Atmospheric Circulation of Eccentric Hot Neptune GJ436b

GJ436b is a unique member of the transiting extrasolar planet population being one of the smallest and least irradiated and possessing an eccentric orbit. Because of its size, mass and density, GJ436b could plausibly have an atmospheric metallicity similar to Neptune (20-60 times solar abundances), which makes it an ideal target to study the effects of atmospheric metallicity on dynamics and radiative transfer in an extrasolar planetary atmosphere. We present three-dimensional atmospheric circulation models that include realistic non-gray radiative transfer for 1, 3, 10, 30, and 50 times solar atmospheric metallicity cases of GJ436b. Low metallicity models (1 and 3 times solar) show little day/night temperature variation and strong high-latitude jets. In contrast, higher metallicity models (30 and 50 times solar) exhibit day/night temperature variations and a strong equatorial jet. Spectra and light curves produced from these simulations show strong orbital phase dependencies in the 50 times solar case and negligible variations with orbital phase in the 1 times solar case. Comparisons between the predicted planet/star flux ratio from these models and current secondary eclipse measurements support a high metallicity atmosphere (30-50 times solar abundances) with disequilibrium carbon chemistry at play for GJ436b. Regardless of the actual atmospheric composition of GJ436b, our models serve to illuminate how metallicity influences the atmospheric circulation for a broad range of warm extrasolar planets.Comment: 25 pages, 13 figure

Plasma steroid profiles in subclinical compared to overt adrenal Cushing's syndrome

CONTEXT Diagnosis of subclinical adrenal hypercortisolism is based on several tests of the hypothalamic-pituitary-adrenal axis to establish mild alterations of cortisol secretion and dysregulated cortisol physiology. OBJECTIVE This study assessed whether plasma steroid profiles might assist diagnosis of subclinical Cushing's syndrome (SC). DESIGN Retrospective cross-sectional study. SETTING Two tertiary medical centers. PATIENTS Two hundred and eight patients were tested for hypercortisolism among whom disease was excluded in 152 and confirmed in 21 with overt clinical Cushing's syndrome due to adrenal tumors (AC) compared to 35 with SC. Another 277 age- and gender-matched hypertensive and normotensive volunteers were included for reference. MAIN OUTCOME MEASURES Panel of 15 plasma steroids measured by mass spectrometry with classification by discriminant analysis. RESULTS Patients with SC showed lower (P<0.05) plasma concentrations of dehydroepiandrosterone and dehydroepiandrosterone-sulfate than subjects without SC. The largest increases (P<0.001) in plasma steroids among patients with SC were observed for 11-deoxycortisol and 11-deoxycorticosterone. Nevertheless, concentrations of 11-deoxycorticosterone, 11-deoxycortisol and pregnenolone in patients with AC were higher (P<0.05) than in those with SC. Patients with SC or AC could be distinguished from subjects without disease using the above combination of steroids as precisely as with use of measurements of serum cortisol after dexamethasone. The steroid combination provided superior diagnostic performance compared to each of the other routine biochemical tests. CONCLUSIONS Distinct plasma steroid profiles in patients with SC may provide a simple and reliable screening method for establishing the diagnosis

Pheochromocytoma and paraganglioma: Clinical feature based disease probability in relation to catecholamine biochemistry and reason for disease suspicion

OBJECTIVE Hypertension and symptoms of catecholamine excess are features of pheochromocytomas and paragangliomas (PPGLs). This prospective observational cohort study assessed whether differences in presenting features in patients tested for PPGLs might assist establishing likelihood of disease. DESIGN AND METHODS Patients were tested for PPGLs because of signs and symptoms, an incidental mass on imaging or routine surveillance due to previous history or hereditary risk. Patients with (n=245) compared to without (n=1820) PPGLs were identified on follow-up. Differences in presenting features were then examined to assess probability of disease and relationships to catecholamine excess. RESULTS Hyperhidrosis, palpitations, pallor, tremor and nausea were 30-90% more prevalent (P<0.001) among patients with than without PPGLs, whereas headache, flushing and other symptoms showed little or no differences. Although heart rates were higher (P<0.0001) in patients with than without PPGLs, blood pressures were not higher and were positively correlated to body mass index (BMI), which was lower (P<0.0001) in patients with than without PPGLs. From these differences in clinical features, a score system was established that indicated a 5.8-fold higher probability of PPGLs in patients with high than low scores. Higher scores among patients with PPGLs were associated, independently of tumor size, with higher biochemical indices of catecholamine excess. CONCLUSIONS This study identifies a complex of five signs and symptoms combined with lower BMI and elevated heart rate as key features in patients with PPGLs. Prevalences of these features, which reflect variable tumoral catecholamine production, may be used to triage patients according to likelihood of disease

Leptospirosis in Germany, 1962–2003

Epidemiologic trends of human leptospirosis in Germany were investigated by analyzing national surveillance data from 1962 to 2003 and by conducting a questionnaire-based survey from 1997 to 2000. After a steady decrease of leptospirosis incidence from 1962 to 1997, surveillance data indicate an increase in disease incidence to 0.06 per 100,000 (1998–2003). Of 102 laboratory-confirmed cases in humans from 1997 to 2000, 30% were related to occupational exposures. Recreational exposures were reported in 30% (including traveling abroad in 16%), whereas residential exposure accounted for 37% of the cases. Direct contact with animals, mostly rats and dogs, was observed in 31% of the cases. We conclude that recent changes in transmission patterns of leptospirosis, partially caused by an expanding rat population and the resurgence of canine leptospirosis, may facilitate the spread of the disease in temperate countries like Germany. Preventive measures should be adapted to the changing epidemiology of leptospirosis

Targeted metabolomics as a tool in discriminating endocrine from primary hypertension

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

Biochemical Diagnosis of Chromaffin Cell Tumors in Patients at High and Low Risk of Disease: Plasma versus Urinary Free or Deconjugated -Methylated Catecholamine Metabolites

BACKGROUND Measurements of plasma or urinary metanephrines are recommended for diagnosis of pheochromocytoma and paraganglioma (PPGL). What test offers optimal diagnostic accuracy for patients at high and low risk of disease, whether urinary free metanephrines offer advantages over deconjugated metanephrines, and what advantages are offered by including methoxytyramine in panels all remain unclear. METHODS A population of 2056 patients with suspected PPGLs underwent prospective screening for disease using mass spectrometric-based measurements of plasma free, urinary deconjugated, and urinary free metanephrines and methoxytyramine. PPGLs were confirmed in 236 patients and were excluded in others on follow-up evaluation. RESULTS Measurements of plasma free metabolites offered higher ( < 0.01) diagnostic sensitivity (97.9%) than urinary free (93.4%) and deconjugated (92.9%) metabolites at identical specificities for plasma and urinary free metabolites (94.2%) but at a lower ( < 0.005) specificity for deconjugated metabolites (92.1%). The addition of methoxytyramine offered little value for urinary panels but provided higher ( < 0.005) diagnostic performance for plasma measurements than either urinary panel according to areas under ROC curves (0.991 vs 0.972 and 0.964). Diagnostic performance of urinary and plasma tests was similar for patients at low risk of disease, whereas plasma measurements were superior to both urinary panels for high-risk patients. CONCLUSIONS Diagnosis of PPGLs using plasma or urinary free metabolites provides advantages of fewer false-positive results compared with commonly measured deconjugated metabolites. The plasma panel offers better diagnostic performance than either urinary panel for patients at high risk of disease and, with appropriate preanalytics, provides the test of choice. Measurements of methoxytyramine in urine show limited diagnostic utility compared with plasma

Machine learning for classification of hypertension subtypes using multi-omics: a multi-centre, retrospective, data-driven study

Background: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. Methods: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. Findings: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers. Interpretation: We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment. Funding: European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1)

Spectronet: A package for computing spectra and median networks

Spectronet is a package that uses various methods for exploring and visualising complex evolutionary signals. Given an alignment in NEXUS format, the package works by computing a collection of weighted splits or bipartitions of the taxa and then allows the user to interactively analyse the resulting collection using tools such as Lento-plots and median networks. The package is highly interactive and available for PCs