ABSTRACTBackground : Acne vulgaris is a
common skin disease, affecting more than 85% of
adolescents and often continuing into adulthood.
People between 11 and 30 years of age and up to
5% of older adults. For most patients acne remains
a nuisance with occasional flares of unsightly
comedones, pustules and nodules. For other less
fortunate persons, the sever inflammatory response
to Propionibacterium acnes (P.acnes) results in
permanent
Methods: Disfiguring scars. (1, 2) Stigmata of sever
acne cane lead to social ostracism, withdrawal
from society and severe psychologic
depression (1-4).
Result Pathogenesis of acne Traditionally, acne
has been thought of as a multifactorial disease of
the folliculosebaceous unit, involving excess
sebum production, abnormal follicular
hyperkeratinization, overgrowth of
Propionibacterium acnes, and inflammation (Fig
2). Recent laboratory and clinical investigations
into the roles of the innate immune system and
extracellular matrix remodeling proteins have shed
additional light on this pathogenetic process (5-7).
Role of androgens: Activity of type 1 5areductase
enzyme was shown to predominate in
human sebaceous glands and epidermis. This
enzyme is responsible for the conversion of
testosterone to the more potent androgen,
dihydrotestosterone (DHT). DHT in turn is thought
to mediate androgen dependent skin diseases such
as acne, hirsutism and androgenetic alopecia (13)
The enzyme 5a-reductase type 1 has been studied
in those with and without acne and it has been
hypothesized that those with acne might have more
active 5a-reductase type 1 .(2)
Conclusion : The prominent role of hormones in
the pathophysiology of acne has long been
recognized and corroborated by clinical and
experimental observations and therapeutic
experience (14). Although acne is not considered a
primary endocrine disorder, androgens, such as
dihydrotestosterone, dehydroepiandrosterone
sulfate, and testosterone, and growth hormone and
insulin-like growth factors, have all been
implicated in the pathogenesis of acne (15).
Corresponding address to :
Dr. Yasir Mansour Mohamed Al-Ani
Islam Mohammad Nabil El Helou
Regular sampling for six months from January to July 2012 were taken in small, shallow, perennial, standing ponds near the Greater Zab River, Gwer district, Erbil. A variety of physicochemical parameters were determined. Air and water temperature were falling between 15.2 - 34.7 ? C and 15.5 and 26.5 ?C. The waters are neutral (pH 7.38-8.27), hard, alkaline, salty, high in TDS and EC (892-966?S/cm, and rich in nutrients (NO3: 2.1-4.1mg/l, PO4: 0.33-0.62 mg / l , SO4: 24.7-80.2 mg / l ). The attention fixed on a filamentous blue- green algae Glaucospira Lagerheim, 1982) which is new to Iraqi flora. It is a filament (trichome), solitary, pale or yellowish blue – green, without sheath, Screw like coiled, motile, some of them are activ
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There is a theoretical controversy in the books of Usul al-Fiqh, in the past and the present, about whether the ruling should be attached to its reason (al-Hikmah), or its apparent and stable cause (al-Illah). Looking at the practical cases of the jurists, we found them sometimes attaching rulings to its reason, and sometimes to its cause, so there is a need to know the factors that affect their choices. By extrapolation, the researcher reached at nine factors that affect referring the ruling to its cause or to its reason in jurisprudential cases.
The aim of this paper is to derive a posteriori error estimates for semilinear parabolic interface problems. More specifically, optimal order a posteriori error analysis in the - norm for semidiscrete semilinear parabolic interface problems is derived by using elliptic reconstruction technique introduced by Makridakis and Nochetto in (2003). A key idea for this technique is the use of error estimators derived for elliptic interface problems to obtain parabolic estimators that are of optimal order in space and time.
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Characterized by the Ordinary Least Squares (OLS) on Maximum Likelihood for the greatest possible way that the exact moments are known , which means that it can be found, while the other method they are unknown, but approximations to their biases correct to 0(n-1) can be obtained by standard methods. In our research expressions for approximations to the biases of the ML estimators (the regression coefficients and scale parameter) for linear (type 1) Extreme Value Regression Model for Largest Values are presented by using the advanced approach depends on finding the first derivative, second and third.