Background: The increasing burden of antimicrobial resistance (AMR) in hospitals necessitates proactive strategies for surveillance and intervention. Inspired by predictive models used in traffic forecasting, we propose a novel machine learning (ML)-based algorithm that integrates multiple hospital parameters—antibiotic consumption patterns, resistance rates, patient admissions, and bed occupancy trends—to predict AMR evolution and guide antimicrobial stewardship programs.

Methods: The algorithm utilizes long short-term memory (LSTM) networks and gradient boosting models (e.g., XGBoost) to analyze temporal patterns in hospital antibiotic usage and AMR incidence. A dataset including historical antibiograms, hospital admission rates, antibiotic prescribing data, and bed occupancy rates was used for training. The model employs autoregressive integrated moving average (ARIMA) and reinforcement learning techniques to enhance predictive accuracy. Performance was assessed using mean absolute percentage error (MAPE) and root mean squared error (RMSE) against real-world AMR data from hospital surveillance reports.

Results: The LSTM-based model demonstrated superior accuracy in predicting resistance trends, identifying early warning signals for carbapenem-resistant Enterobacterales (CRE) and methicillin-resistant Staphylococcus aureus (MRSA) outbreaks. The algorithm outperformed traditional statistical models, enabling real-time optimization of antibiotic prescriptions and resource allocation.

Conclusion: AI-driven predictive analytics, originally developed for traffic modeling, can be repurposed for hospital AMR surveillance. Implementing such models in clinical workflows could improve infection control strategies and reduce resistance development. Future work should focus on real-time deployment and multi-hospital validation.

Background: Inappropriate antibiotic prescribing is a major driver of antimicrobial resistance (AMR) and adverse drug effects. Traditional decision support systems often lack real-time adaptability and struggle to integrate complex patient-specific data. The advent of GPT-based Drug-to-Disease (DtD) checkers offers a novel AI-driven approach to assist clinicians in optimizing antibiotic therapy by ensuring accurate drug-disease matching, minimizing resistance risks, and preventing adverse effects.

Methods: We propose an AI-powered DtD checker based on a large language model (LLM) architecture, trained on clinical guidelines, antibiograms, pharmacokinetics, and patient-specific parameters (e.g., renal function, comorbidities, and prior antibiotic exposure). The model leverages natural language processing (NLP) and deep learning algorithms to cross-reference antibiotic choices with patient characteristics, flagging inappropriate prescriptions and suggesting evidence-based alternatives. Its performance was validated against real-world antibiotic prescribing data in hospitalized patients.

Results: The GPT-based DtD checker significantly reduced inappropriate antibiotic prescriptions by 35%, enhanced compliance with antimicrobial stewardship guidelines, and decreased the incidence of drug-related adverse events by 25%. Additionally, real-time integration with electronic health records (EHRs) improved clinical decision-making efficiency.

Conclusion: AI-driven DtD checkers represent a transformative tool for antimicrobial stewardship, enhancing precision prescribing while mitigating AMR development. Future research should focus on real-time deployment, clinician–AI interaction models, and broader validation in outpatient settings to maximize patient safety and antibiotic effectiveness.

Background: The ability to detect emerging antibiotic resistance trends in real-time is critical for infection control and public health planning. Traditional surveillance systems rely on retrospective data analysis, which often leads to delayed interventions and ineffective containment strategies. Artificial intelligence (AI) and deep learning offer transformative solutions for real-time monitoring, predictive analytics, and early warning systems for antimicrobial resistance (AMR). By integrating hospital data, whole-genome sequencing (WGS), and electronic health records (EHRs), AI can provide timely insights into resistance evolution and guide targeted interventions.

Methods: We developed an AI-powered epidemiological platform that utilizes long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and XGBoost models to analyze vast datasets from microbiology labs, hospital antibiograms, antimicrobial consumption trends, and clinical outcomes. The model applies natural language processing (NLP) to extract key resistance patterns from EHRs and integrates reinforcement learning algorithms to optimize antibiotic prescribing strategies.

Results: The AI-enhanced surveillance system successfully identified early warning signals for carbapenem-resistant Enterobacterales (CRE), vancomycin-resistant Enterococci (VRE), and multidrug-resistant Pseudomonas aeruginosa across multiple healthcare facilities. The predictive model enabled proactive infection control measures, reducing hospital-acquired infections by 20% and optimizing antibiotic stock management.

Conclusion: AI-driven surveillance platforms offer a paradigm shift in AMR monitoring, enabling rapid detection and mitigation strategies. Future research should focus on real-time AI model deployment, data standardization, and integration into hospital antimicrobial stewardship programs to maximize clinical impact.

Introduction

Vascular grafts are essential for cardiovascular reconstruction, haemodialysis access, and aneurysm repair. While synthetic materials like ePTFE and Dacron dominate clinical use, they face challenges including thrombosis, intimal hyperplasia, and infection. Corrugated biopolymeric grafts offer a promising alternative, combining mechanical stability with biocompatibility. However, balancing haemodynamic performance, structural integrity, and infection resistance remains critical. This study evaluates corrugated biopolymeric grafts, emphasising their mechanical behaviour under physiological pressures, computational fluid dynamics (CFD)-guided haemodynamic optimisation, and novel antimicrobial strategies to mitigate biofilm formation.

Methods

Corrugated grafts were fabricated and their mechanical performance was analysed using COMSOL Multiphysics® simulations to model the stress distribution, pressure resistance, and fatigue behaviour under a pulsatile flow. Antimicrobial functionality was integrated. Computational fluid dynamics (CFD) was used to assess their haemodynamic compatibility.

Results

The COMSOL simulations demonstrated that the corrugated designs showed enhanced mechanical stability under cyclic pressure while maintaining compliance comparable to that of the native vessels. The CFD-based haemodynamic modelling confirmed a reduced turbulent flow in the corrugated regions, minimizing thrombogenic risks. Saturability and handling met the surgical standards in the benchtop evaluations.

Conclusions

This work establishes corrugated biopolymeric grafts as a multifunctional solution for vascular reconstruction, uniquely addressing mechanical resilience and CFD-optimized haemodynamics by synergizing the computational design (COMSOL/CFD) with antimicrobial innovation, and these grafts outperformed conventional synthetics in their preclinical metrics. Future work will focus on their in vivo validation and clinical translation, positioning corrugated biopolymeric grafts as a transformative advancement in vascular prosthesis engineering.

The rapid rise of antibiotic-resistant bacteria has emerged as one of the most urgent and critical threats to global public health. Among these, carbapenem-resistant bacteria (CRB) pose a particular challenge, as carbapenem antibiotics are regarded as the last line of defense against multidrug-resistant infections [1]. This study evaluates the antimicrobial properties of probiotic cellulose (PC), a material made of dense cellulose nanofibers colonized by Lactobacillus species, which are classified as GRAS (generally recognized as safe). This method overcomes the limitation of bacterial cellulose produced by Komagataeibacter xylinus, which lacks intrinsic antibacterial activity [2]. We tested various PC samples, each loaded with Lactobacillus fermentum (Lf), Lactobacillus plantarum (Lp), or a combination of both (Lp+Lf), against CRB strains isolated from clinical samples at the Hospital Virgen de las Nieves (Granada). Agar diffusion inhibition assays were performed in a medium favorable for pathogen growth to assess efficacy. The results revealed that all PC samples exhibited inhibition zones against the tested CRB strains. The antimicrobial activity of PC is attributed to the encapsulation of probiotics within the cellulose matrix, as free Lactobacillus species (without bacterial cellulose) showed no inhibitory effect on the pathogenic strains.

Acknowledgments. The results were funded by the research project "PROBCEL - Probiotic cellulose. A new material for the treatment of antibiotic-resistant bacterial infections", reference PDC2022-133234-I00. The project was funded by MCIN/AEI /10.13039/501100011033 and by the European Union Next GenerationEU/ PRTR.

[1] Mancuso, G.; De Gaetano, S.; Midiri, A.; Zummo, S.; Biondo, C. The Challenge of overcoming antibiotic resistance in carbapenem-resistant Gram-negative bacteria: “Attack on Titan”. Microorganisms 2023, 11(8), 1912.
[2] Sabio, L.; González, A.; Ramírez-Rodríguez, G. B.; Gutiérrez-Fernández, J.; Bañuelo, O.; Olivares, M.; Dominguez-Vera, J.M. Probiotic cellulose: Antibiotic-free biomaterials with enhanced antibacterial activity. Acta Biomaterialia 2021,124, 244-253.

Cryptococcus neoformans is a basidiomycetous yeast that rose from being an obscure fungus to an important fungal pathogen. Part of its success is attributed to its arsenal of virulence factors that allow it to subvert the immunological response in a susceptible host, including the production of proteases that can permeabilise the phagosomal membrane, leading to internalised cells escaping. This project sought to assess if a protease inhibitor, decanoyl-RVKR-chloromethyl ketone (DEC-RVKR-CMK), could decrease cryptococcal growth and increase their susceptibility towards macrophage phagocytosis. This compound is used to control unwanted proteolysis in model viral entry studies, including HIV entry studies. The in vitro susceptibility of cryptococcal cells was assessed by measuring the optical density of the cells following exposure to different DEC-RVKR-CMK concentrations. Additionally, the DEC-RVKR-CMK-treated and non-treated cells were co-cultured with macrophages to assess their susceptibility to macrophage phagocytosis. The pHrodo stain was used to assess the internalisation of cryptococcal cells by macrophages. The macrophages were lysed to liberate the internalised cryptococcal cells. A 10-fold increase in DEC-RVKR-CMK concentration led to the growth of cryptococcal cells being inhibited in a dose-dependent manner. In this study, 0.1 mM was defined as the minimum inhibition concentration and led to over 75% growth inhibition. There was no difference in the efficiency of macrophages to internalised DEC-RVKR-CMK-treated and non-treated cells. However, no colonies of DEC-RVKR-CMK-treated cells could be recovered on mycological agar, while the non-treated cells yielded colonies on the agar. The in vitro preliminary data obtained in this study suggest that DEC-RVKR-CMK may be ideal for controlling cryptococcal growth. The clinical relevance of the study may lie in impairing the ability of cryptococcal cells to escape from macrophages, avoiding immunoprocessing. It is suggested to investigate if this quality can be observed in other C. neoformans strains and elucidate the molecular mechanism underpinning the reported observations.

Global widespread extended-spectrum β-lactamase (ESBL)-producing Escherichia coli poses a significant threat in human healthcare and community settings. Chicken meat may be one of the critical transmission routes for those bacteria in the community. This study aimed to characterise 72 ESBL-producing E. coli isolated from 92 samples of chicken meat from 46 butcher shops in La Plata, Argentina. Antimicrobial susceptibility was evaluated using the disk diffusion method according to Clinical and Laboratory Standards Institute (CLSI) guidelines. The Whole Genome Sequencing (WGS) of the isolates were analysed on the Galaxy platform version 23.1. The ESBL resistance was mediated by blaCTX-M-2 (n=42), blaCTX-M-55 (n=14), blaCTX-M-14 (n=5), blaCTX-M-15 (n=5), blaCTX-M-65 (n=3), blaCTX-M-8, blaCTX-M-27, blaCTX-M-2/blaCTX-M-55 (n=1 of each one). Thirty-nine ESBL-producing E. coli harboured other plasmid-mediated genes that confer resistance to highest-priority critically important antimicrobial fosfomycin and ciprofloxacin. The gene fosL1 was principally associated with blaCTX-M-2 (n=15) and fosA3 exclusively to blaCTX-M-55 (n=7). The gene qnrB19 was associated with blaCTX-M-2 (n=13) and to blaCTX-M-55 (n=3) since qnrS1 was associated with blaCTX-M-15 (n=3) and to blaCTX-M-14 (n=1). Fourteen E. coli harbour genes conferred resistance to all three highest priority critically important antimicrobials (HPCIA): 3GC, fosfomycin and fluoroquinolones. All isolates were sensitive to carbapenems and colistin. ESBL-producing E. coli were multidrug-resistant (MDR), showing resistance to other groups of antimicrobial agents such as tetracyclines, amphenicols and aminoglycosides. Our results show a high circulation of ESBL-producing E. coli, resistant to other HPCIA that belong to CTX-M groups predominant in poultry and humans in Argentina. Future studies will be necessary to determine the origin of contamination of chicken meat within the production chain. The information obtained supports the promotion of measures that allow the implementation of strategies for the correct handling of food and prevent its transmission to humans.

Introduction: The search for and study of natural antibacterial and antifungal molecules (including those that overcome pathogen drug resistance) have great scientific and practical importance. Basidiomycetes have a wide range of biosynthetic capabilities and can serve as a source of new biologically active compounds.

The aim of this work was to study the antimicrobial properties of the metabolites that accumulated in basidiomycetes culture liquid.

Methods. The objects of this study were 20 strains of basidiomycetes from the orders Agaricales, Polyporales, and Russulales. The studied strains were grown in a submerged culture. The culture liquids were extracted using ethyl acetate. The extracts were evaporated, and stock solutions with a concentration of 10 mg/mL were prepared. At first, the antimicrobial activity of the extracts was evaluated through agar well diffusion on a wide test culture panel. At the second step, the minimum inhibitory concentration (MIC) of the most active extracts against Gram-negative and Gram-positive bacteria was determined.

Results: Antibacterial activity was detected in all of the strain extracts studied. The largest growth inhibition zones of bacteria were observed for the extracts of Fomes fomentarius 1; Fomitopsis betulina 3; F. pinicola 2, Hericium coralloides 4, and Laetiporus sulphureus 3. Strains F. betulina 3 and H. coralloides 4 demonstrated weak antifungal activity against Aspergillus niger ATCC 16404.

The lowest MIC values were observed for 80 µg/mL of H. coralloides 4 extract against Staphylococcus aureus 25923 ATCC; 160 µg/mL of the former against the clinical strains S. epidermidis 533 and S. haemoliticus 585; and 320 µg/mL of the former against the vancomycin A-resistant strain Enterococcus faecium 569. The MIC of the L. sulphureus 3 extract was 320 µg/mL against S. aureus 25923 ATCC, S. epidermidis 533, and S. haemoliticus 585.

Conclusion: The most active were the H. coralloides 4 and L. sulphureus 3 culture liquid extracts. The H. coralloides 4 extract inhibited the growth of clinical and vancomycin-resistant strains.

Avian pathogenic Escherichia coli (APEC) is responsible for colibacillosis in poultry. E. coli can form biofilms, which facilitate horizontal gene transfer and contribute to the dissemination of antimicrobial resistance. This study aimed to assess the biofilm formation ability of APEC and the influence of temperature variations on this property.

Thirty-four APECs were isolated from diseased chickens (n = 27) and turkeys (n = 7). Biofilm production was assessed according to Laconi et al. (2023). Bacterial suspensions were incubated in microplates at 37°C and 42°C for 24 hours. The analysis was conducted in two biological replicates, each comprising three technical replicates. The optical density (O.D.) of samples (O.D.S) was calculated as the mean of the absorbance of replicates and compared with the mean O.D. of negative control (O.D.NC). The APECs were classified into four classes: non-(O.D.S ≤ O.D.NC); weak (O.D.NC < O.D.S ≤ 2 × O.D.NC); moderate (2 × O.D.NC < O.D.S ≤ 4 × O.D.NC); and strong (O.D.S > 4 × O.D.NC) biofilm producers.

At 37°C, 70.6% (24/34) of APECs were weak biofilm producers, 20.6% (7/34) were moderate, and 8.8% (3/34) were strong, with none classified as non-biofilm producers. The two different temperatures appear to have no significant effect on t APEC's biofilm formation ability, although eight and three isolates exhibited reduced and enhanced biofilm formation ability, respectively, at 42°C compared to 37°C. A similar distribution of biofilm classes was observed between isolates from chickens and turkeys.

Despite being primarily weak biofilm producers, all isolates demonstrated the ability to form biofilms, potentially facilitating the exchange of resistance genes. Future investigations should focus on elucidating the genetic background underlying APEC biofilm-forming ability.

Acknowledgments: This study was co-funded by European Union Project 101136346

Laconi, A., Tolosi, R., Apostolakos, I., Piccirillo, A. (2023). Biofilm formation ability of ESBL/pAmpC-producing Escherichia coli isolated from the broiler production pyramid. Antibiotics, 12(1). https://doi.org/10.3390/antibiotics12010155

Introduction: Cryptococcus (C.) neoformans is a pathogen capable of breaching the blood–brain barrier (BBB) and localising in the brain, presenting challenges for treatment. This is, in part, because amphotericin B cannot cross the BBB, and while fluconazole can cross the BBB, its use is limited by non-fluconazole susceptibility. This study aimed to reformulate aspirin by encapsulating it into D-α-tocopheryl polyethylene glycol succinate (TPGS), to characterise the formulation, and to evaluate its in vitro efficacy against C. neoformans.

Methods: Aspirin was encapsulated into TPGS using a colloidal dispersion method. The aspirin–TPGS micelles were characterised using Fourier transform infrared spectroscopy (FTIR) and a Zeta particle analyser. The EUCAST protocol was used to assess the cryptococcal growth susceptibility to aspirin–TPGS at 0, 1, 2, and 4 mM concentrations. For a comparative analysis, cells were also treated with standard aspirin, TPGS, fluconazole, and amphotericin B at the same concentrations.

Results: The FTIR spectra analysis confirmed the successful encapsulation of aspirin into TPGS. The aspirin–TPGS nanoparticles were 10.97 nm in size with a polydispersity index of 0.175 and a zeta potential of 3.668. Aspirin in TPGS was found to be more potent than standard aspirin powder at 1, 2, and 4 mM, which may have been due to increased lipophilicity that facilitated cellular entry. The aspirin–TPGS formulation was more effective than fluconazole at 1, 2, and 4 mM based on the calculated p values and showed a similar efficacy to that of amphotericin B. The results showed that aspirin–TPGS significantly reduced the cryptococcal growth after 48 hours compared to that in the untreated controls.

Conclusion: These findings indicate that aspirin–TPGS micelles exhibit a potent inhibitory effect on C. neoformans, offering potential as a treatment for cryptococcal infections. Further studies will investigate aspirin–TPGS’s ability to traverse the BBB using an in vitro model of hCMEC/D3 cells, enhancing our understanding of its therapeutic potential in complex biological systems.