Use of umbilical artery Doppler velocimetry in the monitoring of pregnancy in women with pre-existing diabetes

Background: The usefulness of umbilical artery Doppler velocimetry for the monitoring of diabetic pregnancies is controversial. The aim of the present study was to assess whether umbilical artery Doppler velocity waveform analysis can predict adverse perinatal outcomes for pregnancies complicated by pre-existing diabetes mellitus. Methods: All diabetic pregnancies (type 1 and 2) delivered at Mater Mothers' Hospital, Queensland, between 1 January 1995 and 31 December 1999 were included. All pregnant diabetic women were monitored with umbilical artery Doppler velocimetry at 28, 32, 36, and 38 weeks' gestation. Adverse perinatal outcome was defined as pregnancies with one or more of the following: small-for-gestational age, Caesarean section for non-reassuring cardiotocography, fetal acidaemia at delivery, 1-min Apgar of 3 or less, 5-min Apgar of less than 7, hypoxic ischaemic encephalopathy or perinatal death. Abnormal umbilical artery Doppler velocimetry was defined as a pulsatility index of 95th centile or higher for gestation. Results: One hundred and four pregnancies in women with pre-existing diabetes had umbilical arterial Doppler studies carried out during the study period. Twenty-three pregnancies (22.1%) had an elevated pulsatility index. If the scans were carried out within 2 weeks of delivery, 71% of pregnancies with abnormal umbilical Doppler had adverse outcomes (P < 0.01; likelihood ratio, 4.2). However, the sensitivity was 35%; specificity was 94%; positive predictive value was 80%; and negative predictive value was 68%. Only 30% of women with adverse perinatal outcomes had abnormal umbilical arterial Doppler flow. Conclusion: Umbilical artery Doppler velocimetry is not a good predictor of adverse perinatal outcomes in diabetic pregnancies.

Population monitoring for kangaroo management

In wildlife management, the program of monitoring will depend on the management objective. If the objective is damage mitigation, then ideally it is damage that should be monitored. Alternatively, population size (N) can be used as a surrogate for damage, but the relationship between N and damage obviously needs to be known. If the management objective is a sustainable harvest, then the system of monitoring will depend on the harvesting strategy. In general, the harvest strategy in all states has been to offer a quota that is a constant proportion of population size. This strategy has a number of advantages over alternative strategies, including a low risk of over- or underharvest in a stochastic environment, simplicity, robustness to bias in population estimates and allowing harvest policy to be proactive rather than reactive. However, the strategy requires an estimate of absolute population size that needs to be made regularly for a fluctuating population. Trends in population size and in various harvest statistics, while of interest, are secondary. This explains the large research effort in further developing accurate estimation methods for kangaroo populations. Direct monitoring on a large scale is costly. Aerial surveys are conducted annually at best, and precision of population estimates declines with the area over which estimates are made. Management at a fine scale (temporal or spatial) therefore requires other monitoring tools. Indirect monitoring through harvest statistics and habitat models, that include rainfall or a greenness index from satellite imagery, may prove useful.

Sampling times for monitoring tacrolimus in stable adult liver transplant recipients

The aim of this study was to determine the most informative sampling time(s) providing a precise prediction of tacrolimus area under the concentration-time curve (AUC). Fifty-four concentration-time profiles of tacrolimus from 31 adult liver transplant recipients were analyzed. Each profile contained 5 tacrolimus whole-blood concentrations (predose and 1, 2, 4, and 6 or 8 hours postdose), measured using liquid chromatography-tandem mass spectrometry. The concentration at 6 hours was interpolated for each profile, and 54 values of AUC(0-6) were calculated using the trapezoidal rule. The best sampling times were then determined using limited sampling strategies and sensitivity analysis. Linear mixed-effects modeling was performed to estimate regression coefficients of equations incorporating each concentration-time point (C0, C1, C2, C4, interpolated C5, and interpolated C6) as a predictor of AUC(0-6). Predictive performance was evaluated by assessment of the mean error (ME) and root mean square error (RMSE). Limited sampling strategy (LSS) equations with C2, C4, and C5 provided similar results for prediction of AUC(0-6) (R-2 = 0.869, 0.844, and 0.832, respectively). These 3 time points were superior to C0 in the prediction of AUC. The ME was similar for all time points; the RMSE was smallest for C2, C4, and C5. The highest sensitivity index was determined to be 4.9 hours postdose at steady state, suggesting that this time point provides the most information about the AUC(0-12). The results from limited sampling strategies and sensitivity analysis supported the use of a single blood sample at 5 hours postdose as a predictor of both AUC(0-6) and AUC(0-12). A jackknife procedure was used to evaluate the predictive performance of the model, and this demonstrated that collecting a sample at 5 hours after dosing could be considered as the optimal sampling time for predicting AUC(0-6).

Monitoring genetic diversity in tropical trees with multilocus dominant markers

Since no universal codominant markers are currently available, dominant genetic markers, such as amplified fragment length polymorphism (AFLP), are valuable tools for assessing genetic diversity in tropical trees. However, the measurement of genetic diversity (H) with dominant markers depends on the frequency of null homozygotes (Q) and the fixation index (F) of populations. While Q can be estimated for AFLP loci, F is less accessible. Through a modelling approach, we show that the monolocus estimation of genetic diversity is strongly dependent on the value of F, but that the multilocus diversity estimate is surprisingly robust to variations in F. The robustness of the estimate is due to a mechanistic effect of compensation between negative and positive biases of H by different AFLP loci exhibiting contrasting frequency profiles of Q. The robustness was tested across contrasting theoretical frequency profiles of Q and verified for 10 neotropical species. Practical recommendations for the implementation of this analytical method are given for genetic surveys in tropical trees, where such markers are widely applied.