Biomechanical investigation into locomotor pathology in commercial pigs is lacking despite this being a major concern for the industry. Different floor types are used in modern, intensive pig production systems at different stages of the pigs' production cycle. The general perception holds that slatted and/or hard solid concrete surfaces are inferior to soft straw-covered floors regarding healthy musculoskeletal development. Previous studies have compared pigs housed on different floor types using clinical, subjective assessment of leg weakness and lameness. However, reliability studies generally report a low repeatability of clinical lameness scoring. The objective of this study was to quantitatively assess the long-term effect of pen floors, reflected in the biomechanical gait characteristics and associated welfare of the pigs. A cohort of 24 pigs housed on one of three different floor types was followed from 37 to 90 kg average liveweight, with gait analysis (motion capture) starting at 63 kg. The three floor types were fully slatted concrete, partly slatted concrete and deep straw-bedded surfaces, all located within the same building. Pigs underwent five repeated camera-based motion captures, 7 to 10 days apart, during which 3D coordinate data of reflective skin markers attached to leg anatomical landmarks were collected. Pigs walked on the same solid concrete walkway during captures. One-way ANOVA and repeated measures ANOVA were used to analyse the gait data. Results revealed changes over time in the spatiotemporal gait pattern which were similar in magnitude and direction for the pigs from different floor types. Significant increases in elbow joint flexion with age were observed in all pigs (P ⩽ 0.050; +6°). There were few differences between floor groups, except for the step-to-stride ratio in the hind legs being more irregular in pigs housed on partly slatted floors (P = 0.012; 3.6 times higher s.d.) compared with those on 5 to 10 cm straw-bedding in all pen areas. As the level of clinical problems was generally low in this cohort, it may be that floors elicit problems only when there is a primary predisposing factor increasing weakness in susceptible tissues.
A method of kinematic analysis of the fingers using stereo-photogrammetry, referred to as the phalanx transformation technique, has been proposed. Functional methods were used to define the joint axes and subsequently each finger segments' anatomical coordinate system. Thirteen subjects were tested and the accuracy of the technique assessed. The average error across the three joints of the finger was found to be 0.6 mm, which translates to a 2.2% error in predicted joint reaction force when using a biomechanical model. The subjects were required to have sufficient movement in their joints to define the joint axes functionally. Some subjects of clinical interest can have a significantly reduced mobility owing to injury or pathology, therefore, the effect of calibration range of motion on accuracy was analysed. It was found that, for a range of motion typical of a subject with rheumatoid arthritis, the errors in predicted joint reaction force were < 7%. The accuracy of this technique compared favourably with others previously proposed and, considering the other errors inherent in modelling, those found in this study were deemed to be acceptable.
Background: Calls are increasing for widespread SARS-CoV-2 infection testing of people from populations with a very low prevalence of infection. We quantified the impact of less than perfect diagnostic test accuracy on populations, and on individuals, in low prevalence settings, focusing on false positives and the role of confirmatory testing. Methods: We developed a simple, interactive tool to assess the impact of different combinations of test sensitivity, specificity and infection prevalence in a notional population of 100,000. We derived numbers of true positives, true negatives, false positives and false negatives, positive predictive value (PPV, the percentage of test positives that are true positives) and overall test accuracy for three testing strategies: (1) single test for all; (2) add repeat testing in test positives; (3) add further repeat testing in those with discrepant results. We also assessed the impact on test results for individuals having one, two or three tests under these three strategies. Results: With sensitivity of 80%, infection prevalence of 1 in 2,000, and specificity 99.9% on all tests, PPV in the tested population of 100,000 will be only 29% with one test, increasing to >99.5% (100% when rounded to the nearest %) with repeat testing in strategies 2 or 3. More realistically, if specificity is 95% for the first and 99.9% for subsequent tests, single test PPV will be only 1%, increasing to 86% with repeat testing in strategy 2, or 79% with strategy 3 (albeit with 6 fewer false negatives than strategy 2). In the whole population, or in particular individuals, PPV increases as infection becomes more common in the population but falls to unacceptably low levels with lower test specificity. Conclusion: To avoid multiple unnecessary restrictions on whole populations, and in particular individuals, from widespread population testing for SARS-CoV-2, the crucial roles of extremely high test specificity and of confirmatory testing must be fully appreciated and incorporated into policy decisions.
Biomechanical models of the fingers are used to gain a greater understanding of internal loading. This can help guide the treatment of injuries and pathologies. However, to be valid these models require accurate measurement of body kinematics, external reaction forces and soft tissue architecture. This study aimed to quantify the sensitivity of one such model, to errors in these inputs. Experimental data was collected from a single subject carrying out a simple gripping activity and the experimental data altered to introduce artificial errors. We found that the correlations between errors in measurement of body kinematics and the model outputs could be used to express errors in motion capture data in terms of internal loading. However, these correlations were specific to grip type, therefore, if the grip changed significantly a new analysis would be required. Sensitivity analysis of the muscle and tendon locations indicated which parameters were most important to measure accurately; outputs were most sensitive to changes in the most highly loaded muscle-tendon units, these results were applicable across different hand orientations.
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