The World Health Organization (WHO) published an analysis of how antibiotics are used globally. The report is based on 2022 data from the Global Antimicrobial Resistance (AMR) and Use Surveillance System (GLASS) dashboard and the WHO Access, Watch, Reserve (AWaRe) system that classifies antibiotics into three categories: At the 2024 UN General Assembly High-Level Meeting on AMR, countries committed to ensuring that Access antibiotics would account for at least 70% of global antibiotic use by 2030.Since GLASS started to cover antimicrobial use in 2020, 90 countries, territories and areas (CTAs) were enrolled by December 2023, of which 74 have reported national data. However, global participation remains below 50%, with gaps in data from non-European and lower-income countries.The main findings of the report have immediate implications for policy.First, WHO will continue to assist countries in establishing sustainable surveillance systems for collecting high quality antibiotic use data. The WHO Academy will provide an online course to improve measurement, understanding and use of data on antibiotic use to strengthen capacity in CTAs.Second, countries need to implement stewardship policies so that prescribers default to using Access instead of Watch antibiotics whenever possible and avoid unnecessary use of antibiotics in the first place. WHO will work closely with partners, including the World Medical Association, the international organization representing physicians, who have a crucial role in taking forward this report’s next steps, particularly those relating to responsible prescribing.Third, countries need to ensure access to all essential antibiotics, including those in the Reserve category. WHO is working with partners, such as the Global Antibiotic Research and Development Partnership, to develop a framework to improve availability of essential antibiotics for countries with limited resources.

Artificial intelligence (AI) models are promising tools for predicting antimicrobial susceptibility in gram-negative bloodstream infections (GN-BSI). Single-centre study on hospitalized patients with GN-BSI, over 7-year period, aimed to predict resistance to fluoroquinolones (FQ-R), third generation cephalosporins (3GC-R), beta-lactam/beta-lactamase inhibitors (BL/BLI-R) and carbapenems (C-R) was performed. Analyses were carried out within a machine learning framework, developed using the scikit-learn Python package. Overall, 2552 patients were included. Enterobacterales accounted for 85.5% of isolates, with E. coli, Klebsiella spp, and Proteus spp being most common. Distribution of resistance was FQ-R 48.6%, 3GC-R 40.1%, BL/BLI-R 29.9%, and C-R 16.9%. Models’ validation showed good performance predicting antibiotic resistance for all four resistance classes, with the best performance for C-R (AUC-ROC 0.921 ± 0.013). The developed pipeline has been made available (https://github.com/EttoreRocchi/ResPredAI), along with documentation for running the same workflow on a different dataset, to account for local epidemiology and clinical features.

Annually, about 7.7 million people die due to bacterial infections, and 4.95 million deaths are related to antibiotic-resistant pathogens. Low- and middle-income countries (LMICs) disproportionately bear the brunt of antimicrobial resistance (AMR), with around 4.3 million of the nearly 5 million AMR-associated deaths occurring in these regions. AMR arises when microorganisms evolve to survive antibiotic treatment. This review discusses the current strategies being employed by international governmental organizations to address the issue of antibiotic resistance. These strategies include the United Nations’ 17 Sustainable Development Goals and the “One Health Approach,” a system that recognizes the interconnected health of humans, animals, and the environment that incorporates a multi-disciplinary effort to achieve the best possible health outcome. As part of international and regional action plans, there is still a need to emphasize the significance of public awareness. Recent initiatives, such as antibiotic stewardship programs, have demonstrated up to a 30% reduction in healthcare-associated resistant infections. Global surveillance networks have reported multidrug-resistant organisms in over 40% of hospital-acquired infections across LMICs. Furthermore, public awareness campaigns have been shown to improve responsible antibiotic usage by approximately 20% in target populations. Combating AMR requires coordinated action across individuals, communities, and governments. Key strategies include promoting antibiotic stewardship, strengthening infection control, advancing antimicrobial research, improving surveillance, and raising public awareness. Implementing these measures is crucial to preserving antibiotic efficacy and protecting human and animal health for future generations.

Antimicrobial resistance (AMR) is an urgent public health threat. Advancements in artificial intelligence (AI) and increases in computational power have resulted in the adoption of AI for biological tasks. <this review explores the application of AI in bacterial infection diagnostics, AMR surveillance, and antibiotic discovery. Authors summarize contemporary AI models applied to each of these domains, important considerations when applying AI across diverse tasks, and current limitations in the field. AI is not a panacea, only a set of (powerful) tools to support humans with domain expertise. Regardless of the sophistication of the model architecture, predictive ability is reliant on the data used to train the model. To adequately model complex chemical-biological systems, training data must be high quality, diverse, and biochemically relevant. While a lack of training data remains, a limiting factor forms any AI applications in biology, there has been a recent increase in public initiatives to support the development of AI tools for biological tasks. These resources aim to collect and organize robust, high-quality data for model training, and provide standardized benchmarks for model evaluation. Specifically for AMR tasks, TDC contains numerous datasets for infectious diseases and standardized benchmarks.

This study reviews the various aspects of antibacterial resistance, focusing on the genes present in the genome of K. pneumoniae and the mechanisms through which it develops resistance. Through the analysis of these factors, authors intend to highlight the escalating threat posed by antibiotic resistance and the urgent requirement for effective measures to counteract it. Surveillance and diagnostics are vital for controlling outbreaks as they enable early detection of diseases, allowing for timely interventions to prevent further spread. Accurate laboratory diagnostics are essential for confirming cases and informing public health responses, thereby ensuring the effective allocation of resources during an outbreak. In this work, authors have carried out a review on the antibacterial resistance antibiotics, genes found in its genome, as well as the resistance mechanisms involved. Finally, they focused on the main outbreaks causing hospital acquired infections during the last few years.

This study aims to give an understanding of this pattern in the gram-negative bacterial isolates in the past four years. In this retrospective four-year study, the resistance-susceptibility patterns of gram-negative pathogens isolated from blood, urine, sputum, wound drainage, abscess, synovial, pleural, ascitic, and cerebrospinal fluids of the adult and children patients undergoing solid organ transplantation at Shiraz Transplant Centre, the biggest solid organ transplantation centre of Asia, in 2020–2023. During the study follow-up period, 2075 GNB isolates were retrieved from the patients. The most frequent isolates were identified as E. coli with 765 (36.86%), Klebsiella with 684 (32.96%), Pseudomonas with 363 (17.49%), Acinetobacter with 134(6.45%), Enterobacter with 87 (4.19%), Citrobacter with 40 (1.92%), and Stenotrophomonas maltophilia with 2 specimens (0.09%), from the highest to the lowest. Of the retrieved GNB isolates from the transplant patients, 1380 (66.50%) belonged to liver transplant recipients, 658 isolates (31.71%) belonged to kidney transplant recipients, and 23 isolates (1.10%) belonged to simultaneous kidney and pancreas transplant recipients. Carbapenem-resistant Enterobacterales, mainly carbapenem-resistant K. pneumoniae (CRKP) isolates, increased during follow-up periods. The findings of this study reveal an increasing pattern of resistance towards carbapenems in Enterobacterales, which is significant in liver transplant recipients.

The ongoing conflict in Ukraine has exacerbated the risk of MDR bacterial infections in war-injured patients. Authors present the case of a Ukrainian soldier who sustained a traumatic explosive injury to the bilateral lower extremities, underwent surgery and subsequently experienced an infection with a carbapenem-resistant Klebsiella pneumoniae. The isolate was subject to genomic WGS. This case study describes the successful management of a traumatic injury in a Ukrainian soldier infected with a carbapenem-resistant K. pneumoniae isolated from surgical wounds. WGS revealed a hypervirulent ST147 strain carrying multiple resistance and virulence factors, including blaNDM-1. Treatment with ceftazidime/avibactam and aztreonam was effective. Ceftazidime/avibactam and aztreonam is highlighted as a promising regimen for MDR infections in conflict zones.

This multicentre retrospective case-control study, in seven European hospitals, included patients ≥50 years old who underwent elective surgery between 2012 and 2021. Using multivariable logistic regression, authors modelled the risk of postsurgical Klebsiella infection and investigated trial enrichment scenarios. Of 139,778 eligible surgeries identified, 1,781 were included: 840 patients with postsurgical Klebsiella infection and 941 without. The incidence of postsurgical Klebsiella infection was 1.38% (95% CI 1.24-1.54%). Pre-surgical Klebsiella colonisation, gastrointestinal surgery, abdominal surgery, trauma surgery and chronic cardiovascular disease were independent predictors of postoperative Klebsiella infection. Minimally invasive surgery and peri-operative antibiotic prophylaxis predicted a lower risk. Trial enrichment simulation indicated a 72% reduction in required participants when enrolling patients with a predicted risk above 2%. A multivariable model incorporating Klebsiella colonisation status and clinical factors can accurately predict Klebsiella infections in elective surgery patients and this model can select high-risk patients, enhancing the efficiency of phase-III trials of preventive interventions, including vaccination.