Effects of between and within Herd Moves on Elephant Endotheliotropic Herpesvirus (EEHV) Recrudescence and Shedding in Captive Asian Elephants (Elephas maximus)

Haemorrhagic disease associated with elephant endotheliotropic herpesvirus (Elephantid herpesvirus, EEHV) infections is the leading cause of death for Asian elephant (Elephas maximus) calves. This study assessed the effect of captive herd management on EEHV shedding, as evidence of latent infection reactivation, focusing on: (1) the influence of social change on the odds of recrudescence; (2) the respective effects of between and within herd moves; and (3) characteristics of recrudescent viral shedding. Trunk and conjunctival swabs (n = 165) were obtained from six elephants at an EAZA-accredited zoo, collected during a period of social stability, and at times of social change. Longitudinal sampling took place at times of moving two bulls out of the collection and one new bull into an adjacent enclosure to the cow herd (between herd moves), and during a period of mixing this new bull with the cow herd to facilitate mating (within herd moves). Quantitative PCR was employed to detect EEHV 1a/b, 4a/b, and EF–1–α (housekeeping gene). Generalised estimating equations determined EEHV recrudescence odds ratios (OR) and relative viral DNA load. Sixteen EEHV 1a/b shedding events occurred, but no EEHV 4a/b was detected. All management-derived social changes promoted recrudescence (social change OR = 3.27, 95% CI = 0.412–26, p = 0.262; and between herd moves OR = 1.6, 95% CI = 0.178−14.4, p = 0.675), though within herd movements posed the most significant increase of EEHV reactivation odds (OR = 6.86, 95% CI = 0.823−57.1, p = 0.075) and demonstrated the strongest relative influence (post hoc Tukey test p = 0.0425). Shedding onset and magnitude ranged from six to 54 days and from 3.59 to 11.09 ΔCts. Differing challenges are associated with between and within herd movements, which can promote recrudescence and should be considered an exposure risk to naïve elephants.     

灰色数列预测模型在成骨细胞体外培养中的应用

目的：灰色模型是运用一定的数学方法使信息不完全明确的系统经数据处理后能得到较明确结果的一种数学预测模型，体外细胞培养的影响因素较多，属于信息不完全明确的灰色系统，故运用灰色GM（1，1）模型对成骨细胞增殖、分化的变化规律进行预测，验证模型在体外细胞培养中的可应用性。方法：实验于2005—11／2006—03在广东医学院药理教研室完成。①实验过程：应用酶序列消化分离培养法培养新生大鼠颅骨成骨细胞；用MTT法测定体外培养成骨细胞在不含血清培养液A值，以了解成骨细胞的增殖情况；对硝基苯磷酸盐法观察体积分数为0．01的胎牛血清培养液对体外培养成骨细胞分泌碱性磷酸酶活性的影响，代表成骨细胞的分化情况。②灰色GM（1，1）模型建立：运用灰色系统理论，通过SAS8．1软件对体外培养成骨细胞MTT值和碱性磷酸酶OT值进行分析和预测。结果：运用灰色系统理论的后验差检验方法对模型进行检验，MTT这一指标的平均相对误差为4．4％，碱性磷酸酶这一指标的平均相对误差为7．04％，后验差比值为0．048和0．315，综合评定该模型为“好”。结论：灰色GM（1，1）模型对体外培养成骨细胞MTT值和碱性磷酸酶的OT值变化的预测精度高，结果可靠。体外培养成骨细胞MTT值和碱性磷酸酶的OT值的变化可用灰色GM（1，1）模型进行预测。     

Live function in hepatic and extrahepatic diseases

The American Journal of Digestive Diseases - In our patients with acute hepatitis, as a group, the Rose Bengal test was positive in 94 per cent, and the modified glucose tolerance test in 93 per...     

Personalized State-space Modeling of Glucose Dynamics for Type 1 Diabetes Using Continuously Monitored Glucose,Insulin Dose,and Meal Intake: An Extended Kalman Filter Approach

An essential component of any artificial pancreas is on the prediction of blood glucose levels as a function of exogenous and endogenous perturbations such as insulin dose, meal intake, and physical activity and emotional tone under natural living conditions. In this article, we present a new data-driven state-space dynamic model with time-varying coefficients that are used to explicitly quantify the time-varying patient-specific effects of insulin dose and meal intake on blood glucose fluctuations. Using the 3-variate time series of glucose level, insulin dose, and meal intake of an individual type 1 diabetic subject, we apply an extended Kalman filter (EKF) to estimate time-varying coefficients of the patient-specific state-space model. We evaluate our empirical modeling using (1) the FDA-approved UVa/Padova simulator with 30 virtual patients and (2) clinical data of 5 type 1 diabetic patients under natural living conditions. Compared to a forgetting-factor-based recursive ARX model of the same order, the EKF model predictions have higher fit, and significantly better temporal gain and J index and thus are superior in early detection of upward and downward trends in glucose. The EKF based state-space model developed in this article is particularly suitable for model-based state-feedback control designs since the Kalman filter estimates the state variable of the glucose dynamics based on the measured glucose time series. In addition, since the model parameters are estimated in real time, this model is also suitable for adaptive control.