Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections

Gellan gum-based multifunctional hydrogel with enduring sterilization and ROS scavenging for infected wound healing

The progression of severe skin injury healing can be easily impeded by bacterial infections and the resultant overproduction of reactive oxygen species (ROS) within the wound microenvironment. In this study, we developed a multifunctional antibacterial hydrogel by integrating gallium ion-tannic acid and polydopamine particles into gellan gum via a facile heat-cooling process. By harnessing the synergistic effects of polydopamine for short-term photothermal therapy and gallium ion for long-term chemotherapy, the hydrogel obtained shows outstanding antibacterial activities. Sustained release of gallium ion and tannic acid ensures a prolonged sterilization along with ROS-scavenging benefits. Moreover, this hydrogel demonstrates superior cytocompatibility, hemostatic properties, as well as capabilities including promoting cell migration, and adsorption to bacterial cells and toxin. The therapeutic efficacy of the hydrogel was validated using a mouse model of MRSA-induced cutaneous infections. Overall, this work introduces a straightforward yet highly efficient multifunctional hydrogel platform that combines synergetic antibacterial actions, ROS scavenging, and hemostasis to enhance the healing of bacteria-associated wounds.

A new fusion strategy for rapid strain differentiation based on MALDI-TOF MS and Raman spectra

Typing of bacterial subspecies is urgently needed for the diagnosis and efficient treatment during disease outbreaks. Physicochemical spectroscopy can provide a rapid analysis but its identification accuracy is still far from satisfactory. Herein, a novel feature-extractor-based fusion-assisted machine learning strategy has been developed for high accuracy and rapid strain differentiation using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and Raman spectroscopy. Based on this fusion approach, rapid and reliable identification and analysis can be performed within 24 hours. Validation on a panel of important pathogens comprising Staphylococcus aureus, Klebsiella pneumoniae, Escherichia coli, and Acinetobacter baumannii showed that the identification accuracies of k-nearest neighbors (KNNs), support vector machines (SVMs) and artificial neural networks (ANNs) were 100%. In particular, when benchmarked against a MALDI-TOF MS spectral dataset, the new approach improved the identification accuracy of Acinetobacter baumannii from 87.67% to 100%. This work demonstrates the effectiveness of combining MALDI-TOF MS and Raman spectroscopy fusion data in pathogenic bacterial subtyping.

Novel Diagnostic Approach for Acute Pharyngitis: Combining Machine Learning With Thermal Imaging

We evaluated the effect of infrared thermography (IRT) on the clinical assessment of bacterial and viral pharyngitis and its impact on the predictive value of the McIsaac score algorithm for streptococcal pharyngitis in children. We also investigated if IRT could distinguish between bacterial and viral pharyngitis. The study included children aged 2-17 years presenting with sore throat and fever over 38°C from November 1, 2021, to April 30, 2022. Of the 76 assessed children, 16 were excluded due to missing data or technical issues, leaving 60 children (32 males, 28 females) divided into three groups: Group A with streptococcal pharyngitis (N = 30), viral pharyngitis (N = 16), and healthy controls (N = 14). McIsaac score and IRT imaging showed a 90% positive predictive value for streptococcal pharyngitis. While IRT alone could not distinguish between bacterial and viral infections, it significantly increased the predictive value when combined with the McIsaac score.

Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests

Background

Infections caused by antibiotic-resistant bacteria pose a major challenge to modern healthcare. This systematic review evaluates the efficacy of machine learning (ML) approaches in predicting antimicrobial resistance (AMR) in critical pathogens (CP), considering Whole Genome Sequencing (WGS) and antimicrobial susceptibility testing (AST).

Methods

The search covered databases including PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO, from their inception until June 2024. The review protocol was officially registered on PROSPERO (CRD42024543099).

Results

The review included 26 papers, analyzing data from 104,141 microbial samples. Random Forest (RF), XGBoost, and logistic regression (LR) emerged as the top-performing models, with mean Area Under the Receiver Operating Characteristic (AUC) values of 0.89, 0.87, and 0.87, respectively. RF showed superior performance with AUC values ranging from 0.66 to 0.97, while XGBoost and LR showed similar performance with AUC values ranging from 0.83 to 0.91 and 0.76 to 0.96, respectively. Most studies indicate that integrating WGS and AST data into ML models enhances predictive performance, improves antibiotic stewardship, and provides valuable clinical decision support. ML shows significant promise for predicting AMR by integrating WGS and AST data in CP. Standardized guidelines are needed to ensure consistency in future research.

Ecological and evolutionary mechanisms driving within-patient emergence of antimicrobial resistance

The ecological and evolutionary mechanisms of antimicrobial resistance (AMR) emergence within patients and how these vary across bacterial infections are poorly understood. Increasingly widespread use of pathogen genome sequencing in the clinic enables a deeper understanding of these processes. In this Review, we explore the clinical evidence to support four major mechanisms of within-patient AMR emergence in bacteria: spontaneous resistance mutations; in situ horizontal gene transfer of resistance genes; selection of pre-existing resistance; and immigration of resistant lineages. Within-patient AMR emergence occurs across a wide range of host niches and bacterial species, but the importance of each mechanism varies between bacterial species and infection sites within the body. We identify potential drivers of such differences and discuss how ecological and evolutionary analysis could be embedded within clinical trials of antimicrobials, which are powerful but underused tools for understanding why these mechanisms vary between pathogens, infections and individuals. Ultimately, improving understanding of how host niche, bacterial species and antibiotic mode of action combine to govern the ecological and evolutionary mechanism of AMR emergence in patients will enable more predictive and personalized diagnosis and antimicrobial therapies.

A 3D radially aligned nanofiber scaffold co-loaded with LL37 mimetic peptide and PDGF-BB for the management of infected chronic wounds

Diabetic foot ulcers, pressure ulcers, and bedsores can easily develop into chronic wounds with bacterial infections, complicating wound healing. This work reports a two-step strategy for treating infected chronic wounds. Firstly, LL37 mimetic peptide-W379 peptides were rapidly released to eliminate the bacterial biofilm on the wound. Then, 3D radially aligned nanofiber scaffolds loaded with W379 antimicrobial peptide and PDGF-BB were used to treat the wound to prevent bacterial infection recurrence and promote angiogenesis and granulation tissue regeneration, thereby accelerating wound healing. In the presented study, we found that the combined use of burst and controlled release of W379 antimicrobial peptide effectively clears the bacterial biofilm and prevents the recurrence of bacterial infection. Additionally, we found that the removal of the bacterial biofilm contributed to modulating the local inflammatory response from a pro-inflammatory type to a pro-regenerative type. Furthermore, the use of PDGF-BB significantly promotes neovascularization and granulation tissue regeneration in the wound bed, resulting in accelerating re-epithelialization and wound closure. Our study provides a promising treatment method for the repair of infected chronic wounds.

Proof of concept study on early forecasting of antimicrobial resistance in hospitalized patients using machine learning and simple bacterial ecology data

Antibiotic resistance in bacterial pathogens is a major threat to global health, exacerbated by the misuse of antibiotics. In hospital practice, results of bacterial cultures and antibiograms can take several days. Meanwhile, prescribing an empirical antimicrobial treatment is challenging, as clinicians must balance the antibiotic spectrum against the expected probability of susceptibility. We present here a proof of concept study of a machine learning-based system that predicts the probability of antimicrobial susceptibility and explains the contribution of the different cofactors in hospitalized patients, at four different stages prior to the antibiogram (sampling, direct examination, positive culture, and species identification), using only historical bacterial ecology data that can be easily collected from any laboratory information system (LIS) without GDPR restrictions once the data have been anonymised. A comparative analysis of different state-of-the-art machine learning and probabilistic methods was performed using 44,026 instances over 7 years from the Hôpital Européen Marseille, France. Our results show that multilayer dense neural networks and Bayesian models are suitable for early prediction of antibiotic susceptibility, with AUROCs reaching 0.88 at the positive culture stage and 0.92 at the species identification stage, and even 0.82 and 0.92, respectively, for the least frequent situations. Perspectives and potential clinical applications of the system are discussed.

Organic-Inorganic Interfacial Dipole Induced by Energy Level Alignment for Efficient Photocatalytic Sterilization

Photocatalytic molecules are considered to be one of the most promising substitutions of antibiotics against multidrug-resistant bacterial infections. However, the strong excitonic effect greatly restricts their efficiency in antibacterial performance. Inspired by the interfacial dipole effect, a Ti3C2 MXene modified photocatalytic molecule (MTTTPyB) is designed and synthesized to enhance the yield of photogenerated carriers under light irradiation. The alignment of the energy level between Ti3C2 and MTTTPyB results in the formation of an interfacial dipole, which can provide an impetus for the separation of carriers. Under the role of a dipole electric field, these photogenerated electrons can rapidly migrate to the side of Ti3C2 for improving the separation efficiency of photogenerated electrons and holes. Thus, more electrons can be utilized to produce reactive oxygen species (ROS) under light irradiation. As a result, over 97.04% killing efficiency can be reached for Staphylococcus aureus (S. aureus) when the concentration of MTTTPyB/Ti3C2 was 50 ppm under 660 nm irradiation for 15 min. A microneedle (MN) patch made from MTTTPyB/Ti3C2 was used to treat the subcutaneous bacterial infection. This design of an organic-inorganic interface provides an effective method to minimize the excitonic effect of molecules, further expanding the platform of inorganic/organic hybrid materials for efficient phototherapy.

A Supramolecular Fluorescent Sensor Array Composed of Conjugated Fluorophores and Cucurbit[7]uril for Bacterial Recognition

Bacterial infections have emerged as a significant contributor to global mortality and morbidity rates. Herein, we introduce a dual fluorescence "turn-on" supramolecular sensor array composed of three assembled complexes (C1-C3), formed from three positively charged fluorophores (A1-A3) and one cucurbit[7]uril (CB[7]). The ability of this three-element array to simultaneously recognize 10 bacterial species within just 30 s was remarkable, boasting an impressive 100% accuracy. Additionally, the array excelled at distinguishing among various bacterial mixtures and enabled the quantitative detection of common bacterial strains. Notably, it has been skillfully applied to differentiate 10 bacterial samples in urine, achieving excellent differentiation and showcasing promising potential for medical diagnostic applications.

Field-Deployable Colorimetric Array for On-Site Diagnosis of Urinary Tract Infection and Identification of Causative Pathogens

Urinary tract infection (UTI) is a common and prevalent disease caused by a spectrum of pathogens. Lack of access to rapid, portable, and high-quality diagnostics in resource-limited settings aggravates the improper treatment of UTIs, which is also a major driver of antibiotic misuse worldwide. Here, we describe a custom-made portable colorimetric array (PoCA) for reading out polymerase chain reaction (PCR) amplicons, the rationale of which is to transfer the previously developed dsDNA-based photosensitization colorimetric assay (solution) onto paper discs for detection. By integrating mini-LED irradiation and paper discs, the PoCA can read out 96 PCR tests in one pot, thus allowing diagnosis and identification of 12 prevailing UTI pathogens in less than 2 h, coupled with a portable thermal cycler for PCR. After analyzing 200 clinical urine samples, the pathogen profiling accuracy of the PoCA was demonstrated to be higher than the standard urine culture (confirmed with metagenomic next-generation sequencing). The PoCA platform could be used in primary care for rapid UTI diagnosis and pathogen identification.

Nitrogen Vacancy Modulation of Tungsten Nitride Peroxidase-Mimetic Activity for Bacterial Infection Therapy

Bacterial infections claim millions of lives every year, with the escalating menace of microbial antibiotic resistance compounding this global crisis. Nanozymes, poised as prospective substitutes for antibiotics, present a significant frontier in antibacterial therapy, yet their precise enzymatic origins remain elusive. With the continuous development of nanozymes, the applications of elemental N-modulated nanozymes have spanned multiple fields, including sensing and detection, infection therapy, cancer treatment, and pollutant degradation. The introduction of nitrogen into nanozymes not only broadens their application range but also holds significant importance for the design of catalysts in biomedical research. The synergistic interplay between W and N induces pivotal alterations in electronic configurations, endowing tungsten nitride (WN) with a peroxidase-like functionality. Furthermore, the introduction of N vacancies augments the nanozyme activity, thus amplifying the catalytic potential of WN nanostructures. Rigorous theoretical modeling and empirical validation corroborate the genesis of the enzyme activity. The meticulously engineered WN nanoflower architecture exhibits an exceptional ability in traversing bacterial surfaces, exerting potent bactericidal effects through direct physical interactions. Additionally, the topological intricacies of these nanostructures facilitate precise targeting of generated radicals on bacterial surfaces, culminating in exceptional bactericidal efficacy against both Gram-negative and Gram-positive bacterial strains along with notable inhibition of bacterial biofilm formation. Importantly, assessments using a skin infection model underscore the proficiency of WN nanoflowers in effectively clearing bacterial infections and fostering wound healing. This pioneering research illuminates the realm of pseudoenzyme activity and bacterial capture-killing strategies, promising a fertile ground for the development of innovative, high-performance artificial peroxidases.

Transforming Spirulina maxima Biomass into Ultrathin Bioactive Coatings Using an Atmospheric Plasma Jet: A New Approach to Healing of Infected Wounds

The challenge of wound healing, particularly in patients with comorbidities such as diabetes, is intensified by wound infection and the accelerating problem of bacterial resistance to current remedies such as antibiotics and silver. One promising approach harnesses the bioactive and antibacterial compound C-phycocyanin from the microalga Spirulina maxima. However, the current processes of extracting this compound and developing coatings are unsustainable and difficult to achieve. To circumvent these obstacles, a novel, sustainable argon atmospheric plasma jet (Ar-APJ) technology that transforms S. maxima biomass into bioactive coatings is presented. This Ar-APJ can selectively disrupt the cell walls of S. maxima, converting them into bioactive ultrathin coatings, which are found to be durable under aqueous conditions. The findings demonstrate that Ar-APJ-transformed bioactive coatings show better antibacterial activity against Staphylococcus aureus and Pseudomonas aeruginosa. Moreover, these coatings exhibit compatibility with macrophages, induce an anti-inflammatory response by reducing interleukin 6 production, and promote cell migration in keratinocytes. This study offers an innovative, single-step, sustainable technology for transforming microalgae into bioactive coatings. The approach reported here has immense potential for the generation of bioactive coatings for combating wound infections and may offer a significant advance in wound care research and application.

Copper-doped cherry blossom carbon dots with peroxidase-like activity for antibacterial applications

Safety concerns arising from bacteria present a significant threat to human health, underscoring the pressing need for the exploration of novel antimicrobial materials. Nanozymes, as a new type of nanoscale material, have attracted widespread attention for antibacterial applications owing to their ability to mimic the catalytic activity of natural enzymes. In this work, we have constructed copper-doped cherry blossom carbon dots (Cu-CDs) with excellent peroxidase-like (POD) activity using a one-pot hydrothermal method. The utilization of cherry blossom as a natural material precursor significantly enhances its biocompatibility. Furthermore, the incorporation of copper ions initiates Fenton-like reaction-triggered POD-like catalytic activity, effectively eradicating bacteria by converting hydrogen peroxide (H2O2) into hydroxyl radicals (·OH). The antibacterial test results demonstrate that Cu-CDs exhibit a bactericidal efficacy of over 90% against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). This study presents a novel environmentally friendly nanozyme material derived from natural sources, exhibiting significant antimicrobial properties and offering innovative insights for the advancement of antimicrobial materials.

Artificial intelligence and prescription of antibiotic therapy: present and future

INTRODUCTION

In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription.

AREAS COVERED

In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024.

EXPERT OPINION

Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.

Multiomics analysis of the effects of manure-borne doxycycline combined with oversized fiber microplastics on pak choi growth and the risk of antibiotic resistance gene transmission

In this study, oversized microplastics (OMPs) were intentionally introduced into soil containing manure-borne doxycycline (DOX). This strategic approach was used to systematically examine the effects of combined OMP and DOX pollution on the growth of pak choi, analyze alterations in soil environmental metabolites, and explore the potential migration of antibiotic resistance genes (ARGs). The results revealed a more pronounced impact of DOX than of OMPs. Slender-fiber OMPs (SF OMPs) had a more substantial influence on the growth of pak choi than did coarse-fiber OMPs (CF OMPs). Conversely, CF OMPs had a more significant effect on the migration of ARGs within the system. When DOX was combined with OMPs, the negative effects of DOX on pak choi growth were mitigated through the synthesis of indole through the adjustment of carbon metabolism and amino acid metabolism in pak choi roots. In this process, Pseudohongiellaceae and Xanthomonadaceae were key bacteria. During the migration of ARGs, the potential host bacterium Limnobacter should be considered. Additionally, the majority of potential host bacteria in the pak choi endophytic environment were associated with tetG. This study provides insights into the intricate interplay among DOX, OMPs, ARGs, plant growth, soil metabolism, and the microbiome.

Artificial intelligence applications in the diagnosis and treatment of bacterial infections

The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.

Cyr SB, Yaesoubi R. Personalizing the empiric treatment of gonorrhea using machine learning models

Despite the emergence of antimicrobial-resistant (AMR) strains of Neisseria gonorrhoeae, the treatment of gonorrhea remains empiric and according to standardized guidelines, which are informed by the national prevalence of resistant strains. Yet, the prevalence of AMR varies substantially across geographic and demographic groups. We investigated whether data from the national surveillance system of AMR gonorrhea in the US could be used to personalize the empiric treatment of gonorrhea. We used data from the Gonococcal Isolate Surveillance Project collected between 2000-2010 to train and validate machine learning models to identify resistance to ciprofloxacin (CIP), one of the recommended first-line antibiotics until 2007. We used these models to personalize empiric treatments based on sexual behavior and geographic location and compared their performance with standardized guidelines, which recommended treatment with CIP, ceftriaxone (CRO), or cefixime (CFX) between 2005-2006, and either CRO or CFX between 2007-2010. Compared with standardized guidelines, the personalized treatments could have replaced 33% of CRO and CFX use with CIP while ensuring that 98% of patients were prescribed effective treatment during 2005-2010. The models maintained their performance over time and across geographic regions. Predictive models trained on data from national surveillance systems of AMR gonorrhea could be used to personalize the empiric treatment of gonorrhea based on patients' basic characteristics at the point of care. This approach could reduce the unnecessary use of newer antibiotics while maintaining the effectiveness of first-line therapy.

Forecasting antimicrobial resistance evolution

Antimicrobial resistance (AMR) is a major global health issue. Current measures for tackling it comprise mainly the prudent use of drugs, the development of new drugs, and rapid diagnostics. Relatively little attention has been given to forecasting the evolution of resistance. Here, we argue that forecasting has the potential to be a great asset in our arsenal of measures to tackle AMR. We argue that, if successfully implemented, forecasting resistance will help to resolve the antibiotic crisis in three ways: it will (i) guide a more sustainable use (and therefore lifespan) of antibiotics and incentivize investment in drug development, (ii) reduce the spread of AMR genes and pathogenic microbes in the environment and between patients, and (iii) allow more efficient treatment of persistent infections, reducing the continued evolution of resistance. We identify two important challenges that need to be addressed for the successful establishment of forecasting: (i) the development of bespoke technology that allows stakeholders to empirically assess the risks of resistance evolving during the process of drug development and therapeutic/preventive use, and (ii) the transformative shift in mindset from the current praxis of mostly addressing the problem of antibiotic resistance a posteriori to a concept of a priori estimating, and acting on, the risks of resistance.

Meta-analysis of wastewater microbiome for antibiotic resistance profiling

The microbial composition and stress molecules are main drivers influencing the development and spread of antibiotic resistance bacteria (ARBs) and genes (ARGs) in the environment. A reliable and rapid method for identifying associations between microbiome composition and resistome remains challenging. In the present study, secondary metagenome data of sewage and hospital wastewaters were assessed for differential taxonomic and ARG profiling. Subsequently, Random Forest (RF)-based ML models were used to predict ARG profiles based on taxonomic composition and model validation on hospital wastewaters. Total ARG abundance was significantly higher in hospital wastewaters (15 ppm) than sewage (5 ppm), while the resistance towards methicillin, carbapenem, and fluoroquinolone were predominant. Although, Pseudomonas constituted major fraction, Streptomyces, Enterobacter, and Klebsiella were characteristic of hospital wastewaters. Prediction modeling showed that the relative abundance of pathogenic genera Escherichia, Vibrio, and Pseudomonas contributed most towards variations in total ARG count. Moreover, the model was able to identify host-specific patterns for contributing taxa and related ARGs with >90% accuracy in predicting the ARG subtype abundance. More than >80% accuracy was obtained for hospital wastewaters, demonstrating that the model can be validly extrapolated to different types of wastewater systems. Findings from the study showed that the ML approach could identify ARG profile based on bacterial composition including 16S rDNA amplicon data, and can serve as a viable alternative to metagenomic binning for identification of potential hosts of ARGs. Overall, this study demonstrates the promising application of ML techniques for predicting the spread of ARGs and provides guidance for early warning of ARBs emergence.

Antibiotic Resistance of Bacteria Isolated from Clinical Samples and Organs of Rescued Loggerhead Sea Turtles (Caretta caretta) in Southern Italy

Antimicrobial resistance affects all environments, endangering the health of numerous species, including wildlife. Increasing anthropic pressure promotes the acquisition and dissemination of antibiotic resistance by wild animals. Sea turtles, being particularly exposed, are considered sentinels and carriers of potential zoonotic pathogens and resistant strains. Therefore, this study examined the antibiotic resistance profiles of bacteria isolated from loggerhead sea turtles hospitalised in a rescue centre of Southern Italy over a 9-year period. Resistance to ceftazidime, doxycycline, enrofloxacin, flumequine, gentamicin, oxytetracycline and sulfamethoxazole-trimethoprim was evaluated for 138 strains isolated from the clinical samples or organs of 60 animals. Gram-negative families were the most isolated: Vibrionaceae were predominant, followed by Shewanellaceae, Pseudomonadaceae, Enterobacteriaceae and Morganellaceae. These last three families exhibited the highest proportion of resistance and multidrug-resistant strains. Among the three Gram-positive families isolated, Enterococcaceae were the most represented and resistant. The opportunistic behaviour of all the isolated species is particularly concerning for diseased sea turtles, especially considering their resistance to commonly utilised antibiotics. Actually, the multiple antibiotic resistance was higher when the sea turtles were previously treated. Taken together, these findings highlight the need to improve antimicrobial stewardship and monitor antibiotic resistance in wildlife, to preserve the health of endangered species, along with public and environmental health.

Dual elemental doping activated signaling pathway of angiogenesis and defective heterojunction engineering for effective therapy of MRSA-infected wounds

Multi-drug resistant bacterial infections pose a significant threat to human health. Thus, the development of effective bactericidal strategies is a pressing concern. In this study, a ternary heterostructure (Zn-CN/P-GO/BiS) comprised of Zn-doped graphite phase carbon nitride (g-C3N4), phosphorous-doped graphene oxide (GO) and bismuth sulphide (Bi2S3) is constructed for efficiently treating methicillin-resistant Staphylococcus aureus (MRSA)-infected wound. Zn doping-induced defect sites in g-C3N4 results in a reduced band gap (ΔE) and a smaller energy gap (ΔEST) between the singlet state S1 and triplet state T1, which favours two-photon excitation and accelerates electron transfer. Furthermore, the formation of an internal electric field at the ternary heterogeneous interface optimizes the charge transfer pathway, inhibits the recombination of electron-hole pairs, improves the photodynamic effect of g-C3N4, and enhances its catalytic performance. Therefore, the Zn-CN/P-GO/BiS significantly augments the production of reactive oxygen species and heat under 808 nm NIR (0.67 W cm-2) irradiation, leading to the elimination of 99.60% ± 0.07% MRSA within 20 min. Additionally, the release of essential trace elements (Zn and P) promotes wound healing by activating hypoxia-inducible factor-1 (HIF-1) and peroxisome proliferator-activated receptors (PPAR) signaling pathways. This work provides unique insight into the rapid antibacterial applications of trace element doping and two-photon excitation.

The Combination of Membrane Disruption and FtsZ Targeting by a Chemotherapeutic Hydrogel Synergistically Combats Pathogens Infections

The emergence of multidrug-resistant (MDR) bacteria poses a significant challenge to global health. Due to a shortage of antibiotics, alternative therapeutic strategies are urgently needed. Unfortunately, colistin, the last-resort antibiotic, has unavoidable nephrotoxicity and hepatotoxicity, and its single killing mechanism is prone to drug resistance. To address this challenge, a promising combinatorial approach that includes colistin, a membrane-disrupting antimicrobial agent, and chelerythrine (CHE), a FtsZ protein inhibitor is proposed. This approach significantly reduces antibiotic dose and development of resistance, leading to almost complete inactivation of MDR pathogens in vitro. To address solubility issues and ensure transport, the antimicrobial hydrogel system LNP-CHE-CST@hydrogel, which induced reactive oxygen species (ROS) and apoptosis-like cell death by targeting the FtsZ protein, is used. In an in vivo mouse skin infection model, the combination therapy effectively eliminated MDR bacteria within 24 h, as monitored by fluorescence tracking. The findings demonstrate a promising approach for developing multifunctional hydrogels to combat MDR bacterial infections.

Variability in cell division among anatomical sites shapes Escherichia coli antibiotic survival in a urinary tract infection mouse model

Urinary tract infection (UTI), mainly caused by Escherichia coli, are frequent and have a recurrent nature even after antibiotic treatment. Potential bacterial escape mechanisms include growth defects, but probing bacterial division in vivo and establishing its relation to the antibiotic response remain challenging. Using a synthetic reporter of cell division, we follow the temporal dynamics of cell division for different E. coli clinical strains in a UTI mouse model with and without antibiotics. We show that more bacteria are actively dividing in the kidneys and urine compared with the bladder. Bacteria that survive antibiotic treatment are consistently non-dividing in three sites of infection. Additionally, we demonstrate how both the strain in vitro persistence profile and the microenvironment impact infection and treatment dynamics. Understanding the relative contribution of the host environment, growth heterogeneity, non-dividing bacteria, and antibiotic persistence is crucial to improve therapies for recurrent infections.

Porous reticular Co@Fe metal-organic gel: dual-function simulated peroxidase nanozyme for both colorimetric sensing and antibacterial applications

Constructing metal-organic gels (MOGs) with enzyme-catalyzed activity and studying their catalytic mechanism are crucial for the development of novel nanozyme materials. In this study, a Co@Fe MOG with excellent peroxidase activity was developed by a simple and mild one-pot process. The results showed that the material exhibited almost a single peroxidase activity under optimal pH conditions, which allowed it to attract and oxidize the chromogenic substrate 3,3',5,5'-tetramethylbenzidine (TMB). Based on the active electron transfer between the metal centers and the organic ligand in the synthetic material, the Co@Fe MOG-H2O2-TMB system was verified to be able to detect H2O2 and citric acid (CA). The catalytic microenvironment formed by the adsorption and the catalytic center accelerated the electron-transfer rate, which expedited the generation of hydroxyl radicals (˙OH, a kind of reactive oxygen species (ROS)) in the presence of H2O2. The persistence and high intensity of ˙OH generation were proven, which would endow Co@Fe MOG with a certain antibacterial ability, promoting the healing of bacteria-infected wounds. In conclusion, this study contributes to the development efforts toward the application systems of nanozymes for marker detection and antibacterial activity.

Explainable and Interpretable Machine Learning for Antimicrobial Stewardship: Opportunities and Challenges

There is growing interest in exploiting the advances in artificial intelligence and machine learning (ML) for improving and monitoring antimicrobial prescriptions in line with antimicrobial stewardship principles. Against this background, the concepts of interpretability and explainability are becoming increasingly essential to understanding how ML algorithms could predict antimicrobial resistance or recommend specific therapeutic agents, to avoid unintended biases related to the "black box" nature of complex models. In this commentary, we review and discuss some relevant topics on the use of ML algorithms for antimicrobial stewardship interventions, highlighting opportunities and challenges, with particular attention paid to interpretability and explainability of employed models. As in other fields of medicine, the exponential growth of artificial intelligence and ML indicates the potential for improving the efficacy of antimicrobial stewardship interventions, at least in part by reducing time-consuming tasks for overwhelmed health care personnel. Improving our knowledge about how complex ML models work could help to achieve crucial advances in promoting the appropriate use of antimicrobials, as well as in preventing antimicrobial resistance selection and dissemination.

Intracellular infection-responsive macrophage-targeted nanoparticles for synergistic antibiotic immunotherapy of bacterial infection

Intracellular bacteria are considered to play a key role in the failure of bacterial infection therapy and increase of antibiotic resistance. Nanotechnology-based drug delivery carriers have been receiving increasing attention for improving the intracellular antibacterial activity of antibiotics, but are accompanied by disadvantages such as complex preparation procedures, lack of active targeting, and monotherapy, necessitating further design improvements. Herein, nanoparticles targeting bacteria-infected macrophages are fabricated to eliminate intracellular bacterial infections via antibiotic release and upregulation of intracellular reactive oxygen species (ROS) levels and proinflammatory responses. These nanoparticles were formed through the reaction of the amino group on selenocystamine dihydrochloride and the aldehyde group on oxidized dextran (ox-Dex), which encapsulates vancomycin (Van) through hydrophobic interactions. These nanoparticles could undergo targeted uptake by macrophages via endocytosis and respond to the bacteria-infected intracellular microenvironment (ROS and glutathione (GSH)) for controlled release of antibiotics. Furthermore, these nanoparticles could consume intracellular GSH and promote a significant increase in the level of ROS in macrophages, subsequently up-regulating the proinflammatory response to reinforce antibacterial activity. These nanoparticles can accelerate bacteria-infected wound healing. In this work, nanoparticles were fabricated for bacteria-infected macrophage-targeted and microenvironment-responsive antibiotic delivery, cellular ROS generation, and proinflammatory up-regulation activity to eliminate intracellular bacteria, which opens up a new possibility for multifunctional drug delivery against intracellular infection.

Drug repurposing for bacterial infections

Repurposing pharmaceuticals is a technique used to find new, alternate clinical applications for approved drug molecules. It may include altering the drug formulation, route of administration, dose or the dosage regimen. The process of repurposing medicines starts with screening libraries of previously approved drugs for the targeted disease condition. If after an the initial in silico, in vitro or in vivo experimentation, the molecule has been found to be active against a particular target, the molecule is considered as a good candidate for clinical trials. As the safety profile of such molecules is available from the previous data, significant time and resources are saved. These advantages of drug repurposing approach make it especially helpful for finding treatments for rapidly evolving conditions including bacterial infections. An ever-increasing incidence of antimicrobial resistance, owing to the mutations in bacterial genome, leads to therapeutic failure of many approved antibiotics. Repurposing the approved drug molecules for use as antibiotics can provide an effective means for the combating life-threatening bacterial diseases. A number of drugs have been considered for drug repurposing against bacterial infections. These include, but are not limited to, Auranofin, Closantel, and Toremifene that have been repurposed for various infections. In addition, the reallocation of route of administration, redefining dosage regimen and reformulation of dosage forms have also been carried out for repurposing purpose. The current chapter addresses the drug discovery and development process with relevance to repurposing against bacterial infections.

Stimuli‐Responsive Hydrogels for Antibacterial Applications

Hydrogels have emerged as promising candidates for biomedical applications, especially in the field of antibacterial therapeutics, due to their unique structural properties, highly tunable physicochemical properties, and excellent biocompatibility. The integration of stimuli-responsive functions into antibacterial hydrogels holds the potential to enhance their antibacterial properties and therapeutic efficacy, dynamically responding to different external or internal stimuli, such as pH, temperature, enzymes, and light. Therefore, this review describes the applications of hydrogel dressings responsive to different stimuli in antibacterial therapy. The collaborative interaction between stimuli-responsive hydrogels and antibacterial materials is discussed. This synergistic approach, in contrast to conventional antibacterial materials, not only amplifies the antibacterial effect but also alleviates adverse side effects and diminishes the incidence of multiple infections and drug resistance. The review provides a comprehensive overview of the current challenges and outlines future research directions for stimuli-responsive antibacterial hydrogels. It underscores the imperative for ongoing interdisciplinary research aimed at unraveling the mechanisms of wound healing. This understanding is crucial for optimizing the design and implementation of stimuli-responsive antibacterial hydrogels. Ultimately, this review aims to offer scientific guidance for the development and practical clinical application of stimuli-responsive antibacterial hydrogel dressings.

Catechol-Siderophore Mimics Convey Nucleic Acid Therapeutics into Bacteria

Antibacterial resistance is a major threat for human health. There is a need for new antibacterials to stay ahead of constantly-evolving resistant bacteria. Nucleic acid therapeutics hold promise as powerful antibiotics, but issues with their delivery hamper their applicability. Here, we exploit the siderophore-mediated iron uptake pathway to efficiently transport antisense oligomers into bacteria. We appended a synthetic siderophore to antisense oligomers targeting the essential acpP gene in Escherichia coli. Siderophore-conjugated PNA and PMO antisense oligomers displayed potent antibacterial properties. Conjugates bearing a minimal siderophore consisting of a mono-catechol group showed equally effective. Targeting the lacZ transcript resulted in dose-dependent decreased β-galactosidase production, demonstrating selective protein downregulation. Applying this concept to Acinetobacter baumannii also showed concentration-dependent growth inhibition. Whole-genome sequencing of resistant mutants and competition experiments with the endogenous siderophore verified selective uptake through the siderophore-mediated iron uptake pathway. Lastly, no toxicity towards mammalian cells was found. Collectively, we demonstrate for the first time that large nucleic acid therapeutics can be efficiently transported into bacteria using synthetic siderophore mimics.

Gut microbiome correlates of recurrent urinary tract infection: a longitudinal, multi-center study

Background

Urinary tract infections (UTI) affect approximately 250 million people annually worldwide. Patients often experience a cycle of antimicrobial treatment and recurrent UTI (rUTI) that is thought to be facilitated by a gut reservoir of uropathogenic Escherichia coli (UPEC).

Methods

125 patients with UTI caused by an antibiotic-resistant organism (ARO) were enrolled from July 2016 to May 2019 in a longitudinal, multi-center cohort study. Multivariate statistical models were used to assess the relationship between uropathogen colonization and recurrent UTI (rUTI), controlling for clinical characteristics. 644 stool samples and 895 UPEC isolates were interrogated for taxonomic composition, antimicrobial resistance genes, and phenotypic resistance. Cohort UTI gut microbiome profiles were compared against published healthy and UTI reference microbiomes, as well as assessed within-cohort for timepoint- and recurrence-specific differences.

Findings

Risk of rUTI was not independently associated with clinical characteristics. The UTI gut microbiome was distinct from healthy reference microbiomes in both taxonomic composition and antimicrobial resistance gene (ARG) burden, with 11 differentially abundant taxa at the genus level. rUTI and non-rUTI gut microbiomes in the cohort did not generally differ, but gut microbiomes from urinary tract colonized patients were elevated in E. coli abundance 7-14 days post-antimicrobial treatment. Corresponding UPEC gut isolates from urinary tract colonizing lineages showed elevated phenotypic resistance against 11 of 23 tested drugs compared to non-colonizing lineages.

Interpretation

The gut microbiome is implicated in UPEC urinary tract colonization during rUTI, serving as an ARG-enriched reservoir for UPEC. UPEC can asymptomatically colonize the gut and urinary tract, and post-antimicrobial blooms of gut E. coli among urinary tract colonized patients suggest that cross-habitat migration of UPEC is an important mechanism of rUTI. Thus, treatment duration and UPEC populations in both the urinary and gastrointestinal tract should be considered in treating rUTI and developing novel therapeutics.

Funding

This work was supported in part by awards from the U.S. Centers for Disease Control and Prevention Epicenter Prevention Program (grant U54CK000482; principal investigator, V.J.F.); to J.H.K. from the Longer Life Foundation (an RGA/Washington University partnership), the National Center for Advancing Translational Sciences (grants KL2TR002346 and UL1TR002345), and the National Institute of Allergy and Infectious Diseases (NIAID) (grant K23A1137321) of the National Institutes of Health (NIH); and to G.D. from NIAID (grant R01AI123394) and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (grant R01HD092414) of NIH. R.T.'s research was funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation; grant 402733540). REDCap is Supported by Clinical and Translational Science Award (CTSA) Grant UL1 TR002345 and Siteman Comprehensive Cancer Center and NCI Cancer Center Support Grant P30 CA091842. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.

Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation

BACKGROUND

With increasing antibiotic resistance and regulation, the issue of antibiotic combination has been emphasised. However, antibiotic combination prescribing lacks a rapid identification of feasibility, while its risk of drug interactions is unclear.

METHODS

We conducted statistical descriptions on 16 101 antibiotic coprescriptions for inpatients with bacterial infections from 2015 to 2023. By integrating the frequency and effectiveness of prescriptions, we formulated recommendations for the feasibility of antibiotic combinations. Initially, a machine learning algorithm was utilised to optimise grading thresholds and habits for antibiotic combinations. A feedforward neural network (FNN) algorithm was employed to develop antibiotic combination recommendation model (ACRM). To enhance interpretability, we combined sequential methods and DrugBank to explore the correlation between antibiotic combinations and drug interactions.

RESULTS

A total of 55 antibiotics, covering 657 empirical clinical antibiotic combinations were used for ACRM construction. Model performance on the test dataset showed AUROCs of 0.589-0.895 for various antibiotic recommendation classes. The ACRM showed satisfactory clinical relevance with 61.54-73.33% prediction accuracy in a new independent retrospective cohort. Antibiotic interaction detection showed that the risk of drug interactions was 29.2% for strongly recommended and 43.5% for not recommended. A positive correlation was identified between the level of clinical recommendation and the risk of drug interactions.

CONCLUSIONS

Machine learning modelling of retrospective antibiotic prescriptions habits has the potential to predict antibiotic combination recommendations. The ACRM plays a supporting role in reducing the incidence of drug interactions. Clinicians are encouraged to adopt such systems to improve the management of antibiotic usage and medication safety.

Organic-Inorganic Heterointerface-Expediting Electron Transfer Realizes Efficient Plasmonic Catalytic Sterilization via a Carbon-Dot Nanozyme

Plasmonic nanozymes bring enticing prospects for catalytic sterilization by leveraging plasmon-engendered hot electrons. However, the interface between plasmons and nanozymes as the mandatory path of hot electrons receives little attention, and the mechanisms of plasmonic nanozymes still remain to be elucidated. Herein, a plasmonic carbon-dot nanozyme (FeCG) is developed by electrostatically assembling catalytic iron-doped carbon dots (Fe-CDs) with plasmonic gold nanorods. The energy harvesting and hot-electron migration are remarkably expedited by a spontaneous organic-inorganic heterointerface holding a Fermi level-induced interfacial electric field. The accumulated hot electrons are then fully utilized by conductive Fe-CDs to boost enzymatic catalysis toward overproduced reactive oxygen species. By synergizing with localized heating from hot-electron decay, FeCG achieves rapid and potent disinfection with an antibacterial efficiency of 99.6% on Escherichia coli within 5 min and is also effective (94.2%) against Staphylococcus aureus. Our work presents crucial insights into the organic-inorganic heterointerface in advanced plasmonic biocidal nanozymes.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Rapid and sensitive parallel on-site detection of antibiotics and resistance genes in aquatic environments using evanescent wave dual-color fluorescence fiber-embedded optofluidic nanochip

Environmental antibiotics and antibiotic resistance genes (ARGs) pose considerable threat to humans and animals; thus, the rapid and sensitive parallel detection of these pollutants from a single sample is urgently required. However, traditional multiplexed analytic technologies detect only one type of target (e.g., small molecules or nucleic acids) per assay. To address this issue, Evanescent wave Dual-color fluorescence Fiber-embedded Optofluidic Nanochip (EDFON) was fabricated by integrating a fiber-embedded optofluidic nanochip with evanescent wave dual-color fluorescence technology. The EDFON was used for the parallel quantitative detection of sulfamerazine (SMR) and MCR-1 with high sensitivity and specificity by combining a heterogeneous immunoassay with a homogenous hybridization chain reaction based on time-resolved effects. LODs of 0.032 μg/L and 35 pM was obtained for SMR and MCR-1, respectively, within 20 min. To our best knowledge, the EDFON is the first device for the simultaneous detection of two type of targets in each test, which is highly valuable to prevent the global threats of antibiotics and ARGs. Comparison with liquid chromatography-mass spectrometry showed a strong linear relationship (R2 = 0.998) for SMR pollution in the Qinghe River, with spiked SMR and MCR-1 negative surface and wastewater samples showing recovery rates of 91.8-113.4%. These results demonstrate the excellent accuracy and reliability of the EDFON, with features such as multi-analyte detection, field-deployment, and minimal-equipment, rendering it revolutionary for environmental monitoring, food safety, and medical diagnostics.

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

A Dual Fluorescence Turn-On Sensor Array Formed by Poly(para-aryleneethynylene) and Aggregation-Induced Emission Fluorophores for Sensitive Multiplexed Bacterial Recognition

Bacterial infections have emerged as the leading causes of mortality and morbidity worldwide. Herein, we developed a dual-channel fluorescence "turn-on" sensor array, comprising six electrostatic complexes formed from one negatively charged poly(para-aryleneethynylene) (PPE) and six positively charged aggregation-induced emission (AIE) fluorophores. The 6-element array enabled the simultaneous identification of 20 bacteria (OD600=0.005) within 30s (99.0 % accuracy), demonstrating significant advantages over the array constituted by the 7 separate elements that constitute the complexes. Meanwhile, the array realized different mixing ratios and quantitative detection of prevalent bacteria associated with urinary tract infection (UTI). It also excelled in distinguishing six simulated bacteria samples in artificial urine. Remarkably, the limit of detection for E. coli and E. faecalis was notably low, at 0.000295 and 0.000329 (OD600), respectively. Finally, optimized by diverse machine learning algorithms, the designed array achieved 96.7 % accuracy in differentiating UTI clinical samples from healthy individuals using a random forest model, demonstrating the great potential for medical diagnostic applications.

Changing Cinnamaldehyde Skeleton Achieves Antibacterial Nanoswitch

Changeable substituent groups of organic molecules can provide an opportunity to clarify the antibacterial mechanism of organic molecules by tuning the electron cloud density of their skeleton. However, understanding the antibacterial mechanism of organic molecules is challenging. Herein, we reported a molecular view strategy for clarifying the antibacterial switch mechanism by tuning electron cloud density of cinnamaldehyde molecule skeleton. The cinnamaldehyde and its derivatives were self-assembled into nanosheets with excellent water solubility, respectively. The experimental results show that α-bromocinnamaldehyde (BCA) nanosheets exhibits unprecedented antibacterial activity, but there is no antibacterial activity for α-methylcinnamaldehyde nanosheets. Therefore, the BCA nanosheets and α-methylcinnamaldehyde nanosheets achieve an antibacterial switch. Theoretical calculations further confirmed that the electron-withdrawing substituent of the bromine atom leads to a lower electron cloud density of the aldehyde group than that of the electron-donor substituent of the methyl group at the α-position of the cinnamaldehyde skeleton, which is a key point in elucidating the antimicrobial switch mechanism. The excellent biocompatibility of BCA nanosheets was confirmed by CCK-8. The mouse wound infection model, H&E staining, and the crawling ability of drosophila larvae show that as-prepared BCA nanosheets are safe and promising for wound healing. This study provides a new strategy for the synthesis of low-cost organic nanomaterials with good biocompatibility. It is expected to expand the application of natural organic small molecule materials in antimicrobial agents.

Contribution of the patient microbiome to surgical site infection and antibiotic prophylaxis failure in spine surgery

Despite modern antiseptic techniques, surgical site infection (SSI) remains a leading complication of surgery. However, the origins of SSI and the high rates of antimicrobial resistance observed in these infections are poorly understood. Using instrumented spine surgery as a model of clean (class I) skin incision, we prospectively sampled preoperative microbiomes and postoperative SSI isolates in a cohort of 204 patients. Combining multiple forms of genomic analysis, we correlated the identity, anatomic distribution, and antimicrobial resistance profiles of SSI pathogens with those of preoperative strains obtained from the patient skin microbiome. We found that 86% of SSIs, comprising a broad range of bacterial species, originated endogenously from preoperative strains, with no evidence of common source infection among a superset of 1610 patients. Most SSI isolates (59%) were resistant to the prophylactic antibiotic administered during surgery, and their resistance phenotypes correlated with the patient's preoperative resistome (P = 0.0002). These findings indicate the need for SSI prevention strategies tailored to the preoperative microbiome and resistome present in individual patients.

Microneedle System with Biomarker-Activatable Chromophore as Both Optical Imaging Probe and Anti-bacterial Agent for Combination Therapy of Bacterial-Infected Wounds and Outcome Monitoring

Wounds infected with bacteria, if left untreated, have the potential to escalate into life-threatening conditions, such as sepsis, which is characterized by widespread inflammation and organ damage. A comprehensive approach to treating bacterial-infected wounds, encompassing the control of bacterial infection, biofilm eradication, and inflammation regulation, holds significant importance. Herein, a microneedle (MN) patch (FM@ST MN) has been developed, with silk fibroin (SF) and tannic acid-based hydrogel serving as the matrix. Encapsulated within the MNs are the AIEgen-based activatable probe (FQ-H2O2) and the NLRP3 inhibitor MCC950, serving as the optical reporter/antibacterial agent and the inflammation regulator, respectively. When applied onto bacterial-infected wounds, the MNs in FM@ST MN penetrate bacterial biofilms and gradually degrade, releasing FQ-H2O2 and MCC950. The released FQ-H2O2 responds to endogenously overexpressed reactive oxygen species (H2O2) at the wound site, generating a chromophore FQ-OH which emits noticeable NIR-II fluorescence and optoacoustic signals, enabling real-time imaging for outcome monitoring; and this chromophore also exhibits potent antibacterial capability due to its dual positive charges and shows negligible antibacterial resistance. However, the NLRP3 inhibitor MCC950, upon release, suppresses the activation of NLRP3 inflammasomes, thereby mitigating the inflammation triggered by bacterial infections and facilitating wound healing. Furthermore, SF in FM@ST MN aids in tissue repair and regeneration by promoting the proliferation of epidermal cells and fibroblasts and collagen synthesis. This MN system, free from antibiotics, holds promise as a solution for treating and monitoring bacterially infected wounds without the associated risk of antimicrobial resistance.

Antimicrobial carbon dots/pectin-based hydrogel for promoting healing processes in multidrug-resistant bacteria-infected wounds

Multidrug-resistant (MDR) bacterial infections have become a significant threat to global healthcare systems. Here, we developed a highly efficient antimicrobial hydrogel using environmentally friendly garlic carbon dots, pectin, and acrylic acid. The hydrogel had a porous three-dimensional network structure, which endowed it with good mechanical properties and compression recovery performance. The hydrogel could adhere closely to skin tissues and had an equilibrium swelling ratio of 6.21, indicating its potential as a wound dressing. In particular, the bactericidal efficacy following 24-h contact against two MDR bacteria could exceed 99.99 %. When the hydrogel was applied to epidermal wounds infected with methicillin-resistant Staphylococcus aureus (MRSA) on mice, a remarkable healing rate of 93.29 % was observed after 10 days. This was better than the effectiveness of the traditionally used antibiotic kanamycin, which resulted in a healing rate of 70.36 %. In vitro cytotoxicity testing and hemolysis assay demonstrated a high biocompatibility. This was further proved by the in vivo assay where no toxic side effects were observed on the heart, liver, spleen, lung, or kidney of mice. This eco-friendly and easy-to-prepare food-inspired hydrogel provides an idea for the rational use of food and food by-products as a wound dressing to control MDR bacterial infections.

Fast skin healing chitosan/PEO hydrogels: In vitro and in vivo studies

Due to its outstanding qualities, particularly when it takes the shape of hydrogels, chitosan is a well-known biological macromolecule with many applications. When chitosan hydrogels are modified with other polymers, the desirable function as skin regeneration hydrogels is compromised; nevertheless, the mechanical properties can be improved, which is crucial for commercialization. In this study, for the first time, bimetallic zinc silver metal-organic frameworks (ZAg MOF) loaded with ascorbic acid were added to chitosan/polyethylene oxide (PEO) based interpenetrating polymer network (IPN) hydrogels that were crosslinked with biotin to improve their antimicrobial activity, mechanical characteristics, and sustainable treatment of wounds. Significant changes in the microstructure, hydrophilicity level, and mechanical properties were noticed. Ascorbic acid release patterns were upregulated in an acidic environment pH (5.5) that mimics the initial wound pH. Impressive cell viability (98 %), antimicrobial properties, and almost full skin healing in a short time were achieved for the non-replaceable chitosan/PEO developed hydrogels. Enhancing the wound healing of the treated animals using the prepared CS/PEO hydrogel dressing was found to be a result of the inhibition of dermal inflammation via decreasing IL-1β, suppressing ECM degradation (MMP9), stimulating proliferation through upregulation of TGF-β and increasing ECM synthesis as it elevates collagen 1 and α-SMA contents. The findings support the implementation of developed hydrogels as antimicrobial hydrogels dressing for fast skin regeneration.

Potential antimicrobial activity of camel milk as a traditional functional food against foodborne pathogens in vivo and in vitro

Foodborne pathogens are a leading cause of mortality worldwide. Therefore, strategies focused on functional foods are urgently required to tackle this issue. As a result, camel milk is one of the most important traditional functional foods since it contains a variety of bioactive components, which all have antimicrobial activity against foodborne pathogens. The study aims to investigate the potential antimicrobial activity of raw camel milk against foodborne pathogens in both in vitro agar well diffusion and infected mice, especially Listeria monocytogenes, Staphylococcus aureus, Salmonella Typhimurium and Escherichia coli, particularly in societies that rely on consuming camel milk in its raw form. A total of eighty C57BL/6 mice were divided into ten groups and gavaged with or without camel milk for two consecutive weeks. A blood plasma analysis and serum insulin levels were measured. Histological investigations of the liver, pancreas, kidney, spleen, lung and testicles were also performed. In both in vivo and in vitro studies when compared to other pathogenic bacteria, E. coli was the most affected by raw camel milk, with a significant clear zone of 2.9 ± 0.13 cm in vitro and in all measured parameters in vivo (p < 0.05). As a result, we advocated for further research to improve camel breeding, raise milk yield and extend its reproductive capability as one of the most important farm animals.

Machine learning to predict antimicrobial resistance: future applications in clinical practice?

INTRODUCTION

Machine learning (ML) is increasingly being used to predict antimicrobial resistance (AMR). This review aims to provide physicians with an overview of the literature on ML as a means of AMR prediction.

METHODS

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, ACM Digital Library, and IEEE Xplore Digital Library up to December 2023.

RESULTS

Thirty-six studies were included in this review. Thirty-two studies (32/36, 89 %) were based on hospital data and four (4/36, 11 %) on outpatient data. The vast majority of them were conducted in high-resource settings (33/36, 92 %). Twenty-four (24/36, 67 %) studies developed systems to predict drug resistance in infected patients, eight (8/36, 22 %) tested the performances of ML-assisted antibiotic prescription, two (2/36, 6 %) assessed ML performances in predicting colonization with carbapenem-resistant bacteria and, finally, two assessed national and international AMR trends. The most common inputs were demographic characteristics (25/36, 70 %), previous antibiotic susceptibility testing (19/36, 53 %) and prior antibiotic exposure (15/36, 42 %). Thirty-three (92 %) studies targeted prediction of Gram-negative bacteria (GNB) resistance as an output (92 %). The studies included showed moderate to high performances, with AUROC ranging from 0.56 to 0.93.

CONCLUSION

ML can potentially provide valuable assistance in AMR prediction. Although the literature on this topic is growing, future studies are needed to design, implement, and evaluate the use and impact of ML decision support systems.

Ginger extract loaded Fe2O3/MgO-doped hydroxyapatite: Evaluation of biological properties for bone-tissue engineering

Since antiquity, the medicinal properties of naturally sourced biomolecules such as ginger (Zingiber officinale) extract are documented in the traditional Indian and Chinese medical systems. However, limited work is performed to assess the potential of ginger extracts for bone-tissue engineering. Our work demonstrates the direct incorporation of ginger extract on iron oxide-magnesium oxide (Fe2O3 and MgO) co-doped hydroxyapatite (HA) for enhancement in the biological properties. The addition of Fe2O3 and MgO co-doping system and ginger extract with HA increases the osteoblast viability up to ~ 1.4 times at day 11. The presence of ginger extract leads to up to ~ 9 times MG-63 cell viability reduction. The co-doping does not adversely affect the release of ginger extract from the graft surface in the biological medium at pH 7.4 for up to 28 days. Assessment of antibacterial efficacy according to the modified ISO 22196: 2011 standard method indicates that the combined effects of Fe2O3, MgO, and ginger extract lead to ~ 82 % more bacterial cell reduction, compared to the control HA against S. aureus. These ginger extract-loaded artificial bone grafts with enhanced biological properties may be utilized as a localized site-specific delivery vehicle for various bone tissue engineering applications.

Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship-A Systematic Review

Antimicrobial resistance (AMR) is a growing public health problem in the One Health dimension. Artificial intelligence (AI) is emerging in healthcare, since it is helpful to deal with large amounts of data and as a prediction tool. This systematic review explores the use of AI in antimicrobial stewardship programs (ASPs) and summarizes the predictive performance of machine learning (ML) algorithms, compared with clinical decisions, in inpatients and outpatients who need antimicrobial prescriptions. This review includes eighteen observational studies from PubMed, Scopus, and Web of Science. The exclusion criteria comprised studies conducted only in vitro, not addressing infectious diseases, or not referencing the use of AI models as predictors. Data such as study type, year of publication, number of patients, study objective, ML algorithms used, features, and predictors were extracted from the included publications. All studies concluded that ML algorithms were useful to assist antimicrobial stewardship teams in multiple tasks such as identifying inappropriate prescribing practices, choosing the appropriate antibiotic therapy, or predicting AMR. The most extracted performance metric was AUC, which ranged from 0.64 to 0.992. Despite the risks and ethical concerns that AI raises, it can play a positive and promising role in ASP.

Urogenital Infections Among Women Attending Mwingi Hospital, Kitui County, Kenya: Safeguarding Antibiotics Through Microbiological Diagnosis

Background

Urogenital infections pose a considerable public health threat, as almost half of women will experience urinary and reproductive system infections at some point in their lives. However, the urogenital infection burden is often not clear in some regions. Nevertheless, the misuse of antimicrobial agents, including self-prescription, has increased widespread antimicrobial resistance, limiting treatment benefits. Therefore, this study aimed to identify the various urogenital infections, associated risk factors, and profile the bacterial isolates, and assess their antibiotic resistance among women attending Mwingi Hospital.

Methods

A cross-sectional study was conducted on 322 women aged between the ages of 15 to 44 years. Urine and high vaginal swabs were collected from all participants and analyzed within 6 hours. Microscopic examination on wet mounts was done, bacterial isolation was done and those with significant growth were confirmed and subjected to antimicrobial susceptibility testing using specific media. Descriptive statistics were used in expressing the infection frequencies and antimicrobial resistance. Odds ratios were used to determine the risk of urogenital infection. The level of significance was considered at a P value of less than 0.05.

Results

Among the 322 women, 45.3% (146) had a urogenital infection, with bacteria being the primary cause (26.4%). The infections included UTI (22.7%), Candidiasis (15.2%), Trichomoniasis (3.7%), Gonorrhea (2.5%), and Bacterial vaginitis (1.2%). Antibiotic use was 32.9%, with only 2.8% receiving a microbiological diagnosis before antibiotic use. The overall antibiotic resistance was 53%, with the lowest resistance observed against penicillin and combinations (31.4%) and 3rd Cephalosporins (39.4%). The highest resistance was observed against nalidixic acid (74.8%) and cotrimoxazole (62.6%).

Conclusion

Women attending Mwingi Hospital are commonly affected by various urogenital infections. Antibiotic use without microbiological diagnosis was observed. Among the antibiotics tested, 3rd generation cephalosporins and penicillin combination agents were noted as the most effective in treating bacterial urogenital infections, while nalidixic acid and cotrimoxazole were ineffective. Improved diagnosis and targeted treatments are necessary to prevent further development of antibiotic resistance.

The global burden of antimicrobial resistance - urinary tract infections

Antimicrobial resistance (AMR) has emerged as a significant global healthcare problem. Antibiotic use has accelerated the physiologic process of AMR, particularly in Gram-negative pathogens. Urinary tract infections (UTIs) are predominantly of a Gram-negative nature. Uropathogens are evolutionarily highly adapted and selected strains with specific virulence factors, suggesting common mechanisms in how bacterial cells acquire virulence and AMR factors. The simultaneous increase in resistance and virulence is a complex and context-dependent phenomenon. Among known AMR mechanisms, the plenitude of different β-lactamases is especially prominent. The risk for AMR in UTIs varies in different patient populations. A history of antibiotic consumption and the physiology of urinary flow are major factors that shape AMR prevalence. The urinary tract is in close crosstalk with the microbiome of other compartments, including the gut and genital tracts. In addition, pharmacokinetic properties and the physiochemical composition of urinary compartments can contribute to the emergence of AMR. Alternatives to antibiotic treatment and a broader approach to address bacterial infections are needed. Among the various alternatives studied, antimicrobial peptides and bacteriophage treatment appear to be highly promising approaches. We herein summarize the present knowledge of clinical and microbiological AMR in UTIs and discuss innovative approaches, namely new risk prediction tools and the use of non-antibiotic approaches to defend against uropathogenic microbes.

Design, synthesis and antibacterial evaluation of low toxicity amphiphilic-cephalosporin derivatives

Global public health is facing a serious problem as a result of the rise in antibiotic resistance and the decline in the discovery of new antibiotics. In this study, two series of amphiphilic-cephalosporins were designed and synthesized, several of which showed good antibacterial activity against both Gram-positive and Gram-negative bacteria. Structure-activity relationships indicated that the length of the hydrophobic alkyl chain significantly affects the antibacterial activity against Gram-negative bacteria. The best compound 2d showed high activity against drug-susceptible Staphylococcus aureus and methicillin-resistant Staphylococcus aureus (MRSA) with MICs of 0.5 and 2-4 μg/mL, respectively. Furthermore, 2d remained active in complex mammalian body fluids and had a longer post-antibiotic effect (PAE) than vancomycin. Mechanism studies indicated that compound 2d lacks membrane-damaging properties and can target penicillin-binding proteins to disrupt bacterial cell wall structure, inhibit the metabolic activity and induce the accumulation of reactive oxygen species (ROS) in bacteria. Compound 2d showed minimal drug resistance and was nontoxic to HUVEC and HBZY-1 cells with CC50 > 128 μg/mL. These findings suggest that 2d is a promising drug candidate for treating bacterial infections.

Role of Metal-Organic Frameworks (MOFs) in treating and diagnosing microbial infections

This review article highlights the innovative role of metal-organic frameworks (MOFs) in addressing global healthcare challenges related to microbial infections. MOFs, comprised of metal nodes and organic ligands, offer unique properties that can be applied in the treatment and diagnosis of these infections. Traditional methods, such as antibiotics and conventional diagnostics, face issues such as antibiotic resistance and diagnostic limitations. MOFs, with their highly porous and customizable structure, can encapsulate and deliver therapeutic or diagnostic molecules precisely. Their large surface area and customizable pore structures allow for sensitive detection and selective recognition of microbial pathogens. They also show potential in delivering therapeutic agents to infection sites, enabling controlled release and possible synergistic effects. However, challenges like optimizing synthesis techniques, enhancing stability, and developing targeted delivery systems remain. Regulatory and safety considerations for clinical translation also need to be addressed. This review not only explores the potential of MOFs in treating and diagnosing microbial infections but also emphasizes their unique approach and discusses existing challenges and future directions.

Whole Genome Sequencing: Applications in Clinical Bacteriology

The success in determining the whole genome sequence of a bacterial pathogen was first achieved in 1995 by determining the complete nucleotide sequence of Haemophilus influenzae Rd using the chain-termination method established by Sanger et al. in 1977 and automated by Hood et al. in 1987. However, this technology was laborious, costly, and time-consuming. Since 2004, high-throughput next-generation sequencing technologies have been developed, which are highly efficient, require less time, and are cost-effective for whole genome sequencing (WGS) of all organisms, including bacterial pathogens. In recent years, the data obtained using WGS technologies coupled with bioinformatics analyses of the sequenced genomes have been projected to revolutionize clinical bacteriology. WGS technologies have been used in the identification of bacterial species, strains, and genotypes from cultured organisms and directly from clinical specimens. WGS has also helped in determining resistance to antibiotics by the detection of antimicrobial resistance genes and point mutations. Furthermore, WGS data have helped in the epidemiological tracking and surveillance of pathogenic bacteria in healthcare settings as well as in communities. This review focuses on the applications of WGS in clinical bacteriology.