Other specific DSP article suggested by Editorial Board
Whole-genome sequencing and bioinformatic tools powered by machine learning to identify antibiotic-resistant genes and virulence factors in Escherichia coli from sepsis.
Authors: Kumar NR et al
Abstract
Extended-spectrum β-lactamase-producing Escherichia coli poses a global public health threat. Here, a hospital-based study was performed that reinforced the necessity for rapid antimicrobial resistance (AMR) and virulence gene mapping of clinical E. coli isolates. Whole-genome sequencing of 18 sepsis-causing E. coli strains was performed to identify multidrug resistance (MDR) and virulence factor genes and to correlate these with antibiotic use in patients with sepsis. Various global and emerging MDR sequence types were identified, utilizing a supervised machine learning approach to elucidate the relationship between genome content and AMR profiles across 17 antimicrobial classes, ensuring unbiased analysis. Known AMR genes were correlated with resistance phenotypes, and several crucial and novel AMR genes were identified. The feature selection methodology involved processing the genome into overlapping 13 bp k-mer features using a two-step selection process. Logistic regression with nested cross-validation and synthetic minority oversampling technique confirmed the robustness of the model. The combination of Machine Learning (ML) algorithms facilitates the discovery of nonlinear interactions and complex patterns within genomic data, which may not be readily apparent using conventional genomic analysis alone. This will enable the identification of novel biomarkers and genetic determinants of AMR profiles. The integration of genomic data with ML models can be used to quickly predict AMR, allowing for more targeted and personalized treatment strategies that are not typically achieved by traditional AMR surveillance methods. Our findings tailor the research approaches for patients with sepsis, particularly with AMR E. coli, highlighting the importance of prompt surveillance, robust infection control, optimized antibiotic stewardship and integrated genomic and epidemiological analysis to control MDR bacteria transmission, ultimately improving patient outcomes and safeguarding public health.
Other specific DSP article suggested by Editorial Board
A review on fungal surgical site infections: epidemiology, risk factors, main fungal agents, and prevention.
Authors: Shrani K et al
Abstract
Fungal surgical site infections (SSIs) may be less common than bacterial SSIs but are a significant clinical issue due to their challenging diagnosis, higher morbidity, and rising incidence, particularly in immunocompromised patients. The epidemiology, risk factors, prevalent fungal pathogens, and prevention of SSIs caused by fungi are discussed in this narrative review. Systematic literature search for the period 2000 to 2024 was conducted on top databases using relevant MeSH keywords. The most frequent solitary pathogens were Candida spp., followed by Aspergillus and Mucor spp., especially in transplant, cardiac, and GI infections. The greatest challenge is extended length of hospital stay, broad-spectrum antibiotics, immunosuppression, and invasive interventions with prosthetic device or shunts. While it creates added burden, fungal SSIs go unnoticed by clinical practice and are rarely included in SSI prevention strategies. The review declares the significance of enhanced clinical vigilance and tailored antifungal prophylaxis in high-risk exposure surgical procedures. The review, based on the integration of existing information, provides clinicians and infection control practitioners with a framework of fungal SSIs so that they can be better equipped to assess risk, detect infection sooner, and focus prevention efforts.
Other specific DSP article suggested by Editorial Board
Use of the Learning Health System to Redesign and Implement a Clinical Decision Support System for Antimicrobial Stewardship.
DOI: 10.3233/SHTI250845
Authors: Truong NH et al
Abstract
Antimicrobial resistance is a significant global health issue, and Antimicrobial Stewardship (AMS) services in hospitals are key to addressing this. Clinical Decision Support Systems (CDSS) are vital tools that support AMS programs. Guidance MS, a CDSS developed in 2005, has been implemented in over 60 Australian hospitals, yielding positive outcomes such as reduced gram-negative resistance, decreased antimicrobial consumption, and improved prescribing practices. This CDSS was redesigned to continue to support national AMS accreditation standards and meet evolving needs in digital healthcare. The CDSS included features for formulary restriction, clinical decision support, post-prescription review, auditing, and interactive reports for feedback. This paper outlines how the Learning Health System (LHS) framework was applied to redesign, develop, implement and evaluate the updated CDSS. The LHS framework operates through iterative cycles of practice-to-data, data-to-knowledge, and knowledge-to-practice. A Learning Health Community was established, which provided input to guide system design. Key considerations for the new CDSS included a user-centred approach, interoperability with electronic medical records (EMR), and adherence to national regulatory standards for Software As a Medical Device. Following an Agile development process, the redesigned system underwent extensive testing and iterative improvements. The program was then implemented in a beta site, a large, tertiary hospital. Extensive User Acceptance Testing was conducted, with feedback and improvements incorporated into the first version release. The program was then implemented in a network of thirteen hospitals. Formal and anecdotal feedback from the project team and clinicians showed high satisfaction regarding usability, performance, clinician workflow improvement, content quality, and efficiency. The LHS framework enabled user feedback to drive rapid enhancements to the program, ensuring it met identified needs. The implementation projects provided valuable insights into workflows, enhancing project delivery and informing strategies for future application improvements and scalability. Future development will include establishing two-way integration with EMRs and mobile devices. A socio-technical evaluation will assess the program’s perceived usability and usefulness, supporting continuous improvement as part of LHS methodology. A formal evaluation will assess clinical impact on hospital and patient outcomes, providing evidence to guide ongoing optimisation.The LHS is a useful framework for designing, developing, implementing and evaluating digital healthcare solutions for AMS, which continues to inform improvements, enabling provision of an effective, scalable and sustainable, digital solution.
Other specific DSP article suggested by Editorial Board
Interplay between C-reactive protein responses and antibiotic prescribing in people with suspected infection.
Authors: Gu Q et al
Abstract
Background: Serial measurements of C-reactive protein (CRP) are often taken in hospitals to assess recovery from infection, but their utility remains debated. Previous studies, including the development of CRP centile reference charts by authors for suspected bloodstream infections (BSI), suggest variability in CRP responses across infection types. Here the association between serial CRP percentile changes, antibiotic prescribing patterns, and patient outcomes was investigated in a large cohort with suspected infection, acknowledging that CRP is one of multiple factors in clinical decision-making.
Methods: 51,544 suspected infection episodes (defined by blood culture collection) from 36,578 patients in Oxfordshire, UK (2016-2021) were analysed. Episodes were categorised by blood culture results: Gram-positive, Gram-negative, polymicrobial, contaminants, or culture-negative (having previously shown that 51% culture-negatives have CRP responses indistinguishable from culture-positives). The spectrum of antibiotic prescriptions and their changes over time were tracked. Multinomial logistic regression, adjusted for clinical covariates, assessed the association between CRP percentile changes and subsequent prescribing decisions. Linear mixed models evaluated CRP trajectories post-prescribing, and logistic regression associations between early CRP changes (days 1-4) and 5-30-day mortality.
Results: Broad-spectrum antibiotics were predominantly used within the first three days after blood culture collection, followed by a notable shift to narrow-spectrum antibiotics for Gram-positive infections, but with slower de-escalation for Gram-negative and polymicrobial infections. CRP percentile changes were modestly associated with subsequent antibiotic adjustments; in particular, suboptimal recovery, indicated by an increase in CRP centiles, was associated with a higher rate of antibiotic escalation (16.5% vs. 10.7% in expected recovery) and, conversely, faster than expected recovery in CRP was associated with de-escalation (23.6% vs. 17.2%). However, 61.8% of decisions were unchanged despite CRP trends. The relationship between various prescribing decisions and subsequent CRP percentile changes was complex and challenging to estimate, likely due to testing bias. CRP percentile changes during the 4 days post blood culture collection were strongly associated with 5-30-day mortality, highlighting their potential utility as a prognostic indicator. Conclusions: While CRP monitoring can inform antibiotic stewardship, its association with prescribing decisions is probably only modest, underscoring the need to integrate a range of clinical factors to optimise infection management.
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Other specific DSP article suggested by Editorial Board
Formulation and Evaluation of a Licorice-Resveratrol Lollipop for Targeting Streptococcus mutans Biofilm and Antimicrobial Resistance.
DOI: 10.2147/IDR.S537534
Authors: Patil S et al
Abstract
Background: Streptococcus mutans is a key pathogen in dental caries, and the development of novel antimicrobial formulations is crucial to combat its resistance. This study aimed to evaluate a licorice-resveratrol medicated lollipop formulation (LRML) for its antimicrobial and anti-biofilm activity against S. mutans.
Methods: The LRML was developed using a heating and congealing method, incorporating licorice extract (5% w/w) and resveratrol (2% w/w) in a sucrose-based matrix. The physicochemical properties of the formulation, including hardness, drug content uniformity, moisture content, and dissolution profile, were evaluated. The antimicrobial activity was assessed through Minimum Inhibitory Concentration (MIC), Minimum Bactericidal Concentration (MBC), and time-kill assays. Anti-biofilm activity was evaluated using a crystal violet assay. The stability of the formulation was determined under accelerated conditions.
Results: The LRML formulation showed efficient drug release, with formulation number LRML-7 demonstrating 96.87% release within 45 minutes. The antimicrobial tests revealed significant bactericidal effects against S. mutans at concentrations above 0.2 µg/mL, with a notable reduction in bacterial growth in time-kill assays. The formulation also demonstrated substantial inhibition of biofilm formation at both MIC and Minimum Bactericidal Concentration (MBC) levels. Stability studies confirmed that the formulation retained its physicochemical properties over three months.
Conclusion: The LRML exhibited promising antimicrobial and anti-biofilm activities against S. mutans, suggesting its potential as a novel therapeutic option for managing dental infections. Further clinical studies are required to optimize the formulation’s efficacy and clinical applicability.
Other specific DSP article suggested by Editorial Board
Respiratory microbiome and metabolome features associate disease severity and the need for doxycycline treatment in children with macrolide-resistant Mycoplasma pneumoniae-mediated pneumonia.
Authors: Liao WC et al
Abstract
Introduction: Commensal bacterial community along the upper respiratory tract functions against pathogens. The host determinants of Mycoplasma pneumoniae severity should be identified against the increasing threat of macrolide-resistant M. pneumoniae (MRMP) infection. It is hypothesized that respiratory microbiome is involved in the clinical manifestations of M. pneumoniae infection.
Methods: From 2017 to 2020, 92 children with MRMP-mediated pneumonia were enrolled among 845 children with community-associated pneumonia. Oropharyngeal samplings were collected within 48 h after admission. Respiratory microbiome and metabolites were compared based on patients’ later development of prolonged fever and the need for doxycycline treatment (DT, n = 57) and the cured control without fever or doxycycline treatment (WDT, n = 35) by using 16S rRNA-based sequencing and untargeted metabolome analysis.
Results: Significantly higher diversity and different respiratory microbiomes were evaluated in WDT patients in contrast to DT patients. Fusobacterium, Haemophilus, Gemella, Oribacterium, Actinomyces lingnae, Fusobacterium periodonticum, Gemella sanguinis, and Solobacterium moorei were inversely correlated with disease severity. It is assumed that metabolites of divergent microbiomes were related to MRMP development. 15 discriminative amino-acid- and fatty-acid-related metabolites in two groups were identified. F. periodonticum abundance was negatively associated with an inflammatory metabolite: a platelet-activating factor. Fusobacterium and Oribacterium were related to the decrease in LysoPE(18:1(9Z)/0:0) and LysoPC(18:1(9Z)).
Conclusions: Microbiota dysbiosis with dysregulated inflammatory glycerolphospholipid-related metabolites was related to disease severity and the need for doxycycline treatment in children with MRMP-mediated pneumonia. Anaerobic bacteria metabolites and metabolic pathway could be beneficial therapeutic targets against M. pneumoniae infection.