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Using real-time ultrasound for in vivo assessment of carcass and internal adipose depots of dairy sheep
- J. Afonso, C. M. Guedes, A. Teixeira, V. Santos, J. M. T. Azevedo, S. R. Silva
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- Journal:
- The Journal of Agricultural Science / Volume 157 / Issue 7-8 / October 2019
- Published online by Cambridge University Press:
- 23 March 2020, pp. 650-658
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Fifty-one Churra da Terra Quente ewes (4–7 years old) were used to analyse the potential of real-time ultrasound (RTU) to predict the amount of internal adipose depots, in addition to carcass fat (CF). The prediction models were developed from live weight (LW) and RTU measurements taken at eight different locations. After correlation and multiple linear regression analysis, the prediction models were evaluated by k-fold cross-validation and through the ratio of prediction to deviation (RPD). All prediction models included at least one RTU measurement as an independent variable. Prediction models for the absolute weight of the different adipose depots showed higher accuracy than prediction models for fat content per kg of LW. The former showed to be very good or excellent (2.4 ⩽ RPD ⩽ 3.8) for all adipose depots except mesenteric fat (MesF) and thoracic fat, with the model for MesF still providing useful information (RPD = 1.8). Prediction models for fat content per kg of LW were also very good or excellent for subcutaneous fat, intermuscular fat, CF and body fat (2.6 ⩽ RPD ⩽ 3.2), while the best prediction models for omental fat, kidney knob, channel fat and internal fat still provided useful information. Despite some loss in the accuracy of the estimates obtained, there was a similar pattern in terms of RPD for models developed from LW and RTU measurements taken just at the level of the 11th thoracic vertebra. In vivo RTU measurements showed the potential to monitor changes in ewe internal fat reserves as well as in CF.
Application of bioelectrical impedance analysis in prediction of light kid carcass and muscle chemical composition
- S. R. Silva, J. Afonso, A. Monteiro, R. Morais, A. Cabo, A. C. Batista, C. M. Guedes, A. Teixeira
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Carcass data were collected from 24 kids (average live weight of 12.5±5.5 kg; range 4.5 to 22.4 kg) of Jarmelista Portuguese native breed, to evaluate bioelectrical impedance analysis (BIA) as a technique for prediction of light kid carcass and muscle chemical composition. Resistance (Rs, Ω) and reactance (Xc, Ω), were measured in the cold carcasses with a single frequency bioelectrical impedance analyzer and, together with impedance (Z, Ω), two electrical volume measurements (VolA and VolB, cm2/Ω), carcass cold weight (CCW), carcass compactness and several carcass linear measurements were fitted as independent variables to predict carcass composition by stepwise regression analysis. The amount of variation explained by VolA and VolB only reached a significant level (P<0.01 and P<0.05, respectively) for muscle weight, moisture, protein and fat-free soft tissue content, even so with low accuracy, with VolA providing the best results (0.326⩽R2⩽0.366). Quite differently, individual BIA parameters (Rs, Xc and Z) explained a very large amount of variation in dissectible carcass fat weight (0.814⩽R2⩽0.862; P<0.01). These individual BIA parameters also explained a large amount of variation in subcutaneous and intermuscular fat weights (respectively 0.749⩽R2⩽0.793 and 0.718⩽R2⩽0.760; P<0.01), and in muscle chemical fat weight (0.663⩽R2⩽0.684; P<0.01). Still significant but much lower was the variation in muscle, moisture, protein and fat-free soft tissue weights (0.344⩽R2⩽0.393; P<0.01) explained by BIA parameters. Still, the best models for estimation of muscle, moisture, protein and fat-free soft tissue weights included Rs in addition to CCW, and accounted for 97.1% to 99.8% (P<0.01) of the variation observed, with CCW by itself accounting for 97.0% to 99.6% (P<0.01) of that variation. Resistance was the only independent variable selected for the best model predicting subcutaneous fat weight. It was also selected for the best models predicting carcass fat weight (combined with carcass length, CL; R2=0.943; P<0.01) and intermuscular fat weight (combined with CCW; R2=0.945; P<0.01). The best model predicting muscle chemical fat weight combined CCW and Z, explaining 85.6% (P<0.01) of the variation observed. These results indicate BIA as a useful tool for prediction of light kids’ carcass composition.
Chapter 8 - Detection of regional blood flow using arterial spin labeling
- from Section 1 - Physiological MR techniques
- Edited by Jonathan H. Gillard, University of Cambridge, Adam D. Waldman, Imperial College London, Peter B. Barker, The Johns Hopkins University School of Medicine
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- Book:
- Clinical MR Neuroimaging
- Published online:
- 05 March 2013
- Print publication:
- 26 November 2009, pp 94-112
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Summary
Introduction
The motivation to measure regional perfusion is well established in physiology and medicine. Techniques to measure regional blood flow in animal models such as using microspheres [1] and radiolabeled tracers [2] have had a major impact on our understanding of the regulation of microcirculation in normal tissue and changes that occur during a variety of disease processes. A major limitation of these techniques is that, for the most part, they require sacrificing the animal after only one or a few independent measurements of blood flow. Techniques to measure regional blood flow in humans have relied on the wash-in/wash-out kinetics of tracers that can be detected by radiological imaging techniques. Most important have been the use of radiolabeled water detected by positron emission tomography (PET) [3] and regional distribution of inhaled xenon detected by X-ray computed tomography (CT).[4] The results from these techniques show a wide range of problems that perfusion imaging can address, from functional mapping of active brain regions during cognitive task activation to attempts to detect the development of Alzheimer’s disease. These techniques are limited by low spatial resolution compared with MRI and the inability to make numerous serial measurements owing to radiation dose issues. All of these approaches have been inspirational, offering theoretical frameworks and practical motivation to develop MRI techniques to measure regional perfusion. The goal has been to take advantage of the non-invasive nature of MRI and the very high resolution that can be obtained to make maps of tissue blood flow.
Early approaches to measure regional blood flow by MR techniques relied on adapting the well-developed class of techniques that measure tissue-specific wash-in and wash-out of tracers. Tracers such as deuterium oxide [5,6] or fluorinated inhalants [7,8] were first detected using MR spectroscopy (MRS) from specified regions and later images were made that enabled estimates of cerebral blood flow (CBF),[9,10] and blood flow in tumors.[11] A major drawback with the MR techniques that relied on directly detecting tracers was the low spatial resolution that could be obtained compared with normal MRI. A solution to this problem was to follow the tracer kinetics of MRI contrast agents indirectly through their effects on tissue water relaxation.[12,13] After a rapid bolus of gadolinium chelates, the change in contrast in a tissue can be used to calculate regional blood volume and blood flow at the resolution of standard MRI. This approach has become an important technique for assessing hemodynamics during ischemia in heart and brain [14] and is described in detail in Ch. 7.
8 - MRI detection of regional blood flow using arterial spin labeling
- from SECTION 1 - PHYSIOLOGICAL MR TECHNIQUES
- Edited by Jonathan H. Gillard, University of Cambridge, Adam D. Waldman, Charing Cross Hospital, London, Peter B. Barker, The Johns Hopkins University
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- Book:
- Clinical MR Neuroimaging
- Published online:
- 07 December 2009
- Print publication:
- 02 December 2004, pp 119-140
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Summary
Introduction
By the early 1980s MR imaging (MRI) was well on its way to establishing itself as a useful tool for diagnosis of a number of disorders, especially of the central nervous system (CNS) (Atlas, 2002). The reason for the rapid and spectacular success of MRI is the superb soft tissue contrast that imaging water distribution and relaxation times afford. In addition, the non-invasive nature of the technology makes it possible to readily test efficacy. By the end of the 1980s, the great success in generating anatomical images of normal and diseased tissue led a number of groups to seek ways to get functional information from MRI. Indeed, by the early 1990s techniques had been developed that enabled various aspects of tissue function to be assessed. Most important has been blood oxygen level dependent (BOLD) contrast, which enables detection of changes in hemoglobin oxygenation during regional activation of the brain (Kwong et al., 1992; Ogawa et al., 1992). BOLD-based MRI has rapidly grown into a technique that readily enables brain mapping during complex cognitive tasks (Moonen and Bandettini, 1999). Another class of MRI techniques sensitizes images to changes in diffusion of water in tissue (Wesbey et al., 1984) as well as quantifying preferred diffusion directions (Basser et al., 1994). Diffusion-weighted MRI has grown into an important tool for monitoring tissue damage due to ischemia in brain (Warach, 2002), increasing MRI sensitivity to white matter (WM) disorders (Ahrens et al., 1998) and for mapping fiber orientation in the brain (Mori et al., 2000). Another important way to add functional information to MRI is the class of techniques that enable regional measurement of tissue blood flow or perfusion.