{"id":13350,"date":"2024-07-09T18:26:52","date_gmt":"2024-07-09T18:26:52","guid":{"rendered":"https:\/\/dailyai.com\/?p=13350"},"modified":"2024-07-09T18:35:05","modified_gmt":"2024-07-09T18:35:05","slug":"ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/","title":{"rendered":"Un avance de la IA identifica r\u00e1pidamente la fiebre tifoidea farmacorresistente sin exposici\u00f3n a antibi\u00f3ticos"},"content":{"rendered":"

Investigadores de la Universidad de Cambridge han utilizado la IA en la lucha contra la resistencia a los antibi\u00f3ticos.\u00a0<\/strong><\/p>\n

El equipo de investigaci\u00f3n, dirigido por el profesor Stephen Baker, cre\u00f3 una herramienta de aprendizaje autom\u00e1tico utilizando \u00fanicamente im\u00e1genes de microscop\u00eda para distinguir entre las bacterias resistentes a la ciprofloxacina (un antibi\u00f3tico com\u00fan) y las susceptibles a ella.<\/span><\/p>\n

Esto podr\u00eda reducir dr\u00e1sticamente el tiempo necesario para diagnosticar la resistencia a los antibi\u00f3ticos, transformando potencialmente la forma en que tratamos infecciones peligrosas como la fiebre tifoidea.<\/span><\/p>\n

En <\/span>estudiar<\/span><\/a>publicado en Nature Communications, se centraba en la Salmonella Typhimurium, una bacteria que causa enfermedades gastrointestinales graves y puede provocar enfermedades invasivas potencialmente mortales.\u00a0<\/span><\/p>\n

\"AI\"
La salmonela es una bacteria que suele infectar a los humanos a trav\u00e9s de alimentos contaminados, y algunas cepas han adquirido resistencia a los antibi\u00f3ticos. Fuente: Universidad de Cambridge<\/a>.<\/figcaption><\/figure>\n

El Dr. Tuan-Anh Tran, uno de los principales investigadores del proyecto, explic\u00f3 el planteamiento en un comunicado. <\/span>entrada del blog<\/span><\/a>: \"La belleza del modelo de aprendizaje autom\u00e1tico es que puede identificar bacterias resistentes bas\u00e1ndose en unas pocas caracter\u00edsticas sutiles en im\u00e1genes de microscop\u00eda que los ojos humanos no pueden detectar.\"<\/span><\/p>\n

El proceso de investigaci\u00f3n const\u00f3 de varios pasos clave:<\/span><\/p>\n

    \n
  1. Preparaci\u00f3n de muestras bacterianas<\/strong>: El equipo cultiv\u00f3 muestras de S. Typhimurium en medios nutritivos l\u00edquidos, algunas expuestas a diferentes concentraciones de ciprofloxacino y otras no.<\/span><\/li>\n
  2. Im\u00e1genes de alto contenido<\/strong>: Utilizando un sofisticado microscopio, los investigadores tomaron im\u00e1genes detalladas de las bacterias en m\u00faltiples momentos.<\/span><\/li>\n
  3. An\u00e1lisis de im\u00e1genes<\/strong>: Un programa inform\u00e1tico especializado extrajo 65 caracter\u00edsticas diferentes de cada c\u00e9lula bacteriana, entre ellas la forma, el tama\u00f1o y la interacci\u00f3n con colorantes fluorescentes.<\/span><\/li>\n
  4. Desarrollo de modelos de aprendizaje autom\u00e1tico<\/strong>: Los investigadores introdujeron estos datos en varios algoritmos de aprendizaje autom\u00e1tico y los entrenaron para reconocer patrones asociados a la resistencia a los antibi\u00f3ticos.<\/span><\/li>\n
  5. Selecci\u00f3n de caracter\u00edsticas<\/strong>: El equipo identific\u00f3 las caracter\u00edsticas m\u00e1s cruciales para distinguir entre bacterias resistentes y susceptibles.<\/span><\/li>\n<\/ol>\n

    Los resultados de este proceso fueron impresionantes. El sistema de IA identific\u00f3 correctamente las bacterias resistentes a los antibi\u00f3ticos en un 87% de las veces.\u00a0<\/span><\/p>\n

    Quiz\u00e1 lo m\u00e1s significativo es que los investigadores descubrieron que las bacterias resistentes y susceptibles presentaban patrones visuales distintos que la IA pod\u00eda detectar, incluso cuando no hab\u00edan sido expuestas a antibi\u00f3ticos.\u00a0<\/span><\/p>\n

    Esto sugiere que la resistencia a los antibi\u00f3ticos cambia la apariencia de las bacterias de formas demasiado sutiles para que las vean los humanos, pero que la IA puede detectar.<\/span><\/p>\n

    Los m\u00e9todos actuales suelen requerir varios d\u00edas de cultivos bacterianos y pruebas con diversos antimicrobianos. En cambio, el nuevo m\u00e9todo basado en IA podr\u00eda proporcionar resultados en cuesti\u00f3n de horas.\u00a0<\/span><\/p>\n

    Un diagn\u00f3stico m\u00e1s r\u00e1pido permite a los m\u00e9dicos prescribir antes los antibi\u00f3ticos m\u00e1s eficaces, lo que puede mejorar la evoluci\u00f3n de los pacientes y reducir la propagaci\u00f3n de bacterias resistentes.<\/p>\n

    De cara al futuro, el equipo de investigaci\u00f3n pretende ampliar su m\u00e9todo a muestras cl\u00ednicas m\u00e1s complejas, como sangre u orina, y analizarlas con otros tipos de bacterias y antibi\u00f3ticos. Tambi\u00e9n est\u00e1n trabajando para hacer la tecnolog\u00eda m\u00e1s accesible a hospitales y cl\u00ednicas de todo el mundo.<\/span><\/p>\n

    Como explica el profesor Baker: \"Lo que ser\u00eda realmente importante, sobre todo para un contexto cl\u00ednico, ser\u00eda poder tomar una muestra compleja -por ejemplo, sangre u orina o esputo- e identificar la susceptibilidad y la resistencia directamente a partir de ella.\"<\/p>\n

    \"Ese es un problema mucho m\u00e1s complicado y que realmente no se ha resuelto en absoluto, ni siquiera en el diagn\u00f3stico cl\u00ednico en un hospital. Si encontr\u00e1ramos la forma de hacerlo, podr\u00edamos reducir el tiempo que se tarda en identificar la resistencia a los f\u00e1rmacos y a un coste mucho menor. Eso podr\u00eda ser realmente transformador\".<\/span><\/p>\n

    La Dra. Sushmita Sridhar resumi\u00f3 las repercusiones afirmando: \"Dado que este enfoque utiliza im\u00e1genes con resoluci\u00f3n unicelular, todav\u00eda no es una soluci\u00f3n que pueda implantarse f\u00e1cilmente en todas partes. Pero resulta realmente prometedor que, captando s\u00f3lo unos pocos par\u00e1metros sobre la forma y la estructura de las bacterias, pueda darnos informaci\u00f3n suficiente para predecir la resistencia a los f\u00e1rmacos con relativa facilidad.\"<\/span><\/p>\n

    Mientras la resistencia a los antibi\u00f3ticos sigue representando una amenaza creciente para la salud mundial, enfoques innovadores como esta t\u00e9cnica de imagen potenciada por la IA ofrecen nuevas esperanzas.\u00a0<\/span><\/p>\n

    Esto forma parte de una tendencia m\u00e1s amplia de innovaciones impulsadas por la IA en la investigaci\u00f3n de antibi\u00f3ticos. En el MIT, los investigadores han utilizado modelos de aprendizaje profundo para descubrir un una nueva clase de antibi\u00f3ticos<\/a>.<\/p>\n

    En una l\u00ednea similar, otro equipo de cient\u00edficos anunci\u00f3 en mayo del a\u00f1o pasado que hab\u00eda utilizado la IA para identificar un nuevo antibi\u00f3tico<\/a> eficaz contra las bacterias resistentes a los medicamentos.<\/p>\n

    La IA permite una identificaci\u00f3n m\u00e1s r\u00e1pida y precisa de las infecciones farmacorresistentes, allanando el camino para tratamientos m\u00e1s eficaces y mejores resultados para los pacientes.\u00a0<\/span><\/p>\n

    Los pr\u00f3ximos a\u00f1os ser\u00e1n cruciales, ya que el equipo trabaja para trasladar sus \u00e9xitos de laboratorio a aplicaciones cl\u00ednicas en el mundo real.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Investigadores de la Universidad de Cambridge han utilizado la IA en la lucha contra la resistencia a los antibi\u00f3ticos. El equipo de investigaci\u00f3n, dirigido por el profesor Stephen Baker, cre\u00f3 una herramienta de aprendizaje autom\u00e1tico que utiliza \u00fanicamente im\u00e1genes de microscop\u00eda para distinguir entre las bacterias resistentes a la ciprofloxacina (un antibi\u00f3tico com\u00fan) y las susceptibles a ella. Esto podr\u00eda reducir dr\u00e1sticamente el tiempo necesario para diagnosticar la resistencia a los antibi\u00f3ticos, transformando potencialmente la forma en que tratamos infecciones peligrosas como la fiebre tifoidea. El estudio, publicado en Nature Communications, se centr\u00f3 en la Salmonella Typhimurium, una bacteria que causa enfermedades gastrointestinales graves y puede provocar enfermedades invasivas potencialmente mortales. El Dr. Tuan-Anh Tran, investigador clave en el<\/p>","protected":false},"author":2,"featured_media":13352,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[353,105],"class_list":["post-13350","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-biology","tag-machine-learning"],"yoast_head":"\nAI breakthrough rapidly identifies drug-resistant typhoid without antibiotic exposure | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/es\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI breakthrough rapidly identifies drug-resistant typhoid without antibiotic exposure | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers at the University of Cambridge have harnessed AI in the fight against antibiotic resistance.\u00a0 The research team, led by Professor Stephen Baker, created a machine learning tool using only microscopy images to distinguish between bacteria resistant to ciprofloxacin (a common antibiotic) and those susceptible to it. This could dramatically reduce the time required for diagnosing antibiotic resistance, potentially transforming how we treat dangerous infections like typhoid fever. 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