应用混合设计模型研究高浓度氨对肉鸡血浆免疫球蛋白和新城病毒疫苗效价的影响

E. Soliman, S. Moawed
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摘要

肉鸡舍氨浓度高导致生产性能下降、抗性降低和死亡率升高。5个哈伯德肉鸡组(5 × 60)中有4个受到60 ppm氨2;4;6;每天8小时;分别连续5周。高氨浓度对生化的影响对接触后收集的900份(150份血清、150份血浆、150份拭子和450份器官)样本进行免疫学和细菌学状况评估。考虑重复测量,数据分析采用传统和混合模型方差分析。效应大小估计使用Eta和偏Eta平方进行量化。暴露2、4、6和8 h时肉鸡死亡率分别为10%、18%、32%和41.2%;分别。免疫器官重量下降极显著(P小于0.01)。暴露8小时后,血浆免疫球蛋白显著降低(P值小于0.01)。暴露4、6和8小时后,新城病毒疫苗滴度显著降低(P小于0.01)。效应量统计显示,混合模型方差分析在显示研究效应的生物学价值方面是有利的。暴露于超出允许限度的氨的时间越长,可以检测到的负面影响越大。肉鸡养殖场的管理实践应包括减少氨浓度的预防措施,研究人员在进行实验研究时应牢记每种效应大小的定量估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of mixed design models in impact of high Ammonia concentration on Plasma Immunoglobulins and Newcastle virus vaccine titer in broilers
High Ammonia concentration in broiler's houses contributed a reduction in performance, lowering in resistance and increased mortality. Four out of five Hubbard broiler groups (5 × 60) were subjected to 60 ppm ammonia for 2; 4; 6; 8 hours per day; respectively for 5 successive weeks. The impact of high ammonia concentration on biochemical; immunological and bacteriological status was assessed in 900 (150 sera, 150 plasma, 150 swab and 450 organ) samples collected post exposure. Data were analyzed using traditional and mixed model ANOVA considering repeated measures. Effect size estimates were quantified using Eta and partial Eta-squared. Broilers showed 10, 18, 32 and 41.2% mortalities at 2, 4, 6 and 8 hrs of exposure; respectively. A highly significant decline (P ˂ 0.01) was recorded in immune organs’ weight. Plasma immunoglobulins revealed a highly significant decrease (P ˂ 0.01) at 8 hrs of exposure. Newcastle virus vaccine titer revealed a highly significant decrease (P ˂ 0.01) in 4, 6 and 8 hrs of exposure. Effect size statistics revealed that mixed model ANOVA was advantageous in exhibiting the biological value of studied effects. The longer the period of exposure to ammonia beyond permissible limits, the larger negative influences can be detected. Management practice in broiler farms should include preventive measures to reduce ammonia concentrations as well as researchers should keep in mind a quantitative estimate for each effect size when conducting experimental studies.
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