Background : Coronary artery disease is theunderlying cause in approximately two thirds of
patients with systolic heart failure ;
Coronary artery angiogriphy may be useful to
define the presence ,
Anatomical characteristics ,and functional
significance of Coronary artery disease in
selected heart failure patients with or without signs
and aymptoms of Coronary artery disease.
Objectives: to verify the clinical usefulness of
coronary angiography (CA) in congestive heart
failure (CHF) patients with no history of ischemic
heart disease and to identify predictive factors for
performing coronary angiography to patients with
congestive heart failure with no obvious ischemia.
Methods :this is a cross-sestional study conducted
Ibn al –Bitar center for cardiac surgery during a
period between November 2009 and November
2010 ;all included patients have symptomatic LV
dyfunctional with NYHA functional class II or
more with no clinical evidence of IHD .History,
physical examination had been performed for each
patient , in addition ti blood tests ,ECG ,CXR,
echocardiography and Coronary angiogriphy
Results :Among 112 patients with symptomatic
LV dysfunction of unknown cause enrolled in this
study male :female ratio 3:1 their age (17 -77 years
)there were 14 patients (12.5%)with significant
Coronary artery disease , statistical analysis
between CADand non CAD group show :the CAD
were more common in male 11 patients (78% )
smokers(1)Were female , 25 patients (22.3%) were
diabetic , 25 diabetic (22.3%) were hypertensive
,50 diabetic (44.6%) were smokers . 4 diabetic3.5) with family history of dilatedhyperlipidemia and 3 diabetic (2.6%) ) with
family history ischemic heart disease (IHD in
close relative at age <50 years in men and <55
years in women ) as demographic distribution of
patients in table 1 .coronary angiography show
that are 14 patients (12.5%) with significant
coronary artery disease (CAD) and 98 patients
(87.5%) with significant coronary artery disease
. as shown in figure 1 . eleven patients (78%) of
patients (22%) were female statistical analysis
shows that the male is more predominant in
CAD groups as shown in table 2 . twelve
patients (85.7%) of patients with CAD were
smokers while only 38 patients (38.7%) were
smokers in non CAD group . statistical analysis
shows there is significant difference between
the two groups ( p value < 0.01 ) as shown in
table 3 .nine patients (64.2%) of patients with
CAD were diabetics while 16 patients
(16.3%)were diabetics in non CAD group ,
statistical analysis shows there is significant
difference between the two groups (p value <
0.005) as shown in table (3) .there is no
statistical between the mean of left ventricular
ejection fraction of CAD group ( 37 ±10 %) and
that of non CAD (36±9 % ) , p value > 0.5 as
shown in table 6 . figure 2 revealed that LAD
lesion was most common coronary artery lesion
which represent 12 (85%) followed by in order
of frequency LCX 42 % ,RCA 28% and LMS
7% ; in addition to that 57% with single vessel
disease ,14.2% with 2 vessel disease and
28.4%with r vessel disease (2)Leads , ST
depression ≥1 mm or deep T wave inversion
had been excluded
(
The objective of this article is to delve into the intricate dynamics of marriage relationships, exploring the impact of emotions such as fear, love, financial considerations and likability. In our investigation, we adopt a perspective that acknowledges the nonlinear nature of interactions among individuals. Diverging from certain prior studies, we propose that the fear element within the context of marriage is not a singular, isolated factor but rather a manifestation resulting from the amalgamation of numerous social issues. This, in turn, contributes to the emergence of strained and unsuccessful relationships. Unlike conventional approaches, we extensively examine the conditions essential for the existence of all socially signifi
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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