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| http://dx.doi.org/10.1186/s13063-021-05824-2 | DOI Listing |
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Neural networks for predicting etiological diagnosis of uveitis.
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Department of Internal Medicine, Hôpital Universitaire de la Croix-Rousse, Hospices Civils de Lyon, University Claude Bernard-Lyon 1, Lyon, France.
Background/objectives: The large number and heterogeneity of causes of uveitis make the etiological diagnosis a complex task. The clinician must consider all the information concerning the ophthalmological and extra-ophthalmological features of the patient. Diagnostic machine learning algorithms have been developed and provide a correct diagnosis in one-half to three-quarters of cases.
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Animal Breeding and Genetics Program, Institute of Agrifood Research and Technology (IRTA), Caldes de Montbui, Spain.
Breeding animals to produce more robust and disease-resistant pig populations becomes a complementary strategy to the more conventional methods of biosecurity and vaccination. The objective of this study was to explore the ability of a panel of genetic markers and immunity parameters to predict the survival rates during a natural PRRSV outbreak. Ten-week-old female Duroc pigs (n = 129), obtained from 61 sows and 20 boars, were naturally infected with a highly pathogenic PRRSV genotype 1 strain.
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Introduction is a complex process influenced by various physiological and psychological factors. Stress, in particular, has been shown to impair wound healing by affecting the immune response and slowing the repair process. Yoga-based relaxation techniques, such as bhramari pranayama and Om chanting, have demonstrated the potential to reduce stress and improve overall well-being.
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