Moving from MATLAB matrices to NumPy arrays - A Matrix Cheatsheet
Moving from MATLAB matrices to NumPy arrays
- A Matrix Cheatsheet
Over time Python became my favorite programming language for the quick automation of tasks, such as manipulating and analyzing data. Also, I grew fond of the great
plotting library for Python.
MATLAB/Octave was usually my tool of choice when my tasks involved matrices and linear algebra. However, since I feel more comfortable with Python in general, I recently took a second look at Python’s
module to integrate matrix operations more easily into larger programs/scripts.
Last Updated: 04/03/2014
Eventually, I collected some of the basic and most common matrix functions of MATLAB/Octave and compiled a list to compare them to their NumP something I wanted to share in hope that it might be useful to other MATLAB/Octave users who are thinking about a migration to Python NumPy to perform matrix operations.
Note: in order to use "numpy" as "np" as shown in the table, you need to
import NumPy like this:
import numpy as np
MATLAB/Octave
Creating Matrices&(here: 3x3 matrix)
&& A = [1 2 3; 4 5 6; 7 8 9]A =&& 1 & 2 & 3&& 4 & 5 & 6&& 7 & 8 & 9
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])&& Aarray([[1, 2, 3],&& & & [4, 5, 6],&& & & [7, 8, 9]])
Getting the dimensionof a matrix(here: 2D, rows x cols)
&& A = [1 2 3; 4 5 6]A =& &1 & 2 & 3& &4 & 5 & 6&& size(A)ans =& &2 & 3
&& A = np.array([ [1,2,3], [4,5,6] ])&& Aarray([[1, 2, 3],& & & &[4, 5, 6]])&& A.shape(2, 3)
Selecting rows&
&& A = [1 2 3; 4 5 6; 7 8 9]% first row&& A(1,:)ans =&& 1 & 2 & 3% first 2 rows&& A(1:2,:)ans =&& 1 & 2 & 3&& 4 & 5 & 6
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])# first row&& A[0,:]array([1, 2, 3])# first 2 rows&& A[0:2,:]array([[1, 2, 3],
[4, 5, 6]])
Selecting columns
&& A = [1 2 3; 4 5 6; 7 8 9]% first column&& A(:,1)ans =&& 1&& 4&& 7% first 2 columns&& A(:,1:2)ans =&& 1 & 2&& 4 & 5&& 7 & 8
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])# first column (as row vector)&& A[:,0]array([1, 4, 7])# first column (as column vector)&& A[:,[0]]array([[1],&& & & [4],&& & & [7]])# first 2 columns&& A[:,0:2]array([[1, 2],&& & & &[4, 5],&& & & &[7, 8]])
Extracting rows andcolumns by criteria(here: get rows&that have value 9in column 3)
&& A = [1 2 3; 4 5 9; 7 8 9]A =&& 1 & 2 & 3&& 4 & 5 & 9&& 7 & 8 & 9&& A(A(:,3) == 9,:)ans =&& 4 & 5 & 9&& 7 & 8 & 9
&& A = np.array([ [1,2,3], [4,5,9], [7,8,9]])&& Aarray([[1, 2, 3],&& & & [4, 5, 9],&& & & [7, 8, 9]])&&& A[A[:,2] == 9]array([[4, 5, 9],&& & & [7, 8, 9]])
Accessing elements(here: first element)
&& A = [1 2 3; 4 5 6; 7 8 9]&& A(1,1)ans =& 1
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])&& A[0,0]1
Creating an1D column vector
&& a = [1; 2; 3]a =&& 1&& 2&& 3
&& a = np.array([[1],[2],[3]])&& aarray([[1],&& & & [2],&& & & [3]])
Creating an1D row vector
&& b = [1 2 3]b =&& 1 & 2 & 3
&& b = np.array([1,2,3])&& barray([1, 2, 3])
Converting&row to column vectors
&& b = [1 2 3]'b =&& 1&& 2&& 3
&& b = np.array([1, 2, 3])&& b = b[np.newaxis].T# alternatively &b = b[:,np.newaxis]&& barray([[1],&& & & [2],&& & & [3]])
Reshaping Matrices(here: 3x3 matrix to row vector)
&& A = [1 2 3; 4 5 6; 7 8 9]A =&& 1 & 2 & 3&& 4 & 5 & 6&& 7 & 8 & 9&& total_elements = numel(A)&& B = reshape(A,1,total_elements)&% or reshape(A,1,9)B =&& 1 & 4 & 7 & 2 & 5 & 8 & 3 & 6 & 9
&& A = np.array([[1,2,3],[4,5,6],[7,8,9]])&& Aarray([[1, 2, 3],&& & & [4, 5, 9],&& & & [7, 8, 9]])&& total_elements = A.shape[0] * A.shape[1]&& B = A.reshape(1, total_elements)&# or A.reshape(1,9)# Alternative: A.shape = (1,9)&# to&change the array in place&& Barray([[1, 2, 3, 4, 5, 6, 7, 8, 9]])
Concatenating matrices
&& A = [1 2 3; 4 5 6]&& B = [7 8 9; 10 11 12]&& C = [A; B]& &&1& & 2& & 3& & 4& & 5& & 6& & 7& & 8& & 9&& 10 & 11 & 12
&& A = np.array([[1, 2, 3], [4, 5, 6]])&& B = np.array([[7, 8, 9],[10,11,12]])&& C = np.concatenate((A, B), axis=0)&& Carray([[ 1, 2, 3],&& & & &[ 4, 5, 6],&& & & &[ 7, 8, 9],&& & & &[10, 11, 12]])
Stacking&vectors and matrices
&& a = [1 2 3]&& b = [4 5 6]&& c = [a' b']c =&& 1 & 4&& 2 & 5&& 3 & 6&& c = [a; b]c =&& 1 & 2 & 3&& 4 & 5 & 6
&& a = np.array([1,2,3])&& b = np.array([4,5,6])&& np.c_[a,b]array([[1, 4],&& & & [2, 5],&& & & [3, 6]])&& np.r_[a,b]array([[1, 2, 3],&& & & [4, 5, 6]])
Creating a random m x n matrix
&& rand(3,2)ans =&& 0.21977 & 0.10220&& 0.38959 & 0.69911&& 0.15624 & 0.65637
&& np.random.rand(3,2)array([[ 0.,& 0.],&& & & [ 0.,& 0.],&& & & [ 0.,& 0.]])
Creating azero&m x n matrix&
&& zeros(3,2)ans =&& 0 & 0&& 0 & 0&& 0 & 0
&& np.zeros((3,2))array([[ 0.,& 0.],&& & & [ 0.,& 0.],&& & & [ 0.,& 0.]])
Creating anm x n matrix of ones
&& ones(3,2)ans =&& 1 & 1&& 1 & 1&& 1 & 1
&& np.ones([3,2])array([[ 1.,& 1.],&& & & [ 1.,& 1.],&& & & [ 1.,& 1.]])
Creating anidentity matrix
&& eye(3)ans =Diagonal Matrix&& 1 & 0 & 0&& 0 & 1 & 0&& 0 & 0 & 1
&& np.eye(3)array([[ 1.,& 0.,& 0.],&& & & [ 0.,& 1.,& 0.],&& & & [ 0.,& 0.,& 1.]])
Creating adiagonal matrix
&& a = [1 2 3]&& diag(a)ans =Diagonal Matrix&& 1 & 0 & 0&& 0 & 2 & 0&& 0 & 0 & 3
&& a = np.array([1,2,3])&& np.diag(a)array([[1, 0, 0],&& & & [0, 2, 0],&& & & [0, 0, 3]])
Matrix-scalaroperations
&&&A = [1 2 3; 4 5 6; 7 8 9]&& A * 2ans =& & 2& & 4& & 6& & 8 & 10 & 12&& 14 & 16 & 18&& A + 2&& A - 2&& A / 2
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])&& A * 2array([[ 2,& 4,& 6],&& & & [ 8, 10, 12],&& & & [14, 16, 18]])&& A + 2&& A - 2&& A / 2
Matrix-matrixmultiplication
&&&A = [1 2 3; 4 5 6; 7 8 9]&& A * Aans =& & 30& & 36& & 42& & 66& & 81& & 96&& 102 & 126 & 150
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])&& np.dot(A,A) # or A.dot(A)array([[ 30,& 36,& 42],&& & & [ 66,& 81,& 96],&& & & [102, 126, 150]])
Matrix-vectormultiplication
&& A = [1 2 3; 4 5 6; 7 8 9]&& B = [ 1; 2; 3 ]&& A * Bans =&& 14&& 32&& 50
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])&& B = np.array([ [1], [2], [3] ])&& np.dot(A,B) # or A.dot(B)array([[14],
Element-wise&matrix-matrix&operations
&&&A = [1 2 3; 4 5 6; 7 8 9]&& A .* Aans =& & 1& & 4& & 9&& 16 & 25 & 36&& 49 & 64 & 81&& A .+ A&& A .- A&& A ./ A
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])&& A * Aarray([[ 1,& 4,& 9],&& & & [16, 25, 36],&& & & [49, 64, 81]])&& A + A&& A - A&& A / A
Matrix elements&to power n
&&&A = [1 2 3; 4 5 6; 7 8 9]&& A.^2ans =& & 1& & 4& & 9&& 16 & 25 & 36&& 49 & 64 & 81
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])&& np.power(A,2)array([[ 1,& 4,& 9],&& & & [16, 25, 36],&& & & [49, 64, 81]])
Matrix to power n(matrix-matrix&multiplicationwith itself)
&&&A = [1 2 3; 4 5 6; 7 8 9]&& A ^ 2ans =& & 30& & 36& & 42& & 66& & 81& & 96&& 102 & 126 & 150
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])&&& np.linalg.matrix_power(A,2)array([[ 30,& 36,& 42],&& & & [ 66,& 81,& 96],&& & & [102, 126, 150]])
Matrix transpose
&&&A = [1 2 3; 4 5 6; 7 8 9]&& A'ans =&& 1 & 4 & 7&& 2 & 5 & 8&& 3 & 6 & 9
&& A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])&& A.Tarray([[1, 4, 7],&& & & [2, 5, 8],&& & & [3, 6, 9]])
Determinant of a matrix:&A -& |A|
&& A = [6 1 1; 4 -2 5; 2 8 7]A =&& 6 & 1 & 1&& 4& -2 & 5&& 2 & 8 & 7&& det(A)ans = -306
&&&A = np.array([[6,1,1],[4,-2,5],[2,8,7]])&& Aarray([[ 6,& 1,& 1],&& & & [ 4, -2,& 5],&& & & [ 2,& 8,& 7]])&& np.linalg.det(A)-306.0
Inverse of a matrix
&& A = [4 7; 2 6]A =&& 4 & 7&& 2 & 6&& I = [1 0; 0 1]I =&& 1 & 0&& 0 & 1&& A_inv = inv(A)A_inv =& &0.60000& -0.70000& -0.20000 & 0.40000
&& A = np.array([[4, 7], [2, 6]])&& Aarray([[4, 7],&& & & &[2, 6]])&& I = np.array([[1, 0], [0, 1]])&& Iarray([[1, 0],&& & & &[0, 1]])&& A_inverse = np.linalg.inv(A)&& A_inversearray([[ 0.6, -0.7],&& & & &[-0.2,
0.4]])# A * A^-1 should yield the Identity matrixassert(np.dot(A, A_inverse).all() == I.all())
Calculating a&covariance matrix&of 3 random variables(here: covariances of the means&of x1, x2, and x3)
&& x1 = [4.0 3.0 4.1000]&&& x2 = [2.0 2.0 2.2000]'&& x3 = [0.00 0.00 0.63000]&&& cov( [x1,x2,x3] )ans =&& 2.5000e-02 & 7.5000e-03 & 1.7500e-03&& 7.5000e-03 & 7.0000e-03 & 1.3500e-03&& 1.7500e-03 & 1.3500e-03 & 4.3000e-04
&& x1 = np.array([ 4, 4.2, 3.9, 4.3, 4.1])&& x2 = np.array([ 2, 2.1, 2, 2.1, 2.2])&& x3 = np.array([ 0.6, 0.59, 0.58, 0.62, 0.63])&& np.cov([x1, x2, x3])array([[ 0.025 &, &0.0075 , &0.00175],& & & &[ 0.0075 , &0.007 &, &0.00135],& & & &[ 0.00175, &0.00135, &0.00043]])
Calculating&eigenvectors and&eigenvalues
&& A = [3 1; 1 3]A =&& 3 & 1&& 1 & 3&& [eig_vec,eig_val] = eig(A)eig_vec =& -0.70711 & 0.70711&& 0.70711 & 0.70711eig_val =Diagonal Matrix&& 2 & 0&& 0 & 4
&& A = np.array([[3, 1], [1, 3]])&& Aarray([[3, 1],&& & & [1, 3]])&& eig_val, eig_vec = np.linalg.eig(A)&& eig_valarray([ 4.,& 2.])&& eig_vecarray([[ 0., -0.],&& & & [ 0.,& 0.]])
Generating a Gaussion dataset:creating random vectors from the multivariate normal
distribution given mean and covariance matrix(here: 5 random vectors with mean 0, covariance = 0, variance = 2)
% requires statistics toolbox package% how to install and load it in Octave:% download the package from:&% http://octave.sourceforge.net/packages.php% pkg install&% & & ~/Desktop/io-2.0.2.tar.gz &% pkg install&% & & ~/Desktop/statistics-1.2.3.tar.gz&& pkg load statistics&& mean = [0 0]&& cov = [2 0; 0 2]cov =& &2 & 0& &0 & 2&& mvnrnd(mean,cov,5)& &2.480150 &-0.559906& -2.933047 & 0.560212& &0.098206 & 3.055316& -0.985215 &-0.990936& &1.122528 & 0.686977& &&
&& mean = np.array([0,0])&& cov = np.array([[2,0],[0,2]])&& np.random.multivariate_normal(mean, cov, 5)array([[ 1., -1.],&& & & &[-2.,
1.],&& & & &[-2.,
1.],&& & & &[-2., -0.],&& & & &[-1., -1.]])
NumPy matrix vs. NumPy array
While NumPy arrays are the basic types in NumPy, there is also a matrix type with very similar behavior. For most people who are familiar with MATLAB/Octave, NumPy matrix syntax might feel a little bit more natural. One of the differences is, for example, that the "*" operator is used for matrix-matrix multiplication of NumPy matrices, where the same operator performs element-wise multiplication on NumPy arrays. Vice versa, the ".dot()" method is used for element-wise multiplication in NumPy matrices, where "*" is used for the same operation on NumPy matrices.
Most people recommend the use of the NumPy array type over NumPy matrices, since arrays are what most of the NumPy functions return.
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*Piotr Glinicki, Wojciech Jeske
Chromogranina A (CgA): budowa, funkcja biologiczna, przedanalityczne, analityczne i kliniczne aspekty oznaczania jej we krwi
Chromogranin A (CgA): structure, biological function, pre-analytical, analytical, and clinical aspects of its measurement in blood
Department of Endocrinology, Centre of Postgraduate Medical Education, Bielański Hospital, WarszawaHead of Department: prof. Wojciech Zgliczyński, MD, PhD
StreszczenieChromogranina A (CgA) jest g?ównym, niespecyficznym markerem guzów neuroendokrynnych (NET). Obecnie dost?pnych jest kilka komercyjnych testów: RIA, IRMA, ELISA, CLIA, TRACE. Istnieje wiele czynników: in vivo, in vitro i wspó?istniej?cych chorób, które mog? wp?ywa? na st??enie CgA we krwi. Podwy?szone st??enie CgA we krwi obserwujemy zwykle w: guzach neuroendokrynnych przewodu pokarmowego (GEP-NET), pheochromocytoma, neuroblastoma, zespo?ach MEN, guzach neuroendokrynnych oskrzeli, raku rdzeniastym tarczycy, raku drobnokomórkowym p?uc oraz w innych rzadkich guzach NET. Pomiar st??enia CgA jest obecnie rutynowo stosowany w diagnostyce guzów GEP-NET, ale szczególnie jest on przydatny w monitorowaniu efektów leczenia. CgA mo?e by? dodatkowym badaniem w diagnostyce pheochromocytoma. U pacjentów z zespo?em mnogich nowotworów endokrynnych (MEN) i u cz?onków ich rodzin oznaczanie st??enia CgA mo?e s?u?y? ich monitorowaniu, aby jak najwcze?niej wykry? pojawienie si? w toku obserwacji guza neuroendokrynnego trzustki, rakowiaka albo pheochromocytoma.
SummaryChromogranin A (CgA) is a main nonspecific neuroendocrine tumour (NET) marker. Currently few commercial assays are available: RIA, IRMA, ELISA, CLIA, TRACE. There are many factors: in vivo, in vitro and coexisting diseases which can influence the CgA blood concentration. Elevated CgA levels in blood can be usually detected in: gastroenteropancreatic neuroendocrine tumours (GEP-NET), pheochromocytoma, neuroblastoma, MEN syndromes, bronchopulmonary NETs, medullary thyroid carcinoma, small-cell lung carcinoma, and some other very rare NETs. CgA measurement became a routine investigation in the diagnosis of GEP-NET, but is especially helpful in monitoring the effects of their treatment. CgA can be considered as a complementary investigation in the diagnostic procedure of pheochromocytoma. In patients with multiple endocrine neoplasia (MEN) investigation of CgA level may be used in monitoring eventual coexistence or appearance with time of carcinoid, pancreatic neuroendocrine tumour or pheochromocytoma.
S?owa kluczowe: chromogranina A, CgA, guzy neuroendokrynne, NET
Key words: ,
Introduction
Chromogranin A (CgA) is present in some endocrine cells of adrenals, pituitary, pancreas, thyroid and in cells of diffuse endocrine system (DES) of gastrointestinal and respiratory system. It is co-secreted and co-released together with some amines and peptides, that are present in the neurosecretory granules. In functionally active, and non-active neuroendocrine tumors (NETs) blood CgA level is often elevated, therefore, it is accepted as a main nonspecific marker of NETs (1, 2).
Chromogranin A: structure and biological function
Chromogranin A is an acidic protein with a molecular weight of 48 kDa that is composed of 439 amino acids (3). The human CgA gene (CHGA) is located on chromosome 14 (4). There are 10 dibasic sites in human CgA, which are potential sites for proteolytic cleavage (5). CgA occurs in two main conformations: random coil (60-65%) and alpha-helix (25-40%). Alteration of CgA conformation is pH and calcium ions dependent (6, 7). CgA is a protein binding Ca2+ ions. Many regions of CgA are homologous with Ca2+ binding protein & calmodulin (8). CgA is a member of the chromogranin family. The granin family consists of eight proteins including: CgA, chromogranin B (CgB), secretogranin II, secretogranin III, secretogranin IV, secretogranin V, secretogranin VI, VGF (9). Posttranslational processing of CgA molecule leads to the formation of smaller biologically active peptides, such as: vasostatin I, vasostatin II, chromacin, pancreastatin, WE-14, parastatin, catestin (10). These CgA-derived peptides due to their influence on the secretion of other hormone, play an indirect role in the metabolism of lipids, carbohydrates, calcium homeostasis, catecholamine secretion, and possess some activities on the cardiovascular system (e.g. vasoconstriction, vasodilatation). They participate also in regulation of secretion of some hormones (e.g. insulin, glucagon, leptin, LH, FSH, PTH), and play some role in the defense mechanism of the respiratory system (antimicrobial activity against bacteria, fungi) (11-15).
Pre-analytical and analytical aspects of chromogranin A (CgA) measurement in blood
Measurement of blood CgA concentration appeared possible despite the presence in blood of circulating CgA fragments induced by proteolysis (16).
The first radioimmunoassay for measurements of chromogranin A was introduced by O&Connor and Bernstein in 1984 (17). The next generation assays were based on sandwich methods with the use of monoclonal or polyclonal antibodies (18). Currently few commercial assays are available: IRMA (CgA-RIA CT, CIS Bio International-Schering, Gif-sur-Yvette, France), DAKO chromogranin A ELISA kit (DAKO A/S, Glostrup, Denmark), RIA (EuroDiagnostica, Malmo, Sweden), TRACE (Kryptor S B-R-A-H-M-S GmbH, Thermo Scientific, Germany). These assays differ in test structure, use different antibodies and differently calibrated standards. The applied in these assay monoclonal or polyclonal antibodies recognize different epitopes of CgA molecule and bind also some CgA fragments (19-21). Main characteristics of the mentioned method for determination of CgA are presented in table 1.
Table 1. Comparison of the currently available methods for determination of CgA concentration.
MethodKit producerAntibodyStandardUnitSort of biological material(according to thekit producer)Upper referencerange(according to the kit producer)
Immunoradiometric(IRMA)CIS Bio2 monoclonalrh CgAng/mlserumplasmaserum98 ng/mlplasma?
ELISACIS Bio2 monoclonalrh CgAng/mlserumplasmaserum98 ng/mlplasma?
ELISADAKO 2 polyclonal23 kDa C-terminal fragment of CgAU/lplasma(EDTA, heparin)serum?plasma2-18 U/l
Radioimmunoassay(RIA)EuroDiagnostica1 polyclonalCgA fraction purified from urine (patients with carcinoid tumours)nmol/lserumplasma(EDTA, heparin)serum and plasma& 3 nmol/l
ELISAALPCO2 polyclonalrh CgAng/mlplasma (EDTA)serum?plasma100 ng/ml
Automated immunometricassay (Kryptor)&B-R-A-H-M-S2 monoclonalrh CgA&g/lserumserummale: 84.7 &g/lfemale: 43.2 &g/lplasma?
rh CgA & human recombinant CgA
Lack of the recognized international standard for CgA, differences of methodology and specificity of the antibodies used, cause that individual CgA measurements performed with different CgA assays cannot be directly compared (22-23).
Using two commercial assays (IRMA and ELISA), we were able to show that CgA concentration is markedly higher in plasma (EDTA, heparin) than in serum (difference 12-70%), therefore different reference ranges should be applied for serum and for plasma samples. Our finding may be explained by the known fact that CgA can partially aggregate at high concentration of free Ca2+ ions. In plasma samples (EDTA) anticoagulation occurs through the binding of Ca2+ ions, therefore absence of free Ca2+ ions may protect CgA from partial aggregation (24). Apart of the tendency for aggregation CgA is a relatively stable protein, although one recent study has indicated that storage of samples over 24 hrs and 48 hrs at +4&C might decrease CgA concentration by 15% and 44% respectively (25). No significant differences were found between CgA levels in men and women (26, 27). In one study, however, authors noted higher concentrations of CgA in males than females, but in another paper CgA levels were higher in females than men, regardless of age (28, 29). Daily fluctuation of blood CgA concentration can be approximately 20-25%, and higher levels are observed in the late afternoon and at night (30, 31). Moderate exercise may exert a minor effect on CgA concentration (32). The CgA levels may also be influenced by ingestion of a meal, therefore blood for its measurement should be collected on fasting preferably in the morning (33, 34).
There are two groups of drugs which strongly influence CgA concentration: proton pump inhibitors (PPI) and H2-receptor antagonists (H2-RA) (35). Long-term use of PPI, and in some cases even a short-term treatment too (1-2 weeks), cause a significant increase of CgA concentration in blood, reaching sometimes levels like those observed in advanced NET tumours with metastases (36, 37). To avoid the effect of PPIs on CgA, if only possible, proton pump inhibitors should be discontinued for two weeks, before blood sampling, or PPI should be temporarily replaced by H2-RA, and this later drug should be discontinued 3 days before blood collection (38, 39). Treatment with corticosteroids may increase the concentration of CgA about 2-fold. The hypotensive drugs, have a little or no effect on CgA concentration (40, 41). During chemotherapy CgA levels may increase temporarily due to the tissue damage and/or nephrotoxic effect of such drugs (42, 43). The list of drugs and pathological conditions which can have influence on CgA concentration is presented in the table (tab. 2).
Table 2. Drugs and pathological conditions which can have influence on the CgA blood level.
High or moderate increase of the blood CgA levelModerate or little increase of the blood CgA level
& proton-pump inhibitors& histamine H2 receptor-blockers& chronic atrophic gastritis& impaired renal function& prostate cancer and BPH& inflammatory bowel disease& untreated essential hypertension& acute coronary syndrome& cardiac insufficiency& impaired liver function& hepatocellular carcinoma& pancreatic adenocarcinoma& hyperthyroidism& hyperparathyroidism& rheumatoid arthritis (RF IgM)& Parkinson disease& pregnancy
Practical recommendations for monitoring the concentration of CgA are presented in table 3.
Table 3. Practical recommendations for monitoring the concentration of CgA.
& The same method, preferably in the same laboratory& Use the same type of biological material (serum, EDTA-plasma, heparin-plasma)& Results refer to the reference range established for serum or plasma& Order blood collection preferably in the morning on fast (& 8 hr after last meal)& Withdraw drugs (proton pump inhibitors for 2 weeks, H2-receptor blockers for 3 days)& Consider other non-specific factors which can have influence on CgA blood level
Chromogranin A & neuroendocrine tumours marker
Neuroendocrine tumours are considered as rare, heterogeneous neoplasms. Some of these tumours secrete into the blood various substances, such as: hormones, pre-hormones, amines, various peptides which measurements have been applied for diagnostic purposes, assessment of treatment efficacy, and disease prognosis.
CgA is a main nonspecific neuroendocrine tumour marker. Elevated blood CgA level can be usually detected in: gastroenteropancreatic neuroendocrine tumours (GEP-NET), pheochromocytomas, neuroblastomas, MEN syndromes, bronchopulmonary NETs, medullary thyroid carcinoma, small-cell lung carcinoma, and some other very rare NETs (44). Dimension of CgA level elevation depends of the cell type, number of secretory granules, tumour volume, localization and stage of the disease (metastases) (45).
CgA measurement became a routine investigation in the diagnosis of GEP-NET (46), but is especially helpful in monitoring the effects of treatment. In small NETs, however (e.g. insulinoma) CgA level may not be elevated (47), therefore in general the sensitivity of CgA in GEP-NET is 10-100%, and specificity 68-100%. In patient with metastasis, the sensitivity is 60-100%. In some cases CgA level can increase by 100-1000 times above the cut-off value (the highest values are observed in carcinoids with liver metastases). CgA is considered as an independent prognostic factor of survival in patients with midgut GEP-NET (48). The highest sensitivity (80-100%) of CgA is observed in: gastrinoma, carcinoid tumour, and glucagonoma. Concentration of circulating CgA is associated with the degree of differentiation of neuroendocrine tumors (in poorly differentiated tumours CgA level are lower than in the highly differentiated tumors). According to the established recommendation in poorly differentiated tumours, CgA level should be measured every 2-3 months, whereas in other cases, at 6-12 months intervals (49).
Pheochromocytoma and other adrenal tumours
CgA is secreted by the neuroendocrine cells of adrenal medulla. CgA levels correlate with tumour mass and secretion of catecholamines, but this correlation disappears in large tumours (50). The sensitivity of CgA is 80-96% (51, 52). CgA can be considered as a complementary investigation in the diagnostic procedure of pheochromocytoma (together with catecholamines the sensitivity is approx. 100%). In preliminary differential diagnosis of adrenal tumours a markedly increased CgA level might be a useful additional marker of pheochromocytoma, however there are some cases of &silent& pheochromocytoma in which CgA level is not elevated. On the other side we should know that there are some cases of hormonally active adenomas and adrenal carcinomas secreting cortisol in which blood CgA levels may be moderately increased (53).
It is important to note that pheochromocytoma should be excluded before any invasive diagnostic or therapeutic procedure is undertaken to avoid the development of catecholaminergic crisis. It has been reported that in clinically silent cases of pheochromocytoma, after administration of glucocorticoids in a dose exceeding 1 mg of dexamethasone, there is a potential threat of severe hypertensive crisis, with life-threatening symptoms (low dose DST & 1 mg orally & has not been associated with such crisis) (54). Therefore, special care is necessary while planning the performance of high dose dexamethasone suppression tests in insufficiently diagnosed patients with adrenal tumour. Similar caution is advised when prescribing glucocorticoids as a pre-treatment before CT or MRI in patients allergic to contrast dye. For the above reasons, an additional test helping to confirm or exclude pheochromocytoma in patients with an asymptomatic adrenal tumour seems warranted.
MEN syndromes
In patients with multiple endocrine neoplasia (MEN) investigation of CgA level may be used for monitoring eventual coexistence or appearance with time of carcinoid, pancreatic neuroendocrine tumours or pheochromocytoma (55). Sensitivity of CgA in patients with MEN-1 is approximately 60% (56).
Determination of the blood CgA concentration in some other diseases
During long-term gastric acid inhibition serum CgA levels correlate with serum gastrin and reflect the presence and severity of fundic enterochromaffin (ECL) cells proliferative changes. Therefore it is proposed that monitoring blood CgA levels might be a useful screening test for ECL cell hyperplasia. It is suggested, that in cases with markedly increased CgA values an indication for endoscopic and histological examination of the gastric mucosa should be considered (57). CgA level may increase slightly also in patients with liver cirrhosis, chronic hepatitis, pancreatitis, inflammatory bowel diseases, obstructive pulmonary disease, sepsis, and in extreme physical stress (58).
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otrzymano:
zaakceptowano do druku: Adres do korespondencji:*Piotr GlinickiDepartment of Endocrinology The Centre of Postgraduate Medical Education Bielański Hospitalul. Ceg?owska 80, 01-809 Warszawatel. +48 (22) 569-02-93fax +48 (22) 834-31-31glinicki@cmkp.edu.plPost?py Nauk Medycznych 12/2014Strona internetowa
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