Pain assessment in animal models of osteoarthritis

Assessment of pain in animal models of osteoarthritis is integral to interpretation of a model's utility in representing the clinical condition, and enabling accurate translational medicine. Here we describe behavioral pain assessments available for small and large experimental osteoarthritic pain animal models.     

Imaging of experimental osteoarthritis in small animal models

Normally, tissue alterations in small animal models for osteoarthritis (OA) are assessed by time-consuming and destructive histology or biochemical assays. Some high resolution imaging modalities are used for longitudinal monitoring of the OA disease process in vivo. microCT is one of these imaging modalities, which is known for superb high-resolution imaging of bone architecture alterations. A major drawback of microCT is that it has low soft-tissue contrast, which makes direct imaging of cartilage impossible. The use of microCT in combination with negatively charged radiopaque contrast agents enables imaging of cartilage degeneration. We demonstrate the possibility of microCT to image cartilage degeneration as a consequence of experimental OA, by the use contrast enhanced microCT in vivo in a rat model for OA. Furthermore, for the assessment of alterations in molecular processes involved in OA we used the recently developed technique of multi pinhole SPECT. This enables us to assess molecular processes involved in experimental OA in a rat at sub-millimeter level. Here we show quantification of subchondral bone turnover in an OA rat knee. These new techniques demonstrate the possibilities of quantitative experimental OA assessment in small animal models such as mice and rats and might enable substitution of the conventional destructive methods.     

In vivo magnetic resonance imaging of animal models of knee osteoarthritis

Recent technical developments in high-field magnetic resonance (MR) scanners, improvement in radio frequency coil design and gradient performance along with the development of efficient pulse sequences and new methods of enhancing contrast have made high-quality imaging of animal arthritis models feasible. MR can provide high-resolution structural information about the osteoarthritic changes in animal models, and also information about the biophysical properties of cartilage. This paper reviews the MR techniques available for animal knee imaging, and the various MR-derived readouts of knee osteoarthritis in animal models. Pitfalls in interpreting animal joint anatomy and joint composition are highlighted.     

Development of predictive models of laboratory animal growth using artificial neural networks

Traditional regression analysis of body weight growth curvesencounters problems .when the data are extremely variable. Whiletransformations are often employed to meet the criteria of theanalysis, some transformations are inadequate for normalizingthe data. Regression analysis also requires presuppositionsregarding the model to be fit and the techniques to be usedin the analysis. An alternative approach using artificial neuralnetworks is presented which may be suitable for developing predictivemodels of growth. Neural networks are simulators of the processesthat occur in the biological brain during the learning process.They are trained on the data, developing the necessary algorithmswithin their internal architecture, and produce a predictivemodel based on the learned facts. A dataset of Sprague–Dawleyrat (Rattus norvegicus) weights is analyzed by both traditionalregression analysis and neural network training. Predictionsof body weight are made from both models. While both methodsproduce models that adequately predict the body weights, theneural network model is superior in that it combines accuracyand precision, being less influenced by longitudinal variabilityin the data. Thus, the neural network provides another toolfor researchers to analyze growth curve data.     

Animal models of osteoarthritis

Animal models have proved to be of considerable importance in elucidating mechanisms underlying joint damage in osteoarthritis (OA) and providing proof of concept in the development of pharmacologic and biologic agents that may modify structural damage in the OA joint. The utility of animal models in predicting the response to an intervention with a drug or biologic agent in humans, however, can be established only after evidence is obtained of a positive effect of the agent in humans. To date, no agent has been shown unequivocally to have such an effect, although diacerhein and glucosamine have recently been reported to lower the rate of loss of articular cartilage in patients with hip OA and knee OA, respectively, based on measurements of the rate of joint space narrowing in plain radiographs. Furthermore, the predominant manifestation of OA - and the feature that leads people with radiographic changes of the disease to decide to seek medical attention and contributes to the enormous medicoeconomic and socioeconomic burden imposed by the disease - is joint pain. Notably, none of the animal models of OA is a good indicator of the analgesic effects of pharmacologic agents. Indeed, it should not be assumed a priori that reduction in the rate of progression of joint damage in OA will be associated with a reduction in joint pain.     

Role of fibroblast growth factor 8 (FGF8) in animal models of osteoarthritis

Introduction  

Fibroblast growth factor 8 (FGF8) is isolated as an androgen-induced growth factor, and has recently been shown to contribute to limb morphogenesis. The aim of the present study was to clarify the role of FGF8 in animal models of osteoarthritis (OA).     

Testing the predictive performance of distribution models

Distribution models are used to predict the likelihood of occurrence or abundance of a species at locations where census data are not available. An integral part of modelling is the testing of model performance. We compared different schemes and measures for testing model performance using 79 species from the North American Breeding Bird Survey. The four testing schemes we compared featured increasing independence between test and training data: resubstitution, random data hold‐out and two spatially segregated data hold‐out designs. The different testing measures also addressed different levels of information content in the dependent variable: regression R² for absolute abundance, squared correlation coefficient r² for relative abundance and AUC/Somer’s D for presence/absence. We found that higher levels of independence between test and training data lead to lower assessments of prediction accuracy. Even for data collected independently, spatial autocorrelation leads to dependence between random hold‐out test data and training data, and thus to inflated measures of model performance. While there is a general awareness of the importance of autocorrelation to model building and hypothesis testing, its consequences via violation of independence between training and testing data have not been addressed systematically and comprehensively before. Furthermore, increasing information content (from correctly classifying presence/absence, to predicting relative abundance, to predicting absolute abundance) leads to decreasing predictive performance. The current tests for presence/absence distribution models are typically overly optimistic because a) the test and training data are not independent and b) the correct classification of presence/absence has a relatively low information content and thus capability to address ecological and conservation questions compared to a prediction of abundance. Meaningful evaluation of model performance requires testing on spatially independent data, if the intended application of the model is to predict into new geographic or climatic space, which arguably is the case for most applications of distribution models.     

Simple but predictive protein models

The traditional approach to computational biophysics studies of molecular systems is brute force molecular dynamics simulations under the conditions of interest. The disadvantages of this approach are that the time and length scales that are accessible to computer simulations often do not reach biologically relevant scales. An alternative approach, which we call intuitive modeling, is hypothesis-driven and based on tailoring simplified protein models to the systems of interest. Using intuitive modeling, the length and time scales that can be achieved using simplified protein models exceed those of traditional molecular-dynamic simulations. Here, we describe several recent studies that signify the predictive power of simplified protein models within the intuitive-modeling approach.     

SARS病毒感染动物模型

SARS-CoV感染动物模型的建立可加深对SARS病原学的了解和发病机制的研究,加速实验室诊断技术的建立、抗病毒药物的筛选和疫苗的开发,同时也有助于给该病一个更精确的定义。可以说SARS动物模型的建立,不但是SARS研究的瓶颈问题,其应用更是贯穿SARS研究的整个过程。到目前为止,已经报道有4种非人灵长类动物(恒河猴、食蟹猴、绒猴、非洲绿猴)和6种啮齿类动物(大鼠、小鼠、豚鼠、田鼠、仓鼠、转基因鼠),以及雪貂、家猫等可以作为SARS动物模型用于实验研究,并已经开始利用动物模型进行疫苗和药物的安全性和有效性评价。本文就已报道的各类SARS动物模型进行综述,并根据动物模型和SARS患者的比对,提出动物模型建立的技术要点。     

Transgenic animal models of tauopathies

Tauopathies are a group of neurodegenerative disorders that include Alzheimer's disease, frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17) and other related diseases with prominent tau pathology. Research advances in the last several decades have characterized and defined tau neuropathologies of both neuron and glia in these diverse disorders and this has stimulated development of animal models of tauopathies. Indeed, animal models ranging from invertebrate species such as C. elegan to Drosophila melanogaster and mammalian transgenic mouse models of tauopathies have been generated and reported. This review summarizes the salient features of many of the known models of tauopathies.     

Economic models of animal communication

Many models of animal signal evolution fail to incorporate an explicit strategy for receivers prior to the evolution of signals. When reasonable assumptions are made for such strategies, we have shown that there is a minimal accuracy of signal coding that is required before receivers should attend to signals (Bradbury & Vehrencamp 1998, Principles of Animal Communication). Depending upon the relative payoffs of correct and incorrect decisions by receivers, this minimal accuracy can be quite high. Here we use this result to explain why so many signals appear to be traits that provided useful information to receivers before becoming ritualized into signals. Our model also supports one prediction of sensory drive models: that latent preferences may selectively favour some signal precursors over others. However, it imposes a serious constraint on sensory drive by requiring that there be sufficient benefits to a receiver to compensate for the costs of disrupting the optimal receiver strategy used before exploitation. Finally, we discuss the overlap between signal honesty and accuracy and show how senders that completely disagree with receivers about appropriate receiver decisions may still benefit by providing moderately honest and accurate signals. Copyright 2000 The Association for the Study of Animal Behaviour.     

Validating predictive models of food spoilage organisms

The accuracy and bias of a predictive model for the maximum specific growth rate of Pseudomonas spp. were studied by means of percentage discrepancy and bias indicators. These were calculated for observations obtained both in laboratory media and in food. When independent pseudomonad data generated in broth were compared with model predictions, the error was smaller than in the case of food. The extent to which the food structure and composition of the microflora contribute to the overall error of the model was quantified.     

Predicting resource utilization: the utility of optimal foraging models

Predicting how predation changes communities is an important challenge for ecologists. One view assumes that predictions can be made in terms of behavioural phenomena. Static optimality methods have been used to devise models of prey choice and are thought to have performed well. Two examples from the literature show that models of diet choice have either been applied without first testing them at the individual level, or have been tested in a way that does not fulfil all their assumptions. A discussion of the nature of mathematical models shows why it is dangerous to translate theories into natural language and how important it is to appreciate the limitations on the mathematical functions describing behaviour. A final section discusses behavioural phenomena such as satiation and learning, which are likely to limit the predictive capacities of static optimization models. It is likely that dynamic models are the way forward although they may be harder to apply at the ecological level.