(BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab ® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell ® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Infectious Bursal Disease (IBD) is a chicken disease economically important for the poultry industry in function of the immune depression that it causes. Disease control is made with different vaccines and vaccination programs. In present work, the pathogenicity of 3 intermediate vaccines (I1, I2 and I3), 2 intermediate more pathogenic (IP1 and IP2) and 3 vaccines containing strong virus (F1, F2 and F3) was evaluated. Birds vaccinated with IP1, IP2, F1, F2 and F3 showed significantly lower bursa size in relation to control animals and animals vaccinated with I1, I2 and I3. On the other hand, vaccines I1 and I3 induced antibody titers higher than the control and lower than I2, IP1, IP2, F1, F2 and F3. Histological scores showed that vaccines I1, I2 and I3 induced similar injury degree, although I2 and I3 were not different from the control, whereas I1 was slightly different. Strong vaccines induced more pronounced lesions than the other tested vaccines. These findings suggest that strong vaccines are able to cause severe bursal injuries. However, bursometry and relative weight of the bursa of Fabricius were considered inadequate to evaluate vaccine pathogenicity. Moreover, strong vaccines induced higher antibody titers than the other vaccines, although some intermediate vaccines induced similar titers

Background: Avian Pathogenic Escherichia coli is the main agent of colibacillosis, a systemic disease that causes considerable economic losses to the poultry industry. In vivo experiments are used to measure the ability of E. coli to be pathogenic. Generally, these experiments have proposed different criteria for results interpretation and did not take into account the death time. The aim of this study was to propose a new methodology for the classification of E. coli pathogenicity by the establishment of a pathogenicity index based in the lethality, death time and the ability of the strain to cause colibacillosis lesions in challenged animals.Materials, Methods & Results: A total of 293 isolates of E. coli were randomly selected to this study. The strains were isolated from cellulitis lesions, broiler bedding material or respiratory diseases and were previously confirmed through biochemical profile. The bacterial isolates were kept frozen at -20°C. The strains were retrieved from stocks and cultured in brain-heart infusion broth overnight at 37°C to obtain a final concentration of 109 UFC/mL. A total of 2940 one-dayold chicks from commercial breeding hens were randomly assigned to groups containing 10 animals and each group was subcutaneously inoculated in the abdominal region with 0.1 mL of the standard inoculum solution containing each of the strains. A control group of 10 broilers were inoculated with 0.1 mL of brain-heart infusion broth by the same route. The chicks were kept for seven days. They were observed at intervals of 6, 12 and 24 h post-inoculation during the first days. From the second day on, the chicks were observed at intervals of 12 h. According to the death time and to the scores of each lesion (aerosaculitis, pericarditis, perihepatitis, peritonitis and cellulitis), a formula to determine the Individual Pathogenicity Index was established. A value of 10 was established as the maximum pathogenicity rate for an inoculated bird. From this rate, 5 points corresponded to scores for gross lesions present at necropsy. For each lesion present, it represents 1 point. The remaining 5 points corresponded to the death time. To obtain the death time value, an index of 1, corresponding to the maximum value assigned to a death on the first day, was divided by the number of days that the birds were evaluated, resulting in a value of 0.1428, which corresponded to a survival bonus factor. It was possible to classify E. coli strains into four pathogenicity groups according to the pathogenicity index: high pathogenicity (pathogenicity index ranging from 7 to 10), intermediate pathogenicity (pathogenicity index ranging from 4 to 6.99), low pathogenicity (pathogenicity index ranging from 1 to 3.99) and apathogenic (pathogenicity index ranging from 0 to 0.99). The analysis of the strains according to their origin revealed that isolates from broiler bedding material presented a lower pathogenicity index.Discussion: It is possible that the source of isolation implies in different results, depending on the criteria adopted. This data reinforces the importance of use a more accurate mathematical model to represents the biological phenomenon. In the study, all avian pathogenic Escherichia coli strains were classified based on a pathogenicity index and the concept of the death time represents an interesting parameter to measure the ability of the strain to promote acute and septicemic manifestation. The use of a support method for poultry veterinary diagnostic accompanying the fluctuation of the bacteria pathogenicity inside the farms may indicate a rational use of antimicrobial in poultry industry.

Utilização de inteligência artificial (redes neurais artificiais) para a classificação do comportamento bioquímico de amostras de Escherichia coli isoladas de frangos de corte* The use of artificial intelligence (artificial neural networks) to classify the biochemical reactions of Escherichia coli isolates from broilers

Introduction: Avian pathogenic E. coli (APEC) and uropathogenic E. coli (UPEC) are responsible for avian colibacillosis and human urinary tract infections, respectively. There are genetic similarities between the APEC and UPEC pathotypes, suggesting the APEC strains could be a potential reservoir of virulence and antimicrobial-resistance genes for the UPEC strains. This study aimed to characterize and compare APEC and UPEC strains regarding the phylogroup classification, pathogenicity and antimicrobial susceptibility. Methodology: A total of 238 APEC and 184 UPEC strains were selected and characterized. The strains were assayed for antimicrobial susceptibility and classified into phylogenetic groups using a multiplex-PCR protocol. In addition, the APEC strains had previously been classified according to their in vivo pathogenicity. Results: The results showed that both pathotypes had variation in their susceptibility to most of the antimicrobial agents evaluated, with few strains classified as multidrug resistant. The highest resistance rate for both pathotypes was to amoxicillin. Classifying the APEC and UPEC strains into phylogenetic groups determined that the most frequently frequencies were for groups D and B2, respectively. These results reflect the pathogenic potential of these strains, as all the UPEC strains were isolated from unhealthy patients, and most of the APEC strains were previously classified as pathogenic. Conclusions: The results indicate that distribution into phylogenetic groups provided, in part, similar classification to those of in vivo pathogenicity index, as it was possible to adequately differentiate most of the pathogenic and commensal or low-pathogenicity bacteria. However, no relationship could be found between the specific antimicrobial agents and pathogenicity or phylogenetic group for either pathotype.