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Healthcare associated infections (HAI): Insights into epidemiology, microbiology, and diagnostics.
Authors: Asokan S et al
Abstract
Healthcare associated infections remain a major global health concern because they increase illness, mortality, hospital stay, and healthcare costs. This review provides an updated synthesis of recent evidence on the epidemiology, microbiology, diagnostics, and prevention of healthcare associated infections. These infections arise from patient susceptibility, invasive procedures, antibiotic overuse, contaminated equipment, and poor infection control practices. Device associated infections such as catheter associated urinary tract infection, central line associated bloodstream infection, ventilator associated pneumonia, and surgical site infection are common and often involve multidrug resistant pathogens. Biofilm formation on devices and hospital surfaces creates persistent reservoirs that promote resistance spread. Advances in automated culture systems, rapid molecular assays, metagenomics, and whole genome sequencing improve detection and surveillance. This article integrates evidence from 2020 to 2025 to provide a multidisciplinary framework for understanding and controlling HAIs.
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Magnitude and Predictors of Antimicrobial Use-Related Drug Problems in Geriatric Inpatients: Evaluation of Pharmacist Interventions.
Authors: Hussain MW, et al
Abstract
Introduction: Infections remain a major cause of illness and death in older adults. Physiological changes, polypharmacy, and chronic conditions complicate antimicrobial therapy and increase the risk of drug problems. Pharmacist-led interventions improve prescribing, but evidence from the United Arab Emirates is limited. This study evaluated the magnitude and determinants of antimicrobial use-related drug problems and assessed clinical pharmacist interventions in hospitalized older adults with infections.
Methods: A retrospective observational study was conducted from February to July 2022 at a 350-bed tertiary care academic hospital in the United Arab Emirates. Medical records of patients aged 60 years and older admitted with infections and treated with systemic antimicrobials were reviewed. Drug-related problems were classified using the Hepler and Strand framework.
Results: Among 102 patients, 85 antimicrobial-related drug problems were identified, with half experiencing at least one. Common categories were drug-drug interactions (29%), unnecessary use (18%), and overdosing (14%). Cephalosporins (21%), penicillins (12%), and fluoroquinolones (12%) were the most frequently implicated classes. Clinical pharmacists made 79 recommendations; 95% were accepted, and 91% were implemented. Main interventions were drug omission (28%), substitution (25%), and dose modification (23%). Prior antimicrobial use, polypharmacy, and longer hospital stay predicted drug problems.
Discussion: The high prevalence of antimicrobial use-related drug problems highlights the challenges of polypharmacy and prolonged hospitalization. Findings reinforce the feasibility and acceptance of pharmacist-led interventions, supporting their role in antimicrobial stewardship.
Conclusion: Clinical pharmacists are integral to optimizing antimicrobial therapy and improving medication safety in older adults.
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Advances in integrated antimicrobial resistance diagnostics: quantitative, qualitative and AI-driven approaches
Authors: Berinson B, et al
Abstract
The rapid global increase in antimicrobial resistance complicates the treatment of life-threatening infections and makes fast, reliable antimicrobial susceptibility testing (AST) essential. While phenotypic methods such as broth dilution, agar diffusion, gradient diffusion and automated systems remain the diagnostic standard, they are limited by long turnaround times. Rapid phenotypic AST (RAST) approaches shorten the time to first results to 4 to 8 h and allow earlier optimisation of anti-infective therapy, although their clinical benefit has not yet been conclusively demonstrated and their use is restricted to validated pathogens and substances.In parallel, molecular methods such as PCR, isothermal amplification and, increasingly, whole-genome sequencing enable rapid detection of key resistance determinants (e.g., mecA/C, vanA/B, extended-spectrum beta-lactamases [ESBL] and carbapenemase genes), thereby particularly supporting the workup of positive blood cultures and surveillance investigations. Their predictive value is high for Gram-positive pathogens but limited for Gram-negative organisms due to the diversity of resistance mechanisms. Artificial intelligence (AI) offers additional potential for automated interpretation of phenotypic tests, analysis of complex genomic data and mass-spectrometry-based resistance prediction models, but faces challenges regarding standardisation, generalisability and data quality.Overall, novel RAST, molecular and AI-supported approaches usefully complement but do not replace classical methods. Their clinical impact depends on targeted implementation and integration into effective antibiotic and diagnostic stewardship structures.
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Artificial intelligence in combating challenges in antimicrobial resistance: a narrative review.
Authors: Salama R, et al
Abstract
Antimicrobial resistance (AMR) is a major global health challenge that threatens the effective prevention and treatment of infections. It arises from increasing resistance rates, limited diagnostic capacity, inappropriate antimicrobial use, and a declining pipeline of new antibiotics. These challenges highlight the need for innovative approaches to complement existing AMR control strategies. Artificial intelligence (AI) has emerged as a valuable tool to address the complexity and scale of AMR. This narrative review examines how AI can be more effectively integrated into key components of AMR management. By analysing large clinical and laboratory datasets, AI-based surveillance and predictive models enable near real-time monitoring of resistance patterns and early outbreak detection. AI-powered diagnostic tools, including image analysis and genomic methods, improve rapid pathogen identification and prediction of antimicrobial susceptibility. In clinical practice, AI-driven decision support systems strengthen antimicrobial stewardship by optimizing prescribing and monitoring antibiotic use. In addition, deep learning approaches accelerate antimicrobial drug discovery and repurposing, reducing development timelines. AI also enhances the detection and surveillance of resistance genes through genomic and metagenomic analyses across human, animal, and environmental settings. Despite its potential, AI applications in AMR face challenges related to data quality, bias, interoperability, privacy, and clinician adoption. Therefore, AI should be seen as a tool that supports, rather than replaces, existing AMR strategies. When regulated well and integrated within One Health frameworks, AI can strengthen surveillance, improve treatment decisions, and support evidence-based interventions to curb AMR.
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Single-cell hepatitis B sequencing reveals distinct viral infection events consistent with superinfection
Authors: Monika Mani
Abstract
Background: The high error rate of the hepatitis B virus (HBV) polymerase leads to a genetically diverse quasispecies in individuals with chronic hepatitis B (CHB). Data regarding the propagation of these variants in individual hepatocytes may provide insight into viral replication and diversity of covalently closed circular DNA (cccDNA), which is the template for HBV replication.
Methods : We developed sequencing protocols to characterize HBV diversity between and within hepatocytes using RNA extracted from individually isolated hepatocytes. We sequenced HBV in >200 hepatocytes in four liver biopsies from people with HIV/HBV (HB1, HB2, HB3, HB6).
Results : We found that two biopsies (HB1 and HB6) showed HBV diversity between hepatocytes that met an experimentally identified threshold. Specifically, in HB1, HBV sequences from 86 individual cells were from two different haplotypes of genotype D: 70.2% of cells with haplotype a, 5.8% with haplotype b, and 24% with both haplotypes in the same cell. Furthermore, within single hepatocytes, up to three different HBV sequences were present per cell, including some cells with both genotypes A and D. HB6, who received years of lamivudine monotherapy, had evidence of drug-resistance (DR) mutations distributed among hepatocytes and demonstrated up to three different sequences, including wild-type and drug-resistant sequences, in the same hepatocyte. Modeling of infected hepatocytes did not reveal evidence of local HBV spread based on spatial proximity.
Conclusions: Taken together, our findings demonstrate that individual hepatocytes may harbor multiple, transcriptionally active HBV cccDNA molecules, which likely arose from distinct infection events.
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Can fluconazole be used to treat non-resistant Candida (Candidozyma) auris infections? Preclinical PK/PD data from a Galleria mellonella infection model
Authors: Vasiliki Kroustali
Abstract
Background: Azole therapy is currently not used against Candida auris infections. Although fluconazole resistance is prevalent, 10–45% of isolates among clades remain non-resistant (MICs ≤32 mg/L). We evaluated fluconazole pharmacokinetics/pharmacodynamics (PK/PD) against these isolates using a Galleria mellonella model.
Methods: Nine C. auris isolates representing five clades and MICs 1–128 mg/L were studied, and four Candida albicans isolates were included for model validation. Larvae were infected with lethal inocula and treated for four days with human-equivalent fluconazole doses. Efficacy endpoints were 24-hour change in fungal burden and 7-day survival. Free-drug 24-hour area under the concentration-time curve divided by the MIC (fAUC0–24/MIC) targets were derived using a sigmoidal Emax model, and Monte Carlo simulations estimated probability of target attainment (PTA).
Results: For C. albicans, the fAUC0–24/Clinical and Laboratory Standards Institute (CLSI) MIC corresponding to EI50 for 24-hour fungal burden reduction was 35.5, consistent with murine models. EI90 survival targets were 76.5 (CLSI) and 68.9 (European Committee on Antimicrobial Susceptibility Testing, EUCAST), supporting the clinical breakpoints and validating the model. For C. auris, EI90 targets for survival were 93.2 (CLSI) and 63.2 (EUCAST) and PTA >95% were found for isolates with MICs up to 2, 4, and 8 mg/L with fluconazole doses of 400, 800, and 1,200 mg/day, respectively.
Conclusions: Fluconazole demonstrated similar in vivo activity against C. auris and C. albicans. Putative WT isolates with MICs ≤8 mg/L may be treatable with 1,200 mg/day. Clinical studies are needed to verify the efficacy of fluconazole against C. auris.”
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Analysis of positivity rate among contact screening for carbapenemase-producing Enterobacterales by room and carbapenemase type
Authors: Miseo Kim
Abstract
Objectives: We evaluated the real-world implementation of contact screening of Carbapenemase-producing Enterobacterales (CPE), focusing on screening timing and positivity rates by room type and carbapenemase enzyme.
Method: We retrospectively assessed all contacts of CPE index patients at a tertiary hospital in Seoul, Korea, between January and May 2024. Contacts were defined as patients who shared a room with an index case or occupied open beds in the same intensive care unit (ICU). Acquisition rates were compared by screening timing, room type, and carbapenemase type.
Results: Among 2,003 contacts linked to 336 index patients, 1,401 (70%) underwent screening, of whom 37 (2.6%) tested positive. Immediate screening identified 30/1,184 (2.5%) positives, while readmission screening detected 7/217 (3.2%); two additional acquisitions were identified from clinical specimens among unscreened readmitted contacts. Acquisition was more frequent after exposure to KPC-producing organisms than to NDM-producing organisms (3.7% [26/706] vs. 1.5% [8/520]; P = 0.02). By room type, acquisition was higher in multi-patient rooms compared with two-bed or open-bed ICU settings combined (3.4% [34/1004] vs 0.8% [3/397]; P = 0.006).
Conclusion : CPE acquisition was more likely following exposure to KPC-producing organisms and in multi-patient rooms. Systematic readmission screening identified additional carriers who would otherwise have been missed.”
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A Decade of Change: Comparative Findings from the 2015, 2020, and 2025 APIC MegaSurveys on the Infection Prevention Workforce
Authors: Sara M. Reese,
Abstract
Background: Infection preventionists (IPs) are essential in reducing healthcare-associated infections across increasingly diverse care settings. As responsibilities expand, it is important to understand the evolution of workforce demographics, education, and roles.
Methods: The 2025 Association for Professionals in Infection Control and Epidemiology (APIC) MegaSurvey was a cross-sectional, online survey of IPs. Survey development, pilot testing, and administration were led by APIC’s Center for Research, Practice and Innovation. Descriptive statistics and Cochran–Mantel–Haenszel tests were used to compare results with the 2015 and 2020 MegaSurveys.
Results: A total of 4,269 IPs participated. Comparisons of the previous MegaSurvey results found significant differences in IP professional background, highest degree achieved, age range, gender, salary, certification, years of experience and five-year plans. More IPs (82%) reported a nursing background in 2015 and 2020, than IPs in 2025 (68%; p <.0001). Educational attainment increased, with 48% reporting a master’s degree or higher in 2025 compared to 34% in 2015 (p <.0001). Acute care remained the most common practice setting (65.1%), followed by long-term care/skilled nursing facility/rehab (16.7%), outpatient clinics (7.3%), and ambulatory surgical centers (5.3%).
Conclusions: The IP workforce is becoming more diverse, specialized, educated and distributed across settings. Ongoing monitoring, competency-based training, and strategic workforce planning are essential to sustain infection prevention capacity.”