JAC-Antimicrobial Resistance

The inappropriate use of antimicrobials enhances antimicrobial resistance (AMR). Antimicrobial stewardship (AMS) is a coordinated effort of prescribers, pharmacists, and nurses. Still, local data regarding AMS-related knowledge, attitudes, and practices (KAP) are scarce in many low and middle-income countries. We evaluated KAP regarding AMS among the healthcare providers at Komfo Anokye Teaching Hospital (KATH), Ghana, and found the related factors. A cross-sectional survey in the form of a descriptive survey was conducted among medical doctors, pharmacists, and nurses at KATH. Knowledge, attitude, and practice were evaluated using a structured questionnaire. The scores were converted into percentages and classified as good (>=60%) or poor (<60%). Chi-square tests were used to test associations, and logistic regression to predict good KAP (p<0.05). A total of 349 healthcare professionals participated, which comprised: 91 medical doctors (26.1%), 101 pharmacists (28.9%), and 157 nurses (45.0%). The majority of the respondents had formal AMS/AMR training (69.6%), and 37.0% had updated training the previous year. Only 18.6% demonstrated good AMS-related knowledge, although attitudes were largely positive (95.7% good) and reported practices were mostly appropriate (77.4% good). In multivariable models, greater years of practice (5-9 years: adjusted odds ratio [AOR] 2.32; >=15 years: AOR 2.77) and formal training (AOR 2.94) were associated with good knowledge. Formal training was also associated with good attitudes (AOR 5.19). Compared with medical doctors, nurses had lower odds of good practice (AOR 0.29), while pharmacists had higher odds (AOR 1.41). Participants with 10-14 years of experience had higher odds of good practice (AOR 3.18). This study revealed that marked knowledge deficits exist, despite favourable attitudes and generally good self-reported AMS practices. Role-tailored, competency-based AMS training with regular updates and reinforcement through practical stewardship tools is needed to translate positive attitudes into evidence-based prescribing and administration behaviours.

Background Antimicrobial stewardship (AMS) and infection prevention and control (IPC) are complementary strategies to improve patient safety and address antimicrobial resistance (AMR). In low- and middle-income countries (LMICs), they are often implemented separately, reducing effectiveness. Evidence on integrating AMS and IPC in routine hospital practice remains limited. Objective To evaluate the feasibility of an integrated AMS-IPC improvement approach and describe changes in implementation in Vietnamese hospitals. Methods We conducted a multisite quality improvement initiative in four hospitals within the national AMR surveillance network in Viet Nam (March-September 2025). We used US-CDC tools to guide the implementation, including the Global Antibiotic Stewardship Evaluation Tool (G-ASET) and the Infection Control Assessment and Response (ICAR) tool. Baseline assessments were followed by feedback, multidisciplinary action planning, and targeted capacity building. Follow-up occurred 2-5 months later. Changes were analysed descriptively using quantitative scores and qualitative synthesis, and reported following the SQUIRE 2.0 guidelines. Results All hospitals had established IPC programmes at baseline, while AMS maturity varied. G-ASET scores improved across all sites, with greater gains in hospitals starting from lower baselines. Key improvements included leadership and governance, education and training, stewardship actions, and monitoring and reporting. IPC practices aligned with AMS priorities also improved, particularly transmission-based precautions, environmental cleaning, and cross-team coordination. Infrastructure-dependent areas, such as water safety, showed limited short-term progress. Conclusions An integrated AMS-IPC approach using repeated assessment and feedback is feasible and associated with meaningful improvements. This model offers a scalable strategy for strengthening hospital responses to AMR in LMICs and informs national programmes.

The Burkholderia cepacia complex (BCC) is comprised of 24 species of Gram-negative bacteria that cause opportunistic infections. While antimicrobial susceptibility testing (AST) has historically been used to guide treatment for BCC infections, recent work highlighting problems with AST for these organisms led the Clinical and Laboratory Sciences Institute (CLSI) to remove disk diffusion (DD) and minimal inhibitory concentration (MIC) breakpoints for BCC from its M100 standards document. Epidemiological cut-off values (ECVs) may be helpful to clinicians in the absence of breakpoints, as they may be used to determine whether an isolate has a wild-type or non-wild-type phenotype. Here we present an analysis of BCC ECVs for ceftazidime (CAZ), levofloxacin (LVX), meropenem (MEM), minocycline (MIN), and trimethoprim-sulfamethoxazole (TMP-SMX). ECVs were calculated using MIC data from 3 previous studies and 3 independent laboratories for 1,896 BCC isolates. ECVs were 16 g/ml for CAZ, 8 g/ml for LVX, 16 g/ml for MEM, and 8 g/ml for MIN. The ECV for TMP-SMX varied depending on the analysis from 2 g/ml, 8 g/ml, and 16 g/ml and therefore could not be reliably established. Challenges with establishing ECVs for BCC include limitations with the pooled MIC dataset, broad MIC distributions, and high ECVs that are above the obsolete susceptible MIC breakpoints. These challenges limit the clinical utility of ECVs for these organisms and supported removal of ECVs from the CLSI M100 standards document. IMPORTANCEThe Burkholderia cepacia complex is a group of bacterial species that cause difficult-to-treat opportunistic infections. Recently, clinical breakpoints, which are used to determine whether organisms are susceptible to certain antimicrobials, were removed from Clinical and Laboratory Standards Institute (CLSI) standards for these organisms due to problems with antimicrobial susceptibility testing performance. Clinicians are now faced with the challenge of how to treat these complex infections without clinical breakpoints. Here we determine epidemiological cut-off values (ECVs) for relevant antimicrobials for the B. cepacia complex. While we established ECVs for four antimicrobials, we encountered significant challenges in our analyses, including limitations with data for these organisms and high ECVs that are not clinically useful. These challenges limit the practical use of these ECVs in helping guide clinicians on treatment and supported the eventual removal of ECVs from the CLSI M100 standards document.

Background Microbiological confirmation of paediatric pulmonary tuberculosis is frequently unattainable, rendering chest radiography a critical yet underutilised diagnostic tool. Methods We conducted a retrospective diagnostic accuracy study of the qXR version 4.2.1 (Qure.ai), a paediatric optimized computer-aided detection (CAD) algorithm, for pulmonary tuberculosis. Diagnostic performance was assessed against microbiological (MRS) and clinical reference standards (ClRS). Bayesian latent class analysis (LCA) was applied to address the imperfection of both reference standards in children. Performance was quantified using area under the receiver operating characteristic curve (AUROC) and estimates of sensitivity and specificity. Results We included digital chest radiographs of 932 Gambian children (< 15 years) comprising 80 (9%) children with confirmed tuberculosis, 163 (17%) with unconfirmed tuberculosis, and 689 (74%) classified as unlikely tuberculosis. Against MRS, qXR demonstrated AUROC, sensitivity and specificity of 0.68 (95% CI, 0.61 to 0.75), 54% (95% CI, 43 to 64%), and 82% (95% CI, 79 to 84%), respectively. Against ClRS, the AUROC, sensitivity and specificity were 0.73 (95% CI, 0.69 to 0.77), 41% (95% CI, 34 to 49%), and 87% (95% CI, 84 to 89%), respectively. Bayesian LCA, assuming conditional independence, estimated sensitivity of 79% (95% CrI, 65 to 89%) and specificity of 82% (95% CrI, 79 to 84%). Assuming conditional dependence between qXR and expert radiologist, and between culture and Xpert, estimated sensitivity increased to 89% (95% CrI, 71 to 98%), with specificity remaining at 82% (95% CrI, 79 to 84%). Conclusions Paediatric optimized qXR algorithm provides a valuable complementary tool for diagnosis of paediatric pulmonary tuberculosis. Conventional reference standards likely underestimate the true diagnostic performance of CAD systems in children.

Background: Antimicrobial resistance is the world's silent pandemic. The public knowledge, attitudes, and practices (KAP) about antibiotic usage are strongly related to the growing problem in Nepal. Methods: A cross-sectional descriptive survey was done to 263 respondents. Information on KAP regarding antibiotics, primary healthcare sources, and demography was collected through a questionnaire. To identify health literacy gaps and characteristics that contribute to improper antibiotic use, this study assessed these variables across an age group from 18 to 60 years. Descriptive statistics analysis was performed to analyze the data. Results: The majority of respondents were between the ages of 18 and 39 (85.1%), female (63.1%), and had at least a bachelor's degree (67.8%). Significant misunderstandings about antibiotics remained, even though 77.6% of respondents correctly recognized antibiotics as effective against bacteria; 44.1% incorrectly believed that antibiotics cure viral diseases, and 87.8% felt that antibiotics should be stopped right away if adverse effects develop. In practice, 52.9% acknowledged quitting antibiotics as soon as symptoms improved, despite 89.4% consulting doctors. Additionally, 43% of respondents said they have taken antibiotics without a prescription, frequently due to pharmacist recommendations (21.67%) and financial or geographical constraints. The main sources of information were doctors (11.07%) and pharmacist-doctor combinations (14.88%), yet 81.8% of respondents said they had never heard of the phrase antimicrobial resistance. Conclusion: There is a significant lack between theoretical understanding and practical application, despite the high levels of fundamental knowledge toward the prohibition of non-prescription sales. Self-medication and early withdrawal are still common inappropriate practices. It is crucial to implement focused teaching initiatives that highlight the differences between bacterial and viral diseases as well as the risks associated with leftover medicine. It is advised to use digital platforms for younger demographics and to strengthen the role of pharmacists in order to reduce AMR.

The incidence of vancomycin-resistant Enterococcus (VRE) is rising in hospitals in Canada, and resistance to last-resort antimicrobials including linezolid complicates treatment options for multidrug-resistant isolates. Recent reports from around the globe indicate that both linezolid and vancomycin resistance genes can be co-carried and mobilized by linear plasmids (named pELF) in Enterococcus species, often on the same backbone. We aimed to investigate linezolid resistance and linear plasmid prevalence in VRE bloodstream infection isolates collected by the Canadian Nosocomial Infection Surveillance Program from 2009 to 2024. We found that screening for pELF linear plasmid ends in short reads was a reliable way to predict linear plasmid presence in large-scale surveillance data (100 % accuracy on 85 reference samples). Almost half of the isolates in our collection were predicted to carry pELF plasmids (45.4 %, 941/2071) and we found that this proportion has increased from 2018 (32.2 %, 59/183) to 72 % of isolates between 2021 and 2024 (2021: 68.5 % (115/168); 2022: 71.6 % (146/204); 2023: 72.8 % (166/228); 2024: 71.6 % (235/328)). This trend of increasing linear plasmid carriage is evident from 2018 to 2024 across the dominant emerging sequence types (ST80, ST17, ST117). Linezolid resistance based on phenotypic antimicrobial susceptibility testing was low (1.0 %, 21/2071). Using long read sequencing, we characterized the linezolid resistant isolates and confirmed pELF plasmid presence in 13/21 (61.9 %) isolates. Six isolates harboured pELF plasmids encoding linezolid resistance genes (optrA, cfr(D), poxtA) and five of these also encoded vancomycin resistance genes (vanA). We compared these six plasmids to 39 public plasmid sequences and clustered them using MOB-suite and pling. Overall, this study provides further examples of the co-carriage of vancomycin and linezolid resistance genes on mobile linear plasmids and shows that linear plasmid prevalence is detectable and increasing across VRE in Canada. IMPACT STATEMENTGiven the increasing prevalence of multidrug-resistant hospital-acquired pathogens, resistance to last-resort antibiotics is a global public health threat. Linezolid is a last-resort antibiotic used to treat vancomycin-resistant Enterococcus isolates, and the dissemination of linezolid resistance genes is significantly facilitated by mobile elements that can transfer between unrelated strains and species. Linezolid resistance genes have recently been described on linear plasmids and are often co-localized with other resistance genes on the same plasmid backbone. Consequently, understanding the features and distribution of linear plasmids and those harbouring linezolid resistance genes is crucial for pathogen surveillance and mitigation of resistance. In this work, we used long-read and short-read sequencing to characterize genomic epidemiology of linear plasmids across 16 years of Enterococcus surveillance data in Canada. This study furthers knowledge of linear plasmids by demonstrating that they are relatively common across vancomycin-resistant Enterococcus blood isolates and by providing more examples of co-localized vancomycin and linezolid resistance genes on the same linear plasmid backbone. DATA SUMMARYSequencing data and genome sequences were deposited in National Centre for Biotechnology BioProject PRJNA1279082, and accessions are listed in Table S1. Supplementary materials for this study are available at the Figshare portal through DOI: XXX.

Background: Antimicrobial resistance (AMR) is a global public health problem. Infections caused by extended-spectrum beta-lactamase (ESBL) and carbapenemase (CP) -producing Enterobacterales (E) threaten individuals and healthcare systems worldwide. Symptomatic infection caused by Enterobacterales is typically preceded by asymptomatic colonisation and often occurs in the most vulnerable individuals, thus interrupting asymptomatic transmission is desirable. The dominant transmission routes across the healthcare continuum including hospitals, intermediate care, and long-term care facilities are not well understood. Methods: Here we present a protocol describing a genomic surveillance framework developed for the Tracking Antimicrobial Resistance Across Care Settings (TRACS) Liverpool programme, which aims to identify critical ESBL-E transmission points in hospitals and care homes in Liverpool, UK. Our study integrates individual participant and healthcare facility data, validated standard operating procedures for taking and culturing stool, rectal, environmental, and staff samples, and genomic sequencing of ESBL-E, and statistical modelling approaches into a research framework for ESBL-E genomic surveillance. Discussion: There is a need for improved epidemiological and laboratory approaches to studying bacterial transmission. Drug-resistant enteric bacteria are a highly tractable marker of the movement of all enteric bacteria, and interventions designed to interrupt transmission of drug-resistant bacteria are expected to have a broader healthcare impact. This protocol provides a standardised, reproducible approach for identifying ESBL-E, tracking acquisition events, and linking clinical and environmental isolates through whole-genome sequencing.

The global occurrence of sexually transmitted infections (STIs) continues to rise, necessitating accurate diagnosis and treatment to curb their spread and associated complications. With the alarming increase in antimicrobial resistance (AMR) in Neisseria gonorrhoeae, effective STI management relies heavily on etiological diagnosis. The Tanzania National Standard for Medical Laboratories 2017 outlines recommended STI testing protocols based on facility levels, yet adherence to these guidelines and associated challenges remain poorly documented. This study describes the diagnostic capacity for different STIs in northern Tanzania. A cross-sectional study was conducted between May and July 2023, encompassing 14 laboratories across Moshi Municipal Council, Kilimanjaro region. The laboratories assessed were in five hospitals and nine health centres (HCs). Data regarding facility type and STI diagnostic capabilities were gathered through questionnaires administered during site visits and supplemented by observations. All five hospitals were equipped to conduct rapid diagnostic tests for HIV, syphilis, and wet preparation microscopy for Trichomonas vaginalis (TV). Only three hospitals had the capacity to perform culture and sensitivity testing using chocolate and blood agar medium, however none reported isolating Neisseria gonorrhoeae in the past year. Critical STI diagnostic tests including the Treponema pallidum particle agglutination assay (TPPA) and Treponema pallidum hemagglutination assay (TPHA) for the laboratory confirmation of syphilis, assays for Chlamydia trachomatis, Herpes Simplex virus -2, and Human papillomavirus (HPV) were absent across all five hospitals. Conversely, all health centers demonstrated proficiency in rapid treponemal tests for syphilis, together with rapid HIV test and TV testing, although one health center lacked the capacity for wet laboratory preparation for TV detection. Findings underscore a concerning lack of STI testing capacity within surveyed healthcare facilities, posing significant barriers to effective STI management and exacerbating the threat of AMR in Tanzania. In particular, the capacity for conventional microbiology culture was limited in most settings, severely compromising the ability to track and monitor AMR. Urgent investment in laboratory infrastructure and training is imperative to enhance STI diagnosis and treatment, ultimately curtailing transmission and mitigating the impact of AMR.

ObjectiveRecurrent urinary tract infections (rUTIs) significantly decrease quality of life and antibiotics are becoming increasingly less effective due to antimicrobial resistance. Alternative effective treatment strategies are urgently needed for rUTIs. Prior studies have indicated that women can experience resolved or improved rUTI following electrofulguration (EF). To further investigate these findings, we report on the design and methodology behind a randomized trial examining two treatment arms: standard prolonged antibiotic treatment with nitrofurantoin (NF) alone or in combination with EF. Patients and MethodsThe aim of this randomized trial is to determine, at two institutions, the efficacy of two interventions for rUTI associated with early stages of chronic cystitis (stages 1 and 2): conventional 6 months low-dose (100mg) NF daily antibiotic suppression alone (NF) or conventional NF with EF (EF + NF). The study is also designed to analyze changes in the urinary microbiomes in the two different treatment arms and to determine the durability of clinical outcomes in both treatment arms at 2 years after the end of each intervention. The primary outcomes will be obtained from 6 to 18 months, as well as 18 - 30 months following completion of the original 6-month intervention. Failure is defined based on UTI symptoms documented by a validated questionnaire with a documented urine culture confirming a bacterial strain at each UTI episode following the end of the 6-month intervention. ConclusionsThis randomized trial is designed to examine the efficacy and durability of treating women with rUTIs using the standard of care of NF alone, or an EF procedure with NF.

Background: Conjugative plasmids encoding New Delhi metallo-beta-lactamase (blaNDM) pose a threat for the spread of carbapenem resistance among healthcare acquired pathogens. Plasmid-associated outbreaks of blaNDM-producing bacteria can involve multiple bacterial species and persist over long time periods, making their detection and control difficult. We systematically studied the genomic epidemiology of blaNDM-encoding plasmids detected within a single hospital system over a five-year period. Methods: blaNDM-producing isolates were collected from clinical cultures as part of the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT) genomic sequencing active surveillance program, or during infection prevention and control (IP&C) investigations. Isolates were identified as blaNDM producers by polymerase chain reaction (PCR); the presence of plasmid-encoded blaNDM genes was confirmed by sequencing on both Illumina and Oxford Nanopore platforms. Plasmids were clustered using Pling and bacterial relatedness of host isolates was evaluated with split kmer analysis. Electronic health record data were used to identify shared unit-level spatiotemporal exposures and epidemiologic links within both plasmid and host clusters. Results: We identified 61 blaNDM-producing isolates collected from 54 patients sampled between November 2020 and July 2025. Isolates belonged to 15 Enterobacterales species; Enterobacter hormaechei was the most frequently sampled species (n=23, 37%), and blaNDM-5 was the most frequently observed blaNDM allele (n=36, 59%). We observed six clusters of genetically similar blaNDM-encoding plasmids each containing 2-28 isolates, and eight singleton plasmids. The two largest plasmid clusters consisted of a highly conserved 46 kb IncX3 family blaNDM-5-encoding plasmid (n=28 plasmids, 9 species) and a more variable 98-201 kb IncC family blaNDM-1-encoding plasmid (n=12 plasmids, 6 species). Epidemiologic investigation paired with whole genome sequencing identified spatiotemporal associations between shared patient exposures and putative plasmid and bacterial transmission clusters, suggesting that unit-level exposures contribute to plasmid dissemination. Finally, analysis of publicly available sequences showed that the most prevalent plasmids detected, IncX3(blaNDM-5) and IncC(blaNDM-1), also demonstrated high global prevalence. Conclusions: This study demonstrates the diversity of blaNDM carrying plasmids within a single hospital system and their capacity to cause prolonged, multispecies outbreaks. Integrating whole genome sequencing with epidemiologic data identified unit-level spatiotemporal overlap as a likely contributor to plasmid dissemination in the hospital.

Methicillin-resistant Staphylococcus (MRS) infections are a significant public health concern. Anterior nares serve as a major reservoir and source of spread of MRS ssp. People living with HIV (PLWHIV) tend to be at higher risk of colonisation with MRS organisms due to frequent healthcare exposure. We assessed the prevalence of MRS nasal carriage and associated factors among PLWHIV at the HIV clinic of Kiruddu National Referral Hospital, Kampala, Uganda, from May to July 2024. Nasal swabs from 256 PLWHIV were cultured, and microbiological isolation was performed at MBN Clinical Laboratories. Prevalence was calculated as proportions, and logistic regression identified associations with clinical and socio-demographic factors (p < 0.05). Of 256 participants, 163 (63.7%) carried Staphylococcus, with 82 (32%) identified as MRS carriers (8.9% MRSA, 23% MRCoNS). Frequent hospital visits ([&ge;]3) (adjusted incidence risk ratio [A-IRR] = 1.18 x 107, p < 0.001), second-line antiretroviral therapy (ART) (A-IRR = 3.82, p = 0.041), and unsuppressed viral load (>1000 copies/mL) (adjusted odds ratio [AOR] = 11.3, 95% CI: 2.11-60.58, p = 0.005) were significantly associated with MRS carriage. Mask-wearing was protective against MRCoNS (A-IRR = 1.66, 95% CI: 1.06-2.58, p = 0.026). MRS isolates exhibited high resistance to erythromycin (81.7%) and trimethoprim-sulfamethoxazole (79.3%), but susceptibility to linezolid (93.9%). MRS nasal carriage is prevalent among PLWHIV. Individuals with frequent health care contact and those on second-line ART regimens are more susceptible to MRS colonization, while individuals who wear face masks and those with an undetectable HIV viral load are less susceptible. Antimicrobial Resistance (AMR) surveillance within HIV programs, enhanced infection control, ART adherence, and targeted screening for high-risk groups are critical to mitigate colonization.

The Epic Sepsis Model version 2 (ESMv2) is a prediction model embedded into the electronic medical record used to warn clinicians which hospitalized patients are at risk for sepsis. We conducted a retrospective cohort study of 31,951 hospitalizations of 25,760 patients to compare analyses conducted at the commonly used patient-level (where a maximum prediction prior to the onset of sepsis is used to measure performance) vs novel prediction-level (where each prediction is used to measure performance). Sepsis, defined by the Sepsis 3 criteria occurred during 1,049 hospitalizations (3.3%). Patient-level analyses suggested excellent discrimination AUC 0.86; [IQR 0.85, 0.87], whereas prediction-level analyses demonstrated lower performance AUC 0.62; [IQR 0.57, 0.65]. Low estimates of the positive predictive value (14.5% at the patient level vs 4% at the prediction level) imply a high number of false alerts. Common evaluation approaches may overstate the performance of dynamic prediction models and mislead clinical decision-making.

Background: Tuberculosis (TB) remains the leading infectious cause of death worldwide, with India accounting for nearly one-fourth of global TB cases. Ni-kshay, the countrys digital case-based TB notification platform is rich in data pertaining to the continuum of care of TB patients. This study aims to develop a standardized analytical approach to programmatic data to identify predictors of unfavourable treatment outcomes and mortality among adult drug-sensitive TB patients at the state level for Maharashtra during 2021 and 2022. Methods: Two separate analyses were undertaken comparing treatment success with: (1) unfavourable outcomes (death, treatment failure, loss to follow-up, regimen change, or not evaluated); and (2) mortality. Multivariate logistic regression was used to compute adjusted odds ratios (aOR) for key risk factors, adjusting for age, gender, and weight. Results: The final cohort included 323,124 cases for unfavourable outcome analysis and 315,579 cases for mortality analysis. Increasing age, male gender, lower body weight, known HIV and diabetes comorbidities, tobacco and alcohol consumption, and "unknown" status for behavioural risks and comorbidity status were significantly associated with increased odds of both unfavourable outcomes and mortality. Conclusions: This study highlights the utility of programmatic data in identifying high-risk TB patients and offers a reproducible analytic framework.

Background: Multi-drug resistant Bacterial (MDRB) Infections in the intensive care units (ICUs) substantially elevate patient mortality, prolong hospital stays, and impose heavy healthcare cost burdens. Existing predictive models for ICU-acquired MDRB infection predominantly focus on static admission-risk assessment, lacking the capacity to leverage longitudinal treatment data for dynamic risk re-stratification during the ICU stay. Meanwhile, most models suffer from poor clinical interpretability, overreliance on hard-to-collect biomarkers, or absence of deployable clinical tools, limiting real-world translation. Therefore, there is an urgent need to develop a parsimonious, interpretable tool based on routine cumulative data to guide timely intervention. This study aimed to develop a interpretable model with a web calculator to improve clinical applicability. Methods: In this study, we conducted a retrospective analysis of ICU inpatients at the First Affiliated Hospital of Dali University between January 1, 2023, and January 1, 2026. Using the create Data Partition function in R software (random seed = 42), the dataset was stratified and divided into a training group and a validation group in a 7:3 ratio. Feature selection was performed using the Boruta algorithm to validate variable rationality. A multivariable logistic regression model was constructed and visualized as a nomogram, and its performance was compared with six machine learning algorithms (Random Forest, XG Boost, Neural Network, etc.). Model validation was conducted using receiver operating characteristic curves (ROC), Decision Curve Analysis (DCA), and SHAP value interpretation. Finally, an online R Shiny calculator was developed based on the final model. Results: A total of 3,631 patients were enrolled and divided into a training group (n=2,543) and a validation group (n=1,088) using stratified random sampling. Five independent predictors were identified in the training group, which were hypertension combined with diabetes, antibiotic types, ventilator days, urinary catheter days, and PCT abnormality times. The Logistic regression model achieved an AUC of 0.772 (95%CI: 0.733-0.812) in the validation group, outperforming XG Boost (0.763) and Random Forest (0.703). The model demonstrated excellent calibration (Hosmer-Leme show {chi}{superscript 2} = 1.94, P = 0.9829) and positive net clinical benefit across threshold probabilities of 0%-40%. SHAP analysis aligned with regression-derived variable importance rankings, confirming predictor contributions. An open-access online calculator was successfully deployed (https://dongfangshao666.shinyapps.io/MDR_shiny2/), enabling real-time individualized risk stratification at the bedside. Conclusion: This study developed and validated a dynamic, interpretable multi-drug-resistant bacterial infection risk prediction model requiring only five routinely collected clinical indicators. The model balances robust predictive performance with high transparency, overcoming key limitations of prior tools. The accompanying web calculator supports dynamic risk reassessment throughout the ICU stay, facilitating precise antimicrobial stewardship, targeted infection control interventions, and optimized resource allocation, bridging the gap between statistical modeling and frontline clinical decision-making.

Salmonella is a major zoonotic foodborne pathogen, and antimicrobial resistance (AMR) in Salmonella presents a significant public health challenge. Whole-genome sequencing (WGS) offers a more rapid and comprehensive method for AMR characterization compared to conventional antimicrobial susceptibility testing (AST), supporting antimicrobial therapy and surveillance efforts. In this study, Oxford Nanopore Technology (ONT)-based WGS was performed on 1,490 Salmonella isolates collected through nationwide surveillance in Taiwan in 2025. Genotypic resistance inferred from WGS data was compared with phenotypic AST results to assess the performance of ONT-WGS. Overall, WGS-inferred resistance showed high concordance with phenotypic resistance for most antimicrobials. However, major genotype- phenotype discordance was observed, attributed to four categories: (i) breakpoint-dependent classification, (ii) reduced or absent phenotypic expression of resistance genes, (iii) MIC modulation by ramAp, and (iv) absence of known AMR determinants. Notable discrepancies included tigecycline resistance without known genetic determinants, nalidixic acid resistance linked to ramAp-mediated MIC elevation, and a high prevalence of colistin resistance (35.4%) in S. Enteritidis without identifiable AMR determinants. Additionally, a significant proportion of ESBL- and AmpC-producing isolates were classified as susceptible or intermediate to cefotaxime and ceftazidime under CLSI criteria, highlighting the potential for misclassification and treatment failure. These findings demonstrate that ONT-WGS enables accurate, comprehensive AMR characterization, offering direct identification of AMR determinants and minimizing misclassification due to breakpoint-based AST interpretations. When interpreted appropriately, WGS can support better antimicrobial selection and serve as a valuable alternative to conventional susceptibility testing.

Rationale: Efficacious dose selection for anti-tuberculosis drugs has traditionally relied on achieving plasma exposures above the minimum inhibitory concentration, but this approach has not consistently aligned with clinical outcomes. Objectives: We sought to identify early pharmacokinetic-pharmacodynamic targets most predictive of clinical efficacious dose. Methods: We conducted a back-translational, pharmacokinetic-pharmacodynamic simulation-based analysis of 15 anti-tuberculosis drugs. Using pharmacokinetic data from multiple biological matrices and a range of pharmacodynamic metrics, we established candidate exposure-response targets for attainment. We systematically evaluated the predictive accuracy of each target pair against established clinical doses to formulate a decision-making framework linking key drug properties to the most predictive targets. Measurements and Main Results: Depending on the target used, projected clinical doses varied widely - both within and across compounds - highlighting the importance of target selection for dose projection and go/no-go decisions. In general, targeting cellular lesion-level drug exposures relative to in vivo preclinical potency provided an effective approach for early dose selection. However, for highly penetrating drugs, targeting site-of-action therapeutic exposures in the caseum was more predictive of clinical dose. Based on these findings, we developed a preliminary dose prediction tool that enables drug developers to estimate clinically relevant dose ranges of compounds using in vitro and early in vivo data. Conclusions: This work establishes and validates a simple, evidence-based framework to standardize early translational decision-making on dose selection of anti-tuberculosis candidates in development.

Biofilms pose a significant challenge to antimicrobial therapy. Bacteria in biofilms differ from planktonic counterpart in their altered metabolism, collective behavior, protective role of extracellular matrix and diversified microbial subpopulations. These attributions significantly influence bioavailability and activity of antibiotics. The presence of bacterial aggregates during acute infections expands the problem to many other conditions previously not discussed in the biofilm context. Klebsiella pneumoniae is a leading cause of life-threatening hospital-acquired infections and is included in the WHO Bacterial Priority Pathogens List due to increasing antimicrobial resistance. The combination of antimicrobial resistance and the ability to form biofilms severely limits the efficacy of antibiotic treatments. In this study, we investigated the in vitro susceptibility of mature biofilms to 13 antimicrobials of K. pneumoniae clinical isolates from a single hospital. The resistance profiles of the local clinical isolates were consistent with the global epidemiology of K. pneumoniae. Minimal biofilm eradication concentrations (MBEC) for mature biofilms were defined with two assays (biomass and metabolic activity measurements) and brought into relation with susceptibility breakpoints and plasma (Cmax). Colistin sulfate, tigecycline, cephalosporins and combination of imipenem with cilastatin were the most potent biomass eradicators, while suppression of metabolic activity was barely reachable. Moreover, we observed a notable increase in metabolic activity upon exposure to sub-MBEC concentrations of antibiotics. Finally, our data broach a subject of antibiotic prioritization with respect to biofilm tolerance. IMPORTANCEThis study addresses the critical gap between standard antibiotic susceptibility testing and the tolerance of biofilm and microbial aggregates during infections caused by K. pneumoniae. By systematically evaluating mature biofilms from a significant number of clinical isolates, we demonstrate that colistin and tigecycline show potent activity against both biofilm biomass and metabolic activity, whereas cephalosporins primarily reduce biomass without effectively suppressing bacterial metabolism, and other drugs have only weak effects on biofilms at clinically achievable concentrations. Furthermore, the alarming observation that sub-inhibitory biofilm eradication concentration (sub-MBEC) of antibiotic can paradoxically increase the metabolic activity of biofilms highlights a potential risk factor for therapy failure and resistance development. Our findings contribute to the necessary evidence base for prioritizing existing antibiotics in the limited armamentarium against biofilm-forming K. pneumoniae.

Introduction: The use of artificial intelligence (AI) by clinicians has increased rapidly in recent years, with large language models (LLMs) emerging as tools that can equal clinician diagnostic performance in simulated settings. However, limited data exist regarding physicians use of LLMs in real-world clinical practice. This study aimed to evaluate the frequency of LLM use among practicing hospitalists, identify which LLMs are most commonly utilized, and assess hospitalists' perceptions of the benefits and limitations of LLM use in clinical care. Methods: We conducted a cross-sectional survey study of academic hospital medicine faculty across 8 institutions within the Hospital Medicine Reengineering Network (HOMERuN), a collaborative research consortium. Eligible participants included hospitalists practicing within participating HOMERuN sites during the study period. The survey assessed the frequency of LLM use, types of LLMs used, clinical applications, and physician perceptions regarding usefulness, efficiency, and concerns associated with LLM adoption. Results: 170 respondents (67.1%) reported ever using an LLM in clinical practice. Among LLM users, OpenEvidence was the most used tool (88.9%), followed by ChatGPT (58.5%), Google Gemini (26.9%), and Microsoft Copilot (20.5%). Only a minority of hospitalists reported using LLMs daily while seeing patients. The most common use cases of LLMs were answering diagnostic (77.1%) and management (77.6%) questions. A majority also reported using LLMs to identify or summarize primary literature (60.0%). Lack of trust in outputs (49.8%), uncertainty around institutional policies (48.6%), and lack of access to secure applications (43.1%) were cited as the most frequent barriers to using LLMs in practice. Discussion: The use of LLMs in clinical practice is already widespread, though regular or daily use is not yet typical. Concerns regarding reliability, patient privacy, and safe integration into clinical workflows remain significant barriers to broader adoption. The responsible implementation of LLMs in hospital medicine will require addressing these barriers.

Background India accounts for nearly one-fourth of the global tuberculosis (TB) burden. The country's progress towards elimination of TB is hindered by considerable heterogeneity in behavioural, social, and health system determinants, which influence transmission dynamics and care access. Evidence from the recent national TB prevalence survey showed that almost half of individuals with active disease were asymptomatic, underscoring the limitations of symptom -based case finding. Achieving the End TB targets will therefore require strategies that simultaneously address the substantial pool of individuals with undiagnosed, asymptomatic disease and those symptomatic individuals who do not seek care. Methods We developed a transmission model of TB that explicitly incorporates individuals with asymptomatic disease, and those who do not seek care. Model calibration was performed within a Bayesian framework using epidemiological and programmatic data for India. The calibrated model was then used to project the potential impact of intervention on TB incidence and mortality. Results Under the baseline scenario, the estimated TB incidence and mortality rates for 2024 were 180 (163-203) and 24 (18-31) per 100,000 population, respectively. Across all intervention scenarios targeting improved diagnosis, active case finding, nutrition support and their combination the reduction in incidence rate by 2030 ranged from 13% to 60% compared with 2025, while the corresponding decline in mortality rate ranged from 16% to 66%. Conclusion While individual interventions yield measurable reductions in TB incidence and mortality, but greater impact is achieved when implemented in combination reflecting the need for a comprehensive, multi-component response towards TB elimination.

Background: Shigella is a leading cause of childhood diarrhea in low- and middle-income countries and is increasingly resistant to first-line antibiotics. We conducted a surveillance study to determine the incidence, genomic characteristics, and AMR profiles of Shigella infections in children under five with moderate to severe diarrhea (MSD) in Lusaka, Zambia. Methods: Between 15 September 2020 and 30 November 2021, a prospective cohort study of 1,400 children under five was enrolled during a community census in a peri-urban setting and passively followed for 9.5 months for MSD. During enrollment, socio-demographic data were collected using electronic questionnaires, while clinical data were collected through the DHIS platform. The main outcome, Shigella in diarrheal stool in under 5 children, was detected using culture and Loop-mediated Isothermal Amplification (LAMP) targeting the ipaH gene. Cox proportional hazards models were used to assess the incidence and risk factors of Shigella (ipaH) infections. Whole-genome sequencing (WGS) was used to characterize the genomic diversity and antimicrobial resistance genes, complemented by phenotypic antibiotic susceptibility testing. Results: There were 230 first episodes of Shigella over a follow-up time of 9,581.7 child-months, yielding an incidence of 24.0 (95% CI 21.1-27.3) cases per 1,000 child-months, with the highest incidence among 2 to 3-year-olds. The key risk factors identified were the water source (p=0.025) and age group (p=0.014). Genotypic characterization revealed 10 S. flexneri, 9 S. sonnei, and 3 S. boydii. The S. sonnei isolates formed two clusters, differing in virulence factors and plasmid profiles, indicating two possible circulating strains. Shigella isolates exhibited phenotypic and genotypic multidrug resistance, including against trimethoprim, aminoglycosides, and beta-lactams. Plasmid-mediated quinolone resistance (qnrS1) was identified in four S. flexneri isolates, with these genes located on the IncFIB(K) plasmid, highlighting the potential for horizontal transmission and spread of quinolone resistance in this region. No phenotypic and genotypic resistance to macrolides, the first-line treatment for Shigella in Zambia, was observed. Interpretation: We report a high burden of Shigella with multidrug resistance, including resistance to fluoroquinolones. These findings highlight the increasing resistance of Shigella to first-line antibiotics and underscore the importance of developing safe and effective vaccines, improving WASH conditions, and ongoing AMR surveillance. Funding: The EDCTP2 program, supported by the European Union, the Faculty for the Future Foundation (FFTF), the Netherlands Organization for Health Research and Development (ZonMw), and Health-Holland AMR-Global, Gloria, and Track-AMR.