Antibiotic resistance is one of the biggest public health threats worldwide. Currently, antibiotic-resistant bacteria kill 700,000 people every year. These data represent the near future in which w... Antibiotic resistance is currently one of the biggest public health threats worldwide. Right now, antibiotic-resistant bacteria kill 700,000 people every year. Many of the available antibiotics are...

The progressive emergence of antimicrobial resistance has become a global health problem in need of rapid solution. Research into new antimicrobial drugs is imperative. Drug repositioning, together with computational mathematical prediction models, could be a fast and efficient method of searching for new antibiotics. The aim of this study was to identify compounds with potential antimicrobial capacity against Escherichia coli from US Food and Drug Administration-approved drugs, and the similarity between known drug targets and E. coli proteins using a topological structure-activity data analysis model. This model has been shown to identify molecules with known antibiotic capacity, such as carbapenems and cephalosporins, as well as new molecules that could act as antimicrobials. Topological similarities were also found between E. coli proteins and proteins from different bacterial species such as Mycobacterium tuberculosis, Pseudomonas aeruginosa and Salmonella Typhimurium, which could imply that the selected molecules have a broader spectrum than expected. These molecules include antitumor drugs, antihistamines, lipid-lowering agents, hypoglycemic agents , antidepressants, nucleotides, and nucleosides, among others. The results presented in this study prove the ability of computational mathematical prediction models to predict molecules with potential antimicrobial capacity and/or possible new pharmacological targets of interest in the design of new antibiotics and in the better understanding of antimicrobial resistance.

In May 2022, the UK International Health Regulations National Focal Point notified World Health Organization of 176 cases of severe acute hepatitis of unknown etiology in children under 10 years of age. From that moment on, cases of severe acute hepatitis of unknown origin in children began to be reported in several countries. As of June 17, 2022, a total of 991 cases had been reported in 35 countries worldwide, 50 children needed a liver transplant and 28 patients died. According to information published by ECDC, 449 cases have been detected in 21 EU countries. The children were between 1 month and 16 years of age. Adenovirus was detected in 62.2% of the analyzed samples. So far, the cause of these cases is unknown and many hypotheses remain open, but hepatitis A–E viruses and COVID-19 vaccines have been ruled out. A possible hypothesis has been published to explain the cause of these cases of severe hepatitis, according to which it could be a consequence of adenovirus infection in the intestine in healthy children previously infected with SARS-CoV-2. No other clear epidemiological risk factors have been identified to date. Thus, at this time, the etiology of the current cases of hepatitis remains under active investigation.

Antimicrobial resistance (AMR) is one of the most significant public health threats today. The need for new antimicrobials against multidrug-resistant infections is growing. The development of computational models capable of predicting new drug–target interactions is an interesting strategy to reposition already known drugs into potential antimicrobials. The objective of this review was to compile the latest advances in the development of computational models capable of identifying drugs already registered by the Food and Drug Administration for other indications with potential capacity to be applied as antimicrobials. We present studies that apply in silico methods such as machine learning, molecular docking, molecular dynamics and deep learning. Some of these studies have in vitro/in vivo results that demonstrate the reliability of this computational methodology in terms of the identification of effective molecules and new targets of interest in the treatment of infections. In addition, we present the methods that are under development and their future prospects in terms of the search for new antimicrobials. We highlight the need to implement these strategies in the research of effective drugs in the treatment of infectious diseases and to continue to improve the available models and approaches to gain an advantage against the rapid emergence of AMR.

The current big data era has vast amounts of biomedical data that present great interest in processes related to the search of new drugs. Due to the information available in open access databases, many efforts have been directed towards the application of in silico discovery strategies aimed at drug discovery and drug repurposing. These approaches could be useful in finding solutions for antimicrobial resistance, which already causes 700,000 deaths every year. Mathematical prediction models provide a time- and cost-effective solution to this global health threat, as they save all the resources necessary for the development of the molecule. The literature provides computational techniques that have been successfully used in the discovery and repurposing of drugs. For this reason, the aim of this article is to present the different types of prediction models that have been used for the discovery and repurposing of new antimicrobial drugs and to prove the efficiency of these strategies to provide new molecules and therapeutic opportunities to drugs found in biomedical datasets.

Antimicrobial resistance (AMR) represents one of the greatest global health threats of the 21st century, yet public understanding of this silent pandemic remains limited. This study presents the design, implementation and evaluation of the SWICEU project, developed at CEU Cardenal Herrera University (Spain), which integrates citizen science, gamification and creative communication strategies to raise awareness of AMR and promote scientific vocations and the responsible use of antibiotics among young people and the general population. Over eight editions (2017–2025), 287 university students and 853 pre‐university students aged between 14 and 16 participated in activities combining experimental microbiology with creative formats such as storytelling, music, board games and digital campaigns to promote becoming the Flemings of the future and raise awareness of AMR. The project's impact was assessed through pre‐ and post‐surveys, satisfaction measures and analytics from media and social platforms including YouTube, X and Instagram. Results showed significant knowledge gains among participants (from 68.8% to 80.9% correct responses, P < 0.05), high satisfaction ratings (4.86–5/5) and wide public engagement, reaching more than 3.5 million people through media coverage and over 819 000 impressions on social media. These findings demonstrate that combining citizen participation, gamified learning and creative media strategies can make complex microbiological concepts both accessible and engaging. The SWICEU model provides a transferable framework for enhancing science communication and health literacy, not only in AMR education but also across other public health areas requiring societal awareness and behavior change.