18F-FDOPA PET/CT Uptake Parameters Correlate with Catecholamine Secretion in Human Pheochromocytomas

International audienceBackground: 18 F-FDOPA positron emission tomography/ computed tomography (PET/CT) is a sensitive nuclear imaging technology for the diagnosis of pheochromocytomas (PHEO). However, its utility in determining predictive factors for the secretion of catecholamines remains poorly studied. Methods: Thirty-nine histologically confirmed PHEO were included in this retrospective single-center study. Patients underwent 18 F-FDOPA PET/CT before surgery, with an evaluation of several uptake parameters (standardized uptake values [SUV max and SUV mean ] and the metabolic burden [MB] calculated as follows: MB = SUV mean × tumor volume) and measurement of plasma and/or urinary metanephrine (MN), normetanephrine (NM), and chromogranin A. Thirty-five patients were screened for germline mutations in the RET, SDHx, and VHL genes. Once resected, primary cultures of 5 PHEO were used for real-time measurement of catechol-amine release by carbon fiber amperometry. Results: The MB of the PHEO positively correlated with 24-h urinary excre-tion of NM (r = 0.64, p < 0.0001), MN (r = 0.49, p = 0.002), combined MN and NM (r = 0.75, p < 0.0001), and eventually plasma free levels of NM (r = 0.55, p = 0.006). In the mutated patients (3 SDHD, 2 SDHB, 3 NF1, 1 VHL, and 3 RET), a similar correlation was observed between MB and 24-h urinary combined MN and NM (r = 0.86, p = 0.0012). For the first time, we demonstrate a positive correlation between the PHEO-to-liver SUV max ratio and the mean number of secretory granule fusion events of the corresponding PHEO cells revealed by amperometric spikes (p = 0.01). Conclusion: While the 18 F-FDOPA PET/CT MB of PHEO strongly correlates with the concentration of MN, amperometric recordings suggest that 18 F-FDOPA uptake could be enhanced by overactivity of cat-echolamine exocytosis

The Structural Basis of Cryptosporidium-Specific IMP Dehydrogenase Inhibitor Selectivity

Cryptosporidium parvum is a potential biowarfare agent, an important AIDS pathogen, and a major cause of diarrhea and malnutrition. No vaccines or effective drug treatment exist to combat Cryptosporidium infection. This parasite relies on inosine 5?-monophosphate dehydrogenase (IMPDH) to obtain guanine nucleotides, and inhibition of this enzyme blocks parasite proliferation. Here, we report the first crystal structures of CpIMPDH. These structures reveal the structural basis of inhibitor selectivity and suggest a strategy for further optimization. Using this information, we have synthesized low-nanomolar inhibitors that display 103 selectivity for the parasite enzyme over human IMPDH2

Structure–activity relationship study of EphB3 receptor tyrosine kinase inhibitors

A structure–activity relationship study for a 2-chloroanilide derivative of pyrazolo[1,5-a]pyridine revealed that increased EphB3 kinase inhibitory activity could be accomplished by retaining the 2-chloroanilide and introducing a phenyl or small electron donating substituents to the 5-position of the pyrazolo[1,5-a]pyridine. In addition, replacement of the pyrazolo[1,5-a]pyridine with imidazo[1,2-a]pyridine was well tolerated and resulted in enhanced mouse liver microsome stability. The structure–activity relationship for EphB3 inhibition of both heterocyclic series was similar. Kinase inhibitory activity was also demonstrated for representative analogs in cell culture. An analog (32, LDN-211904) was also profiled for inhibitory activity against a panel of 288 kinases and found to be quite selective for tyrosine kinases. Overall, these studies provide useful molecular probes for examining the in vitro, cellular and potentially in vivo kinase-dependent function of EphB3 receptor

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio

Prediction of Clinical Deep Brain Stimulation Target for Essential Tremor From 1.5 Tesla MRI Anatomical Landmarks

International audienceBackground: Deep brain stimulation is an efficacious treatment for refractory essential tremor, though targeting the intra-thalamic nuclei remains challenging. Objectives: We sought to develop an inverse approach to retrieve the position of the leads in a cohort of patients operated on with optimal clinical outcomes from anatomical landmarks identifiable by 1.5 Tesla magnetic resonance imaging. Methods: The learning database included clinical outcomes and post-operative imaging from which the coordinates of the active contacts and those of anatomical landmarks were extracted. We used machine learning regression methods to build three different prediction models. External validation was performed according to a leave-one-out cross-validation. Results: Fifteen patients (29 leads) were included, with a median tremor improvement of 72% on the Fahn-Tolosa-Marin scale. Kernel ridge regression, deep neural networks, and support vector regression (SVR) were used. SVR gave the best results with a mean error of 1.33 ± 1.64 mm between the predicted target and the active contact position. Conclusion: We report an original method for the targeting in deep brain stimulation for essential tremor based on patients' radio-anatomical features. This approach will be tested in a prospective clinical trial

The biogeochemical impact of glacial meltwater from Southwest Greenland

Biogeochemical cycling in high-latitude regions has a disproportionate impact on global nutrient budgets. Here, we introduce a holistic, multi-disciplinary framework for elucidating the influence of glacial meltwaters, shelf currents, and biological production on biogeochemical cycling in high-latitude continental margins, with a focus on the silica cycle. Our findings highlight the impact of significant glacial discharge on nutrient supply to shelf and slope waters, as well as surface and benthic production in these regions, over a range of timescales from days to thousands of years. Whilst biological uptake in fjords and strong diatom activity in coastal waters maintains low dissolved silicon concentrations in surface waters, we find important but spatially heterogeneous additions of particulates into the system, which are transported rapidly away from the shore. We expect the glacially-derived particles – together with biogenic silica tests – to be cycled rapidly through shallow sediments, resulting in a strong benthic flux of dissolved silicon. Entrainment of this benthic silicon into boundary currents may supply an important source of this key nutrient into the Labrador Sea, and is also likely to recirculate back into the deep fjords inshore. This study illustrates how geochemical and oceanographic analyses can be used together to probe further into modern nutrient cycling in this region, as well as the palaeoclimatological approaches to investigating changes in glacial meltwater discharge through time, especially during periods of rapid climatic change in the Late Quaternary

The seasonal nitrogen cycle in Wilkinson Basin, Gulf of Maine, as estimated by 1-D biological model optimization

Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 78 (2009): 77-93, doi:10.1016/j.jmarsys.2009.04.001.The objective of this study was to fit a simple ecosystem model to climatological nitrogen cycle data in the Gulf of Maine, in order to calibrate the biological model for use in future 3-D modelling studies. First depth-dependent monthly climatologies of nitrate, ammonium, chlorophyll, zooplankton, detritus and primary production data from Wilkinson Basin, Gulf of Maine, were created. A 6-box nitrogen-based ecosystem model was objectively fitted to the data through parameter optimization. Optimization was based on weighted least squares with model-data misfits nondi- mensionalized by assigned uncertainties in the monthly climatological estimates. These uncertainties were estimated as the standard deviations of the raw data from the 6-meter and monthly bin averages. On average the model fits the monthly means almost within their assigned uncertainties. Several statistics are examined to assess model-data misfit. Pattern statistics such as the correlation coefficient lack practical significance when data errors are large relative to the signal, as in this application. Thus Taylor diagrams were not found to be useful. The RMSE and model bias normalized by the data error were found to be the most useful skill metrics as they indicate whether the model fits the data within its estimated error. The optimal simulated nitrogen cycle budgets are presented, as an estimate of the seasonal nitrogen cycle in Wilkinson Basin, and discussed in context of the available data.Wilkinson Basin has spring and fall phytoplankton blooms, and strong summer stratification with a deep chlorophyll maximum near 21 m, just above the nitracline. The mean euphotic zone depth is estimated to be 25 m, relatively constant with season. The model estimates annual primary production as 176 g C m−2 yr−1, annual new production as 71 g C m−2 yr−1 and sinking PON fluxes of 9.7 and 4.7 g N m−2 yr−1 at 24 and 198 m respectively. Areas for improvement in the biological model, the model optimization method, and significant data gaps are identified.This work was supported by ONR, NSF, and NOAA grant to Dennis McGillicuddy