Multiplex PCR is a key modality of nucleic acid amplification testing with growing applications in clinical diagnostics, especially in infectious diseases. Recent work has demonstrated that thermodynamic and kinetic information embedded in amplification curves (ACs) can be leveraged for target identification in the multiplex setting. This technology, named Amplification Curve Analysis (ACA), requires a mechanistic simulation tool linking biochemical design choices to AC features. We present DYNAMIC, an open-source Python implementation of a kinetic model acting as a digital twin of singleplex TaqMan PCR. Based on established kinetic and stoichiometric principles, DYNAMIC predicts fluorescence values over a wide range of experimental conditions. Key features include separate modeling of primer and probe annealing, a flexible 2-parameter thermal degradation model of Taq activity, and support for atypical regimes relevant to ACA, such as asymmetric primer concentrations. A global optimization algorithm identifies thermodynamic hyperparameters linking assay characteristics to AC features. In comparison with experimental data, DYNAMIC reproduces AC variations driven by changes in primer and probe concentrations, captures late-cycle efficiency loss from enzyme degradation, and yields realistic cycle threshold trends across orders of magnitude in input DNA. Tested against a dilution series of four previously published assays, the model robustly identifies key kinetic hyperparameters. Overall, DYNAMIC provides a mechanistic framework for predicting TaqMan PCR kinetics that can streamline assay development, reduce empirical optimization, and support the rational design of multiplex panels where target identification relies on AI-enabled classification of AC features.

A HPG electrode with a rifampicin-imprinted polymer selectively binds rifampicin from serum, producing a characteristic decrease in electrochemical signal compared with the baseline.

Loop-mediated isothermal amplification (LAMP) is increasingly recognised as a practical alternative to PCR for pathogen detection, offering rapid turnaround time, a constant operating temperature, and compatibility with a wide range of detection methods. Colourimetric LAMP has gained popularity due to its potential for instrument-free readout, making it suitable for molecular diagnostics in low-resource settings. Despite these advantages, its adoption at the point-of-care remains limited as it has been widely used in liquid format and therefore restricted to the availability of cold-chain storage and trained personnel. This work introduces the development and optimisation of ready-to-use lyophilised colourimetric LAMP (lyo-cLAMP) that does not require cold-chain, additional reagents, or manual intervention beyond the addition of extracted nucleic acids from a sample, which indicates the presence of a target of interest by colour change after amplification. A variety of pH and metal indicators were screened and combined to evaluate their synergy, identifying four combinations with high discrimination between positive and negative amplification. The performance of lyo-cLAMP was assessed with synthetic SARS-CoV-2 RNA, comparing it to the liquid format and achieving the same limit-of-detection. Lastly, the translation of lyo-cLAMP to diagnostic applications was demonstrated by screening positive SARS-CoV-2 residual clinical samples, achieving high accuracy. The developed lyo-cLAMP is compatible with any LAMP assay, allowing for rapid adaptation to new targets, which is particularly valuable in outbreak scenarios.

Nucleic acid extraction remains the principal infrastructure barrier to molecular influenza testing outside centralised laboratories, since bead-based purification is normally tied to mains-powered extractors and trained operators. We evaluated SmartLid, a centrifugation-free format in which a removable magnetic key shuttles paramagnetic beads through pre-aliquoted lysis/binding, wash, and elution buffers without pipetting or powered instrumentation, against an automated magnetic-bead extractor (Nextractor NX-48S) on 311 nasopharyngeal specimens from the 2024-2025 influenza season at a National Influenza Centre. Paired eluates were amplified under identical monoplex RT-qPCR conditions for influenza A(H1N1)pdm09, A(H3), and B/Victoria. Both methods gave 100% specificity (47/47 negatives; no false positives). Subtyping succeeded in 263/264 reference-positive specimens after SmartLid extraction versus 241/264 after automated extraction (99.62% versus 91.29%; difference 8.33 percentage points; discordant pairs 23 versus 1; McNemar P < 0.001). Across 240 complete pairs, cycle threshold (Ct) values were lower after SmartLid extraction (median paired difference -2.78 cycles; estimated location shift -2.60 cycles, 95% CI -2.82 to -2.37; P < 0.001) with rank-ordering of specimens conserved between methods (Spearman rho = 0.84). The advantage was preserved across all three subtypes and in both fresh and frozen specimens (adjusted P < 0.001). Specimens recovered only after SmartLid extraction had higher Ct values than dual-detected specimens (median 34.37 versus 28.54; P < 0.001), locating the gain near the assay detection limit. An instrument-free manual format can therefore exceed the extraction efficiency of an automated reference workflow, extending quality-assured influenza subtyping beyond centralised laboratories.

Abstract Background Digital antimicrobial stewardship (AMS) interventions, such as clinical decision support systems, audit-and-feedback platforms, and electronic prescribing tools, have been increasingly adopted to improve antibiotic use. However, the effectiveness of these interventions across healthcare settings remains uncertain, and the certainty of the evidence has not been comprehensively evaluated. The objective of this study was to provide a comprehensive understanding of the role of digital interventions in optimizing antimicrobial use and improving clinical outcomes within a broad spectrum of healthcare settings. Methods We conducted a systematic review and meta-analysis of randomized controlled trials evaluating digital AMS interventions that followed PRISMA 2020 guidelines and registered in PROSPERO (CRD420251178854) and funded by the Wellcome Trust CAMO-Net programme. Searches were performed across major databases. Primary outcomes included the appropriateness of antibiotic prescriptions and the antibiotic prescription rate. Secondary outcomes included 30-day mortality, 30-day hospital readmission, and length of hospital stay (LOS). Random-effects models were used to pool effect sizes. Risk of bias was assessed using RoB 2.0, and certainty of evidence was rated using GRADE. A Summary of Findings table was prepared to present effect estimates, sample sizes, and evidence certainty. Results Eleven RCTs met the inclusion criteria, and nine were included in the quantitative synthesis. Digital AMS interventions did not show a significant effect on appropriateness of antibiotic prescribing (RR 0.99, 95%CI 0.93–1.05; very low certainty). There was no reduction in antibiotic prescription (RR 0.98, 95%CI 0.88–1.09), with substantial statistical heterogeneity (I² = 71%) and very low certainty. Across clinical outcomes, digital AMS showed no effect on 30-day mortality (RR 0.91, 95%CI 0.77–1.09; very low certainty) or 30-day readmission (RR 0.95, 95%CI 0.79–1.14; very low certainty). For LOS, results were inconsistent across studies, and the pooled effect showed no clinically meaningful change (MD 0.17 days, 95%CI –0.01 to 0.35; very low certainty). Most trials had “some concerns” of bias due to deviations from intended interventions. Conclusion Meta-analyses of digital AMS RCTs showed a lack of evidence with a high level of certainty on antibiotic prescribing or clinical outcomes due to high heterogeneity in interventions and study designs, as well as RCTs’ limitations (no adoption/fidelity metrics).

AIMS:Urban aquatic environments are increasingly recognized as important components in the persistence and dissemination of antimicrobial-resistant bacteria. This study investigated the occurrence, antimicrobial resistance profiles, and genomic characteristics of carbapenem-resistant Klebsiella pneumoniae complex isolated from urban rivers in São Caetano do Sul, part of the São Paulo Metropolitan Region, Brazil. METHODS AND RESULTS:Between October 2023 and October 2024, 39 surface water samples were collected from two urban streams and processed using selective culture, MALDI-TOF identification, antimicrobial susceptibility testing, and whole-genome sequencing. Among 144 K. pneumoniae complex isolates recovered, 43 (29.9%) were resistant to meropenem. All meropenem-resistant K. pneumoniae isolates carried the blaKPC gene, while one Klebsiella quasipneumoniae isolate harbored blaNDM. Multilocus sequence typing revealed high genetic diversity, with predominance of global disseminated high-risk lineages, including ST11 and ST258. The repeated detection of identical sequence types across different sampling sites and time points suggests environmental persistence within the urban river network. CONCLUSION:These findings demonstrate that urban surface waters act as reservoirs for clinically relevant carbapenem-resistant Klebsiella lineages, highlighting the importance of environmental surveillance as part of applied strategies to monitor antimicrobial resistance in urban settings.

Abstract Infections caused by carbapenem-producing Enterobacterales (CPEs) are a persistent and growing threat in healthcare settings. Yet, current infection prevention and control (IPC) surveillance methods, which largely rely on the spatial and temporal proximity of patients, often misattribute or miss infection transmission events. Here, we develop and retrospectively evaluate an integrated methodology that combines analyses of ward-level patient movement data and whole-genome sequencing (WGS) data analyses, which provide measures of bacterial and plasmid similarity. Specifically, we evaluate this methodology across two datasets: a CPE outbreak of diverse carbapenem types (103 genomes, January 2021–March 2021) and an Imipenem-Hydrolysing β-lactamase-positive CPE outbreak (82 genomes, June 2016–October 2019), using standard clinical criteria and conservative genomic thresholds to quantify how often current IPC surveillance methods correctly identify genomically confirmed transmission events. Findings show that, across 3,423 patient contact–genome pairs, current IPC surveillance methods detected only 20.5% of genomically confirmed transmission events whilst maintaining 98.5% specificity, with missed events arising from temporal, spatial, and cross-species, mechanistic blindspots. In contrast, WGS-enabled IPC surveillance methods provided a 25–47-day earlier detection window and, in a linked economic evaluation, delivered annualised savings of up to £3.6 million, as well as a return on investment exceeding 2-fold in 7 of 8 cost scenarios. By operationalising high-throughput WGS data analysis with clinically relevant patient movement data, we evidence that it may be possible to disrupt and thereby mitigate the effects of AMR-driven CPE outbreaks, supporting investigations into the adoption of WGS-enabled IPC surveillance as a standard-of-care tool.

OBJECTIVE:Sepsis management and antimicrobial resistance (AMR) are linked priorities. Early identification and treatment of sepsis with broad-spectrum antibiotics are key to reducing morbidity and mortality. We aimed to investigate clinicians' decision-making about broad-spectrum antibiotics for suspected maternal sepsis in women admitted to hospital for childbirth, to identify if, how and why overtreatment may occur. DESIGN:Qualitative study. Semistructured interviews were conducted online, transcribed verbatim, coded in MAXQDA and analysed using the thematic Framework Approach. SETTING:National Health Service (NHS) in England, Scotland and Wales, March-June 2024. PARTICIPANTS:24 clinicians, purposively sampled for representation from relevant professions with roles in decision-making (n=15 obstetricians; n=4 anaesthetists; n=3 midwives; n=2 microbiologists). RESULTS:Participants report increasing numbers of women in labour are being prescribed broad-spectrum antibiotics for suspected maternal sepsis per guidelines. Fear of missing sepsis, absence of evidence about sepsis during labour and beliefs about antibiotic use informed clinicians' views, impacting workloads and women's experiences. 9/24 participants said overtreatment is a problem; 21/24 believed overtreatment occurs. Clinicians' beliefs, views and experiences are reported in three themes that provide a framework to explain how clinicians navigate guidelines including; why overtreatment can occur (Theme 1: The sepsis guideline is more than a guideline); how decisions to prescribe broad-spectrum antibiotics during labour are made (Theme 2: Trying to balance the whole picture) and where clinicians' think the bigger picture problems lie (Theme 3: If we continue down this path where might we be). The framework encapsulates clinicians' tacit knowledge governing decision-making and guideline use. CONCLUSIONS:Clinicians link priorities for sepsis management and AMR, but antimicrobial stewardship comes second to concerns about missing sepsis in high-stakes maternity care. Overtreatment may be inevitable until better evidence to support decision-making for suspected maternal sepsis and point-of-care diagnostic tests for women in labour exist and are embedded in clinical guidelines and clinicians' mindlines.

Background: Healthcare-associated infections and antimicrobial resistance threaten hospitalised neonates in low-resource settings. Infection prevention and control (IPC) and antimicrobial stewardship (AMS) are shaped by interacting system constraints that linear approaches may not capture. Qualitative system dynamics (SD) offers tools to capture feedback mechanisms, but applications frequently rely on group model-building workshops and limited qualitative rigour, undermining credibility. Methods: We developed and applied an interview-led qualitative SD workflow in a tertiary neonatal unit in Botswana. Data comprised 67 semi-structured interviews across stakeholder groups spanning the unit, hospital services, and health system governance, complemented by a one-day group model building (GMB) workshop. Interview data were analysed using hybrid inductive-deductive thematic analysis. Themes were translated into SD variables and causal relationships, mapped into ‘seed’ causal loop diagrams (CLDs), then integrated into a comprehensive CLD. Findings and diagrams were validated through expert review and stakeholder feedback. Results: The workflow produced ten seed CLDs and an integrated CLD comprising 62 variables and 92 causal relationships. Four dominant reinforcing feedback structures explained persistent IPC/AMS challenges: (1) overcrowding-transmission-length of stay, (2) workload-burnout-turnover, (3) workload-reduced IPC adherence-infections, and (4) IPC support-shared accountability-communication norms. Material shortages and governance gaps amplified harmful feedback loops. Conclusion: A qualitative SD approach, grounded in extensive stakeholder interviews and established qualitative analytic practices, can generate credible CLDs whose variables, causal relationships, and feedback structures are traceable to qualitative evidence when repeated workshops are not feasible. The resulting feedback structures highlight leverage points for strengthening neonatal IPC and AMS in comparable resource-constrained hospital settings, while providing a replicable methodological template for future systems-informed implementation research.

BACKGROUND:Infection prevention and control (IPC) is critical in neonatal units to prevent healthcare-associated infections, yet research on IPC has primarily focused on healthcare workers, overlooking the role of family caregivers. In low-resource settings, mothers often take on essential caregiving responsibilities including IPC-related tasks, both a logistic necessity and a cultural norm, but their perspectives and experiences remain underexplored. This qualitative descriptive study aimed to explore mothers' experiences and perceptions of IPC in a neonatal unit in Gaborone, Botswana. Understanding maternal involvement in IPC is essential for developing effective interventions, particularly in settings where health systems face workforce shortages and resource constraints. METHODS:Fifteen semi-structured interviews were conducted with mothers of infants admitted to the neonatal unit between June and December 2022. Data were analysed using thematic analysis. RESULTS:Six themes were identified. Major themes related to (1) mothers' strong sense of responsibility for IPC and concerns about infection risks, (2) health system constraints (including supply shortages) that limited consistent IPC practice, (3) competing demands within restricted visiting times that required mothers to balance IPC with feeding and bonding, (4) variable communication with health care workers (HCWs) and limited comfort reminding staff about IPC, and (5) peer dynamics between mothers, including mutual reminders as well as tensions arising from shared feeding equipment. A minor theme that emerged was (6) mothers' emotional distress and its perceived impact on remembering and enacting IPC behaviours. Overall, mothers described high motivation to protect their infants but reported that time pressures, insufficient communication with HCWs, and resource limitations constrained IPC adherence, underscoring the need for structured IPC orientation and ongoing support for families in neonatal care. CONCLUSION:Mothers in this neonatal unit viewed themselves as key partners in IPC and were highly motivated to protect their infants, but their ability to consistently follow IPC practices was constrained by limited supplies, staffing pressures, restricted time for caregiving and bonding, and variable communication with HCWs. Mothers also expressed concerns about HCW adherence to IPC and described how emotional distress and peer dynamics could shape IPC behaviour. These findings underscore the need for structured IPC education and support for mothers in neonatal units, and the importance of integrating family-centred IPC policies into neonatal care. Future research should evaluate interventions that strengthen caregiver support, improve communication, and address contextual barriers and constraints to sustained IPC practice.

BACKGROUND:Mpox continues to spread across east and central Africa, with Uganda among the most affected countries. The diagnostic reference standard, centralised real-time quantitative PCR (qPCR), requires specialised infrastructure and sample-transport logistics, producing extended turnaround times that delay public health responses, particularly in remote or underserved settings. Point-of-care molecular diagnostics could address this, but prospective clinical evaluation data from affected regions remain limited. METHODS:We conducted a prospective diagnostic accuracy study across six health facilities in Kampala and Wakiso, Uganda, within routine outpatient and inpatient pathways. Individuals of any age were consecutively enrolled if they presented at a participating site during the enrolment period with signs or symptoms meeting the WHO suspected-case definition for mpox and had at least one active cutaneous lesion amenable to swabbing. Cutaneous lesion swabs were tested at the point-of-care with Dragonfly, a sample-to-result molecular platform using a dual-target design to detect orthopoxvirus (OPXV) and monkeypox virus (MPXV), followed by confirmatory qPCR. The primary outcome was the diagnostic accuracy (sensitivity and specificity) of Dragonfly for MPXV and OPXV against qPCR. Usability and acceptability were assessed in a focus group discussion with front-line users. Clinical and epidemiological associations were examined among concordant participants using a prespecified, literature-informed binary feature set with Fisher's exact tests and Benjamini-Hochberg correction. FINDINGS:Between Sept 17 and Nov 28, 2025, of 300 enrolled participants, 196 (65%) were positive for MPXV by qPCR (median cycle threshold 21·1 [IQR 19·1-23·9]). Among confirmed MPXV cases, 108 (55%) were male and 88 (45%) were female by self-report. For the primary outcome, Dragonfly showed 98·7% (95% CI 96·6-99·5) overall agreement with qPCR, with results available in under 40 min. For MPXV, sensitivity was 98·5% (95·6-99·5) and specificity was 96·2% (90·5-98·5); for OPXV, sensitivity was 100% (98·1-100) and specificity 96·2% (90·6-98·5). Front-line users reported high acceptability, attributing this to avoidance of centralised laboratory logistics, while noting training and supply-chain requirements for routine use. INTERPRETATION:Dragonfly showed high sensitivity and specificity for mpox detection across a broad range of viral loads in the field, supporting its potential as a point-of-care diagnostic in high-burden, resource-limited settings (eg, low-income and middle-income countries, remote environments, or small clinics). Further research should assess integration with existing diagnostics, cost-effectiveness, and performance across clades and key populations. FUNDING:UK Biotechnology and Biological Sciences Research Council, UK Medical Research Council, and Wellcome Trust funded Centres for Antimicrobial Optimisation Network programme.

Background The Centres for Antimicrobial Optimization Network Brazil aims to implement an antimicrobial stewardship program in Brazilian municipality. This study explores barriers and enablers to its implementation, through understanding the context and beliefs regarding antimicrobial use in this environment. Methods The study occurred in 12 primary health care units, where a mixed-method study was conducted. A total of 208 out of 450 health care workers completed a Theoretical Domain Framework-based survey, and 16 patients and 12 health workers were interviewed. Survey results were compared by professional category; interviews were analyzed using Critical Discourse Analysis. Results Professionals with higher education scored higher across most domains. In the “Optimism” domain, these professionals scored ≥6.0, while others scored ≤5.0. Similar patterns were observed in the domains “Knowledge” (≥6.0 vs ≤5.5), “Social/professional role and identity” (≥6.36 vs ≤5.79), and “Intentions” (≥6.0 vs ≤5.0). Qualitative data highlighted breaks in the continuity of care and gaps in patient knowledge about antimicrobial use. Key barriers included disparities in training, physician-centered decision-making, and patient knowledge gaps. Enablers included health care workers' willingness to learn and home caregivers' understanding of patient conditions. Conclusions The implementation of the antimicrobial stewardship program depends on addressing training disparities and leveraging health care workers' willingness to learn.

Multiplex PCR plays a critical role in diagnostics by enabling the detection of multiple targets in a single reaction. However, its use is often limited by the need for multiple fluorescent channels, which are restricted in standard PCR instrumentation. Amplification curve analysis (ACA) is a data-driven multiplexing (DDM) approach that overcomes this limitation by using real-time PCR data and machine learning to differentiate targets in a single-channel, single-well format, without requiring instrument modifications. As part of this DDM strategy, we previously introduced Smart-Plexer 1.0, a tool that simulates multiplex assays using empirical singleplex data to identify optimal assay combinations in silico, maximizing kinetic feature distances between targets to support ACA-based discrimination. While Smart-Plexer 1.0 performs reliably in controlled reactions and offers a strong framework for the ACA assay design, it relies on a single kinetic feature and a median-based distance metric, which limits its accuracy in reactions with variable target concentrations or efficiencies. Here, we present Smart-Plexer 2.0, a more robust and accurate version designed to improve the performance in amplification reactions affected by such variability. This version introduces three new kinetic features that are stable across different template concentrations and uses clustering-based distance measures to better capture the variability between targets. Compared to its predecessor, Smart-Plexer 2.0 reduces accuracy variance by an order of magnitude and improves ACA classification by 1.5 and 1% in retrospective 3-plex and 7-plex assays, respectively. In a multi-experiment, cross-concentration evaluation of a newly developed 7-plex assay, it achieved 97.6% ACA accuracy, confirming its robustness across complex scenarios. Smart-Plexer 2.0 offers a reliable and scalable way to design high-performance multiplex PCR assays using standard real-time PCR instruments.

The World Health Organization’s designation of mpox as a public health emergency of international concern in August 2024 underscores the urgent need for effective diagnostic solutions to combat this escalating threat. The rapid global spread of clade II mpox, coupled with the sustained human-to-human transmission of the more virulent clade I mpox in the Democratic Republic of Congo, highlights a critical gap in point-of-care diagnostics for this emergent disease. In response, we developed Dragonfly, a portable molecular diagnostic platform for point-of-care use that integrates power-free nucleic acid extraction (<5 minutes) with lyophilised colourimetric LAMP chemistry. The platform demonstrated an analytical limit-of-detection of 100 genome copies per reaction for monkeypox virus, effectively distinguishing it from other orthopoxviruses, herpes simplex virus, and varicella-zoster virus. Clinical validation on 164 samples, including 51 mpox-positive cases, yielded 96.1% sensitivity and 100% specificity for orthopoxviruses, and 94.1% sensitivity and 100% specificity for monkeypox virus. Here, we present a rapid, accessible, and robust point-of-care diagnostic solution for mpox, suitable for both low- and high-resource settings, addressing the global resurgence of orthopoxviruses in the context of declining smallpox immunity.

Introduction: Antimicrobial stewardship is crucial in combating the global problem of antimicrobial resistance. A key aspect of stewardship is intravenous-to-oral switching (IVOS). Research indicates oral antibiotics are often non-inferior to intravenous treatment. However, individual decisions regarding IVOS are complex and under-researched, impacting healthcare costs, patient comfort, and length of stay (LOS). We developed a machine learning model to predict when IVOS may be appropriate for an individual to support antimicrobial stewardship decision making. This clinical decision support system (CDSS) was evaluated through a prospective dataset and a user study involving simulated and real case vignettes. Methods: Patients receiving intravenous antibiotics for less than eight days followed by oral treatment were identified from electronic health record data in Imperial College Healthcare NHS Trust's Clinical Analytics, Research, and Evaluation (iCARE) environment. Patient demographics, co-morbidities, and vital signs were extracted as features. Disease embeddings and a set transformer were employed for processing co-morbidities, while 253 features were derived from vital signs using the 'CAnonical Time-series CHaracteristics' framework. Models were trained and tested using 10-fold cross-validation. A prospective dataset was generated from new data released into iCARE during research (June 2023). The best-performing model from cross-validation was applied to this dataset. Simulated case vignettes, based on iCARE data and real patient cases from MIMIC-IV, were integrated into a web app developed using Django. Clinicians involved in antimicrobial prescribing were recruited to review these cases and provide IVOS decisions. Results: 5,610 unique stays were used for cross-validation with the prospective dataset containing 547. The model achieved an AUROC of 0.77, accuracy of 0.70, true positive rate of 0.61, and false positive rate of 0.21 on the unseen prospective dataset, predicting that 51% of patients could have switched earlier than they did. Minimal differences between the real and predicted switch events correlated with reduced mean patient LOS. A statistically significant (p-value <0.01) difference in remaining LOS was observed between patients receiving oral versus intravenous treatment until five days of intravenous treatment. Data collection for the case vignette study is ongoing and will be completed before ICID. Preliminary qualitative results indicate that if a user trusts the CDSS, it sees increased engagement during decision-making, and can enable faster prescribing choices. Discussion: This CDSS aims to facilitate early switching when appropriate. Our model's predictions align with current guidelines, suggesting that switching in the first 48 hours is generally inappropriate, but subsequent early IVOS can be determined based on a patient's clinical status. Through case vignettes, we aim to quantify differences in prescribing behaviors between the CDSS and standard care, as well as evaluate user acceptability of the technology. Conclusion: AI-based CDSSs have the potential to support antimicrobial stewardship decision-making, though their impact on prescribing and safety remains unanswered.

BACKGROUND:The ability to monitor host- and bacteria-specific biomarkers along with antimicrobial drug concentration at the site of infection offers potential for individualised approaches to antimicrobial therapy. Although urine collection is straightforward and directly linked to the infection site, the assessment of urinary tract infection (UTI) biomarkers during infection has not been extensively explored. The aim of this study is to evaluate the potential of monitoring urinary nitrite levels as a biomarker for antimicrobial pharmacodynamics in UTI treatment. METHODS:Resistant and susceptible E. coli strains were cultured in oxygen-free artificial urine, with amoxicillin added after 15 h. Colony-forming unit (CFU) counts, nitrite, and creatinine levels were measured at 5 timepoints over 66 h. Urine samples from 25 UTI patients and 25 non-UTI controls were analysed for bacterial growth, nitrite, and creatinine. Spearman rank correlation and Mann-Whitney U-tests were used for statistical analysis. RESULTS:Our in-vitro model demonstrates that measuring the bacteria-specific urinary biomarker nitrite during E. coli growth in artificial urine can effectively be applied to assess antimicrobial pharmacodynamics over the course of UTI treatment. In an in-vitro UTI model, nitrite concentration can differentiate between resistant and susceptible E. coli strains and correlates with CFU counts. Analysis of 25 clinical UTI samples is consistent with these findings, showing correlations between nitrite levels and CFU counts. CONCLUSIONS:Here we show that nitrite generation by E. coli may have clinical relevance as a biomarker for infection progression and antimicrobial treatment outcomes, offering a valuable tool for monitoring the pharmacodynamic responses to antimicrobial therapy in UTIs.

Background The majority of countries (88%) have an Antimicrobial Resistance (AMR) National Action Plan (NAP V.1.0), but many remain unimplemented, and lack funding for interventions. Intervention selection requires a systematic approach to explain and predict progress. Looking beyond AMR is important to ensure the capture of systemic factors at the country level, which can impede or accelerate success. Aim To provide innovative policy analysis to allow country comparison and refine targeted action, while developing and implementing NAPs (V.2.0). Methods Mixed-method multi-country case study of policies and implementation strategies to address AMR across One Health. Starting with 17 countries, the sample includes each WHO region and emerging economies. This investigation of structures, processes, and outcomes has three components: a. Textual analysis of peer-reviewed literature, policy documents, global, national and state level progress reports, validated by global and in-country experts. An all-language article search conducted for 2000-2024, using broad search terms: ‘Antimicrobial resistance policies’, ‘national action plan’, ‘surveillance’, ‘AMR systems’ supplemented by hand searches. Deductive analysis using multi-disciplinary frameworks including the Expert Consensus for Implementation Research (ERIC). b. Longitudinal quantitative analysis assessing country contextual determinants and Antimicrobial Use (AMU) and AMR outcomes. Data from global health indicator repositories and international and national AMU and AMR surveillance networks are analysed using econometrics and machine learning approaches. c. Interactive Tableau dashboard development to display insights from a & b to allow visualisation and comparison of case-country AMR intervention context and components. Discussion This protocol provides a systematic, transparent approach for countries to benchmark their own AMR strategies. The interactive dashboard will allow comparisons between country clusters by geography or economy, and enable rapid knowledge mobilisation among strategic and operational stakeholders including policy makers and planners. This protocol facilitates others to perform this structured assessment and nominate their country for the next wave of analysis.

OBJECTIVE:Staphylococcus capitis is part of the human microbiome and an opportunistic pathogen known to cause catheter-associated bacteraemia, prosthetic joint infections, skin and wound infections, among others. Detection of S. capitis in normally sterile body sites saw an increase over the last decade in England, where a multidrug-resistant clone, NRCS-A, was widely identified in blood samples from infants in neonatal intensive care units. To address a lack of complete genomes and antibiograms of S. capitis in public databases, we performed long- and short-read whole-genome sequencing, hybrid genome assembly, and antimicrobial susceptibility testing of 22 diverse isolates. DATA DESCRIPTION:We present complete genome assemblies of two S. capitis type strains (subspecies capitis: DSM 20326; subspecies urealyticus: DSM 6717) and 20 clinical isolates (NRCS-A: 10) from England. Each genome is accompanied by minimum inhibitory concentrations of 13 antimicrobials including vancomycin, teicoplanin, daptomycin, linezolid, and clindamycin. These 22 genomes were 2.4-2.7 Mbp in length and had a GC content of 33%. Plasmids were identified in 20 isolates. Resistance to teicoplanin, daptomycin, gentamicin, fusidic acid, rifampicin, ciprofloxacin, clindamycin, and erythromycin was seen in 1-10 isolates. Our data are a resource for future studies on genomics, evolution, and antimicrobial resistance of S. capitis.

The increasing threat from infection with drug-resistant pathogens is among the most serious public health challenges of our time. Formed by Wellcome in 2018, the Surveillance and Epidemiology of Drug-Resistant Infections Consortium (SEDRIC) is an international think tank whose aim is to inform policy and change the way countries track, share, and analyse data relating to drug-resistant infections, by defining knowledge gaps and identifying barriers to the delivery of global surveillance. SEDRIC delivers its aims through discussions and analyses by world-leading scientists that result in recommendations and advocacy to Wellcome and others. As a result, SEDRIC has made key contributions in furthering global and national actions. Here, we look back at the work of the consortium between 2018-2024, highlighting notable successes. We provide specific examples where technical analyses and recommendations have helped to inform policy and funding priorities that will have real-world impact on the surveillance and epidemiology of infections with drug-resistant pathogens.