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Contains fulltext : 232525.pdf (Publisher’s version ) (Open Access), CONTEXT: Identification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL], and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension. OBJECTIVE: Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability. METHODS: Retrospective analyses of PHT and EHT patients from a European multicenter study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a "classical approach" (CA) (performing a series of univariate and multivariate analyses) and a "machine learning approach" (MLA) (using random forest) were used.The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n = 59) and EHT (n = 223 with 40 CS, 107 PA, and 76 PPGL), respectively. RESULTS: From 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 16 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The receiver operating characteristic curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83). CONCLUSION: TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

Contains fulltext : 232525.pdf (Publisher’s version ) (Open Access), CONTEXT: Identification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL], and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension. OBJECTIVE: Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability. METHODS: Retrospective analyses of PHT and EHT patients from a European multicenter study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a "classical approach" (CA) (performing a series of univariate and multivariate analyses) and a "machine learning approach" (MLA) (using random forest) were used.The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n = 59) and EHT (n = 223 with 40 CS, 107 PA, and 76 PPGL), respectively. RESULTS: From 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 16 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The receiver operating characteristic curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83). CONCLUSION: TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

Contains fulltext : 232525.pdf (Publisher’s version ) (Open Access), CONTEXT: Identification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL], and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension. OBJECTIVE: Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability. METHODS: Retrospective analyses of PHT and EHT patients from a European multicenter study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a "classical approach" (CA) (performing a series of univariate and multivariate analyses) and a "machine learning approach" (MLA) (using random forest) were used.The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n = 59) and EHT (n = 223 with 40 CS, 107 PA, and 76 PPGL), respectively. RESULTS: From 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 16 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The receiver operating characteristic curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83). CONCLUSION: TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

While retinal images (RI) assist in the diagnosis of various eye conditions and diseases such as glaucoma and diabetic retinopathy, their innate features including low contrast homogeneous and nonuniformly illuminated regions, present a particular challenge for retinal image registration (RIR). Recently, the hybrid similarity measure, Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) has been proposed for RIR. This paper investigates incorporating various fixed and adaptive bin size selection strategies to estimate the probability distribution in the mutual information (MI) stage of EMPCA-MI, and analyses their corresponding effect upon RIR performance. Experimental results using a clinical mono-modal RI dataset confirms that adaptive bin size selection consistently provides both lower RIR errors and superior robustness compared to the empirically determined fixed bin sizes.

While retinal images (RI) assist in the diagnosis of various eye conditions and diseases such as glaucoma and diabetic retinopathy, their innate features including low contrast homogeneous and nonuniformly illuminated regions, present a particular challenge for retinal image registration (RIR). Recently, the hybrid similarity measure, Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) has been proposed for RIR. This paper investigates incorporating various fixed and adaptive bin size selection strategies to estimate the probability distribution in the mutual information (MI) stage of EMPCA-MI, and analyses their corresponding effect upon RIR performance. Experimental results using a clinical mono-modal RI dataset confirms that adaptive bin size selection consistently provides both lower RIR errors and superior robustness compared to the empirically determined fixed bin sizes.