Cross‐Sectional Study of Seroprevalence and Associated Risk Factors of Bovine Brucellosis in Selected Districts of Jimma Zone, South Western Oromia, Ethiopia

Etefa, Monenus; Kabeta, Tadele; Merga, Desalegn; Debelo, Motuma

doi:10.1155/2022/9549942

Cited by 7 publications

(5 citation statements)

References 66 publications

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“…Research into Brucellosis persists to be a focal point of exploration. In 2022, a seroprevalence study and evaluation of risk factors were conducted in the Jimma region of Ethiopia, with data from 424 bovine blood samples and 114 households being scrutinized [14]. Univariate analysis with a Chi-square test and multivariate logistic regression models were employed to investigate the relationship between seropositivity and risk factors.…”

Section: Related Workmentioning

confidence: 99%

DeepBrucel: A Deep Learning Approach for Automated Risk Detection of Brucellosis in Cattle Farms in Ecuador

Aza-Espinosa,

Herrera-Granda,

Ibarra-Rosero

2023

ISI

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An automated risk model for Brucellosis detection in cattle farms, termed DeepBrucel, was developed and validated. A comprehensive survey encompassing 51 variables related to farm characteristics, management practices, and reproductive pathologies was administered across 632 cattle farms in Ecuador. The extensive dataset thus obtained was utilized to implement and compare classifiers based on regression, neural networks, and deep learning methodologies. A wide-ranging primary experimentation protocol enabled the identification of critical variables and the optimal topology for the neural networks. Superior performance was exhibited by a deep neural network model with three hidden layers, which achieved an impressive accuracy of 98.4% in predicting Brucellosis risk. DeepBrucel, now publicly available, provides a highly accessible and robust tool for the diagnosis and control of Brucellosis in cattle farms.

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Section: Related Workmentioning

confidence: 99%

DeepBrucel: A Deep Learning Approach for Automated Risk Detection of Brucellosis in Cattle Farms in Ecuador

Aza-Espinosa,

Herrera-Granda,

Ibarra-Rosero

2023

ISI

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“…In addition, in many of these 34 studies, a random selection of animals included the epidemiologically irrelevant young ones, thus reducing the probability of detecting infected herds/flocks. One or more of these problems are also common in more recent works, thus limiting the value of the studies [ 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 ].…”

Section: One-health Challenges For Brucellosismentioning

confidence: 99%

“…Similarly overlooked is the significance of cross-infections ( Table 5 ). For instance, most studies investigate only one species, usually cattle, in areas where the existence of other livestock and the particular breeding system strongly suggests that they are bred together or in contact with other ruminants [ 52 , 53 , 55 , 57 , 65 , 66 , 67 , 68 , 73 , 74 , 75 , 76 ]. Regrettably, this is accompanied by the assumption that B. melitensis cannot infect bovines or that, if this happens, cattle spontaneously clear B. melitensis once contact with infected small ruminants is prevented [ 77 ].…”

Section: One-health Challenges For Brucellosismentioning

confidence: 99%

“…In any case, these assays do not outperform the diagnostic sensitivity and specificity (DSe and DSp) of the classical buffered Brucella agglutination tests (Rose Bengal [RBT], Card and Buffered Plate Agglutination tests) that do not suffer from cut-off adjustment problems [ 50 ]. There are abundant examples of these problems in the recent literature [ 52 , 53 , 54 , 55 , 56 , 61 , 62 , 66 , 68 , 71 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 ].…”

Section: One-health Challenges For Brucellosismentioning

confidence: 99%

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Brucellosis and One Health: Inherited and Future Challenges

et al. 2023

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One Health is the collaborative efforts of multiple disciplines to attain optimal health for people, animals and the environment, a concept that historically owes much to the study of brucellosis, including recent political and ethical considerations. Brucellosis One Health actors include Public Health and Veterinary Services, microbiologists, medical and veterinary practitioners and breeders. Brucellosis awareness, and the correct use of diagnostic, epidemiological and prophylactic tools is essential. In brucellosis, One Health implementation faces inherited and new challenges, some aggravated by global warming and the intensification of breeding to meet growing food demands. In endemic scenarios, disease awareness, stakeholder sensitization/engagement and the need to build breeder trust are unresolved issues, all made difficult by the protean characteristics of this zoonosis. Extended infrastructural weaknesses, often accentuated by geography and climate, are critically important. Capacity-building faces misconceptions derived from an uncritical adoption of control/eradication strategies applied in countries with suitable means, and requires additional reference laboratories in endemic areas. Challenges for One Health implementation includee the lack if research in species other than cattle and small ruminants, the need for a safer small ruminant vaccine, the need to fill in the infrastructure gap, the need for realistic capacity-building, the creation of reference laboratories in critical areas, and the stepwise implementation of measures not directly transposed from the so-called developed countries.

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“…Brucella causes productive and reproductive loss in livestock. Notably, in dairies, Brucella causes abortion, accompanied by retained placenta, birth of weak calves, low milk production in females [2,3], and epididymitis and orchitis in males [4].…”

Section: Introductionmentioning

confidence: 99%

Comparison of diagnostic tests for detecting bovine brucellosis in animals vaccinated with S19 and RB51 strain vaccines

Ibarra,

Campos,

Hernán

et al. 2023

Vet World

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Background and Aim: The diagnosis of bovine brucellosis in animals vaccinated with strain-19 (S19) and Rose Bengal (RB)-51 strain vaccines can be misinterpreted due to false positives. This study aimed to compare diagnostic tests for detecting bovine brucellosis in animals vaccinated with S19 and RB51 vaccine strains. Materials and Methods: Two groups of 12 crossbred Holstein calves between 6 and 8 months of age were used. On day 0, blood samples were collected from the animals, and the competitive enzyme-linked immunosorbent assay was used for serological diagnosis of bovine Brucellosis. All animals tested negative. After the first blood collection, the animals were subcutaneously vaccinated: one group received the S19 vaccine and the other received the RB51 vaccine. From the 3rd month after vaccination, all animals were sampled. Sampling was repeated every 2 months until the 7th month. Serological diagnosis of bovine brucellosis was performed using RB, tube serum agglutination test (SAT), SAT with 2-mercaptoethanol (SAT-2Me), and fluorescence polarization assay (FPA). Results: Animals vaccinated with S19 showed positive results with the RB, SAT, and SAT-2Me tests in all months of post-vaccination diagnosis. In animals vaccinated with S19, FPA showed positive results at months 3 and 5 and negative results at month 7, indicating that this test discriminates vaccinated animals from infected animals 7 months after vaccination. Rose Bengal, SAT, SAT-2Me, and FPA tests showed negative results in animals vaccinated with RB51 in all months of diagnosis. Conclusion: Animals vaccinated with S19 may test positive for brucellosis using RB, SAT, or SAT-2Me tests 7 months later. Fluorescence polarization assay is an optimal alternative for diagnosing animals in the field, thereby preventing false positives, and consequently, unnecessary confiscations of animals. Animals vaccinated with RB51 tested negative with RB, SAT, SAT-2Me, and FPA tests in all months of diagnosis, confirming that the tests are ineffective for diagnosing brucellosis caused by rough strains. Keywords: agglutination, bovine, brucellosis, vaccination.

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Cross‐Sectional Study of Seroprevalence and Associated Risk Factors of Bovine Brucellosis in Selected Districts of Jimma Zone, South Western Oromia, Ethiopia

Cited by 7 publications

References 66 publications

DeepBrucel: A Deep Learning Approach for Automated Risk Detection of Brucellosis in Cattle Farms in Ecuador

DeepBrucel: A Deep Learning Approach for Automated Risk Detection of Brucellosis in Cattle Farms in Ecuador

Brucellosis and One Health: Inherited and Future Challenges

Comparison of diagnostic tests for detecting bovine brucellosis in animals vaccinated with S19 and RB51 strain vaccines

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