Experimental models of autoimmune inflammatory ocular diseases

Ocular inflammation is one of the leading causes of blindness and loss of vision. Human uveitis is a complex and heterogeneous group of diseases characterized by inflammation of intraocular tissues. The eye may be the only organ involved, or uveitis may be part of a systemic disease. A significant number of cases are of unknown etiology and are labeled idiopathic. Animal models have been developed to the study of the physiopathogenesis of autoimmune uveitis due to the difficulty in obtaining human eye inflamed tissues for experiments. Most of those models are induced by injection of specific photoreceptors proteins (e.g., S-antigen, interphotoreceptor retinoid-binding protein, rhodopsin, recoverin, phosducin). Non-retinal antigens, including melanin-associated proteins and myelin basic protein, are also good inducers of uveitis in animals. Understanding the basic mechanisms and pathogenesis of autoimmune ocular diseases are essential for the development of new treatment approaches and therapeutic agents. The present review describes the main experimental models of autoimmune ocular inflammatory diseases.

Animal models of prenatal immune challenge and their contribution to the study of schizophrenia: a systematic review

Prenatal immune challenge (PIC) in pregnant rodents produces offspring with abnormalities in behavior, histology, and gene expression that are reminiscent of schizophrenia and autism. Based on this, the goal of this article was to review the main contributions of PIC models, especially the one using the viral-mimetic particle polyriboinosinic-polyribocytidylic acid (poly-I:C), to the understanding of the etiology, biological basis and treatment of schizophrenia. This systematic review consisted of a search of available web databases (PubMed, SciELO, LILACS, PsycINFO, and ISI Web of Knowledge) for original studies published in the last 10 years (May 2001 to October 2011) concerning animal models of PIC, focusing on those using poly-I:C. The results showed that the PIC model with poly-I:C is able to mimic the prodrome and both the positive and negative/cognitive dimensions of schizophrenia, depending on the specific gestation time window of the immune challenge. The model resembles the neurobiology and etiology of schizophrenia and has good predictive value. In conclusion, this model is a robust tool for the identification of novel molecular targets during prenatal life, adolescence and adulthood that might contribute to the development of preventive and/or treatment strategies (targeting specific symptoms, i.e., positive or negative/cognitive) for this devastating mental disorder, also presenting biosafety as compared to viral infection models. One limitation of this model is the incapacity to model the full spectrum of immune responses normally induced by viral exposure.

Testing phenomenological and theoretical models of dark matter density profiles with galaxy clusters

We use the stacked gravitational lensingmass profile of four high-mass (M 1015M ) galaxy clusters around z≈0.3 from Umetsu et al. to fit density profiles of phenomenological [Navarro– Frenk–White (NFW), Einasto, S´ersic, Stadel, Baltz–Marshall–Oguri (BMO) and Hernquist] and theoretical (non-singular Isothermal Sphere, DARKexp and Kang & He) models of the dark matter distribution. We account for large-scale structure effects, including a two-halo term in the analysis.We find that the BMO model provides the best fit to the data as measured by the reduced χ2. It is followed by the Stadel profile, the generalized NFW profile with a free inner slope and by the Einasto profile. The NFW model provides the best fit if we neglect the two-halo term, in agreement with results from Umetsu et al. Among the theoretical profiles, the DARKexp model with a single form parameter has the best performance, very close to that of the BMO profile. This may indicate a connection between this theoretical model and the phenomenology of dark matter haloes, shedding light on the dynamical basis of empirical profiles which emerge from numerical simulations.