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Management of patients with acute myocardial infarction (AMI) has improved considerably over the last several decades, although the incidence of AMI continues to rise. Current acute myocardial infarction reperfusion therapy has been embraced enthusiastically for use within the first three hours of the onset of symptoms because it is associated with a very high mortality in the first few hours of therapy. However, improvement in the outcomes of AMI as a result of reperfusion therapy has been limited because of the restriction of its use to a small percentage of patients who experience symptoms and cardiac ischemia for only a few hours. It is now apparent that it is possible to identify those patients with AMI who have an elevated risk of mortality from early reperfusion therapy; thus, strategies are needed to re-direct the use of reperfusion to the high-risk patient. Two major strategies have been suggested for re-directing the use of reperfusion to the high-risk patient. An early rule-in strategy would use the results of a limited exercise stress test, serial cardiac monitoring, serial cardiac enzymes, or both to direct the use of reperfusion therapy. A strategy to treat all patients with clinical AMI would evaluate risk using prediction rules or decision analysis and identify patients at low risk for death. The vast majority of patients with AMI can be treated with reperfusion therapy as primary therapy. However, cardiologists who are aggressively pursuing reperfusion for all patients with AMI need to be prepared to assess the risk of treatment in the light of each patient’s individual circumstances.Molecular typing of Campylobacter jejuni isolates from Greek broilers with reduced susceptibility to florfenicol.
Campylobacter jejuni isolates recovered from broiler batches marketed in Greece were typed by PFGE following digestion with the restriction enzyme NotI and further molecular
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Iterate over date range while conditional checking
I have a dataframe that looks like this
I want to iterate over date range to check if a transaction has a credit and if so, then take out the qty_deb and assign it to qty_cred.
I have tried this but the problem is the df[‘nbr’][y-1]!= y-1 gets turned into an error.
df.at[4:6, ‘nbr’] = df.at[4:6, ‘nbr’].shift(1)
df[‘qty_deb’] = 0
df[‘qty_cred’] = 0
x_start_date = pd.to_datetime(‘6/23/2019’)
x_end_date = pd.to_datetime(‘6/29/2019’)
for i in range(1, df[‘nbr’].max()+1):
if (df[‘nbr’].iloc[i-1] == df[‘nbr’].iloc[i] and df[‘nbr’].iloc[i] >= x_start_date and df[‘nbr’].iloc[i]