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Logistic regression models, with camp as random effect, were evaluated for the outcome human retinol deficiency.
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The adenylate cyclase deletion model (cyr1Δ) was simulated with cAMP concentration set to 1.
We decided not to use a multi-level model, nesting the data with camp, so as to simplify the analyses.
We evaluated associations between cholinesterase levels measured over the four time points, and genotyped SNPs using a mixed effect model, where random intercept was applied with camp site as the clustering variable; the covariance matrix was estimated at the individual within-camp level.
Factors possibly associated with seropositivity in humans and livestock (explanatory variables with P ≤ 0.2 in univariable analysis) were evaluated with multivariate logistic regression models (with RE at the nomadic camp level) using backward stepwise selection and a removal level for covariates at P = 0.10 based on the likelihood-ratio test (LRT).
History of ON was best predicted by a combination of tLat and tAUC, with a clear increase in sensitivity compared to the model with cLat and cAmp (sens: 88 vs. 77; spec: 83 vs. 85; YI: 0.71 vs. 0.62).
The inhibition curves suggest a binding mode that is competitive with cAMP for the catalytic site of the enzyme, consistent with the docked models (Fig. 2).
Camp residents are forbidden from traveling outside the camp without pre-arranged permission with camp authorities.
Linear regression models with random effects for correlation within camps were applied with human retinol concentration as outcome.
With Camp Lo, Astro and more.
The correlation between cAMP alone and cAMP and N was 0.79, while the correlation with cAMP and P was 0.87.
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