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Lipid Profile and Menopausal Status
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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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Publication Date
Sat Apr 01 2023
Journal Name
Baghdad Science Journal
Spectrophotometric Analysis of Vancomycin Hydrochloride in Pure and Pharmaceutical Injections via Batch and Cloud Point Extraction Techniques
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Development of a precise and delicate reaction has been acquired for the determination of vancomycin hydrochloride using batch and cloud point extraction (CPE) methods. The first method is based on the formation of azo dye as a result of diazotized dapsone coupled with vancomycin HCl (VAN) in a basic medium. The sensitivity of this reaction was enhanced by utilizing a nonionic surfactant (Triton X-114) and the cloud point extraction technique (second method). The azo dye formed was extracted into the surfactant-rich phase, dissolved in ethanol and detected at λmax 440 nm spectrophotometrically. The reaction was investigated using both batch and CPE methods (with and without extraction), and a simple comparison between the two

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Publication Date
Thu Mar 20 2025
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Isolation and identification of pathogenic bacteria from drinking tap water and Tigris River water sources in Baghdad
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Water is a resource and a crucial aspect of living and surviving. In Iraq, the Tigris River is one of the most critical water sources. The present study aimed to provide an insight analysis of some water quality parameters including the microbial content of drinkable tap water and river water. Ten Water samples (T1- T10) in triplicate were collected from sampling sites -Site I (Tap water) from home water taps, supplied by the Water Filtration Station/ Al Karama Project/ Al-Karkh> 10 from Site II (R1- R10)River water from Tigris River (around or near the Water Filtration Station/ Al Karama Project) every week (from September to half of November 2022), then were immediately placed in sterile bottles and transported to Microbiolo

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Role of Artificial Intelligence in Enhancing Efficiency and Sustainability in Middle Eastern Oil and Gas: A Review
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This review examines how artificial intelligence (AI) including machine learning (ML), deep learning (DL), and the Internet of Things (IoT) is transforming operations across exploration, production, and refining in the Middle Eastern oil and gas sector. Using a systematic literature review approach, the study analyzes AI adoption in upstream, midstream, and downstream activities, with a focus on predictive maintenance, emission monitoring, and digital transformation. It identifies both opportunities and challenges in applying AI to achieve environmental and economic goals. Although adoption levels vary across the region, countries such as Saudi Arabia, the UAE, and Qatar are leading initiatives that align with global sustainability targets.

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