{"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\/it\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/","title":{"rendered":"Una scoperta dell'intelligenza artificiale identifica rapidamente il tifo resistente ai farmaci senza esposizione agli antibiotici"},"content":{"rendered":"

I ricercatori dell'Universit\u00e0 di Cambridge hanno sfruttato l'intelligenza artificiale per combattere la resistenza agli antibiotici.\u00a0<\/strong><\/p>\n

Il team di ricerca, guidato dal professor Stephen Baker, ha creato uno strumento di apprendimento automatico utilizzando solo immagini di microscopia per distinguere i batteri resistenti alla ciprofloxacina (un antibiotico comune) da quelli sensibili.<\/span><\/p>\n

Questo potrebbe ridurre drasticamente il tempo necessario per diagnosticare la resistenza agli antibiotici, trasformando potenzialmente il modo in cui trattiamo infezioni pericolose come la febbre tifoidea.<\/span><\/p>\n

Il <\/span>studio<\/span><\/a>pubblicato su Nature Communications, si \u00e8 concentrato sulla Salmonella Typhimurium, un batterio che causa gravi malattie gastrointestinali e pu\u00f2 portare a malattie invasive potenzialmente letali.\u00a0<\/span><\/p>\n

\"AI\"
La salmonella \u00e8 un batterio che infetta comunemente gli esseri umani attraverso alimenti contaminati e alcuni ceppi hanno acquisito una resistenza agli antibiotici. Fonte: Universit\u00e0 di Cambridge<\/a>.<\/figcaption><\/figure>\n

Il dott. Tuan-Anh Tran, ricercatore chiave del progetto, ha spiegato l'approccio in una <\/span>post sul blog<\/span><\/a>: \"La bellezza del modello di apprendimento automatico \u00e8 che pu\u00f2 identificare i batteri resistenti sulla base di alcune sottili caratteristiche delle immagini al microscopio che gli occhi umani non possono rilevare\".<\/span><\/p>\n

Il processo di ricerca ha comportato diverse fasi chiave:<\/span><\/p>\n

    \n
  1. Preparazione del campione batterico<\/strong>: Il team ha coltivato campioni di S. Typhimurium in terreni nutritivi liquidi, alcuni esposti a diverse concentrazioni di ciprofloxacina e altri no.<\/span><\/li>\n
  2. Imaging ad alto contenuto<\/strong>: Utilizzando un sofisticato microscopio, i ricercatori hanno scattato immagini dettagliate dei batteri in pi\u00f9 momenti.<\/span><\/li>\n
  3. Analisi dell'immagine<\/strong>: Un software specializzato ha estratto 65 caratteristiche diverse da ogni cellula batterica, tra cui forma, dimensioni e interazione con i coloranti fluorescenti.<\/span><\/li>\n
  4. Sviluppo di modelli di apprendimento automatico<\/strong>: I ricercatori hanno inserito questi dati in diversi algoritmi di apprendimento automatico, addestrandoli a riconoscere i modelli associati alla resistenza agli antibiotici.<\/span><\/li>\n
  5. Selezione delle caratteristiche<\/strong>: Il team ha identificato le caratteristiche pi\u00f9 cruciali per distinguere i batteri resistenti da quelli sensibili.<\/span><\/li>\n<\/ol>\n

    I risultati di questo processo sono stati impressionanti. Il sistema di intelligenza artificiale ha identificato correttamente i batteri resistenti agli antibiotici circa 87% delle volte.\u00a0<\/span><\/p>\n

    Forse la cosa pi\u00f9 significativa \u00e8 che i ricercatori hanno scoperto che i batteri resistenti e quelli suscettibili avevano schemi visivi distinti che l'intelligenza artificiale era in grado di rilevare, anche quando non erano stati esposti agli antibiotici.\u00a0<\/span><\/p>\n

    Ci\u00f2 suggerisce che la resistenza agli antibiotici modifica l'aspetto dei batteri in modi troppo sottili per gli esseri umani, ma che l'intelligenza artificiale pu\u00f2 rilevare.<\/span><\/p>\n

    I metodi attuali richiedono in genere diversi giorni di coltura batterica e test con vari antimicrobici. Il nuovo metodo basato sull'intelligenza artificiale, invece, potrebbe fornire risultati in poche ore.\u00a0<\/span><\/p>\n

    Una diagnosi pi\u00f9 rapida consente ai medici di prescrivere prima gli antibiotici pi\u00f9 efficaci, migliorando potenzialmente i risultati dei pazienti e riducendo la diffusione di batteri resistenti.<\/p>\n

    In prospettiva, il team di ricerca intende espandere il proprio approccio a campioni clinici pi\u00f9 complessi, come sangue o urine, e testarli su altri tipi di batteri e antibiotici. Sta inoltre lavorando per rendere la tecnologia pi\u00f9 accessibile agli ospedali e alle cliniche di tutto il mondo.<\/span><\/p>\n

    Come spiega il professor Baker: \"Ci\u00f2 che sarebbe davvero importante, in particolare per un contesto clinico, sarebbe poter prelevare un campione complesso - ad esempio sangue, urina o espettorato - e identificare la suscettibilit\u00e0 e la resistenza direttamente da l\u00ec\".<\/p>\n

    \"Si tratta di un problema molto pi\u00f9 complicato e che non \u00e8 stato affatto risolto, nemmeno nella diagnostica clinica in ospedale. Se riuscissimo a trovare un modo per farlo, potremmo ridurre il tempo necessario per identificare la resistenza ai farmaci e a costi molto pi\u00f9 bassi. Questo potrebbe essere davvero trasformativo\".<\/span><\/p>\n

    La dottoressa Sushmita Sridhar ha riassunto gli impatti, affermando: \"Dato che questo approccio utilizza la risoluzione delle immagini di una singola cellula, non \u00e8 ancora una soluzione che potrebbe essere prontamente impiegata ovunque. Ma \u00e8 davvero promettente che, catturando solo alcuni parametri sulla forma e la struttura dei batteri, possa darci informazioni sufficienti per prevedere la resistenza ai farmaci con relativa facilit\u00e0\".<\/span><\/p>\n

    Poich\u00e9 la resistenza agli antibiotici continua a rappresentare una minaccia crescente per la salute globale, approcci innovativi come questa tecnica di imaging alimentata dall'intelligenza artificiale offrono nuove speranze.\u00a0<\/span><\/p>\n

    Questo fa parte di una tendenza pi\u00f9 ampia di innovazioni guidate dall'intelligenza artificiale nella ricerca sugli antibiotici. Al MIT, i ricercatori hanno utilizzato modelli di apprendimento profondo per scoprire un classe di antibiotici completamente nuova<\/a>.<\/p>\n

    In modo simile, un altro team di scienziati ha annunciato nel maggio dello scorso anno di aver utilizzato l'intelligenza artificiale per identificare un nuovo antibiotico<\/a> efficace contro i batteri resistenti ai farmaci.<\/p>\n

    L'intelligenza artificiale consente di identificare con maggiore rapidit\u00e0 e precisione le infezioni resistenti ai farmaci, aprendo la strada a trattamenti pi\u00f9 efficaci e a migliori risultati per i pazienti.\u00a0<\/span><\/p>\n

    I prossimi anni saranno cruciali, in quanto il team lavorer\u00e0 per tradurre i successi ottenuti in laboratorio in applicazioni cliniche reali.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    I ricercatori dell'Universit\u00e0 di Cambridge hanno sfruttato l'intelligenza artificiale per combattere la resistenza agli antibiotici. Il team di ricerca, guidato dal professor Stephen Baker, ha creato uno strumento di apprendimento automatico utilizzando solo immagini di microscopia per distinguere i batteri resistenti alla ciprofloxacina (un antibiotico comune) da quelli sensibili. Questo potrebbe ridurre drasticamente il tempo necessario per diagnosticare la resistenza agli antibiotici, trasformando potenzialmente il modo in cui trattiamo infezioni pericolose come la febbre tifoidea. Lo studio, pubblicato su Nature Communications, si \u00e8 concentrato sulla Salmonella Typhimurium, un batterio che causa gravi malattie gastrointestinali e pu\u00f2 portare a malattie invasive potenzialmente letali. Il dott. Tuan-Anh Tran, ricercatore chiave dello studio<\/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\/it\/2024\/07\/ai-breakthrough-rapidly-identifies-drug-resistant-typhoid-without-antibiotic-exposure\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\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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