Variation of pro‐vasopressin processing in parvocellular and magnocellular neurons in the paraventricular nucleus of the hypothalamus: Evidence from the vasopressin‐related glycopeptide copeptin

Arginine vasopressin (AVP) is synthesized in parvocellular‐ and magnocellular neuroendocrine neurons in the paraventricular nucleus (PVN) of the hypothalamus. Whereas magnocellular AVP neurons project primarily to the posterior pituitary, parvocellular AVP neurons project to the median eminence (ME) and to extrahypothalamic areas. The AVP gene encodes pre‐pro‐AVP that comprises the signal peptide, AVP, neurophysin (NPII), and a copeptin glycopeptide. In the present study, we used an N‐terminal copeptin antiserum to examine copeptin expression in magnocellular and parvocellular neurons in the hypothalamus in the mouse, rat, and macaque monkey. Although magnocellular NPII‐expressing neurons exhibited strong N‐terminal copeptin immunoreactivity in all three species, a great majority (~90%) of parvocellular neurons that expressed NPII was devoid of copeptin immunoreactivity in the mouse, and in approximately half (~53%) of them in the rat, whereas in monkey hypothalamus, virtually all NPII‐immunoreactive parvocellular neurons contained strong copeptin immunoreactivity. Immunoelectron microscopy in the mouse clearly showed copeptin‐immunoreactivity co‐localized with NPII‐immunoreactivity in neurosecretory vesicles in the internal layer of the ME and posterior pituitary, but not in the external layer of the ME. Intracerebroventricular administration of a prohormone convertase inhibitor, hexa‐d‐arginine amide resulted in a marked reduction of copeptin‐immunoreactivity in the NPII‐immunoreactive magnocellular PVN neurons in the mouse, suggesting that low protease activity and incomplete processing of pro‐AVP could explain the disproportionally low levels of N‐terminal copeptin expression in rodent AVP (NPII)‐expressing parvocellular neurons. Physiologic and phylogenetic aspects of copeptin expression among neuroendocrine neurons require further exploration

Diagnostic Value of Serum Amylase Levels Indicating Computed Tomography-Defined Post-Endoscopic Retrograde Cholangiopancreatography Pancreatitis: A Prospective Multicenter Observational Study.

Objective:Post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis involves persistent serum amylase levels of 3 times or more the standard upper limit. However, these criteria were mostly based on retrospective studies and not necessarily supported by diagnostic imaging. Our prospective study aimed to investigate cutoff serum amylase levels suggesting post-ERCP pancreatitis using computed tomography as the criterion standard.Methods:We prospectively followed 2078 cases. Computed tomography was performed in patients whose serum amylase levels exceeded the institutional upper limit 12 to 24 hours after ERCP. Two expert radiologists blindly assessed the images and judged the presence or absence of pancreatitis. Correlations between serum amylase levels with pancreatitis were investigated using receiver operating characteristic analysis.Results:Amylase levels increased in 416 (23.2%) of 1789 cases included, and 350 cases were analyzed using computed tomography. Post-endoscopic retrograde cholangiopancreatography pancreatitis was diagnosed in 12.0% (214/1789). The cutoff amylase levels for judging pancreatitis after 12 to 24 hours was 2.75 times higher than the institutional upper limit, with an area under the curve of 0.77.Conclusions:The appropriate cutoff serum amylase level for judging post-ERCP pancreatitis at 12 to 24 hours after ERCP was 2.75 times higher than the institutional upper limit. These results may clarify the definition of post-ERCP pancreatitis

Mapping Genes Related to Early Onset Major Depressive Disorder in Dagestan Genetic Isolates 2) who genotyped AFFX SNP 6 in selected MDD cases

SUMMARY Aim: The purpose of this study was to determine the molecular epidemiology of early onset major depressive disorder (MDD) in genetic isolates of the Caucasus Dagestan indigenous ethnic populations using molecular and statistical population-genetic approaches. Methods: Two multigenerational pedigrees from two diverse remote highland isolates with aggregation of early onset MDD were ascertained within our long-term research program titled 'Dagestan Genetic Heritage, DGH'. The first isolate included 48 cases of MDD (19 living) with 11 suicides committed, and the second included 60 MDD cases (30 living) with 12 suicides committed. The phenotypes of the affected family members were determined using a database containing diagnoses from a regional psychiatric hospital and through our own clinical examinations, which were based on a Russian translation of DIGS software based on the DSM-IV criteria . A 10 cM genomic scan (Weber/CHLC 9.0 STRs) of the 64 affected and non-affected members of the pedigrees was performed and the data was used for multipoint parametric linkage analyses. Following this scan, selected cases were analyzed by Affymetrix 6.0 SNP arrays in order to refine the contribution of copy number variations (CNVs) to the genetic basis of MDD. Results: We found a total of 18 genomic regions with nominal (LOD>1.3) linkage to MDD across the two isolates. Three genomic regions had genome-wide significant (LOD>3) linkages and were found at 2p13.2-p11.2, 14q31.12-q32.13 and 22q12.3. We also confirmed previous findings for MDD at 4q25, 11p15, 12q23-24, 13q31-32, 18q21-22 and 22q11-13. Six linkage regions were observed in both genetic isolates, while 12 other linkages demonstrated population-specific heterogeneity. We detected CNV rearrangements within 12 of the 18 linkage regions. Affected subjects had the highest rate of genomic instability within the linkage regions at 2p13.2-p11.2, 4q25-q28.2, 7p14.1, 8p23, 14q31.12-q32.13, 18q22.1 and 20p13. Conclusion: The results obtained in this study suggest that mapping genes of complex diseases, including MDD, across genetically homogeneous isolates can help detect linkage signals and expedite the search for susceptibility genes when combined with methods that detect structural genomic variation in linkage regions