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Geetha. T. Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 7, Issue 2, ( Part -3) February 2017, pp.24-26
www.ijera.com DOI: 10.9790/9622- 0702032426 24 | P a g e
EMW Distribution for Human Hormone
Geetha. T* and Poongothai. T**
* Asst. Professor of Mathematics, K.N. Govt. Arts College, Thanjavur, Tamilnadu, India
** Research Scholar, K.N. Govt, Arts College, Thanjavur.
ABSTRACT
A stochastic modeling of biological systems is crucial to effectively and efficiently developing treatments for
medical conditions that plague humanity. The study of challenge tests designed to evaluate serotoninergic
pathways have widely used intravenous citalopram. Oral citalopram has also been used, but unsatisfactory
results were obtained with a dose of 20 mg. We evaluated cortisol, growth hormone and prolactin levels and
determine whether a higher oral dose would reproduce similar to those described for intravenous administration.
Under the assumption that the threshold level of cortisol is a random variable follows exponentiated modified
weibull distribution. The survival function of cortisol and its p.d.f are derived.
Keywords: Cortisol, threshold, EMWD, cumulative Damage process.
I. INTRODUCTION
K.Chandrasekar and R.Kannan has been
derived the stochastic model for estimating the
expected time to seroconversion of HIV infected
using exponentiated modified modified weibull
distribution. In this paper, the stochastic model for
the expected time to cortisol and variance of the
cortisol time are derived under the assumption that
threshold level of stress effect is a random variable
which follows exponentiated modified weibull
distribution.
Challenge tests to evaluate serotoninergic
function in psychiatric disorders are common and
usually evaluate corticotrophin-releasing hormone,
growth hormone and prolactin. Fenfluramine has
given way to other agents, including 5-HT reuptake
inhibitors (SSRI antidepressants). Although
paroxetine has been used in challenge tests,
citalopram is often the SSRI of choice due to its
higher selectivity. In the only double-blind
randomized study addressing 5-HT-mediated
neuroendocrine response, no difference was found
between citalopram, a racemic compound, and
escitalopram, its active isomer. Pharmacokinetic
studies have shown that maximum plasma levels of
approximately 130-160 nmol/l are generally
obtained within 2-4 h after a 50 –mg oral dose. A
second, lower, peak is sometimes observed. The
decline is considered to be monoexponential with a
of approximately 30 h. Although intravenous
citalopram has previously been used in
serotoninergic challenge tests, this formulation is
not available in many countries, and the only
challenge test with oral citalopram (at a dose of 20
mg) produced less than satisfactory results. The
objectives of this study were: 1) to measure plasma
levels of cortisol, growth hormone and prolactin as
a means of evaluating the neuroendocrine response
to a higher dose (40 mg) of oral citalopram used as
a tool to stimulate hormone secretion in
neuroendocrine challenge paradigms; 2) to
compare the kinetics of serum citalopram levels
and neuroendocrine response using this higher dose
of citalopram.
APPLICATION
RESEARCH ARTICLE OPEN ACCESS
Geetha. T. Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 7, Issue 2, ( Part -3) February 2017, pp.24-26
www.ijera.com DOI: 10.9790/9622- 0702032426 25 | P a g e
Growth hormone levels increased in three
of the four subjects who presented a rise I cortisol
level and a peak at 2-3 h after citalopram intake.
Curiously, the three subjects who did not present an
increase in growth hormone levels after citalopram
intake actually a peak at 2-3 h after placebo intake.
The reason for this is unknown. In two of the six
subjects, prolactin levels increased after citalopram
challenge. All measurements were within the
normal range, except for that of one subject who
had a baseline prolactin level above the upper
normal limit, which might have been related to
stress or even to a normally occurring pulse outside
normal limits.
II. MATHEMATICAL MODEL
: time for cortisol level during exercise.
Y : Cortisol level.
: a continuous random variable denoting the inter
arrival times between successive contacts with p.d.f
f(.) and c.d.f F(.).
(.) : the p.d.f of random variable.
T : a continuous random variable denoting the time
of cortisol with p.d.f l(.) and c.d.f L(.).
(t) : probability of exactly i contacts in (0,t]
(s) : is the Laplace stieltje’s transform of l(t).
(s): is the Laplace stieltje’s transform of f(t).
The probability density function of exponentiated
modified weibull distribution is,
h(x) = ( + )
x>0 and >0
and its distribution function is
H(x) = ,x>0
then its survival function is
(x) = 1-
Put = 2, =1 in (x), then it becomes
(x) = [2 - ]
since f(.) follows exp(c), then
If (s) = and
(s) = -
where
( ) = , (2( )) =
E(T) =
V(T) =
GRAPH: 1
X 0.6 1.2 2.4 4.7 7.1 9.4
E(T) 1.7494 0.8748 0.4374 0.2233 0.1478 0.1117
V(T) 3.0568 0.7642 0.1910 0.0498 0.0218 0.0125
GRAPH: 2
X 0.6 1.2 2.4 4.7 7.1 9.4
E(T) 1.7272 0.8636 0.4319 0.2205 0.1459 0.1102
V(T) 2.9821 0.7455 0.1864 0.0486 0.0213 0.0121
GRAPH: 1
Geetha. T. Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 7, Issue 2, ( Part -3) February 2017, pp.24-26
www.ijera.com DOI: 10.9790/9622- 0702032426 26 | P a g e
GRAPH: 2
GRAPH : 1
X 0 0.6 1.2 2.4 4.7 7.1 9.4
Y1 4.3 3.4 3.1 3 2.2 2 1.9
Y2 6.1 5.2 5.3 3.8 2.8 3.4 2.7
GRAPH: 2
X 0 0.6 1.2 2.4 4.7 7.1 9.4
Y1 4.3 4 3.5 2.8 2.6 2.3 2.1
Y2 5.4 5.4 4.8 5.3 5.5 2.8 3.7
III. CONCLUSION
The threshold level of cortisol is a random
variable follows exponentiated modified weibull
distribution and the survival function of cortisol
and its p.d.f. also found.
REFERENCES
[1]. Attenburrow MJ, Mitter PR, Whale R, Terao
T, Cowen PJ. Low-dose Citalpram as a 5-
HT neuroendocrine probe.
Psychopharmacology (Berl). 2001; 155(3) :
323-6.
[2]. Chandrasekar. K and Kannan.R (2013), A
Stochastic Model for Estimation of Expected
time to Seroconversion of HIV infected
Geetha. T. Int. Journal of Engineering Research and Application www.ijera.com
ISSN : 2248-9622, Vol. 7, Issue 2, ( Part -3) February 2017, pp.24-26
www.ijera.com DOI: 10.9790/9622- 0702032426 27 | P a g e
using exponential-geometric distribution,
Bio-Science Research Bulletin, Vol. 29(1),
29-38.
[3]. Elbatal.L., (2011), Exponentaited modified
Weibull distribution, Economic Quality
Control, 26, 189-200.
[4]. Esary, J.D., Marshall, A.W. and Proschan, F.
(1973), Shock models and Wear processes,
Ann. Probability, 1(4) : 627-649.
[5]. Hanley NR, Van de Kar LD. Serotonin and
the neuroendocrine regulation of the
hypothalamic – pituitary adrenal axis in
health and diseases. Vitam Horm. 2003; 66:
189-255.
[6]. Kannan. R., Kavitha. S., and Sathiyamoorthi
.R., (2011), A Stochastic Model for the
Estimation of Time to Seroconversion of
HIV Transmission using Exponentiated
Exponential Distribution , Bio-Science
Research Bulletin, Vol.27(2): 67-76.
[7]. Kannan. R., Venkatachalam. K. A.,
Sathiyamoorthi. R. and Malarvizi. G.,
(2007), A Stochastic Approach to Determine
the Expected Time to Seroconversion of
HIV infected. Journal of Indian Acad. of
Math., 29(1), 237-249.
[8]. Sathiyamoorthi, R. and Kannan, R., (2001),
A Stochastic Model for Time to
Seroconversion of HIV Transmission,
Journal of Kerala Statistical Association,
Vol. 12: 23-28.

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EMW Distribution for Human Hormone

  • 1. Geetha. T. Int. Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 7, Issue 2, ( Part -3) February 2017, pp.24-26 www.ijera.com DOI: 10.9790/9622- 0702032426 24 | P a g e EMW Distribution for Human Hormone Geetha. T* and Poongothai. T** * Asst. Professor of Mathematics, K.N. Govt. Arts College, Thanjavur, Tamilnadu, India ** Research Scholar, K.N. Govt, Arts College, Thanjavur. ABSTRACT A stochastic modeling of biological systems is crucial to effectively and efficiently developing treatments for medical conditions that plague humanity. The study of challenge tests designed to evaluate serotoninergic pathways have widely used intravenous citalopram. Oral citalopram has also been used, but unsatisfactory results were obtained with a dose of 20 mg. We evaluated cortisol, growth hormone and prolactin levels and determine whether a higher oral dose would reproduce similar to those described for intravenous administration. Under the assumption that the threshold level of cortisol is a random variable follows exponentiated modified weibull distribution. The survival function of cortisol and its p.d.f are derived. Keywords: Cortisol, threshold, EMWD, cumulative Damage process. I. INTRODUCTION K.Chandrasekar and R.Kannan has been derived the stochastic model for estimating the expected time to seroconversion of HIV infected using exponentiated modified modified weibull distribution. In this paper, the stochastic model for the expected time to cortisol and variance of the cortisol time are derived under the assumption that threshold level of stress effect is a random variable which follows exponentiated modified weibull distribution. Challenge tests to evaluate serotoninergic function in psychiatric disorders are common and usually evaluate corticotrophin-releasing hormone, growth hormone and prolactin. Fenfluramine has given way to other agents, including 5-HT reuptake inhibitors (SSRI antidepressants). Although paroxetine has been used in challenge tests, citalopram is often the SSRI of choice due to its higher selectivity. In the only double-blind randomized study addressing 5-HT-mediated neuroendocrine response, no difference was found between citalopram, a racemic compound, and escitalopram, its active isomer. Pharmacokinetic studies have shown that maximum plasma levels of approximately 130-160 nmol/l are generally obtained within 2-4 h after a 50 –mg oral dose. A second, lower, peak is sometimes observed. The decline is considered to be monoexponential with a of approximately 30 h. Although intravenous citalopram has previously been used in serotoninergic challenge tests, this formulation is not available in many countries, and the only challenge test with oral citalopram (at a dose of 20 mg) produced less than satisfactory results. The objectives of this study were: 1) to measure plasma levels of cortisol, growth hormone and prolactin as a means of evaluating the neuroendocrine response to a higher dose (40 mg) of oral citalopram used as a tool to stimulate hormone secretion in neuroendocrine challenge paradigms; 2) to compare the kinetics of serum citalopram levels and neuroendocrine response using this higher dose of citalopram. APPLICATION RESEARCH ARTICLE OPEN ACCESS
  • 2. Geetha. T. Int. Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 7, Issue 2, ( Part -3) February 2017, pp.24-26 www.ijera.com DOI: 10.9790/9622- 0702032426 25 | P a g e Growth hormone levels increased in three of the four subjects who presented a rise I cortisol level and a peak at 2-3 h after citalopram intake. Curiously, the three subjects who did not present an increase in growth hormone levels after citalopram intake actually a peak at 2-3 h after placebo intake. The reason for this is unknown. In two of the six subjects, prolactin levels increased after citalopram challenge. All measurements were within the normal range, except for that of one subject who had a baseline prolactin level above the upper normal limit, which might have been related to stress or even to a normally occurring pulse outside normal limits. II. MATHEMATICAL MODEL : time for cortisol level during exercise. Y : Cortisol level. : a continuous random variable denoting the inter arrival times between successive contacts with p.d.f f(.) and c.d.f F(.). (.) : the p.d.f of random variable. T : a continuous random variable denoting the time of cortisol with p.d.f l(.) and c.d.f L(.). (t) : probability of exactly i contacts in (0,t] (s) : is the Laplace stieltje’s transform of l(t). (s): is the Laplace stieltje’s transform of f(t). The probability density function of exponentiated modified weibull distribution is, h(x) = ( + ) x>0 and >0 and its distribution function is H(x) = ,x>0 then its survival function is (x) = 1- Put = 2, =1 in (x), then it becomes (x) = [2 - ] since f(.) follows exp(c), then If (s) = and (s) = - where ( ) = , (2( )) = E(T) = V(T) = GRAPH: 1 X 0.6 1.2 2.4 4.7 7.1 9.4 E(T) 1.7494 0.8748 0.4374 0.2233 0.1478 0.1117 V(T) 3.0568 0.7642 0.1910 0.0498 0.0218 0.0125 GRAPH: 2 X 0.6 1.2 2.4 4.7 7.1 9.4 E(T) 1.7272 0.8636 0.4319 0.2205 0.1459 0.1102 V(T) 2.9821 0.7455 0.1864 0.0486 0.0213 0.0121 GRAPH: 1
  • 3. Geetha. T. Int. Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 7, Issue 2, ( Part -3) February 2017, pp.24-26 www.ijera.com DOI: 10.9790/9622- 0702032426 26 | P a g e GRAPH: 2 GRAPH : 1 X 0 0.6 1.2 2.4 4.7 7.1 9.4 Y1 4.3 3.4 3.1 3 2.2 2 1.9 Y2 6.1 5.2 5.3 3.8 2.8 3.4 2.7 GRAPH: 2 X 0 0.6 1.2 2.4 4.7 7.1 9.4 Y1 4.3 4 3.5 2.8 2.6 2.3 2.1 Y2 5.4 5.4 4.8 5.3 5.5 2.8 3.7 III. CONCLUSION The threshold level of cortisol is a random variable follows exponentiated modified weibull distribution and the survival function of cortisol and its p.d.f. also found. REFERENCES [1]. Attenburrow MJ, Mitter PR, Whale R, Terao T, Cowen PJ. Low-dose Citalpram as a 5- HT neuroendocrine probe. Psychopharmacology (Berl). 2001; 155(3) : 323-6. [2]. Chandrasekar. K and Kannan.R (2013), A Stochastic Model for Estimation of Expected time to Seroconversion of HIV infected
  • 4. Geetha. T. Int. Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 7, Issue 2, ( Part -3) February 2017, pp.24-26 www.ijera.com DOI: 10.9790/9622- 0702032426 27 | P a g e using exponential-geometric distribution, Bio-Science Research Bulletin, Vol. 29(1), 29-38. [3]. Elbatal.L., (2011), Exponentaited modified Weibull distribution, Economic Quality Control, 26, 189-200. [4]. Esary, J.D., Marshall, A.W. and Proschan, F. (1973), Shock models and Wear processes, Ann. Probability, 1(4) : 627-649. [5]. Hanley NR, Van de Kar LD. Serotonin and the neuroendocrine regulation of the hypothalamic – pituitary adrenal axis in health and diseases. Vitam Horm. 2003; 66: 189-255. [6]. Kannan. R., Kavitha. S., and Sathiyamoorthi .R., (2011), A Stochastic Model for the Estimation of Time to Seroconversion of HIV Transmission using Exponentiated Exponential Distribution , Bio-Science Research Bulletin, Vol.27(2): 67-76. [7]. Kannan. R., Venkatachalam. K. A., Sathiyamoorthi. R. and Malarvizi. G., (2007), A Stochastic Approach to Determine the Expected Time to Seroconversion of HIV infected. Journal of Indian Acad. of Math., 29(1), 237-249. [8]. Sathiyamoorthi, R. and Kannan, R., (2001), A Stochastic Model for Time to Seroconversion of HIV Transmission, Journal of Kerala Statistical Association, Vol. 12: 23-28.