通过形态特征估算脑重和肝重及其比例,诊断新生仔猪宫内生长受限症

IF 4 2区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE Animal Pub Date : 2024-07-19 DOI:10.1016/j.animal.2024.101262
R. Ruggeri , G. Bee , P. Trevisi , C. Ollagnier
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引用次数: 0

摘要

宫内生长受限(IUGR)是指胎儿在妊娠期间生长不足。为了应对胎盘不足,IUGR 仔猪会优先考虑大脑发育,以此作为一种生存机制。这种适应性导致仔猪出生时脑重/肝重比率(BrW/LW)较高。本研究评估了利用形态特征估算脑重(BrW)和肝重(LW)的潜力,从而对新生仔猪的 IUGR 进行无创诊断。出生时,记录每头仔猪(n = 144)的体重(BtW)。出生后一天(±1),用计算机断层扫描(n = 94)或自然死亡或安乐死后的器官称重(n = 50)测量BrW和LW。此外,还从每头仔猪的图像中捕获了 20 个形态特征,并将其与胸围和体重相关联。挑选出与净重或长重线性相关的 r≥0.70 的形态特征。将每个选定的性状作为自变量与BtW相结合,建立多元线性回归模型,以预测BrW和LW。根据最高的调整 R2 值选择了六个模型:三个用于估算净重,三个用于估算长重。然后将数据集随机分为训练子集(75% 的数据)和测试子集(剩余的 25%)。在训练子集中,从选定的六个模型中推导出三个用于预测胸围的方程和三个用于预测长臂的方程。然后将这些方程应用于测试子集。预测器官重量的方程的准确性是通过计算 BrW 和 LW 预测值与实际值之间的平均绝对误差和平均绝对百分比误差(MAE 和 MAPE)来评估的。为了预测BrW/LW,使用了一个包括BtW和两个形态特征的方程,该方程能更好地预测BrW和LW。在测试数据集中,结合耳距和净重的方程能更好地预测净重。该方程对脑的真实重量和估计重量的 MAE 为 1.95,MAPE 为 0.06。在肝脏方面,将腹部正方形区域和 BtW 相结合的方程显示出最佳性能,其 MAE 为 9.29,真实重量和估计重量之间的 MAPE 为 0.17。最后,实际 BrW/LW 与估计 BrW/LW 之间的 MAE 和 MAPE 分别为 0.14 和 0.17。这些研究结果表明,特定的形态特征可用于估算脑重和肝重,从而有助于准确、无创地鉴定新生仔猪的 IUGR。
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Morphometric traits to estimate brain and liver weight and their ratio for the diagnosis of intrauterine growth restriction in newborn piglets

Intrauterine growth restriction (IUGR) is defined as inadequate foetal growth during gestation. In response to placenta insufficiency, IUGR piglets prioritise brain development as a survival mechanism. This adaptation leads to a higher brain-to-liver weight ratio (BrW/LW) at birth. This study assessed the potential of using morphometric traits to estimate brain (BrW) and liver (LW) weights, enabling non-invasive diagnosis of IUGR in newborn piglets. At birth, body weight (BtW) of individual piglets (n = 144) was recorded. One day (± 1) after birth, BrW and LW were measured with computed tomography (n = 94) or by weighing the organs after natural death or euthanasia (n = 50). Additionally, 20 morphometric traits were captured from images of each piglet and correlated with the BrW and LW. The morphometric traits that showed a r ≥ 0.70 in linear correlation with the BrW or LW were selected. Each selected trait was combined as an independent variable with BtW to develop multiple linear regression models to predict the BrW and LW. Six models were chosen based on the highest adjusted R2 value: three for estimating BrW and three for LW. The dataset was then randomly divided into a training (75% of the data) and a testing (remaining 25%) subsets. Within the training subset, three equations to predict the BrW and three to predict the LW were extrapolated from the six selected models. The equations were then applied to the testing subset. The accuracy of the equations in predicting organ weight was assessed by calculating mean absolute and mean absolute percentage error (MAE and MAPE) between predicted and actual BrW and LW. To predict the BrW/LW, an equation including BtW and the two morphometric traits which better predicted BrW and LW was used. In the testing dataset, the equation combining ear distance and BtW better estimated the BrW. The equation performed with a MAE of 1.95 and a MAPE of 0.06 between the true and estimated weight of the brain. For the liver, the equation combining the abdominal area delimited by a square and BtW displayed the best performance, with a MAE of 9.29 and a MAPE of 0.17 between the true and estimated weight. Finally, the MAE and MAPE between the actual and estimated BrW/LW were 0.14 and 0.17, respectively. These findings suggest that specific morphometric traits can be used to estimate brain and liver weights, facilitating accurate and non-invasive identification of IUGR in newborn piglets.

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来源期刊
Animal
Animal 农林科学-奶制品与动物科学
CiteScore
7.50
自引率
2.80%
发文量
246
审稿时长
3 months
期刊介绍: Editorial board animal attracts the best research in animal biology and animal systems from across the spectrum of the agricultural, biomedical, and environmental sciences. It is the central element in an exciting collaboration between the British Society of Animal Science (BSAS), Institut National de la Recherche Agronomique (INRA) and the European Federation of Animal Science (EAAP) and represents a merging of three scientific journals: Animal Science; Animal Research; Reproduction, Nutrition, Development. animal publishes original cutting-edge research, ''hot'' topics and horizon-scanning reviews on animal-related aspects of the life sciences at the molecular, cellular, organ, whole animal and production system levels. The main subject areas include: breeding and genetics; nutrition; physiology and functional biology of systems; behaviour, health and welfare; farming systems, environmental impact and climate change; product quality, human health and well-being. Animal models and papers dealing with the integration of research between these topics and their impact on the environment and people are particularly welcome.
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