Human HCG Beta free ELISA Kit

Context: Cocaine- and amphetamine-regulated transcript (CART) codes for a peptide widely distributed in nervous and endocrine tissues. CART immunoreactivity (CART-LI) has been detected in human insulinomas. Objective: The objective of the study was to investigate the measurement of plasma CART-LI as a tumor marker of neuroendocrine malignancy. Design and Subjects: Plasma CART-LI levels were measured in 401 patients with a range of diagnoses: neuroendocrine malignancy (n = 131), after removal of neuroendocrine malignancy (n = 27), without any form of tumor or renal impairment (n = 192), with renal impairment (n = 17) and with nonneuroendocrine tumors (n = 34). Chromatography methods were used to investigate CART-LI circulating in human plasma. Results: The upper limit of normal calculated for CART-LI was 150 pmol/liter. Mean circulating plasma CART-LI among neuroendocrine tumor patients was 440 pmol/liter, 56% of subjects having levels greater than 150 pmol/liter. Measuring CART-LI in addition to chromogranin (Cg)-A improved the sensitivity for neuroendocrine malignancy from 85 to 91%, whereas combined use of CgA and CgB had a joint sensitivity of 89%. Of 38 patients with pancreatic neuroendocrine tumors, 71% had plasma CART-LI levels greater than 150 pmol/liter, increasing to 95% in those classified with progressive disease (n = 20, mean CART-LI 625 pmol/liter), compared with 80% for CgA. Chromatographic analysis suggests that circulating CART-LI is present as one major form, which may correspond to CART (62-102) or another unknown form. Conclusions: We demonstrate CART-LI as a specific tumor marker in patients with a range of neuroendocrine tumors. Used in combination with CgA, CART-LI measurement has the potential to improve sensitivity in diagnosis and follow-up of neuroendocrine tumors, in particular progressive pancreatic neuroendocrine tumors.

The transfer of energy between two adjacent parts of rock mainly depends on its thermal conductivity. Present study supports the use of artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) in the study of thermal conductivity along with other intrinsic properties of rock due to its increasing importance in many areas of rock engineering, agronomy and geo environmental engineering field. In recent years, considerable effort has been made to develop techniques to determine these properties. Comparative analysis is made to analyze the capabilities among six different models of ANN and ANFIS. ANN models are based on feedforward backpropagation network with training functions resilient backpropagation (RP), one step secant (OSS) and Powell-Beale restarts (CGB) and radial basis with training functions generalized regression neural network (GRNN) and more efficient design radial basis network (NEWRB). A data set of 136 has been used for training different models and 15 were used for testing purposes. A statistical analysis is made to show the consistency among them. ANFIS is proved to be the best among all the networks tried in this case with average absolute percentage error of 0.03% and regression coefficient of 1, whereas best performance shown by the FFBP (RP) with average absolute error of 2.26%. Thermal conductivity is predicted using P-wave velocity, porosity, bulk density, uniaxial compressive strength of rock as input parameters. (c) 2005 Elsevier Ltd. All rights reserved.