Background: Dyslipidemia is a major cause of
cardiovascular disease, which in turn, is the most
common cause of female morbidity and mortality.
Postmenopausal women (natural and surgical) are at
higher risk of developing cardiovascular disease,
especially coronary artery atherosclerosis.
Objective: To observe the relationship between blood
lipids: total cholesterol (TC), high density lipoproteincholesterol (HDL-C), low density lipoproteincholesterol (LDL-C), triglycerides (TGs), and very low
density lipoprotein- cholesterol (VLDL-C), LDL-C/
HDL-C ratio (atherogenic index) and menopausal
status, and to determine the co-factors that may explain
this relationship
Methods: A prospective, cross-sectional study, which
includes 279 women, age range from 35-55 years
agreed to participate in this study. They were divided
into 4 groups according to their menopausal status.
These were pre-, peri post- natural and surgical postmenopausal. Data were collected from participants in a
pre-coded questionnaire and an overnight fasting blood
sample was collected for biochemical analysis.
Results: Postmenopausal women had higher levels of
lipids than pre or peri-menopausal. TC concentration
and LDL-cholesterol levels were higher in natural and
surgical menopause than in pre and pri-menopausal
women (p<0.01 and p<0.05 respectively). While
LDL/HDL-C ratio (atherogenic index) were higher in
the surgical postmenopausal women than in premenopausal group (p<0.05). No significant inter-group
differences were found in HDL-C. Triglycerides, and
VLDL levels were higher in surgical menopause group
than in both pre- and peri-menopause groups (p<0.05).
No significant differences were demonstrated in pre-,
peri-, and natural menopausal women with regard to
triglyceride and VLDL levels and LDL/HDL-C ratio.
Conclusion : Dyslipidemia is more frequent among
women with natural and surgical menopause groups
than in the other groups. This makes those women
more susceptible to CVD. Certain co factors appear to
have direct associations with lipid levels in each group
and those were discussed.
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The current study was designed to evaluate the anti-inflammatory effect of GKB in the rat model of granulomatous inflammation. Thirty rats were distributed into five groups: The first group served as negative control group that received distilled water (DW) only without inducting inflammation, positive control group; treated with DW with the induction of inflammation and they were assigned to cotton pellet-induced granuloma, ginkgo biloba (GKB) treated group (200mg/kg/day), dexamethasone-treated group (1mg/kg), and Prednisolone treated group (5mg/kg). All the treatments were given orally for seven consecutive days. On day eight, the rats were anesthetized and the pellets together with granulation tissue were carefully removed
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The Na-alginate bead is commonly used in biotechnology fields such as adsorption due to ion exchange between Ca and Na with elements. Scanning electron microscopy (SEM-EDX) has proven to be a comparative method in the detections of these adsorbed elements, but the un-flat forming area of beads that can introduce impossible of the detection of element adsorbed. In contrast, X-ray fluorescence (XRF) documents analysis of elements, direct examination, which may analysis the adsorbents of elements. Here, this Study evaluated the possibility by using XRF for the direct analysis for examples of Cd and Ag in a bench stand. This Study compared this to commonly use
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Purpose: To identify the size of the food gap for the main agricultural products and crops in Iraq, which reflects to us the extent to which agricultural production in particular and the agricultural sector in general have declined.Theoretical framework: The theoretical side of the research dealt with the definition of self-sufficiency and the food gap, as well as identifying the reality of agricultural production in Iraq during the study period, as well as the reality of the food gap for the most important agricultural, plant and animal products.Design/methodology/approach: In reviewing the research problem, the researcher adopted the method of deductive and descriptive analysis based on the presentation and detail of official data
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Channel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T
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