, specificity and cross-reactivity assays showed statistically significant differences between the average Ct value obtained for Molva molva DNA (19.45±0.65) and the average Ct for non-target species DNA (38.3±2.8), even with closely related species such as Molva dypterygia (34.9±0.09). The proposed methodology has been validated with 31 commercial samples.

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Background: International consensus on the use of continuous glucose monitoring (CGM) recommends coefficient of variation (CV) as the metric of choice to express glycemic variability (GV) with a cutoff of 36% to define unstable diabetes. Even though, CV is associated with hypoglycemia in T2DM patients, the evidence on the use of one particular measure of GV in T1DM patients as a predictor of hypoglycemia is limited. Methods: A cohort of T1DM ambulatory patients was evaluated using CGM. Number and incidence rate of events < 54 and < 70 mg/dL were calculated. Bivariate and multivariate analysis of different glycemic indexes and clinical variables were performed to identify those associated with hypoglycemia. ROC curve analysis for each of the glycemic indexes were performed to define the best indexes and optimal cutoff thresholds to discriminate patients at risk of hypoglycemia. Results: Seventy-three patients were included. A total of 128 events of hypoglycemia < 54 mg/dL were recorded in 34 patients and 350 events < 70 mg/dL were registered in 51 patients. CV was the only variable significantly associated with hypoglycemia <54 mg/dL in the multivariate analysis (aRR: 1.44, 95% CI: 1.10-1.88; p=0.008). %CV, HbA1c and mean glucose were associated with events <70 mg/dL. Analysis of ROC curves showed that, among GV metrics, CV had the best performance to discriminate patients with events < 54 mg/dL (AUC: 0.87, 95% CI: 0.79-0.95) and events <70 mg/dL (AUC: 0.79, 95% CI: 0.68-0.90) with optimal cutoff thresholds values of 34% and 31% respectively. Among glycemic risk indexes, LBGI showed the best performance. Conclusions: This analysis shows that CV is the the best GV index, and LBGI the best glycemic risk index, to identify patients at risk of clinically significant hypoglycemia and hypoglycemia alert events in T1DM patients. Disclosure A. Gomez: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc., Novo Nordisk Inc. Speaker’s Bureau; Self; Abbott, AstraZeneca, Medtronic MiniMed, Inc., Merck Sharp & Dohme Corp. D. Henao: Research Support; Self; Novo Nordisk Inc. Speaker’s Bureau; Self; Abbott, Medtronic MiniMed, Inc., Novo Nordisk Inc. A.M. Imitola: None. L.B. Taboada: Other Relationship; Self; Medtronic MiniMed, Inc. M. Robledo: None. K.V. Cruz: None. M. Rondón: None. O.M. Munoz: None. M. Garcia Jaramillo: None. F. León-Vargas: None.