IBD高免蛋黄抗体注射及口服被动免疫保护效果评价

本文报道了IBD高免蛋黄抗体蛋黄液(yAbs)及yAbs离心上清液(yAbss)和三氯甲烷萃提液(yAbsa)三种样品皮下注射和口服给药被动免疫保护效果。人工攻毒试验结果:皮下注射IBDyAbs和yAbss的两组获得全保护,发病率为0;注射yAbsa组发病率为10.0％(1/10),无死亡,而这三种样品口服给药的三个组与空白对照组的发病率和死亡率差异不显著(P>0.05).发病率分别为90％(9/10),70％(7/10)和50.0％(5/10),死亡率分别为30％(3/10)、20％(2/10)和10.0％(1/10)。琼脂扩散试验检测血清抗体阳转及消长结果:IBD蛋黄抗体大剂量口服及手术后直接十二指肠注入组鸣只血清抗体始终全部为阴性;而注射组则在注射后3-12h内血清中可以检出IBD抗体且持续至48h以上。试验结果表明,IBD高免蛋黄抗体只能采取皮下或肌肉注射才能产生被动免疫保护怍用,胃肠道给药不能产生被动免疫保护作用,制备方法对IBD蛋黄抗体的保护效果无影响。     

四川主要地区鸡大肠杆菌病病原分离鉴定及免疫预防研究(二)——免疫期的测定

为探明接种E.coli氢氧化铝多价灭活苗鸡只血清中E.coli抗体消长规律,以及血清抗体与保护率的关系,以便合理制定免疫程序,我们采用试管凝集法对免疫鸡只进行血清中E.coli抗体测定,并对不同免疫期和不同抗体水平的鸡进行同源菌株的攻毒保护试验。结果表明:抗体凝集价与攻毒保护率的关系是:1:32=90％;≥1:64=100％;1:24=50％。抗体消长规律是:免疫一周后抗体凝集价达1:32,第三周达1:128,第4—6周达最高峰1:190,以后逐渐下降,第15周降至1:24。免疫期为15周,免疫临界线初步拟定在1:24。建议当鸡(特别是种鸡和蛋鸡)血清抗体水平处于免疫临界线时,应适时接种,以有效地预防本病。     

Improved estimation procedures for multilevel models with binary response: a case-study

During recent years, analysts have been relying on approximate methods of inference to estimate multilevel models for binary or count data. In an earlier study of random-intercept models for binary outcomes we used simulated data to demonstrate that one such approximation, known as marginal quasi-likelihood, leads to a substantial attenuation bias in the estimates of both fixed and random effects whenever the random effects are non-trivial. In this paper, we fit three-level random-intercept models to actual data for two binary outcomes, to assess whether refined approximation procedures, namely penalized quasi-likelihood and second-order improvements to marginal and penalized quasi-likelihood, also underestimate the underlying parameters. The extent of the bias is assessed by two standards of comparison: exact maximum likelihood estimates, based on a Gauss–Hermite numerical quadrature procedure, and a set of Bayesian estimates, obtained from Gibbs sampling with diffuse priors. We also examine the effectiveness of a parametric bootstrap procedure for reducing the bias. The results indicate that second-order penalized quasi-likelihood estimates provide a considerable improvement over the other approximations, but all the methods of approximate inference result in a substantial underestimation of the fixed and random effects when the random effects are sizable. We also find that the parametric bootstrap method can eliminate the bias but is computationally very intensive.