Dysregulation of matrix metalloproteinases-9 (MMP-9) and tissue inhibitors of
matrix metalloproteinases-1 (TIMP-1) may contribute to the development of
cardiovascular diseases in type 2 diabetes mellitus (T2DM) patients. The aim of this
study was to determine the effects of chronic hyperglycemia on serum
concentrations of MMP-9 and TIMP-1of T2DM patients without dyslipidemia (one
of atherosclerosis risk factors) and with duration less than 5 years in comparison
with T2DM patients with dyslipidemia and with duration more than 10 years and
controls. Also to investigate if serum levels of MMP-9 and TIMP-1 could be
potential markers for early detection of the development of cardiovascular
complications in T2DM patients without dyslipidemia. This study consisted of 24
T2DM patients without dyslipidemia, 30 T2DM patients with dyslipidemia, and 26
healthy subjects. A variety of inflammatory markers including: MMP-9, TIMP-1,
IL18 and hs-CRP were compared among the three groups. The BMI was similar
among the three groups. A significant increase of WHR, WHtR, FPG, TC, TG,
LDL-C, VLDL-C, AIP, atherogenic ratio-1, atherogenic ratio-2, hs-CRP, MMP-9,
TIMP-1 and IL-18 with a significant decrease of HDL-C, β Cell% and S% among
the three groups. MMP-9 of T2DM patients without dyslipidemia and with duration
less than 5 years showed a significant positive correlation with FPG and a
significant negative correlation with TC. MMP-9 of T2DM patients with
dyslipidemia and with duration more than 10 years showed significant negative
correlation with LDL-C. TIMP-1 of T2DM patients with dyslipidemia and with
duration more than 10 years showed a significant negative correlation with TC, TG,
VLDL-C and atherogenic ratio-1. The significant increased levels of both MMP-9
and TIMP-1 in T2DM patients without dyslipidemia and with duration less than 5
years compared to controls showed that those patients have risk factor for
cardiovascular complications. This study suggests that MMP-9 and TIMP-1 may be
potentially useful as markers in T2DM patients at risk of progression of
cardiovascular diseases.
This study aimed to study the inhibition activity of purified bacteriocin produced from the local isolation Lactococcuslactis ssp. lactis against pathogenic bacteria species isolated from clinical samples in some hospitals Baghdad city. Screening of L. lactis ssp. Lactis and isolated from the intestines fish and raw milk was performed in well diffusion method. The results showed that L. lactis ssp. lactis (Lc4) was the most efficient isolate in producing the bacteriocin as well observed inhibitory activity the increased that companied with the concentration, the concentration of the twice filtrate was better in obtaining higher inhibition diameters compared to the one-fold concentration. The concentrate
... Show MoreLet G be a graph, each edge e of which is given a weight w(e). The shortest path problem is a path of minimum weight connecting two specified vertices a and b, and from it we have a pre-topology. Furthermore, we study the restriction and separators in pre-topology generated by the shortest path problems. Finally, we study the rate of liaison in pre-topology between two subgraphs. It is formally shown that the new distance measure is a metric
In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne
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The theory of Multi-Criteria Decision Making (MCDM) was introduced in the second half of the twentieth century and aids the decision maker to resolve problems when interacting criteria are involved and need to be evaluated. In this paper, we apply MCDM on the problem of the best drug for rheumatoid arthritis disease. Then, we solve the MCDM problem via -Sugeno measure and the Choquet integral to provide realistic values in the process of selecting the most appropriate drug. The approach confirms the proper interpretation of multi-criteria decision making in the drug ranking for rheumatoid arthritis.
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Alzheimer’s Disease (AD) is the most prevailing type of dementia. The prevalence of AD is estimated to be around 5% after 65 years old and is staggering 30% for more than 85 years old in developed countries. AD destroys brain cells causing people to lose their memory, mental functions and ability to continue daily activities. The findings of this study are likely to aid specialists in their decision-making process by using patients’ Magnetic Resonance Imaging (MRI) to distinguish patients with AD from Normal Control (NC). Performance evolution was applied to 346 Magnetic Resonance images from the Alzheimer's Neuroimaging Initiative (ADNI) collection. The Deep Belief Network (DBN) classifier was used to fulfill classification f
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