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- 저자 : Ahmad Reza Sharifi (검색결과 2 건)
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Annette Forro,Georgios Tsousis,Nicola Beindorff,Ahmad Reza Sharifi,Christos Brozos,Heinrich Bollwein 대한수의학회 2015 Journal of Veterinary Science Vol.16 No.1
The objective of this study was to investigate factors that influence the success of resynchronization protocols for bovines with and withoutprogesterone supplementation. Cow synchronized and not found pregnant were randomly assigned to two resynchronization protocols:ovsynch without progesterone (P4) supplementation (n = 66) or with exogenous P4 administered from Days 0 to 7 (n = 67). Progesteronelevels were measured on Days 0 and 7 of these protocols as well as 4 and 5 days post-insemination. Progesterone supplementation raised theP4 levels on Day 7 (p < 0.05), but had no overall effect on resynchronization rates (RRs) or pregnancy per artificial insemination (P/AI). However, cows with Body Condition Score (BCS) > 3.5 had increased P/AI values while cows with BCS < 2.75 had decreased P/AI ratesafter P4 supplementation. Primiparous cows had higher P4 values on Day 7 than pluriparous animals (p = 0.04) and tended to have higherRRs (p = 0.06). Results of this study indicate that progesterone supplementation in resynchronization protocols has minimal effects onoutcomes. Parity had an effect on the levels of circulating progesterone at initiation of the protocol, which in turn influenced the RR.
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Kazem Khalagi,Mohammad Ali Mansournia,Afarin Rahimi-Movaghar,Keramat Nourijelyani,Masoumeh Amin-Esmaeili,Ahmad Hajebi,Vandad Sharifi,Reza Radgoodarzi,Mitra Hefazi,Abbas Motevalian 한국역학회 2016 Epidemiology and Health Vol.38 No.-
Latent class analysis (LCA) is a method of assessing and correcting measurement error in surveys. The local independence assumption in LCA assumes that indicators are independent from each other condition on the latent variable. Violation of this assumption leads to unreliable results. We explored this issue by using LCA to estimate the prevalence of illicit drug use in the Iranian Mental Health Survey. The following three indicators were included in the LCA models: five or more instances of using any illicit drug in the past 12 months (indicator A), any use of any illicit drug in the past 12 months (indicator B), and the self-perceived need of treatment services or having received treatment for a substance use disorder in the past 12 months (indicator C). Gender was also used in all LCA models as a grouping variable. One LCA model using indicators A and B, as well as 10 different LCA models using indicators A, B, and C, were fitted to the data. The three models that had the best fit to the data included the following correlations between indicators: (AC and AB), (AC), and (AC, BC, and AB). The estimated prevalence of illicit drug use based on these three models was 28.9%, 6.2% and 42.2%, respectively. None of these models completely controlled for violation of the local independence assumption. In order to perform unbiased estimations using the LCA approach, the factors violating the local independence assumption (behaviorally correlated error, bivocality, and latent heterogeneity) should be completely taken into account in all models using well-known methods.
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