The WHO’s critical bacteria list: scientific response eight years after its implementation and development of an AI-based tool for its monitoring

Background: In 2017, the World Health Organization (WHO) issued a global alert identifying 12 bacteria in urgent need of new treatments. Main body: This study assesses the scientific community’s response to this alert by analyzing original research publications using LLMzCor, an AI-based tool de...

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Detalles Bibliográficos
Autores principales: Almonacid, Juan Eduardo Robledo, Lombardo, Christian, Romano, Mariana, Quiroga, Agustina, Cambuli Bianchi, Paula, Hualpa, Mauricio, Giai, Constanza, Oviedo, Xiomara María, Salgado Mansur, Ramiro Alejo, Vallejo, Mariana Guadalupe, Quintero, Cristián Andrés
Formato: Artículo Científico
Lenguaje:Inglés
Publicado: 2025
Materias:
antibiotic
antibiotic resistance
automatic classifier
treatment
WHO
Acceso en línea:https://repositorio.umaza.edu.ar/handle/00261/3591
Aporte de:
UMAZA Digital (Universidad MAZA - Mendoza) de Universidad Juan Agustín MAZA
Descripción
Sumario:Background: In 2017, the World Health Organization (WHO) issued a global alert identifying 12 bacteria in urgent need of new treatments. Main body: This study assesses the scientific community’s response to this alert by analyzing original research publications using LLMzCor, an AI-based tool developed and validated by our group. To compare trends, we focused on publications from 5 years before and after the alert, specifically on three bacteria listed in the WHO alert, sorted by priority level: Acinetobacter baumannii (Critical), Shigella spp (High), and Neisseria gonorrhoeae (Medium) and three non-listed as controls (Rickettsia spp., C. trachomatis, and C. difficile). Articles were classified into three categories: (i) identification of Resistant strains, (ii) development of New treatments, and (iii) Immunization strategies. Results: Although overall publications increased after the WHO alert, no statistically significant changes were found in the reports of Resistant strains over time. The development of New treatments for the listed bacteria showed a slight increase, between 2% and 10%. Furthermore, Immunization strategies remained relatively unchanged, with less than 2%. Meanwhile, LLMzCor demonstrated robust performance across categories, F1-scores ranging from 0.65 to 0.72 in key classifications, while recall peaked at 0.75, indicating a high capacity to identify relevant articles. These results support the model’s reliability for large-scale automated classification of scientific abstracts. Conclusion: These findings, supported by LLMzCor, underscore the urgency of a stronger WHO alert and action plans to develop new strategies against bacterial resistance.

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