One Health and a Computational Biology approach

Authors

DOI:

https://doi.org/10.33448/rsd-v11i14.37105

Keywords:

Human health; Animal; Environment; Antimicrobial resistance; Computational biology.

Abstract

The current concept of One Health is based on the union of three inseparable pillars: human, animal, and environmental health, principles that must be paramount in any project or action in a society. The holistic view becomes fundamental to ensure levels of excellence in the health area as a whole, in addition to numerous diseases and pathologies being prevented and combated through the integrated action of professionals in these three areas. Nevertheless, One Health emerges as a worldwide concept and several projects are being based on this common good practice integrated with the most prominent technologies today, such as computational biology. In this way, national measures and laws are also being amended in pursuit of the principle being based on all places and situations that need to use environmental or animal resources for any circumstance. The primary objective of this brief literature review is to exemplify the concept of the One Health approach based on articles that applied the concept practically, with emphasis on prophylactic measures, applications in bioinformatics, and results presented with this well-known foundation.

References

Aguiar-Oliveira, M. D. L., Campos, A., R. Matos, A., Rigotto, C., Sotero-Martins, A., Teixeira, P. F., & Siqueira, M. M. (2020). Wastewater-based epidemiology (WBE) and viral detection in polluted surface water: A valuable tool for COVID-19 surveillance—A brief review. International journal of environmental research and public health, 17(24), 9251.

Ahmad, F., Saeed, Q., Shah, S. M. U., Gondal, M. A., & Mumtaz, S. (2022). Environmental sustainability: challenges and approaches. Natural Resources Conservation and Advances for Sustainability, 243-270.

Anishchenko, I., Pellock, S. J., Chidyausiku, T. M., Ramelot, T. A., Ovchinnikov, S., Hao, J., ... & Baker, D. (2021). De novo protein design by deep network hallucination. Nature, 600(7889), 547-552.

Butler, C. C., Dorward, J., Yu, L. M., Gbinigie, O., Hayward, G., Saville, B. R., ... & Hobbs, F. R. (2021). Azithromycin for community treatment of suspected COVID-19 in people at increased risk of an adverse clinical course in the UK (PRINCIPLE): a randomized, controlled, open-label, adaptive platform trial. The Lancet, 397(10279), 1063-1074.

Ceballos, M. C., Sant'Anna, A. C., Boivin, X., de Oliveira Costa, F., Monique, V. D. L., & da Costa, M. J. P. (2018). Impact of good practices of handling training on beef cattle welfare and stock people attitudes and behaviors. Livestock Science, 216, 24-31.

Chakraborty, T., & Barbuddhe, S. B. (2021). Enabling One Health solutions through genomics. The Indian Journal of Medical Research, 153(3), 273.

Crampon, K., Giorkallos, A., Deldossi, M., Baud, S., & Steffenel, L. A. (2021). Machine-learning methods for ligand-protein molecular docking. Drug discovery today, 27, 151–164.

de Souza Suguiura, I. M. (2019). Leptospirose no estado do Paraná, Brasil: uma abordagem de saúde única. Revista de Saúde Pública do Paraná, 2(2), 77-84.

dos Santos, R. D. S. B., Mendes, D. C., Muniz, M. F. A. A., da Conceição, L. H. C., de Mello, M. L. V., & Martins, A. V. (2020). Saúde Única nas atividades de campo com estudantes da Faculdade De Medicina Veterinária Do Unifeso. Revista da JOPIC, 3(7).

Galvão, LB, Gomes, P. da S., Assis, NA de, Amaral, AVC do, Ramos, DG de S., Sousa, DB de S., Gitti, CB, Galarza, MFC, Romani, AF, Cruz, C. de A., Mathias, LA, & Meirelles-Bartoli, RB (2020). Análise da distribuição geográfica e caracterização soroepidemiológica da leptospirose em bovinos abatidos em frigoríficos do Sudoeste de Goiás, Brasil. Research, Society and Development, 9 (7), e390974235. https://doi.org/10.33448/rsd-v9i7.4235

Galvao, M. C. B., Pluye, P., & Ricarte, I. L. M. (2017). Métodos de pesquisa mistos e revisões de literatura mistas: conceitos, construção e critérios de avaliação. InCID: Revista De Ciência Da Informação E Documentação, 8(2), 4-24. https://doi.org/10.11606/issn.2178-2075.v8i2p4-24

Garcia, S. N., Osburn, B. I., & Cullor, J. S. (2019). A one health perspective on dairy production and dairy food safety. One Health, 7, 100086.

Greger, M. (2021). Primary pandemic prevention. American Journal of Lifestyle Medicine, 15(5), 498-505.

Limongi, J. E., & de Oliveira, S. V. (2020). COVID-19 e a abordagem One Health (Saúde Única): uma revisão sistemática. Vigilância Sanitária em Debate: Sociedade, Ciência & Tecnologia, 8(3), 139-149.

McEwen, S. A., & Collignon, P. J. (2018). Antimicrobial resistance: a one health perspective. Microbiology spectrum, 6(2), 6-2.

Metcalf, C. J. E., Morris, D. H., & Park, S. W. (2020). Mathematical models to guide pandemic response. Science, 369(6502), 368-369.

Mwanga, G., Mbega, E., Yonah, Z., & Chagunda, M. G. G. (2020). How Information Communication Technology Can Enhance Evidence-Based Decisions and Farm-to-Fork Animal Traceability for Livestock Farmers. The Scientific World Journal, 2020.

Nicastro, R., & Carillo, P. (2021). Food loss and waste prevention strategies from farm to fork. Sustainability, 13(10), 5443.

Nguyen, N. D., & Wang, D. (2020). Multiview learning for understanding functional multiomics. PLoS computational biology, 16(4), e1007677.

Peng, M., Tabashsum, Z., Anderson, M., Truong, A., Houser, A. K., Padilla, J., ... & Biswas, D. (2020). Effectiveness of probiotics, prebiotics, and prebiotic‐like components in common functional foods. Comprehensive reviews in food science and food safety, 19(4), 1908-1933.

Pérez Santín, E., Rodríguez Solana, R., González García, M., García Suárez, M. D. M., Blanco Díaz, G. D., Cima Cabal, M. D., ... & López Sánchez, J. I. (2021). Toxicity prediction based on artificial intelligence: A multidisciplinary overview. Wiley Interdisciplinary Reviews: Computational Molecular Science, 11(5), e1516.

Resende, J. A., Lúcia da Silva, V., & Diniz, C. G. (2020). Aquatic environments in the One Health context: modulating the antimicrobial resistance phenomenon. Acta Limnologica Brasiliensia, 32.

Shivaprakash, K. N., Swami, N., Mysorekar, S., Arora, R., Gangadharan, A., Vohra, K., ... & Kiesecker, J. M. (2022). Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India. Sustainability, 14(12), 7154.

Shurson, G. C., Urriola, P. E., & van de Ligt, J. L. (2022). Can we effectively manage parasites, prions, and pathogens in the global feed industry to achieve One Health? Transboundary and Emerging Diseases, 69(1), 4-30.

Silvestrini, A. R., Heinemann, M. B., & de Castro, A. M. M. G. (2019). Leptospirose no contexto da Saúde Única e diretrizes de vacinação. Pubvet, 14, 137.

Sinclair, J. R. (2019). Importance of a One Health approach in advancing global health security and the Sustainable Development Goals. Revue scientifique et technique (International Office of Epizootics), 38(1), 145-154.

Straathof, A. J., Wahl, S. A., Benjamin, K. R., Takors, R., Wierckx, N., & Noorman, H. J. (2019). Grand research challenges for sustainable industrial biotechnology. Trends in biotechnology, 37(10), 1042-1050.

Tesfaye, W., Suarez-Lepe, J. A., Loira, I., Palomero, F., & Morata, A. (2019). Dairy and nondairy-based beverages as a vehicle for probiotics, prebiotics, and symbiotics: Alternatives to health versus disease binomial approach through food. In Milk-based beverages (pp. 473-520). Woodhead Publishing.

Vishnoi, S., Matre, H., Garg, P., & Pandey, S. K. (2020). Artificial intelligence and machine learning for protein toxicity prediction using proteomics data. Chemical Biology & Drug Design, 96(3), 902-920.

World Health Organization. (2020). The future of food safety: transforming knowledge into action for people, economies and the environment: technical summary by FAO and WHO.

Downloads

Published

07/11/2022

How to Cite

RODRIGUES, S. de O. .; OLIVEIRA, G. F. de .; FRANCO, J. C. .; ASSIS, I. B. de .; BANWO, K.; PAGNOSSA, J. P. One Health and a Computational Biology approach. Research, Society and Development, [S. l.], v. 11, n. 14, p. e02111437105, 2022. DOI: 10.33448/rsd-v11i14.37105. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/37105. Acesso em: 26 nov. 2022.

Issue

Section

Health Sciences