Background : It has been suggested that pretreatment with a statin agent prior to
myocardial infarction limits myocardial
creatine kinase release, and thus may act to
limit myocardial infarct size in humans.
Objective : To examine the effect of very
early statin initiation for acute myocardial
infarction (AMI), to the extent of
myonecrosis as manifested by peak serum
creatine kinase levels.
Methods : Patients with AMI admitted to AlKindy teaching hospital cardiac care unit
from 1st February 2007 to 28th February
2008, who fulfilled the inclusion criteria
cited in the present study, were randomly
assigned into two study groups. The statin
group patients have received a single oral
dose of 40 mg atorvastatin at time of
admission and repeated for the next days
until discharge, patients not receiving statin
were considered as controls, blood samples
were obtained on admission and every 8 h for
another three consecutive samples to identify
peak creatine kinase levels.
Results : Patients who had statin therapy
initiated immediately after hospital
admission have similar peak creatine kinase
concentrations as compared to those not
receiving statin therapy ( P= 0.332).
Conclusion : statin initiation in AMI patients
fails to show any observable effect on
creatine kinase release, the need of an
extended period for the statin agent to
achieve the predictable outcome may suggest
the necessity of statin pretreatment in
patients at high risk for AMI
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This paper presents a hybrid energy resources (HER) system consisting of solar PV, storage, and utility grid. It is a challenge in real time to extract maximum power point (MPP) from the PV solar under variations of the irradiance strength. This work addresses challenges in identifying global MPP, dynamic algorithm behavior, tracking speed, adaptability to changing conditions, and accuracy. Shallow Neural Networks using the deep learning NARMA-L2 controller have been proposed. It is modeled to predict the reference voltage under different irradiance. The dynamic PV solar and nonlinearity have been trained to track the maximum power drawn from the PV solar systems in real time.
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