Palabras clave
Tecnología en Salud
Lepra
Mycobacterium leprae
Cómo citar
Resumen
Introducción: la lepra es una enfermedad infecciosa causada por la bacteria Mycobacterium leprae y sigue siendo una causa importante de morbilidad y mortalidad en países como India, Brasil e Indonesia. Objetivo: realizar un mapeo sistemático de las investigaciones primarias disponibles en la literatura sobre el uso de herramientas tecnológicas aplicadas en el campo de la lepra. Métodos: la pregunta de investigación fue: “¿Qué herramientas existen para el estudio remoto de la lepra?”. Se aplicó una estrategia de búsqueda específica en las bases de datos PubMed, Scopus y Web of Science, incluyendo todos los artículos científicos publicados en inglés, portugués o español, en el período comprendido entre 2015 y 2021, que estuvieran dentro del alcance de la investigación. Los datos se extrajeron mediante un cuestionario estructurado y se evaluó el riesgo de sesgo de los estudios incluidos. Resultados: la metodología empleada permitió la selección de 15 artículos científicos. Predominaron los estudios realizados en Brasil, India e Indonesia, indexados en PubMed y publicados entre 2020 y 2021. Los estudios evaluados mostraron el uso de herramientas tecnológicas en lepra en una amplia variedad de plataformas, con resultados prometedores para la salud primaria, la gestión de casos y la investigatión. Sin embargo, todavía están en sus inicios. Conclusión: este mapeo sistemático indica la necesidad de más estudios, con mayor robustez, sobre el uso de herramientas tecnológicas en el combate a la lepra a nivel de salud y de investigación.
Citas
1. Fischer M. Leprosy – an overview of clinical features, diagnosis, and treatment. J Dtsch Dermatol Ges. 2017;15(8):801-27. doi: https://doi.org/10.1111/ddg.13301.
2. Maymone MBC, Laughter M, Venkatesh S, Dacso MM, Rao PN, Stryjewska BM, et al. Leprosy: clinical aspects and diagnostic techniques. J Am Acad Dermatol. 2020;83(1):1-14. doi: https://doi.org/10.1016/j.jaad.2019.12.080.
3. Maymone MBC, Venkatesh S, Laughter M, Abdat R, Hugh J, Dacso MM, et al. Leprosy: treatment and management of complications. J Am Acad Dermatol. 2020;83(1):17-30. doi: https://doi.org/10.1016/j.jaad.2019.10.138.
4. Doraiswamy S, Abraham A, Mamtani R, Cheema S. Use of telehealth during the COVID-19 pandemic: scoping review. J Med Internet Res. 2020;22(12):e24087. doi: https://doi.org/10.2196/24087.
5. Ding X, Clifton D, Ji N, Lovell NH, Bonato P, Chen W, et al. Wearable sensing and telehealth technology with potential applications in the coronavirus pandemic. IEEE Rev Biomed Eng. 2021;14:48-70. doi:https://doi.org/10.1109/RBME.2020.2992838.
6. Abbott PA, Liu Y. A scoping review of telehealth. Yearb Med Inform. 2013 [cited 2023 May 23];8:51-8. Available from: https://www.thieme-connect.com/products/ejournals/pdf/10.1055/s-0038-1638832.pdf.
7. Huang F, Brouqui P, Boudjema S. How does innovative technology impact nursing in infectious diseases and infection control?: a scoping review. Nurs Open. 2021;8(5):2369-84. doi: https://doi.org/10.1002/nop2.863.
8. Scott RE, Mars M. Telehealth in the developing world: current status and future prospects. Smart Homecare Technol Telehealth. 2015;3(1):25-37. doi: https://doi.org/10.2147/SHTT.S75184.
9. Kernebeck S, Busse TS, Böttcher MD, Weitz J, Ehlers J, Bork U. Impact of mobile health and medical applications on clinical practice in gastroenterology. World J Gastroenterol. 2020;26(29):4182-97. doi: https://doi.org/10.3748/wjg.v26.i29.4182.
10. Bousquet J, Ansotegui IJ, Anto JM, Arnavielhe S, Bachert C, Basagaña X, et al. Mobile technology in allergic rhinitis: evolution in management or revolution in health and care? J Allergy Clin Immunol Pract. 2019;7(8):2511-23. doi: https://doi.org/10.1016/j.jaip.2019.07.044.
11. Fernandez A, Black J, Jones M, Wilson L, Salvador-Carulla L, Astell-Burt T, et al. Flooding and mental health: a systematic mapping review. PLoS One. 2015;10(4):e0119929. doi: https://doi.org/10.1371/journal.pone.0119929.
12. Nisha J, Shanthi V. Characterization of ofloxacin interaction with mutated (A91V) quinolone resistance determining region of DNA gyrase in mycobacterium leprae through computational simulation. Cell Biochem Biophys. 2018;76(1-2):125-34. doi: https://doi.org/10.1007/s12013-017-0822-5.
13. Nisha J, Shanthi V. Computational simulation techniques to understand rifampicin resistance mutation (S425L) of rpoB in M. leprae. J Cell Biochem. 2015;116(7):1278-85. doi: https://doi.org/10.1002/jcb.25083.
14. Rachmani E, Hsu CY, Chang PWS, Jumanto J, Fuad A, Ningrum DNA, et al. Encouraging on-time completion of leprosy patients treatment: implementing e-leprosy framework to primary health care in Indonesia. Asia Pac J Public Health. 2019;31(4):296-305. doi: https://doi.org/10.1177/1010539519847355.
15. Dhane DM, Maity M, Mungle T, Bar C, Achar A, Kolekar M, et al. Fuzzy spectral clustering for automated delineation of chronic wound region using digital images. Comput Biol Med. 2017;89:551-60. doi: https://doi.org/10.1016/j.compbiomed.2017.04.004.
16. Souza MLM, Lopes GA, Castelo Branco A, Fairley JK, Fraga LAO. Leprosy screening based on artificial intelligence: development of a crossplatform app. JMIR mHealth uHealth. 2021;9(4):e23718. doi: https://doi.org/10.2196/23718.
17. Choo SW, Ang MY, Dutta A, Tan SY, Siow CC, Heydari H, et al. MycoCAP – mycobacterium comparative analysis platform. Sci Rep. 2015;5:18227. doi: https://doi.org/10.1038/srep18227.
18. Portelli S, Myung Y, Furnham N, Vedithi SC, Pires DEV, Ascher DB. Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches. Sci Rep. 2020;10(1):18120. doi: https://doi.org/10.1038/s41598-020-74648-y.
19. Vedithi SC, Malhotra S, Acebrón-García-de-Eulate M, Matusevicius M, Torres PHM, Blundell TL. Structure-guided computational approaches to unravel druggable proteomic landscape of Mycobacterium leprae. Front Mol Biosci. 2021;8:663301. doi: https://doi.org/10.3389/fmolb.2021.663301.
20. Sosa EJ, Burguener G, Lanzarotti E, Defelipe L, Radusky L, Pardo AM, et al. Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens. Nucleic Acids Res. 2018;46(D1):D413-8. doi: https://doi.org/10.1093/nar/gkx1015.
21. Rachmani E, Lin MC, Hsu CY, Jumanto J, Iqbal U, Shidik GF, et al. The implementation of an integrated e-leprosy framework in a leprosy control program at primary health care centers in Indonesia. Int J Med Inform. 2020;140:104155. doi: https://doi.org/10.1016/j.ijmedinf.2020.104155.
22. Irawatia Y, Bani AP, Gabriella K, Fitriana A, Paramita C, Susiyanti M, et al. Peek Acuity vs Snellen Chart for visual impairment screening in leprosy: a cross-sectional study. Lepr Rev. 2020;91(3):262-73. doi: https://doi.org/10.47276/lr.91.3.262.
23. Mieras LF, Taal AT, Post EB, Ndeve AGZ, Van Hees CLM. The development of a mobile application to support peripheral health workers to diagnose and treat people with skin diseases in resource-poor settings. Trop Med Infect Dis. 2018;3(3):102. doi: https://doi.org/10.3390/tropicalmed3030102.
24. Canci B, Pereira EG, Sakata-So K, Nichiata L. The development of a Portuguese mobile application for clinical support in detecting leprosy suspects. Lepr Rev. 2021;92(2):141-51. doi: https://doi.org/10.47276/lr.92.2.141.
25. Cavalheiro AL, Costa DT, Menezes AL, Pereira JM, Carvalho EM. Thermographic analysis and autonomic response in the hands of patients with leprosy. An Bras Dermatol. 2016;91(3):274-83. doi: https://doi.org/10.1590/abd1806-4841.20164612.
26. Soares PFC, Andrade MJO, Andrade SLE, Santos NA. Visual processing of color and shape in people with leprosy. Psicol Reflex Crit. 2020;33:14. doi: https://doi.org/10.1186/s41155-020-00153-w.
27. Sarode G, Sarode S, Anand R, Patil S, Jafer M, Baeshen H, et al. Epidemiological aspects of leprosy. Dis Mon. 2020;66(7):100899. doi: https://doi.org/10.1016/j.disamonth.2019.100899.
28. Wong CK, Ho DTY, Tam AR, Zhou M, Lau YM, Tang MOY, et al. Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial. BMJ Open. 2020;10(7):e038555. doi: https://doi.org/10.1136/bmjopen-2020-038555.
29. Alonso SG, De La Torre Díez I, Zapiraín BG. Predictive, personalized, preventive and participatory (4P) medicine applied to telemedicine and eHealth in the literature. J Med Syst. 2019;43(5):140. doi: https://doi.org/10.1007/s10916-019-1279-4.
30. Belachew WA, Naafs B. Position statement: leprosy: diagnosis, treatment and follow-up. J Eur Acad Dermatol Venereol. 2019;33(7):1205-13. doi: https://doi.org/10.1111/jdv.15569.
31. Lau KHV. Neurological complications of leprosy. Semin Neurol. 2019;39(4):462-71. doi: https://doi.org/10.1055/s-0039-1687884.
32. Cambau E, Saunderson P, Matsuoka M, Cole ST, Kai M, Suffys P, et al. Antimicrobial resistance in leprosy: results of the first prospective open survey conducted by a WHO surveillance network for the period 2009-15. Clin Microbiol Infect. 2018;24(12):1305-10. doi: https://doi.org/10.1016/j.cmi.2018.02.022.
33. Carrion C, Robles N, Sola-Morales O, Aymerich M, Ruiz Postigo JA. Mobile health strategies to tackle skin neglected tropical diseases with recommendations from innovative experiences: systematic review. JMIR Mhealth Uhealth. 2020;8(12):e2. doi: https://doi.org/10.2196/22478.
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2024 Rafael Everton Assunção Ribeiro da Costa, Fergus Tomas Rocha de Oliveira, Vitoria Neris Rebelo Veras, Juliana do Nascimento Sousa, Sandra Marina Gonçalves Bezerra, Dario Brito Calçada
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