Microbiota-based Antimicrobial resistance Risk Screening (MARS): a novel PCR method for dynamic assessment of AMR colonization risk and ESBL carrier identification

Microbiota-based Antimicrobial resistance Risk Screening (MARS): a novel PCR method for dynamic assessment of AMR colonization risk and ESBL carrier identification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Microbiota-based Antimicrobial resistance Risk Screening (MARS): a novel PCR method for dynamic assessment of AMR colonization risk and ESBL carrier identification Hitoshi Kawasuji, Yoshitomo Morinaga, Honoka Watanabe, Mika Morita, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7842064/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background The gut microbiome serves as a reservoir for antimicrobial-resistant (AMR) bacteria, making the prevention of silent colonization and transmission an urgent public health priority. Colonization risk often precedes established AMR colonization, yet no rapid method currently exists to identify individuals at risk. Results We developed a microbiota-based AMR risk screening (MARS) method, comprising two complementary real-time PCR assays to quantify the bacterial loads of Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae (LRB; indicators of colonization resistance) and Enterobacteriaceae (ETB; indicator of colonization permissiveness). Assay performance was evaluated in an antibiotic-treated mouse model and validated in clinical samples from 80 patients with diarrhea and 20 healthy controls. Both assays demonstrated high specificity and correlated with 16S rRNA gene sequencing. In mice, LRB assay cycle threshold (Ct) values measured just before inoculation with extended-spectrum β-lactamase-producing Escherichia coli (ESBL-Eco) correlated with fecal bacterial load and shedding duration. In patients, LRB bacterial loads declined during antibiotic treatment and recovered after discontinuation. Combined ETB and LRB assay results identified both high-risk individuals and ESBL carriers, with strong discriminatory performance (AUCs = 0.86 and 0.76). Conclusions The MARS method provides a novel, rapid, and clinically applicable approach to assess AMR colonization risk and detect actual colonization, offering a valuable tool for early intervention in high-risk individuals before AMR carriage becomes established. gut microbiome antimicrobial resistance colonization resistance dysbiosis extended-spectrum β-lactamase microbiota-based diagnostics Enterobacteriaceae Lachnospiraceae Ruminococcaceae Bacteroidaceae Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Antimicrobial resistance (AMR) poses a major public health threat, and reducing its spread is a global priority(1, 2). Among AMR pathogens, extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-PE) are classified as critical priority organisms by the World Health Organization(3, 4). These organisms often emerge in the human gut after antibiotic use and can be silently transmitted between individuals(5, 6). Asymptomatic and persistent intestinal carriage of AMR bacteria is now recognized as a key driver of transmission and is of increasing concern. Recent studies report that intestinal colonization by ESBL-producing Escherichia coli (ESBL-Eco) has increased 10-fold in community settings and 3-fold in healthcare environments over the last two decades, with more than one in five hospitalized patients (21.1%) identified as carriers(5, 6). The human gut hosts a diverse microbial community, and a balanced microbiome provides colonization resistance against exogenous AMR organisms(7, 8). Antibiotic therapy can disrupt this ecosystem, causing dysbiosis and transforming the gut into a reservoir for AMR bacteria and resistance genes(2). The loss of colonization resistance and subsequent expansion of AMR organisms are associated with increased risks of septicemia, morbidity, and mortality(9, 10). Despite its clinical relevance, the microbial features underlying protection from or susceptibility to AMR bacterial colonization remain poorly defined(11). Using an antibiotic-treated mouse model with disrupted gut microbiota, we previously showed that Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae (collectively referred to as LRB) predominated in mice that rapidly cleared ESBL-Eco, whereas Enterobacteriaceae (ETB) predominated in those with persistent colonization(12). These findings suggest that LRB taxa are linked to colonization resistance, whereas ETB is associated with colonization permissiveness, consistent with human studies(11, 13-19). For example, in liver transplant recipients, taxa such as Bacteroides , Blautia , Prevotella , and members of Lachnospiraceae and Ruminococcaceae were enriched in patients not colonized by multidrug-resistant organisms (MDROs), whereas ETB were enriched in colonized individuals(19). Although quantitative assessment of LRB and ETB abundances could provide a practical measure of colonization risk, no validated method currently exists for predicting AMR acquisition in the gut. Given the dynamic nature of the gut microbiome, which shifts with antibiotic use or cessation(11), there is an urgent need for a rapid and accessible approach to assess colonization risk. Transitioning from metagenomic sequencing to real-time PCR-based assays, which became widely implemented in clinical laboratories during the coronavirus disease 2019 pandemic, represents a practical step toward clinical application. Here, we developed two complementary real-time PCR assays, termed the microbiota-based AMR risk screening (MARS) method, to quantify LRB and ETB bacterial loads as indicators of colonization resistance and permissiveness, respectively. While primers targeting specific taxa such as Lachnospiraceae, Ruminococcaceae, and Bacteroides spp. have been described(20-22), no universal primer set exists for comprehensive detection of LRB. We therefore designed novel primers from first principles and evaluated the performance of the MARS method in both an antibiotic-treated mouse model and a clinical cohort of patients with diarrhea and healthy individuals. Results Development and specificity of the MARS method Group-specific amplification using the newly developed LRB and ETB primer sets successfully generated PCR products of the expected sizes (data not shown). The specificities of both assays were evaluated using DNA from 40 reference bacterial strains covering diverse species and 18 clinical isolates obtained from fecal specimens of patients with diarrhea, identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) ( Table 1 ). The primer sets produced positive PCR results for their intended target bacteria with no cross-reactivity to nontarget organisms. Exceptions were noted for Parabacteroides johnsonii , P. merdae , Clostridium butyricum , and C. sporogenes ,which yielded positive signals in the LRB assay due to sequence similarity at primer-binding sites ( Table 1 and Supplementary F ig. S1 ). Validity of the MARS method in an animal model The experimental design is illustrated in Fig. 1A . No ESBL-Eco was detected in feces before oral inoculation. At 24 h post-inoculation, control mice exhibited resistance to ESBL-Eco colonization, with counts of 3.9 ± 0.2 (mean ± standard error of mean [SEM], log 10 colony-forming units [CFU]/g feces). In contrast, mice pretreated with ampicillin or metronidazole showed significantly higher loads of 8.7 ± 0.4 and 6.5 ± 0.4 log 10 CFU/g feces, respectively ( Fig. 1A–C ). ESBL-Eco was cleared from the feces of both control and metronidazole-treated mice by day 3, whereas in ampicillin-treated mice, shedding persisted until day 12. Ct values from real-time PCR assays were used to estimate bacterial loads. In the LRB assay, baseline Ct values (day 0) were 18.2 ± 0.6 in control mice, 39.3 ± 1.9 in ampicillin-treated mice, and 29.1 ± 2.4 in metronidazole-treated mice. LRB bacterial loads were lowest in ampicillin-treated mice, in which ESBL-Eco persisted longest. Ct values in antibiotic-treated groups gradually decreased over time, indicating recovery of LRB populations, and converged with control values in parallel with reductions in ESBL-Eco colony counts ( Fig. 1B ). In the ETB assay, baseline Ct values were 29.8 ± 0.9, 42.9 ± 1.3, and 17.0 ± 0.3 in control, ampicillin-, and metronidazole-treated mice, respectively. In ampicillin-treated mice, the Ct value decreased by day 3, consistent with ESBL-Eco colonization. As with the LRB assay, ETB Ct values gradually normalized alongside reductions in ESBL-Eco counts ( Fig. 1C ). Study cohort clinical characteristics A total of 80 clinical fecal specimens were collected from 55 hospitalized patients (n = 76 specimens) and three outpatients (n = 4 specimens) with diarrhea. Samples were categorized according to antibiotic exposure at the time of collection: patients who had received antibiotics (n = 45; ABX group) and those who had not (n = 35; No ABX group). In addition, 57 samples were stratified by ESBL carriage status into ESBL carriers (n = 41) and non-carriers (n = 16). For comparison, fecal specimens were also obtained from 20 healthy volunteers without gastrointestinal symptoms (Healthy group; n = 20). Demographic and clinical characteristics are summarized in Table 2 . Patients (n = 80) were generally older than healthy volunteers, with mean ages of 61.8 and 53.6 years, respectively. No significant differences were observed in age, sex, length of hospital stay at the time of sampling, or most comorbidities between the ABX and No ABX groups or between ESBL carriers and non-carriers. Exceptions included higher proportions of diabetes mellitus in the ABX and ESBL carrier groups, and chronic heart disease in the ESBL carrier group. In the ABX group, the mean (± standard deviation [SD]) duration of antibiotic use before fecal sampling was 9.3 ± 9.7 days (range: 1–41). Both the proportion and duration of antibiotic use were comparable between ESBL carriers and non-carriers. The proportion of ESBL carriers also did not differ significantly between the ABX and No ABX groups. Among the ESBL carriers, isolates included Escherichia coli (n = 9), Citrobacter freundii (n = 1), Citrobacter spp. (n = 1), Klebsiella oxytoca (n = 1), K. aerogenes (n = 1), Raoultella ornithinolytica (n = 1), other identified species (n = 2), and unidentified species (n = 2). Correlations between MARS method and 16S rRNA gene sequencing To validate the MARS method as an indicator of microbiota status and to assess its concordance with the relative abundances of LRB and ETB, we performed 16S rRNA gene sequencing on DNA extracted from 57 fecal samples from 51 patients and 20 healthy individuals. Four samples were excluded because their total read counts after DADA2 denoising were < 1000(23). Among the remaining 73 samples, the combined relative abundance of LRB taxa correlated strongly with LRB assay Ct values (r = 0.67, P < 0.0001) ( Fig. 2A ). Likewise, ETB relative abundance correlated with ETB assay Ct values (r = 0.59, P < 0.001) ( Fig. 2B ). Risk screening using the LRB assay in patients and healthy individuals The ABX group exhibited the highest Ct values in the LRB assay, indicating the lowest LRB bacterial loads ( Fig. 3A–B ). Median Ct values differed significantly among groups: 31.1 (interquartile range [IQR], 25.1–36.5) in the ABX group, 24.7 (IQR: 21.1–29.5) in the No ABX group, and 19.8 (IQR: 18.6–21.4) in the Healthy group ( Fig. 3B ). Similarly, the combined relative abundance of LRB was highest in the Healthy group, intermediate in the No ABX group and lowest in the ABX group ( Fig. 3C and Supplementary F igs. S2A and S2B ). Although the ABX and No ABX groups did not differ significantly, patients consistently showed lower LRB abundance than healthy individuals, likely reflecting overall low LRB levels in both patient groups ( Fig. 3C ). Receiver operating characteristic (ROC) curve analysis demonstrated that the LRB assay effectively discriminated high-risk patients from low-risk and healthy individuals with a corresponding area under the curve (AUC) of 0.86 (95% confidence interval [CI], 0.79–0.93; P 21.92 ( Fig. 3D ). When high risk was defined as Ct ≥ 22, the proportions of individuals classified as high risk were 82.2% in the ABX group, 74.3% in the No ABX group, and 10.0% in Healthy group ( Fig. 3A ). Risk changes in parallel with antibiotic use We next evaluated the relationship between antibiotic exposure and LRB assay Ct values. In the ABX group, patients receiving antibiotics for >10 days (Longer ABX group) had significantly higher Ct values than those treated for ≤10 days (Shorter ABX group) and the (Before [No] group), indicating greater depletion of LRB ( Fig. 3E ). No significant differences were observed between the After and Before (No) groups suggesting partial recovery after antibiotic discontinuation. Ct values correlated positively with antibiotic duration ( Fig. 3F ), but not with time since discontinuation ( Fig. 3G ), indicating that colonization resistance decreases during antibiotic treatment, while the recovery trajectories vary between individuals. To further validate recovery, longitudinal monitoring of 13 patients revealed dynamic shifts consistent with antibiotic exposure and subsequent withdrawal. Ct values consistently increased during treatment and decreased after discontinuation. In Case A, Ct values declined steadily after antibiotic cessation ( Supplementary F ig. S3A ). In Case B, Ct values fluctuated in close parallel with antibiotic use ( Supplementary F ig. S3B ). In Case C, carbapenemase-producing Enterobacteriaceae (CPE) detected in the feces on day 51 became undetectable by day 79 as Ct values decreased, reflecting microbiota rebound ( Supplementary F ig. S3C ). In Case D, however, Clostridioides difficile infection (CDI) (which was diagnosed and treated with vancomycin for 10 days, during which broad-spectrum antibiotics were continued) persisted despite antibiotic discontinuation, with high Ct values and clinical relapse of CDI ( Supplementary F ig. S3D ). Collectively, these results demonstrate that prolonged antibiotic use depletes LRB, whereas recovery after cessation is variable and patient dependent. Detection of the ESBL carriers using MARS method Sixteen fecal samples from 15 patients tested positive on ESBL-selective chromogenic medium (CHROMagar ESBL), and the corresponding patients were defined as ESBL carriers. The Ct values of the LRB assay and the combined relative abundance of LRB taxa, based on 16S rRNA gene sequencing, did not differ significantly between ESBL carriers and non-carriers ( Fig. 4A–C ). This likely reflects the fact that both groups consisted of hospitalized patients at high risk of AMR colonization. In contrast, ETB assay Ct values were significantly lower in ESBL carriers than in non-carriers or healthy individuals ( Fig. 4D ). Consistently, the relative abundance of Enterobacteriaceae from 16S rRNA gene sequencing was significantly greater in ESBL carriers compared to both non-carriers and healthy individuals ( Fig. 4E and Supplementary F igs. S4A and 4B ). ROC analysis demonstrated that the ETB assay distinguished ESBL carriers from non-carriers and healthy individuals with an AUC of 0.76 (95% CI, 0.63–0.88; P = 0.0018), yielding 50.0% sensitivity and 88.5% specificity at a Ct value > 21.05 ( Fig. 4F ). Together, these findings show that the MARS method, combining LRB- and ETB-specific assays, effectively identifies both individuals at high risk of AMR colonization and ESBL carriers ( Figs. 3B, 3D, 4D, 4F ). Discussion Colonization resistance is influenced not only by the diversity of the gut microbiota but also by its compositional makeup(24). Specific bacterial taxa or microbial consortia have been linked with protective or antagonistic roles in colonization and infection by AMR bacteria(11). Although shifts in the gut microbial profile may signal colonization risk, no widely accessible, laboratory-based diagnostic method currently exists for identifying dysbiosis or assessing the risk of AMR colonization. To address this gap, we developed two novel real-time PCR assays from first principles to quantify the bacterial loads of LRB and ETB and validated the MARS method in an antibiotic-treated mouse model and fecal samples from patients with diarrhea and healthy individuals. This method enables rapid and sensitive assessment of AMR colonization risk by quantifying LRB levels—providing insight into antibiotic-induced microbiota disruption—and accurately identifies ESBL-Eco carriers via the ETB assay. Consistent with our findings, LRB has been associated with colonization resistance, whereas ETB is linked to colonization permissiveness in both animal models and human studies. Similar microbial patterns have also been reported for other AMR pathogens(14, 15), including carbapenem-resistant Enterobacteriaceae (CRE)(19), vancomycin-resistant enterococci (VRE)(13, 19), and C. difficile (16), suggesting broad relevance. For example, in a case-control study comparing CDI patients with non-diarrheal controls, protective taxa such as Bacteroides , Lachnospiraceae, and Ruminococcaceae were more prevalent in controls, whereas ETB was enriched in CDI cases(16). A large-scale study analyzing over 12,000 gut metagenomes from 45 countries further identified 172 microbial species as co-colonizers and 135 as co-excluders of Enterobacteriaceae, with nine of the top ten co-excluders (except Barnesiella intestinihominis ) belonging to the LRB group(25). The reproducibility of these microbiota signatures across species (mice and humans), clinical contexts, geographic regions underscores a broadly applicable principle of colonization resistance. Taken together, these findings highlight the potential utility of the MARS method for evaluating AMR colonization risk, not only for ESBL-producing bacteria but also for other clinically relevant AMR pathogens such as CRE, VRE, and C. difficile . Moreover, MARS could serve as a biomarker for evaluating the efficacy of microbiota-targeted interventions, including probiotics, prebiotics, and synbiotics. A recent study demonstrated that a defined consortium of 31 gut-derived commensal bacterial strains (F31-mix), isolated from a single healthy human donor, effectively eliminated K. pneumoniae colonization in germ-free mice after prior mono-colonization(26). Notably, 20 of these strains were LRB members, highlighting this taxonomic group as a central driver of colonization resistance. A streamlined 18-strain version (F18-mix), which retained 10 LRB taxa, provided comparable protection against K. pneumoniae , carbapenemase-producing K. pneumoniae, K. aerogenes , and ESBL-Eco. Furthermore, the broader-spectrum F31-mix also suppressed VRE, reinforcing the role of LRB in colonization resistance across diverse AMR organisms. Mechanistically, nutrient competition was a key factor: eight strains in the F18-mix (F8-mix) consumed gluconate—a preferred carbon source for ETB—six of which were LRB members, thereby restricting K. pneumoniae expansion in the gut(26). These findings suggest that the MARS method could provide a rapid, clinically applicable means of assessing colonization resistance and predicting the engraftment and therapeutic potential of microbiota-based interventions. Antibiotic exposure is a well-established driver of gut microbiome disruption, typically causing acute reductions in alpha diversity and marked compositional shifts(27). Antibiotic-specific effects are increasingly recognized, with substantial decreases documented across key taxa in Firmicutes and Bacteroidetes. Many species commonly depleted during antibiotic treatment are LRB members, including Roseburia , Dorea , Coprococcus , and Anaerostipes spp. (Lachnospiraceae) (28-34); Faecalibacterium prausnitzii and Ruminococcus spp . (Ruminococcaceae)(28, 31-33, 35, 36); and Bacteroides spp. (Bacteroidaceae)(33, 37, 38). In our study, LRB bacterial loads estimated using the MARS method, were significantly reduced during antibiotic use and inversely correlated with the duration of exposure ( Fig. 3F–G ). The gut microbiome also has an inherent capacity to rebound after antibiotic cessation. In this study, LRB bacterial loads increased after discontinuation ( Fig. 3E ), although recovery trajectories varied among patients ( Fig. 3G and Supplementary Fig. S2 ). Previous work reported that microbial diversity begins to recover within approximately 1 month in children(39, 40). In adults, while overall gut microbiota composition approached baseline within 1.5 months after treatment with meropenem, gentamicin, and vancomycin, nine common species—including Coprococcus eutactus (Lachnospiraceae)—remained undetectable for up to 6 months(31). The extent and pace of recovery depend heavily on the antibiotic regimen, with wide variability in effect size, dysbiosis duration, and restoration timing(11, 40). Given this variability, a clinically applicable tool is urgently needed to capture rapid, dynamic changes in colonization resistance and identify AMR risk in real time. Currently, no PCR-based assay universally and specifically detects LRB as a group. Although some assays targeting ETB-related taxa have been proposed(41), none have been clinically validated for identifying actual ESBL colonization. To address this shortcoming, we developed two complementary real-time PCR assays with the MARS method, designed to specifically quantify bacterial loads of both LRB and ETB. LRB and ETB abundances measured by MARS correlated well with their relative abundances derived from 16S rRNA gene sequencing ( Fig. 2A–B ). Clinical validation using fecal samples demonstrated that MARS effectively distinguished high-risk patients from healthy individuals ( Fig. 3A–D ). Moreover, combining ETB and LRB assays enabled the specific identification of ESBL carriers ( Fig. 4B, 4D, and 4F ). This study has several limitations. First, as an exploratory study conducted at a single center, its generalizability to other institutions may be limited. Second, 16S rRNA sequencing and ESBL testing were performed on only 57 of 80 patient stool samples, while 23 longitudinal samples were excluded to avoid duplication bias when analyzing antibiotic-associated changes. Although the sample size was modest, it was appropriate for this proof-of-concept study. Third, we did not prospectively track whether individuals with low LRB loads subsequently developed AMR colonization. Nevertheless, prior studies consistently show that gut dysbiosis predisposes to MDRO overgrowth(11, 19). A prospective multicenter study is underway to validate these findings and assess institutional variation in AMR carriage. Fourth, ESBL isolates were not further characterized by antimicrobial susceptibility testing or β-lactamase genotyping (e.g., bla TEM , bla SHV , bla CTX-M ), though this is unlikely to alter our main conclusions. Finally, nucleic acid extraction and PCR performance may vary by platform, and detection limits were not determined using pure cultures, underscoring the need for further standardization prior to widespread implementation. Conclusions This study establishes a practical approach for assessing the risk of AMR bacterial colonization by quantifying the bacterial loads of LRB as indicators of colonization resistance and ETB as indicators of permissiveness. The MARS method provides a rapid and sensitive tool to detect antibiotic-induced microbiota disruption and identify ESBL carriers. Beyond ESBL, MARS holds promise for broader application to other MDROs, including CRE and C. difficile . While antibiotics remain indispensable, they disrupt the microbiota and weaken colonization resistance. By enabling early identification of high-risk individuals, MARS offers clinically actionable strategy to guide microbiota-based interventions aimed at restoring gut resilience and may serve as a predictive biomarker for the engraftment success and therapeutic efficacy of microbiota-targeted therapies. Methods Reference strains and culture conditions The bacterial strains listed in Table 1 were obtained from the American Type Culture Collection (ATCC; Rockville, MD, USA), the National Collection of Type Cultures (NCTC; London, UK), and the Japan Collection of Microorganisms (JCM; Wako, Japan). Most anaerobic strains were cultured in an anaerobic chamber (Bactron IV SHEL LAB, Cornelius, OR) on brain heart infusion (BHI; BD Diagnosis Systems, Sparks, MD) agar supplemented with yeast extract (5 mg/mL), L-cysteine (1 mg/mL), hemin (5 μg/mL), and menadione (1 μg/mL) at 37 °C for 48 h. Aerobic strains were grown overnight at 37 °C on trypticase soy agar (Becton Dickinson [BD], Sunnyvale, CA) or Luria-Bertani (LB) agar (BD). Extraction and purification of DNA from fecal samples and bacterial cultures Bacterial DNA was extracted using a modified alkaline lysis and heat method(42). Briefly, a loopful of bacteria from a single colony was suspended in 50 μL of 0.05 mol/L NaOH, incubated at 95 °C for 10 min, and neutralized with 11 μL of Tris-HCl buffer (pH 7.0)(43). One microliter of the crude DNA extract, diluted 10-fold with distilled water, was used as a PCR template. Fecal samples were collected and processed for colony counting (animal samples) or routine microbiological examination (clinical samples). Residual material was refrigerated for up to 5 days and then frozen at −80 °C until DNA extraction. DNA was isolated using either the Quick-DNA Fecal/Soil Microbe Miniprep Kit (Zymo Research, Irvine, CA, USA) or the QIAamp PowerFecal Pro DNA Kit (QIAGEN, Crawley, UK), following the manufacturers’ protocols. Extracted DNA was analyzed with both the MARS method and 16S rRNA gene sequencing. Development of 16S rDNA-targeted LRB- and ETB-specific primers To design primers targeting LRB and ETB, 16S rRNA gene sequences were retrieved from the DNA Data Bank of Japan (DDBJ), GenBank, and European Molecular Biology Laboratory (EMBL) databases. Multiple sequence alignments of target families and reference organisms were performed using CLC Genomics Workbench v20.0.4 (CLC Bio, Aarhus, Denmark). Regions unique to LRB and ETB were identified and compared with numerous reference strains, yielding one candidate region for LRB-specific detection and three for ETB-specific detection. Based on these, ten forward and six reverse primers for LRB, and four forward and three reverse primers for ETB were designed using Primer3Plus (https://www.primer3plus.com/index.html). All primers were synthesized by Thermo Fisher Scientific (https://www.thermofisher.com/order/custom-standard-oligo), and optimal primer sets were determined experimentally. Real-time PCR assays for LRB- and ETB-specific detection All real-time PCR assays were performed using THUNDERBIRD or THUNDERBIRD Next SYBR qPCR Mix (TOYOBO, Osaka, Japan) on a LightCycler 96 Real-Time PCR System (Roche Diagnostics KK, Tokyo, Japan), following the manufacturer’s instructions. Each 20 μL reaction contained 10 μL SYBR qPCR mix, 10 pmol of each primer, 1 μL template DNA, and nuclease-free water. For the LRB-specific assay, the optimal forward primer was an equimolar mix of LRB-F1, LRB-F2, and LRB-F3, targeting positions 912–933 bp of the Bacteroides finegoldii 16S rRNA gene (strain JCM 13345; NCBI Reference Sequence: AB222699). The reverse primers were an equimolar mix of LRB-R1 and LRB-R2, targeting positions 1030–1011 bp of the same sequence ( Supplementary Table S1 and Supplementary Fig. S1 ). PCR cycling included an initial denaturation at 95 °C for 60 s, followed by 45 cycles of 95 °C for 15 s, 62 °C for 5 s, and 72 °C for 30 s. The expected amplicon size was 119 bp. For the ETB-specific assay, the forward primer ETB-F1 targeted positions 169–191 bp of the E. coli 16S rRNA gene (strain ATCC 35218; NCBI Reference Sequence: AM980865), with reverse primer ETB-R1 targeting positions 322–304 bp ( Supplementary Table S1 and Supplementary Fig. S1 ). Cycling conditions were 95 °C for 30 s, followed by 45 cycles of at 95 °C for 5 s and at 67 °C for 30 s. The expected amplicon size was 144 bp. Animal model Four- to six-week-old female C57BL/6J mice were purchased from Charles River Laboratories, Japan, Inc. (Kanagawa, Japan). Mice were co-housed for at least 2 weeks before experimentation to normalize gut microbiota. They were then divided into three groups and provided with filter-sterilized water (n = 4, control group), ampicillin (1 g/L; n = 4), or metronidazole (1 g/L; n = 4) in drinking water for 3 consecutive days. After a 1-day washout period with regular water, all groups were orally inoculated with a clinical ESBL-Ecostrain (sequence type 131, cefotaximase-Munich (CTX-M)-15-producing isolate hTYM0002). For inoculum preparation, a single colony of ESBL-Eco hTYM0002 was cultured overnight in LB broth at 37 °C with shaking, transferred to fresh broth for 6–8 h, and adjusted to the desired concentration by turbidimetry. Mice were gavaged with 4 × 10 3 CFU of ESBL-Eco. Fecal samples were collected at baseline (day 0) and at multiple time points up to 12 days post-inoculation. At each time point, one or two fecal pellets were collected, weighed, suspended in 500 μL phosphate-buffered saline (PBS), homogenized, serially diluted, and plated on MacConkey agar containing cefoperazone (32 μg/mL). Plates were incubated overnight at 37 °C, and ESBL-Eco colonies were enumerated. Remaining fecal material was stored at −80 °C for DNA extraction and subsequent analysis with the MARS assays. Clinical evaluation Clinical stool specimens (n = 80) were collected from 58 patients (55 inpatients and 3 outpatients) with diarrhea between March and August 2022 at Toyama University Hospital, a 612-bed tertiary care facility in Japan. Residual fecal material from routine clinical microbiological examinations, including C. difficile testing, was used for this study. Of these, 57 specimens from 51 patients were additionally cultured on a commercial ESBL-selective chromogenic medium (CHROMagar ESBL; Kanto Chemical, Tokyo, Japan) alongside standard microbiological tests before freezing. Patients whose fecal specimens yielded growth on ESBL-selective medium were classified as ESBL carriers, while those without growth were defined as non-carriers. The remaining 23 fecal samples were collected longitudinally from eight patients at different time points during treatment to assess antibiotic-induced changes in colonization. Specimens were categorized into two main groups: patients who had received antibiotics (n = 45; ABX group) and those who had not (n = 35; No ABX group). The No ABX group comprised patients with previous antibiotic use within the preceding 60 days, with the group being further categorized as Before (No) group which comprised those with no history of antibiotic use and After group with those who had received antibiotics within the last 60 days. Samples were also classified by ESBL colonization status as carriers (n = 41) or non-carriers (n = 16). Stool samples from 20 healthy volunteers were collected and designated as the Healthy group (n = 20). PCR amplification and preparation for 16S metagenomic sequencing DNA quality and concentration were measured using a NanoDrop One spectrophotometer (Thermo Fisher Scientific). Library preparation followed the Illumina 16S Metagenomic Sequencing Library Preparation protocol. Briefly, the V3–V4 hypervariable region of the 16S rRNA gene was amplified using primers 341F and 805R, with Illumina sequencing adapters and dual-index barcodes from the Nextera XT kit appended to the amplicons ( Supplementary Table S1 ). Sequencing was performed on an Illumina MiSeq platform with a 2 × 300 bp paired-end protocol (44). Sequencing data from both patient and healthy control samples were deposited in the DNA Data Bank of Japan (DDBJ) under BioProject accession number PRJDB20682. Metagenome profiling Sequencing reads were processed into amplicon sequence variants (ASVs) using the DADA2 pipeline via the q2-dada2 plugin(23) within QIIME2 (v2023.5). Reads were filtered, trimmed, denoised, dereplicated, merged (forward and reverse), chimera-checked. Truncation positions were set at 250 bp for forward reads and 230 bp for reverse reads, based on quality score assessments. Samples with low sequencing depth (< 1000 total reads) were excluded(45). Taxonomic assignment of ASVs was performed using the q2-feature-classifier plugin(46) with a naïve Bayes classifier (classify-sklearn) trained against the SILVA v138 99% 16S rRNA reference database(47), trimmed to the V3–V4 region bound by the 341F/805R primer pair. The resulting taxonomy table was collapsed at the genus, family, and phylum levels, and the merged abundance table was used for downstream analysis. Statistical analyses All statistical analyses were performed using GraphPad Prism version 9.5.1 (GraphPad Software, San Diego, CA, USA). The Mann–Whitney U test was used for pairwise comparisons between non-parametric groups, and the Kruskal–Wallis test was applied for comparisons among three or more groups. Pearson’s correlation coefficient was calculated to assess associations between continuous variables. ROC curves and AUC values were generated to evaluate diagnostic performance. Statistical significance was defined as P < 0.05. Data are presented as means with SDs or SEMs, or as medians with IQRs, as appropriate. Declarations Ethics approval This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Review Board of the University of Toyama (approval nos. R2021167 and R2022089). The requirement for written informed consent was waived due to the observational design. All animal experiments were approved by the Ethics Review Committee for Animal Experimentation (Institutional Animal Care and Use Committee. University of Toyama; approval no. A2020med-18). Data availability All data are included in the main text or supplementary materials. Sequencing data are available in the DDBJ BioProject database under accession number PRJDB20682. Acknowledgments We thank Dr. Kazuyuki Tobe for providing nucleosides extracted from stool samples of healthy individuals. Funding This work was supported by the Japanese Association for Infectious Diseases, Grant for Clinical Research Promotion [6th (2023)] (H.K.); JSPS KAKENHI (Grant no. JP20K08821, Y.M.); and the Japan Agency for Medical Research and Development (AMED) (Grant no. JP22fk0108133, Y.M.). The funders had no role in study design, data collection or analysis, decision to publish, or manuscript preparation. Author contributions Conceptualization: H.K., Y.Mo.; Methodology: H.K., Y.Mo.; Validation: H.K., Y.Mo.; Formal analysis: H.K., Y.Mo.; Investigation: H.K., Y.Mo., H.W., M.M., K.A., T.F., M.E., Y.K., Y.T., M.K., Y.Mu., K.K., K.N.; Resources: Y.Mo., S.F., T.S.; Data curation: H.K., Y.Mo.; Writing—original draft: H.K., Y.Mo.; Writing—review & editing: H.K., Y.Mo.; Visualization: H.K., Y.Mo.; Supervision: Y.Mo., Y.Y.; Project administration: Y.Mo., Y.Y.; Funding acquisition: H.K., Y.M. Corresponding authors Correspondence to Yoshitomo Morinaga. 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Family Species Strain Reaction with the following primer set LRB ETB Reference strains Lachnospiraceae Lacrimsipora indolis ATCC 25771 + − Ruminococcaceae Rumonococcus gauvreauii JCM 14987 T + − Ruminococcaceae Ruminococcus gnavus ATCC 29149 + − Oscillospiraceae Ruminiclostridium cellobioparum ATCC 15832 + − Oscillospiraceae Pseudoflavonifractor capillosus JCM 32126 T + − Rikenellaceae Alistipes indistinctus JCM 16068 T + − Bacteroidaceae Bacteroides fragilis ATCC 25285 + − Bacteroidaceae Phocaeicola vulgatus ATCC 8482 + − Bacteroidaceae Bacteroides acidifaciens JCM 10556 T + − Bacteroidaceae Bacteroides uniforims ATCC 8492 + − Bacteroidaceae Bacteroides dorei JCM 13471 T + − Bacteroidaceae Bacteroides finegoldii JCM 13345 T + − Bacteroidaceae Bacteroides intestinales JCM 13265 T + − Bacteroidaceae Bacteroides thetaiotaomicron ATCC 29148 + − Tannerellaceae Parabacteroides johnsoii JCM 13406 T + − Tannerellaceae Parabacteroides merdae ATCC 43184 + − Clostridiaceae Clostridium butyricum ATCC 19398 + − Clostridiaceae Clostridium sporogenes ATCC 3584 + − Clostridiaceae Clostridium ramosum ATCC 25582 − − Clostridiaceae Clostridium intestinales ATCC 49213 − − Clostridiaceae Clostridium innocuum ATCC 14501 − − Clostridiaceae Clostridium spiroforme ATCC 29900 − − Eggerthellaceae Eggerthella lenta ATCC 25559 − − Peptoniphilaceae Finegoldia magna ATCC 14904 − − Lactobacillaceae Lactobacillus casei ATCC 393 − − Lactobacillaceae Lactobacillus acidophilus ATCC 4356 − − Lactobacillaceae Lactobacillus gasseri ATCC 33323 − − Lactobacillaceae Ligilactobacillus animalis ATCC 35046 − − Corynebacteriaceae Corynebacterium striatum ATCC 6940 − − Corynebacteriaceae Corynebacterium ulcerans ATCC 51799 − − Corynebacteriaceae Corynebacterium pseudodiphtheriticum ATCC 10700 − − Staphylococcaceae Staphylococcus lugdunensis ATCC 49576 − − Staphylococcaceae Staphylococcus saprophyticus ATCC 15305 − − Enterobacteriaceae Escherichia coli ATCC 35218 − + Enterobacteriaceae Klebsiella pneumoniae ATCC 13883 − + Enterobacteriaceae Klebsiella oxytoca ATCC 700324 − + Enterobacteriaceae Klebsiella varicola ATCC 31488 − + Enterobacteriaceae Proteus vulgaris ATCC 6380 − + Enterobacteriaceae Enterobacter cloacae NCTC 13406 − + Pasteurellaceae Haemophilus influenzae ATCC 43335 − − Clinical isolates Enterobacteriaceae Escherichia coli No.1 − + Enterobacteriaceae Escherichia coli No.2 − + Enterobacteriaceae Escherichia coli No.3 − + Enterobacteriaceae Escherichia coli No.4 − + Enterobacteriaceae Escherichia coli No.5 − + Enterobacteriaceae Escherichia coli No.6 − + Enterobacteriaceae Escherichia coli No.7 − + Enterobacteriaceae Escherichia coli No.8 − + Enterobacteriaceae Escherichia coli No.9 − + Enterobacteriaceae Escherichia coli No.10 − + Enterobacteriaceae Klebsiella aerogenes No.11 − + Enterobacteriaceae Klebsiella aerogenes No.12 − + Enterobacteriaceae Klebsiella oxytoca No.13 − + Enterobacteriaceae Klebsiella oxytoca No.14 − + Enterobacteriaceae Citrobacter freundii No.15 − + Enterobacteriaceae Raoultella ornithinolytica No.16 − + Moraxellaceae Acinetobacter species No.17 − − Aeromonadaceae Aeromonas veronii No.18 − − LRB, Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae; ETB, Enterobacteriaceae Table 2. Demographic and clinical characteristics of the study cohort. Variable Healthy (n = 20) No ABX (n = 35) ABX (n = 45) P - value ESBL non-carriers (n = 41) ESBL carriers (n = 16) P -value Age (y) 53.6 ± 5.6 (43–70) 57.2 ± 25.1 (1–92) 65.4 ± 18.6 (0.5–86) 0.16 63.4 ± 19.3 (0.5–92) 64.6 ± 29.7 (1–00) 0.074 Sex Men, n (%) 3 (15.0) 17 (48.6) 19 (42.2) 0.65 24 (58.5) 8 (50.0) 0.57 Women, n (%) 17 (85.0) 18 (51.4) 26 (57.8) 17 (41.5) 8 (50.0) Hospitalized patients, n (%) – 31 (88.6) 45 (100.0) 0.029 38 (92.7) 15 (93.8) 1.00 Length of hospital stay at the time of sampling (d) – 25.7 ± 33.7 (0–145) 30.1 ± 38.2 (0–208) 0.20 21.8 ± 38.0 (0–208) 20.5 ± 18.3 (0–59) 0.31 Comorbidities Solid tumor, n (%) – 14 (40.0) 21 (46.7) 0.65 18 (43.9) 10 (62.5) 0.25 Hematologic tumor, n (%) – 3 (8.6) 8 (17.8) 0.33 2 (4.9) 1 (6.3) 1.00 Chemotherapy, n (%) – 9 (25.7) 13 (28.9) 0.81 11 (26.8) 3 (18.8) 0.73 Radiotherapy, n (%) – 1 (2.9) 3 (6.7) 0.63 4 (9.8) 0 (0.0) 0.57 Gastrointestinal disease, n (%) – 11 (31.4) 10 (22.2) 0.44 11 (26.8) 8 (50.0) 0.12 Hypertension, n (%) – 10 (28.6) 18 (40.0) 0.35 10 (24.4) 5 (31.3) 0.74 Diabetes mellitus, n (%) – 6 (17.1) 19 (42.2) 0.028 6 (14.6) 8 (50.0) 0.013 Dyslipidemia, n (%) – 2 (5.7) 8 (17.8) 0.17 3 (7.3) 4 (25.0) 0.088 Chronic heart disease, n (%) – 6 (17.1) 13 (28.9) 0.29 4 (9.8) 8 (50.0) 0.0020 Chronic lung disease, n (%) – 2 (5.7) 2 (4.4) 1.00 3 (7.3) 1 (6.3) 1.00 Chronic renal disease, n (%) – 7 (20.0) 13 (28.9) 0.44 8 (19.5) 4 (25.0) 0.72 Chronic liver disease, n (%) – 5 (14.3) 6 (13.3) 1.00 8 (19.5) 0 (0.0) 0.090 Intracranial vascular disease, n (%) – 13 (37.1) 14 (31.1) 0.64 7 (17.1) 4 (25.0) 0.48 Autoimmune disease, n (%) – 5 (14.3) 6 (13.3) 1.00 5 (12.2) 3 (18.8) 0.67 Immune suppression, n (%) – 4 (11.4) 3 (6.7) 0.69 4 (9.8) 3 (18.8) 0.39 Antibiotic use, n (%) – – – – 23 (56.1) 10 (62.5) 0.77 Duration of antibiotics at the time of sampling (d) a – – 9.3 ± 9.7 (1–41) – 6.6 ± 7.4 (1–35) 5.8 ± 4.2 (1–12) 0.95 ESBL carriers, n (%) – 6 / 24 (25.0) 10 / 33 (30.3) 0.77 – – – Ages (y), hospital stay duration (d), and duration of antibiotics at the time of fecal sampling (d) are expressed as mean ± standard deviation (SD; range). Other values are shown as number (percentage) of patients. a One patient with secondary acute myeloid leukemia who received trimethoprim-sulfamethoxazole prophylaxis for Pneumocystis pneumonia was excluded. Additional Declarations No competing interests reported. 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Toyama","correspondingAuthor":false,"prefix":"","firstName":"Kentaro","middleName":"","lastName":"Nagaoka","suffix":""},{"id":542845185,"identity":"2e14d869-0791-454f-a061-4e607f9e526d","order_by":15,"name":"Yoshihiro Yamamoto","email":"","orcid":"","institution":"University of Toyama","correspondingAuthor":false,"prefix":"","firstName":"Yoshihiro","middleName":"","lastName":"Yamamoto","suffix":""}],"badges":[],"createdAt":"2025-10-12 16:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7842064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7842064/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95797810,"identity":"8663423b-971e-46dc-8bb3-78d1650d48f6","added_by":"auto","created_at":"2025-11-13 08:11:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":186483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMonitoring antibiotic-induced gut dysbiosis and recovery, along with ESBL-Eco colonization dynamics, using the MARS method in a mouse model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Experimental design. Female C57BL/6J mice (n = 4 per group) received filter-sterilized water (Control), ampicillin, or metronidazole for 3 days. After a 1-day washout, all groups were orally inoculated with an ESBL-producing \u003cem\u003eEscherichia coli \u003c/em\u003e(ESBL-Eco) ST131 strain carrying \u003cem\u003ebla\u003c/em\u003e\u003csub\u003e\u003cem\u003eCTX-M-15\u003c/em\u003e\u003c/sub\u003e. Fecal samples were collected on days 0, 1, 3, 5, 8, and 12 post-inoculation, resuspended in PBS (500 μL), and used for both CFU quantification on selective media and DNA extraction. Extracted DNA was analyzed by the MARS method to quantify LRB and ETB loads. (B) Longitudinal relationship between LRB assay Ct values (dots, primary y-axis) and fecal ESBL-Eco loads (bars, secondary y-axis). (C) Longitudinal relationship between ETB assay Ct values (dots, primary y-axis) and fecal ESBL-Eco loads (bars, secondary y-axis). Each dot represents one mouse. Bars indicate mean ± SEM.\u003c/p\u003e\n\u003cp\u003eMARS, microbiota-based antimicrobial resistance risk screening; ESBL-Eco, extended-spectrum β-lactamase-producing \u003cem\u003eE. coli\u003c/em\u003e; LRB, Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae; ETB, Enterobacteriaceae; PBS, phosphate-buffered saline; Ct, cycle threshold; CFU, colony-forming units; SEM, standard error of mean.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7842064/v1/26f1dc0fdf8c77532849d9f8.png"},{"id":95670197,"identity":"ffe7c541-f931-4614-9eb2-77ffffdc4de2","added_by":"auto","created_at":"2025-11-11 17:17:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between MARS assay results and 16S rRNA gene sequencing in clinical fecal samples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Correlation between LRB assay Ct values and the combined relative abundance of Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae in clinical samples from patients (n = 53) and healthy individuals (n = 20). (B) Correlation between ETB assay Ct values and the relative abundance of Enterobacteriaceae in the same sample set. Zero relative abundance values were assigned a log₂-transformed value of −12 for visualization on a log scale. Pearson’s correlation coefficient (r) was used to assess correlations. Each dot represents a single sample.\u003c/p\u003e\n\u003cp\u003eMARS, microbiota-based antimicrobial resistance risk screening; Ct, cycle threshold; LRB, Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae; ETB, Enterobacteriaceae.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7842064/v1/3163a6e5d1ef268cfa81619c.png"},{"id":95670198,"identity":"3fa968ed-57b3-4fb3-aa35-e57d435f3614","added_by":"auto","created_at":"2025-11-11 17:17:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":201236,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLRB assay Ct values reflect antibiotic-induced gut dysbiosis and identify high-risk individuals.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Clinical sample groups: patients who received antibiotics (ABX, n = 45), patients who did not (No ABX, n = 35), and healthy individuals (Healthy, n = 20). High-risk (Ct ≥ 22) and low-risk (Ct \u0026lt; 22) categories were defined by the LRB assay. (B) LRB Ct values were highest in the ABX group, followed by the No ABX and Healthy groups. (C) Combined relative abundance of Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae was reduced in both patient groups compared to the Healthy group. (D) ROC curve analysis showed the LRB assay discriminated high-risk patients (ABX and No ABX) from low-risk individuals (Healthy). (E) LRB Ct values were significantly higher in the Longer ABX group (\u0026gt; 10 days) than in the Shorter ABX (≤ 10 days) and Before (No ABX) groups, indicating greater depletion during prolonged antibiotic use. No difference was observed between the After and Before (No) groups, suggesting partial recovery after discontinuation. (F, G) LRB Ct values increased with longer antibiotic duration but did not correlate with time since discontinuation.\u003c/p\u003e\n\u003cp\u003e(B, C, E) Kruskal–Wallis test. (D) ROC/AUC analysis. (F, G) Pearson correlation. Each dot represents one sample. Bars show medians with IQRs. Solid lines indicate significant correlations; dashed lines indicate nonsignificant correlations.\u003c/p\u003e\n\u003cp\u003e*, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ****, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; ns, not significant.\u003c/p\u003e\n\u003cp\u003eLRB, Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae; Ct, cycle threshold; ABX, antibiotics; ROC, receiver operating characteristic; AUC, area under the ROC curve.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7842064/v1/54137e62798d7c9a51cb484c.png"},{"id":95670201,"identity":"1bd02e21-52e3-4add-adb6-298e30db5692","added_by":"auto","created_at":"2025-11-11 17:17:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150269,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of ESBL carriers using the MARS method.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Overview of study groups: ESBL carriers (n = 16), ESBL non-carriers (n = 41), and healthy individuals (n = 20). High-risk (Ct ≥ 22) and low-risk (Ct \u0026lt; 22) classifications were based on the LRB assay. (B) LRB Ct values were significantly higher in both ESBL carriers and non-carriers compared to healthy individuals. (C) The combined relative abundance of Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae was significantly lower in both ESBL carriers and non-carriers than in healthy individuals. (D) ETB Ct values were significantly higher in ESBL carriers than in non-carriers and healthy individuals. (E) The relative abundance of Enterobacteriaceae was significantly elevated in ESBL carriers. (F) ROC curve analysis showed that the ETB assay effectively distinguished ESBL carriers from non-carriers and healthy individuals.\u003c/p\u003e\n\u003cp\u003e(B–E) Group differences were tested with the Kruskal–Wallis test. (F) ROC curves and AUC values were calculated. Each dot represents one sample. Bars show medians with interquartile ranges.\u003c/p\u003e\n\u003cp\u003e*, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ****, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; ns, not significant.\u003c/p\u003e\n\u003cp\u003eESBL, extended-spectrum β-lactamase; MARS, microbiota-based antimicrobial resistance risk screening; Ct, cycle threshold; LRB, Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae; ETB, Enterobacteriaceae; ROC, receiver operating characteristic; AUC, area under the curve.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7842064/v1/617d8e10a913b19a8ff9deda.png"},{"id":95804455,"identity":"50f63ca0-911c-49ce-a661-8c51e3fe94f9","added_by":"auto","created_at":"2025-11-13 08:36:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2279111,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7842064/v1/29a796a0-ff32-429f-962c-272f8df0831b.pdf"},{"id":95797522,"identity":"dba27ffc-33ce-4ea7-b4bb-536741929c16","added_by":"auto","created_at":"2025-11-13 08:06:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2118633,"visible":true,"origin":"","legend":"","description":"","filename":"1007SupplementalMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7842064/v1/44f031eed1f93d10543cd3ec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microbiota-based Antimicrobial resistance Risk Screening (MARS): a novel PCR method for dynamic assessment of AMR colonization risk and ESBL carrier identification","fulltext":[{"header":"Background","content":"\u003cp\u003eAntimicrobial resistance (AMR) poses a major public health threat, and reducing its spread is a global priority(1, 2). Among AMR pathogens, extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-PE) are classified as critical priority organisms by the World Health Organization(3, 4). These organisms often emerge in the human gut after antibiotic use and can be silently transmitted between individuals(5, 6). Asymptomatic and persistent intestinal carriage of AMR bacteria is now recognized as a key driver of transmission and is of increasing concern. Recent studies report that intestinal colonization by ESBL-producing \u003cem\u003eEscherichia coli\u003c/em\u003e (ESBL-Eco) has increased 10-fold in community settings and 3-fold in healthcare environments over the last two decades, with more than one in five hospitalized patients (21.1%) identified as carriers(5, 6).\u003c/p\u003e\n\u003cp\u003eThe human gut hosts a diverse microbial community, and a balanced microbiome provides colonization resistance against exogenous AMR organisms(7, 8). Antibiotic therapy can disrupt this ecosystem, causing dysbiosis and transforming the gut into a reservoir for AMR bacteria and resistance genes(2). The loss of colonization resistance and subsequent expansion of AMR organisms are associated with increased risks of septicemia, morbidity, and mortality(9, 10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite its clinical relevance, the microbial features underlying protection from or susceptibility to AMR bacterial colonization remain poorly defined(11). Using an antibiotic-treated mouse model with disrupted gut microbiota, we previously showed that Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae (collectively referred to as LRB) predominated in mice that rapidly cleared ESBL-Eco, whereas Enterobacteriaceae (ETB) predominated in those with persistent colonization(12). These findings suggest that LRB taxa are linked to colonization resistance, whereas ETB is associated with colonization permissiveness, consistent with human studies(11, 13-19). For example, in liver transplant recipients, taxa such as \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, and members of Lachnospiraceae and Ruminococcaceae were enriched in patients not colonized by multidrug-resistant organisms (MDROs), whereas ETB were enriched in colonized individuals(19).\u003c/p\u003e\n\u003cp\u003eAlthough quantitative assessment of LRB and ETB abundances could provide a practical measure of colonization risk, no validated method currently exists for predicting AMR acquisition in the gut. Given the dynamic nature of the gut microbiome, which shifts with antibiotic use or cessation(11), there is an urgent need for a rapid and accessible approach to assess colonization risk. Transitioning from metagenomic sequencing to real-time PCR-based assays, which became widely implemented in clinical laboratories during the coronavirus disease 2019 pandemic, represents a practical step toward clinical application.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we developed two complementary real-time PCR assays, termed the microbiota-based AMR risk screening (MARS) method, to quantify LRB and ETB bacterial loads as indicators of colonization resistance and permissiveness, respectively. While primers targeting specific taxa such as Lachnospiraceae, Ruminococcaceae, and \u003cem\u003eBacteroides\u003c/em\u003e spp. have been described(20-22), no universal primer set exists for comprehensive detection of LRB. We therefore designed novel primers from first principles and evaluated the performance of the MARS method in both an antibiotic-treated mouse model and a clinical cohort of patients with diarrhea and healthy individuals.\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003eDevelopment and specificity of the MARS method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGroup-specific amplification using the newly developed LRB and ETB primer sets successfully generated PCR products of the expected sizes (data not shown). The specificities of both assays were evaluated using DNA from 40 reference bacterial strains covering diverse species and 18 clinical isolates obtained from fecal specimens of patients with diarrhea, identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) (\u003cstrong\u003eTable 1\u003c/strong\u003e). The primer sets produced positive PCR results for their intended target bacteria with no cross-reactivity to nontarget organisms. Exceptions were noted for \u003cem\u003eParabacteroides johnsonii\u003c/em\u003e, \u003cem\u003eP. merdae\u003c/em\u003e, \u003cem\u003eClostridium butyricum\u003c/em\u003e, and \u003cem\u003eC. sporogenes\u003c/em\u003e,which yielded positive signals in the LRB assay due to sequence similarity at primer-binding sites (\u003cstrong\u003eTable 1 and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary F\u003c/strong\u003e\u003cstrong\u003eig. S1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidity of the MARS method in an animal model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental design is illustrated in \u003cstrong\u003eFig. 1A\u003c/strong\u003e.\u0026nbsp;No\u0026nbsp;ESBL-Eco was detected in feces before oral inoculation. At 24 h post-inoculation, control mice exhibited resistance to ESBL-Eco colonization, with counts of 3.9 ± 0.2 (mean ± standard error of mean [SEM], log\u003csub\u003e10\u003c/sub\u003e colony-forming units [CFU]/g feces). In contrast, mice pretreated with ampicillin or metronidazole showed significantly higher loads of 8.7 ± 0.4 and 6.5 ± 0.4 log\u003csub\u003e10\u003c/sub\u003e CFU/g feces, respectively (\u003cstrong\u003eFig. 1A–C\u003c/strong\u003e). ESBL-Eco was cleared from the feces of both control and metronidazole-treated mice by day 3, whereas in ampicillin-treated mice, shedding persisted until day 12.\u003c/p\u003e\n\u003cp\u003eCt values from real-time PCR assays were used to estimate bacterial loads. In the LRB assay, baseline Ct values (day 0) were 18.2 ± 0.6 in control mice, 39.3 ± 1.9 in ampicillin-treated mice, and 29.1 ± 2.4 in metronidazole-treated mice. LRB bacterial loads were lowest in ampicillin-treated mice, in which ESBL-Eco persisted longest. Ct values in antibiotic-treated groups gradually decreased over time, indicating recovery of LRB populations, and converged with control values in parallel with reductions in ESBL-Eco colony counts (\u003cstrong\u003eFig. 1B\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the ETB assay, baseline Ct values were 29.8 ± 0.9, 42.9 ± 1.3, and 17.0 ± 0.3 in control, ampicillin-, and metronidazole-treated mice, respectively. In ampicillin-treated mice, the Ct value decreased by day 3, consistent with ESBL-Eco colonization. As with the LRB assay, ETB Ct values gradually normalized alongside reductions in ESBL-Eco counts (\u003cstrong\u003eFig. 1C\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy cohort clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 80 clinical fecal specimens were collected from 55 hospitalized patients (n = 76 specimens) and three outpatients (n = 4 specimens) with diarrhea. Samples were categorized according to antibiotic exposure at the time of collection: patients who had received antibiotics (n = 45; ABX group) and those who had not (n = 35; No ABX group). In addition, 57 samples were stratified by ESBL carriage status into ESBL carriers (n = 41) and non-carriers (n = 16). For comparison, fecal specimens were also obtained from 20 healthy volunteers without gastrointestinal symptoms (Healthy group; n = 20).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDemographic and clinical characteristics are summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e. Patients (n = 80) were generally older than healthy volunteers, with mean ages of 61.8 and 53.6 years, respectively. No significant differences were observed in age, sex, length of hospital stay at the time of sampling, or most comorbidities between the ABX and No ABX groups or between ESBL carriers and non-carriers. Exceptions included higher proportions of diabetes mellitus in the ABX and ESBL carrier groups, and chronic heart disease in the ESBL carrier group. In the ABX group, the mean (± standard deviation [SD]) duration of antibiotic use before fecal sampling was 9.3 ± 9.7 days (range: 1–41). Both the proportion and duration of antibiotic use were comparable between ESBL carriers and non-carriers. The proportion of ESBL carriers also did not differ significantly between the ABX and No ABX groups. Among the ESBL carriers, isolates included \u003cem\u003eEscherichia coli\u003c/em\u003e (n = 9), \u003cem\u003eCitrobacter freundii\u003c/em\u003e (n = 1), \u003cem\u003eCitrobacter\u0026nbsp;\u003c/em\u003espp. (n = 1), \u003cem\u003eKlebsiella oxytoca\u003c/em\u003e (n = 1), \u003cem\u003eK. aerogenes\u003c/em\u003e (n = 1), \u003cem\u003eRaoultella ornithinolytica\u003c/em\u003e (n = 1), other identified species (n = 2), and unidentified species (n = 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations between MARS method and 16S rRNA gene sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the MARS method as an indicator of microbiota status and to assess its concordance with the relative abundances of LRB and ETB, we performed 16S rRNA gene sequencing on DNA extracted from 57 fecal samples from 51 patients and 20 healthy individuals. Four samples were excluded because their total read counts after DADA2 denoising were \u0026lt; 1000(23). Among the remaining 73 samples, the combined relative abundance of LRB taxa correlated strongly with LRB assay Ct values (r = 0.67, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001) (\u003cstrong\u003eFig. 2A\u003c/strong\u003e). Likewise, ETB relative abundance correlated with ETB assay Ct values (r = 0.59, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (\u003cstrong\u003eFig. 2B\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk screening using the LRB assay in patients and healthy individuals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ABX group exhibited the highest Ct values in the LRB assay, indicating the lowest LRB bacterial loads (\u003cstrong\u003eFig. 3A–B\u003c/strong\u003e). Median Ct values differed significantly among groups: 31.1 (interquartile range [IQR], 25.1–36.5) in the ABX group, 24.7 (IQR: 21.1–29.5) in the No ABX group, and 19.8 (IQR: 18.6–21.4) in the Healthy group (\u003cstrong\u003eFig. 3B\u003c/strong\u003e). Similarly, the combined relative abundance of LRB was highest in the Healthy group, intermediate in the No ABX group and lowest in the ABX group (\u003cstrong\u003eFig. 3C and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary F\u003c/strong\u003e\u003cstrong\u003eigs. S2A and S2B\u003c/strong\u003e). Although the ABX and No ABX groups did not differ significantly, patients consistently showed lower LRB abundance than healthy individuals, likely reflecting overall low LRB levels in both patient groups (\u003cstrong\u003eFig. 3C\u003c/strong\u003e). Receiver operating characteristic (ROC) curve analysis demonstrated that the LRB assay effectively discriminated high-risk patients from low-risk and healthy individuals with a corresponding area under the curve (AUC) of 0.86 (95% confidence interval [CI], 0.79–0.93; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001), sensitivity of 90.0%, and specificity of 78.8% at a Ct value \u0026gt; 21.92 (\u003cstrong\u003eFig. 3D\u003c/strong\u003e). When high risk was defined as Ct ≥ 22, the proportions of individuals classified as high risk were 82.2% in the ABX group, 74.3% in the No ABX group, and 10.0% in Healthy group (\u003cstrong\u003eFig. 3A\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk changes in parallel with antibiotic use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe next evaluated the relationship between antibiotic exposure and LRB assay Ct values. In the ABX group, patients receiving antibiotics for \u0026gt;10 days (Longer ABX group) had significantly higher Ct values than those treated for ≤10 days (Shorter ABX group) and the (Before [No] group), indicating greater depletion of LRB (\u003cstrong\u003eFig. 3E\u003c/strong\u003e). No significant differences were observed between the After and Before (No) groups suggesting partial recovery after antibiotic discontinuation. Ct values correlated positively with antibiotic duration (\u003cstrong\u003eFig. 3F\u003c/strong\u003e), but not with time since discontinuation (\u003cstrong\u003eFig. 3G\u003c/strong\u003e), indicating that colonization resistance decreases during antibiotic treatment, while the recovery trajectories vary between individuals.\u003c/p\u003e\n\u003cp\u003eTo further validate recovery, longitudinal monitoring of 13 patients revealed dynamic shifts consistent with antibiotic exposure and subsequent withdrawal. Ct values consistently increased during treatment and decreased after discontinuation. In Case A, Ct values declined steadily after antibiotic cessation (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003eig. S3A\u003c/strong\u003e). In Case B, Ct values fluctuated in close parallel with antibiotic use (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003eig. S3B\u003c/strong\u003e). In Case C, carbapenemase-producing Enterobacteriaceae (CPE) detected in the feces on day 51 became undetectable by day 79 as Ct values decreased, reflecting microbiota rebound (\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003eig. S3C\u003c/strong\u003e). In Case D, however, \u003cem\u003eClostridioides difficile\u003c/em\u003e infection (CDI) (which was diagnosed and treated with vancomycin for 10 days, during which broad-spectrum antibiotics were continued) persisted despite antibiotic discontinuation, with high Ct values and clinical relapse of CDI (\u003cstrong\u003eSupplementary F\u003c/strong\u003e\u003cstrong\u003eig. S3D\u003c/strong\u003e). Collectively, these results demonstrate that prolonged antibiotic use depletes LRB, whereas recovery after cessation is variable and patient dependent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of the ESBL carriers using MARS method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSixteen fecal samples from 15 patients tested positive on ESBL-selective chromogenic medium (CHROMagar ESBL), and the corresponding patients were defined as ESBL carriers. The Ct values of the LRB assay and the combined relative abundance of LRB taxa, based on 16S rRNA gene sequencing, did not differ significantly between ESBL carriers and non-carriers (\u003cstrong\u003eFig. 4A–C\u003c/strong\u003e). This likely reflects the fact that both groups consisted of hospitalized patients at high risk of AMR colonization. In contrast, ETB assay Ct values were significantly lower in ESBL carriers than in non-carriers or healthy individuals (\u003cstrong\u003eFig. 4D\u003c/strong\u003e).\u0026nbsp;Consistently, the relative abundance of Enterobacteriaceae from 16S rRNA gene sequencing was significantly greater in ESBL carriers compared to both non-carriers and healthy individuals (\u003cstrong\u003eFig. 4E and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSupplementary F\u003c/strong\u003e\u003cstrong\u003eigs. S4A and 4B\u003c/strong\u003e). ROC analysis demonstrated that the ETB assay distinguished ESBL carriers from non-carriers and healthy individuals with an AUC of 0.76 (95% CI, 0.63–0.88; \u003cem\u003eP\u003c/em\u003e = 0.0018), yielding 50.0% sensitivity and 88.5% specificity at a Ct value \u0026gt; 21.05 (\u003cstrong\u003eFig. 4F\u003c/strong\u003e). Together, these findings show that the MARS method, combining LRB- and ETB-specific assays, effectively identifies both individuals at high risk of AMR colonization and ESBL carriers (\u003cstrong\u003eFigs. 3B, 3D, 4D, 4F\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eColonization resistance is influenced not only by the diversity of the gut microbiota but also by its compositional makeup(24).\u0026nbsp;Specific bacterial taxa or microbial consortia have been linked with protective or antagonistic roles in colonization and infection by AMR bacteria(11). Although shifts in the gut microbial profile may signal colonization risk, no widely accessible, laboratory-based diagnostic method currently exists for identifying dysbiosis or assessing the risk of AMR colonization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address this gap, we developed two novel real-time PCR assays from first principles to quantify the bacterial loads of LRB and ETB and validated the MARS method in an antibiotic-treated mouse model and fecal samples from patients with diarrhea and healthy individuals. This method enables rapid and sensitive assessment of AMR colonization risk by quantifying LRB levels—providing insight into antibiotic-induced microbiota disruption—and accurately identifies ESBL-Eco carriers via the ETB assay.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsistent with our findings, LRB has been associated with colonization resistance, whereas ETB is linked to colonization permissiveness in both animal models and human studies. Similar microbial patterns have also been reported for other AMR pathogens(14, 15), including carbapenem-resistant Enterobacteriaceae (CRE)(19), vancomycin-resistant enterococci (VRE)(13, 19), and \u003cem\u003eC. difficile\u003c/em\u003e(16), suggesting broad relevance. For example, in a case-control study comparing CDI patients with non-diarrheal controls, protective taxa such as \u003cem\u003eBacteroides\u003c/em\u003e, Lachnospiraceae, and Ruminococcaceae were more prevalent in controls, whereas ETB was enriched in CDI cases(16). A large-scale study analyzing over 12,000 gut metagenomes from 45 countries further identified 172 microbial species as co-colonizers and 135 as co-excluders of Enterobacteriaceae, with nine of the top ten co-excluders (except \u003cem\u003eBarnesiella intestinihominis\u003c/em\u003e) belonging to the LRB group(25).\u0026nbsp;The reproducibility of these microbiota signatures across species (mice and\u0026nbsp;humans), clinical contexts, geographic regions underscores a broadly applicable principle of colonization resistance. Taken together, these findings highlight the potential utility of the MARS method for evaluating AMR colonization risk, not only for ESBL-producing bacteria but also for other clinically relevant AMR pathogens such as CRE, VRE, and \u003cem\u003eC. difficile\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, MARS could serve as a biomarker for evaluating the efficacy of microbiota-targeted interventions, including probiotics, prebiotics, and synbiotics. A recent study demonstrated that a defined consortium of 31 gut-derived commensal bacterial strains (F31-mix), isolated from a single healthy human donor, effectively eliminated \u003cem\u003eK. pneumoniae\u003c/em\u003e colonization in germ-free mice after prior mono-colonization(26). Notably, 20 of these strains were LRB members, highlighting this taxonomic group as a central driver of colonization resistance. A streamlined 18-strain version (F18-mix), which retained 10 LRB taxa, provided comparable protection against \u003cem\u003eK. pneumoniae\u003c/em\u003e, carbapenemase-producing \u003cem\u003eK. pneumoniae,\u003c/em\u003e \u003cem\u003eK. aerogenes\u003c/em\u003e, and ESBL-Eco. Furthermore, the broader-spectrum F31-mix also suppressed VRE, reinforcing the role of LRB in colonization resistance across diverse AMR organisms. Mechanistically, nutrient competition was a key factor: eight strains in the F18-mix (F8-mix) consumed gluconate—a preferred carbon source for ETB—six of which were LRB members, thereby restricting \u003cem\u003eK. pneumoniae\u003c/em\u003e expansion in the gut(26). These findings suggest that the MARS method could provide a rapid, clinically applicable means of assessing colonization resistance and predicting the engraftment and therapeutic potential of microbiota-based interventions.\u003c/p\u003e\n\u003cp\u003eAntibiotic exposure is a well-established driver of gut microbiome disruption, typically causing acute reductions in alpha diversity and marked compositional shifts(27). Antibiotic-specific effects are increasingly recognized, with substantial decreases documented across key taxa in Firmicutes and Bacteroidetes. Many species commonly depleted during antibiotic treatment are LRB members, including \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eDorea\u003c/em\u003e, \u003cem\u003eCoprococcus\u003c/em\u003e, and \u003cem\u003eAnaerostipes\u003c/em\u003e spp. (Lachnospiraceae)\u0026nbsp;(28-34);\u003cem\u003e\u0026nbsp;Faecalibacterium prausnitzii\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Ruminococcus\u0026nbsp;\u003c/em\u003espp\u003cem\u003e.\u0026nbsp;\u003c/em\u003e(Ruminococcaceae)(28, 31-33, 35, 36); and \u003cem\u003eBacteroides\u0026nbsp;\u003c/em\u003espp. (Bacteroidaceae)(33, 37, 38). In our study, LRB bacterial loads estimated using the MARS method, were significantly reduced during antibiotic use and inversely correlated with the duration of exposure (\u003cstrong\u003eFig. 3F–G\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe gut microbiome also has an inherent capacity to rebound after antibiotic cessation. In this study, LRB bacterial loads increased after discontinuation (\u003cstrong\u003eFig. 3E\u003c/strong\u003e), although recovery trajectories varied among patients (\u003cstrong\u003eFig. 3G and Supplementary Fig. S2\u003c/strong\u003e). Previous work reported that microbial diversity begins to recover within approximately 1 month in children(39, 40). In adults, while overall gut microbiota composition approached baseline within 1.5 months after treatment with meropenem, gentamicin, and vancomycin, nine common species—including \u003cem\u003eCoprococcus eutactus\u003c/em\u003e (Lachnospiraceae)—remained undetectable for up to 6 months(31). The extent and pace of recovery depend heavily on the antibiotic regimen, with wide variability in effect size, dysbiosis duration, and restoration timing(11, 40). Given this variability, a clinically applicable tool is urgently needed to capture rapid, dynamic changes in colonization resistance and identify AMR risk in real time. Currently, no PCR-based assay universally and specifically detects LRB as a group. Although some assays targeting ETB-related taxa have been proposed(41), none have been clinically validated for identifying actual ESBL colonization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address this shortcoming, we developed two complementary real-time PCR assays with the MARS method, designed to specifically quantify bacterial loads of both LRB and ETB. LRB and ETB abundances measured by MARS correlated well with their relative abundances derived from 16S rRNA gene sequencing (\u003cstrong\u003eFig. 2A–B\u003c/strong\u003e). Clinical validation using fecal samples demonstrated that MARS effectively distinguished high-risk patients from healthy individuals (\u003cstrong\u003eFig. 3A–D\u003c/strong\u003e). Moreover, combining ETB and LRB assays enabled the specific identification of ESBL carriers (\u003cstrong\u003eFig. 4B, 4D, and 4F\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, as an exploratory study conducted at a single center, its generalizability to other institutions may be limited. Second, 16S rRNA sequencing and ESBL testing were performed on only 57 of 80 patient stool samples, while 23 longitudinal samples were excluded to avoid duplication bias when analyzing antibiotic-associated changes. Although the sample size was modest, it was appropriate for this proof-of-concept study. Third, we did not prospectively track whether individuals with low LRB loads subsequently developed AMR colonization. Nevertheless, prior studies consistently show that gut dysbiosis predisposes to MDRO overgrowth(11, 19). A prospective multicenter study is underway to validate these findings and assess institutional variation in AMR carriage. Fourth, ESBL isolates were not further characterized by antimicrobial susceptibility testing or β-lactamase genotyping (e.g., \u003cem\u003ebla\u003csub\u003eTEM\u003c/sub\u003e\u003c/em\u003e, \u003cem\u003ebla\u003csub\u003eSHV\u003c/sub\u003e\u003c/em\u003e, \u003cem\u003ebla\u003csub\u003eCTX-M\u003c/sub\u003e\u003c/em\u003e), though this is unlikely to alter our main conclusions. Finally, nucleic acid extraction and PCR performance may vary by platform, and detection limits were not determined using pure cultures, underscoring the need for further standardization prior to widespread implementation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study establishes a practical approach for assessing the risk of AMR bacterial colonization by quantifying the bacterial loads of LRB as indicators of colonization resistance and ETB as indicators of permissiveness. The MARS method provides a rapid and sensitive tool to detect antibiotic-induced microbiota disruption and identify ESBL carriers. Beyond ESBL, MARS holds promise for broader application to other MDROs, including CRE and \u003cem\u003eC. difficile\u003c/em\u003e. While antibiotics remain indispensable, they disrupt the microbiota and weaken colonization resistance. By enabling early identification of high-risk individuals, MARS offers clinically actionable strategy to guide microbiota-based interventions aimed at restoring gut resilience and may serve as a predictive biomarker for the engraftment success and therapeutic efficacy of microbiota-targeted therapies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eReference strains and culture conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bacterial strains listed in \u003cstrong\u003eTable 1\u003c/strong\u003e were obtained from the American Type Culture Collection (ATCC; Rockville, MD, USA), the National Collection of Type Cultures (NCTC; London, UK), and the Japan Collection of Microorganisms (JCM; Wako, Japan). Most anaerobic strains were cultured in an anaerobic chamber (Bactron IV SHEL LAB, Cornelius, OR) on brain heart infusion (BHI; BD Diagnosis Systems, Sparks, MD) agar supplemented with yeast extract (5 mg/mL), L-cysteine (1 mg/mL), hemin (5 μg/mL), and menadione (1 μg/mL) at 37 °C for 48 h. Aerobic strains were grown overnight at 37 °C on trypticase soy agar (Becton Dickinson [BD], Sunnyvale, CA) or Luria-Bertani (LB) agar (BD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtraction and purification of DNA from fecal samples and bacterial cultures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBacterial DNA was extracted using a modified alkaline lysis and heat method(42). Briefly, a loopful of bacteria from a single colony was suspended in 50 μL of 0.05 mol/L NaOH, incubated at 95 °C for 10 min, and neutralized with 11 μL of Tris-HCl buffer (pH 7.0)(43). One microliter of the crude DNA extract, diluted 10-fold with distilled water, was used as a PCR template.\u003c/p\u003e\n\u003cp\u003eFecal samples were collected and processed for colony counting (animal samples) or routine microbiological examination (clinical samples). Residual material was refrigerated for up to 5 days and then frozen at −80 °C until DNA extraction. DNA was isolated using either the Quick-DNA Fecal/Soil Microbe Miniprep Kit (Zymo Research, Irvine, CA, USA) or the QIAamp PowerFecal Pro DNA Kit (QIAGEN, Crawley, UK), following the manufacturers’ protocols. Extracted DNA was analyzed with both the MARS method and 16S rRNA gene sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of 16S rDNA-targeted LRB- and ETB-specific primers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo design primers targeting LRB and ETB, 16S rRNA gene sequences were retrieved from the DNA Data Bank of Japan (DDBJ), GenBank, and European Molecular Biology Laboratory (EMBL) databases. Multiple sequence alignments of target families and reference organisms were performed using CLC Genomics Workbench v20.0.4 (CLC Bio, Aarhus, Denmark). Regions unique to LRB and ETB were identified and compared with numerous reference strains, yielding one candidate region for LRB-specific detection and three for ETB-specific detection. Based on these, ten forward and six reverse primers for LRB, and four forward and three reverse primers for ETB were designed using Primer3Plus (https://www.primer3plus.com/index.html). All primers were synthesized by Thermo Fisher Scientific (https://www.thermofisher.com/order/custom-standard-oligo), and optimal primer sets were determined experimentally.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReal-time PCR assays for LRB- and ETB-specific detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll real-time PCR assays were performed using THUNDERBIRD or THUNDERBIRD Next SYBR qPCR Mix (TOYOBO, Osaka, Japan) on a LightCycler 96 Real-Time PCR System (Roche Diagnostics KK, Tokyo, Japan), following the manufacturer’s instructions. Each 20 μL reaction contained 10 μL SYBR qPCR mix, 10 pmol of each primer, 1 μL template DNA, and nuclease-free water.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the LRB-specific assay, the optimal forward primer was an equimolar mix of LRB-F1, LRB-F2, and LRB-F3, targeting positions 912–933 bp of the \u003cem\u003eBacteroides finegoldii\u003c/em\u003e 16S rRNA gene (strain JCM 13345; NCBI Reference Sequence: AB222699). The reverse primers were an equimolar mix of LRB-R1 and LRB-R2, targeting positions 1030–1011 bp of the same sequence (\u003cstrong\u003eSupplementary Table S1 and Supplementary Fig. S1\u003c/strong\u003e). PCR cycling included an initial denaturation at 95 °C for 60 s, followed by 45 cycles of 95 °C for 15 s, 62 °C for 5 s, and 72 °C for 30 s. The expected amplicon size was 119 bp.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the ETB-specific assay, the forward primer ETB-F1 targeted positions 169–191 bp of the \u003cem\u003eE. coli\u003c/em\u003e 16S rRNA gene (strain ATCC 35218; NCBI Reference Sequence: AM980865), with reverse primer ETB-R1 targeting positions 322–304 bp (\u003cstrong\u003eSupplementary Table S1 and Supplementary Fig. S1\u003c/strong\u003e). Cycling conditions were 95 °C for 30 s, followed by 45 cycles of at 95 °C for 5 s and at 67 °C for 30 s. The expected amplicon size was 144 bp.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimal model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour- to six-week-old female C57BL/6J mice were purchased from Charles River Laboratories, Japan, Inc. (Kanagawa, Japan). Mice were co-housed for at least 2 weeks before experimentation to normalize gut microbiota. They were then divided into three groups and provided with filter-sterilized water (n = 4, control group), ampicillin (1 g/L; n = 4), or metronidazole (1 g/L; n = 4) in drinking water for 3 consecutive days. After a 1-day washout period with regular water, all groups were orally inoculated with a clinical ESBL-Ecostrain (sequence type 131, cefotaximase-Munich (CTX-M)-15-producing isolate hTYM0002).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor inoculum preparation, a single colony of ESBL-Eco hTYM0002 was cultured overnight in LB broth at 37 °C with shaking, transferred to fresh broth for 6–8 h, and adjusted to the desired concentration by turbidimetry.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMice were gavaged with 4 × 10\u003csup\u003e3\u003c/sup\u003e CFU of ESBL-Eco. Fecal samples were collected at baseline (day 0) and at multiple time points up to 12 days post-inoculation. At each time point, one or two fecal pellets were collected, weighed, suspended in 500 μL phosphate-buffered saline (PBS), homogenized, serially diluted, and plated on MacConkey agar containing cefoperazone (32 μg/mL). Plates were incubated overnight at 37 °C, and ESBL-Eco colonies were enumerated. Remaining fecal material was stored at −80 °C for DNA extraction and subsequent analysis with the MARS assays.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical stool specimens (n = 80) were collected from 58 patients (55 inpatients and 3 outpatients) with diarrhea between March and August 2022 at Toyama University Hospital, a 612-bed tertiary care facility in Japan. Residual fecal material from routine clinical microbiological examinations, including \u003cem\u003eC.\u003c/em\u003e\u003cem\u003e\u0026nbsp;difficile\u003c/em\u003e testing, was used for this study. Of these, 57 specimens from 51 patients were additionally cultured on a commercial ESBL-selective chromogenic medium (CHROMagar ESBL; Kanto Chemical, Tokyo, Japan) alongside standard microbiological tests before freezing. Patients whose fecal specimens yielded growth on ESBL-selective medium were classified as ESBL carriers, while those without growth were defined as non-carriers. The remaining 23 fecal samples were collected longitudinally from eight patients at different time points during treatment to assess antibiotic-induced changes in colonization.\u003c/p\u003e\n\u003cp\u003eSpecimens were categorized into two main groups: patients who had received antibiotics (n = 45; ABX group) and those who had not (n = 35; No ABX group). The No ABX group comprised patients with previous antibiotic use within the preceding 60 days, with the group being further categorized as Before (No) group which comprised those with no history of antibiotic use and After group with those who had received antibiotics within the last 60 days. Samples were also classified by ESBL colonization status as carriers (n = 41) or non-carriers (n = 16). Stool samples from 20 healthy volunteers were collected and designated as the Healthy group (n = 20).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCR amplification and preparation for 16S metagenomic sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA quality and concentration were measured using a NanoDrop One spectrophotometer (Thermo Fisher Scientific). Library preparation followed the Illumina 16S Metagenomic Sequencing Library Preparation protocol. Briefly, the V3–V4 hypervariable region of the 16S rRNA gene was amplified using primers 341F and 805R, with Illumina sequencing adapters and dual-index barcodes from the Nextera XT kit appended to the amplicons (\u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e). Sequencing was performed on an Illumina MiSeq platform with a 2 × 300 bp paired-end protocol\u0026nbsp;(44). Sequencing data from both patient and healthy control samples were deposited in the DNA Data Bank of Japan (DDBJ) under BioProject accession number PRJDB20682.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetagenome profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing reads were processed into amplicon sequence variants (ASVs) using the DADA2 pipeline via the q2-dada2 plugin(23)\u0026nbsp;within QIIME2 (v2023.5). Reads were filtered, trimmed, denoised, dereplicated, merged (forward and reverse), chimera-checked. Truncation positions were set at 250 bp for forward reads and 230 bp for reverse reads, based on quality score assessments. Samples with low sequencing depth (\u0026lt; 1000 total reads) were excluded(45). Taxonomic assignment of ASVs was performed using the q2-feature-classifier plugin(46)\u0026nbsp;with a naïve Bayes classifier (classify-sklearn) trained against the SILVA v138 99% 16S rRNA reference database(47), trimmed to the V3–V4 region bound by the 341F/805R primer pair. The resulting taxonomy table was collapsed at the genus, family, and phylum levels, and the merged abundance table was used for downstream analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using GraphPad Prism version 9.5.1 (GraphPad Software, San Diego, CA, USA). The Mann–Whitney \u003cem\u003eU\u003c/em\u003e test was used for pairwise comparisons between non-parametric groups, and the Kruskal–Wallis test was applied for comparisons among three or more groups. Pearson’s correlation coefficient was calculated to assess associations between continuous variables. ROC curves and AUC values were generated to evaluate diagnostic performance. Statistical significance was defined as \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. Data are presented as means with SDs or SEMs, or as medians with IQRs, as appropriate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Review Board of the University of Toyama (approval nos. R2021167 and R2022089). The requirement for written informed consent was waived due to the observational design. All animal experiments were approved by the Ethics Review Committee for Animal Experimentation (Institutional Animal Care and Use Committee. University of Toyama; approval no. A2020med-18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data are included in the main text or supplementary materials. Sequencing data are available in the DDBJ BioProject database under accession number PRJDB20682.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Kazuyuki Tobe for providing nucleosides extracted from stool samples of healthy individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Japanese Association for Infectious Diseases, Grant for Clinical Research Promotion [6th (2023)] (H.K.); JSPS KAKENHI (Grant no. JP20K08821, Y.M.); and the Japan Agency for Medical Research and Development (AMED) (Grant no. JP22fk0108133, Y.M.). The funders had no role in study design, data collection or analysis, decision to publish, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization: H.K., Y.Mo.; Methodology: H.K., Y.Mo.; Validation: H.K., Y.Mo.; Formal analysis: H.K., Y.Mo.; Investigation: H.K., Y.Mo., H.W., M.M., K.A., T.F., M.E., Y.K., Y.T., M.K., Y.Mu., K.K., K.N.; Resources: Y.Mo., S.F., T.S.; Data curation: H.K., Y.Mo.; Writing—original draft: H.K., Y.Mo.; Writing—review \u0026amp; editing: H.K., Y.Mo.; Visualization: H.K., Y.Mo.; Supervision: Y.Mo., Y.Y.; Project administration: Y.Mo., Y.Y.; Funding acquisition: H.K., Y.M.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Yoshitomo Morinaga.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eCollaborators AR. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet (London, England). 2022;399(10325):629-55.\u003c/li\u003e\n\u003cli\u003eKelly SA, Rodgers AM, O\u0026rsquo;Brien SC, Donnelly RF, Gilmore BF. Gut Check Time: Antibiotic Delivery Strategies to Reduce Antimicrobial Resistance. Trends in Biotechnology. 2020;38(4):447-62.\u003c/li\u003e\n\u003cli\u003eHeinemann M, Kleinjohann L, Rolling T, Winter D, Hackbarth N, Ramharter M, et al. Impact of antibiotic intake on the incidence of extended-spectrum \u0026beta;-lactamase\u0026ndash;producing Enterobacterales in sub-Saharan Africa: results from a community-based longitudinal study. Clinical Microbiology and Infection. 2023;29(3):340-5.\u003c/li\u003e\n\u003cli\u003eSati H, Carrara E, Savoldi A, Hansen P, Garlasco J, Campagnaro E, et al. The WHO Bacterial Priority Pathogens List 2024: a prioritisation study to guide research, development, and public health strategies against antimicrobial resistance. 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DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581-3.\u003c/li\u003e\n\u003cli\u003eKorach-Rechtman H, Hreish M, Fried C, Gerassy-Vainberg S, Azzam Zaher S, Kashi Y, et al. Intestinal Dysbiosis in Carriers of Carbapenem-Resistant Enterobacteriaceae. mSphere. 2020;5(2):10.1128/msphere.00173-20.\u003c/li\u003e\n\u003cli\u003eYin Q, da Silva AC, Zorrilla F, Almeida AS, Patil KR, Almeida A. Ecological dynamics of Enterobacteriaceae in the human gut microbiome across global populations. Nature Microbiology. 2025;10(2):541-53.\u003c/li\u003e\n\u003cli\u003eFuruichi M, Kawaguchi T, Pust M-M, Yasuma-Mitobe K, Plichta DR, Hasegawa N, et al. Commensal consortia decolonize Enterobacteriaceae via ecological control. Nature. 2024;633(8031):878-86.\u003c/li\u003e\n\u003cli\u003eFishbein SRS, Mahmud B, Dantas G. Antibiotic perturbations to the gut microbiome. Nature Reviews Microbiology. 2023;21(12):772-88.\u003c/li\u003e\n\u003cli\u003eReijnders D, Goossens GH, Hermes GD, Neis EP, van der Beek CM, Most J, et al. Effects of Gut Microbiota Manipulation by Antibiotics on Host Metabolism in Obese Humans: A Randomized Double-Blind Placebo-Controlled Trial. Cell Metab. 2016;24(1):63-74.\u003c/li\u003e\n\u003cli\u003eKabbani TA, Pallav K, Dowd SE, Villafuerte-Galvez J, Vanga RR, Castillo NE, et al. Prospective randomized controlled study on the effects of Saccharomyces boulardii CNCM I-745 and amoxicillin-clavulanate or the combination on the gut microbiota of healthy volunteers. Gut Microbes. 2017;8(1):17-32.\u003c/li\u003e\n\u003cli\u003eMacPherson CW, Mathieu O, Tremblay J, Champagne J, Nantel A, Girard SA, et al. Gut Bacterial Microbiota and its Resistome Rapidly Recover to Basal State Levels after Short-term Amoxicillin-Clavulanic Acid Treatment in Healthy Adults. Sci Rep. 2018;8(1):11192.\u003c/li\u003e\n\u003cli\u003ePalleja A, Mikkelsen KH, Forslund SK, Kashani A, Allin KH, Nielsen T, et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat Microbiol. 2018;3(11):1255-65.\u003c/li\u003e\n\u003cli\u003eTh\u0026auml;nert R, Th\u0026auml;nert A, Ou J, Bajinting A, Burnham CD, Engelstad HJ, et al. Antibiotic-driven intestinal dysbiosis in pediatric short bowel syndrome is associated with persistently altered microbiome functions and gut-derived bloodstream infections. Gut Microbes. 2021;13(1):1940792.\u003c/li\u003e\n\u003cli\u003eHe C, Xie Y, Zhu Y, Zhuang K, Huo L, Yu Y, et al. Probiotics modulate gastrointestinal microbiota after Helicobacter pylori eradication: A multicenter randomized double-blind placebo-controlled trial. Front Immunol. 2022;13:1033063.\u003c/li\u003e\n\u003cli\u003eHu Y, Xu X, Ouyang YB, He C, Li NS, Xie C, et al. Altered Gut Microbiota and Short-Chain Fatty Acids After Vonoprazan-Amoxicillin Dual Therapy for Helicobacter pylori Eradication. Front Cell Infect Microbiol. 2022;12:881968.\u003c/li\u003e\n\u003cli\u003eDubinsky V, Reshef L, Bar N, Keizer D, Golan N, Rabinowitz K, et al. Predominantly Antibiotic-resistant Intestinal Microbiome Persists in Patients With Pouchitis Who Respond to Antibiotic Therapy. Gastroenterology. 2020;158(3):610-24.e13.\u003c/li\u003e\n\u003cli\u003eStewardson AJ, Ga\u0026iuml;a N, Fran\u0026ccedil;ois P, Malhotra-Kumar S, Del\u0026eacute;mont C, Martinez de Tejada B, et al. Collateral damage from oral ciprofloxacin versus nitrofurantoin in outpatients with urinary tract infections: a culture-free analysis of gut microbiota. Clin Microbiol Infect. 2015;21(4):344.e1-11.\u003c/li\u003e\n\u003cli\u003eBasolo A, Hohenadel M, Ang QY, Piaggi P, Heinitz S, Walter M, et al. Effects of underfeeding and oral vancomycin on gut microbiome and nutrient absorption in humans. Nat Med. 2020;26(4):589-98.\u003c/li\u003e\n\u003cli\u003eReyman M, van Houten MA, Watson RL, Chu M, Arp K, de Waal WJ, et al. Effects of early-life antibiotics on the developing infant gut microbiome and resistome: a randomized trial. Nat Commun. 2022;13(1):893.\u003c/li\u003e\n\u003cli\u003eYassour M, Vatanen T, Siljander H, H\u0026auml;m\u0026auml;l\u0026auml;inen AM, H\u0026auml;rk\u0026ouml;nen T, Ryh\u0026auml;nen SJ, et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci Transl Med. 2016;8(343):343ra81.\u003c/li\u003e\n\u003cli\u003eRamirez J, Guarner F, Bustos Fernandez L, Maruy A, Sdepanian VL, Cohen H. Antibiotics as Major Disruptors of Gut Microbiota. Front Cell Infect Microbiol. 2020;10:572912.\u003c/li\u003e\n\u003cli\u003eHansen R, Russell RK, Reiff C, Louis P, McIntosh F, Berry SH, et al. Microbiota of de-novo pediatric IBD: increased Faecalibacterium prausnitzii and reduced bacterial diversity in Crohn\u0026apos;s but not in ulcerative colitis. Am J Gastroenterol. 2012;107(12):1913-22.\u003c/li\u003e\n\u003cli\u003eKulski JK, Pryce T. Preparation of mycobacterial DNA from blood culture fluids by simple alkali wash and heat lysis method for PCR detection. J Clin Microbiol. 1996;34(8):1985-91.\u003c/li\u003e\n\u003cli\u003eKawasuji H, Ikezawa Y, Morita M, Sugie K, Somekawa M, Ezaki M, et al. High Incidence of Metastatic Infections in Panton-Valentine Leucocidin-Negative, Community-Acquired Methicillin-Resistant Staphylococcus aureus Bacteremia: An 11-Year Retrospective Study in Japan. Antibiotics (Basel). 2023;12(10).\u003c/li\u003e\n\u003cli\u003eB\u0026aacute;rcena C, Vald\u0026eacute;s-Mas R, Mayoral P, Garabaya C, Durand S, Rodr\u0026iacute;guez F, et al. Healthspan and lifespan extension by fecal microbiota transplantation into progeroid mice. Nature Medicine. 2019;25(8):1234-42.\u003c/li\u003e\n\u003cli\u003eHuang B, Chau SWH, Liu Y, Chan JWY, Wang J, Ma SL, et al. Gut microbiome dysbiosis across early Parkinson\u0026rsquo;s disease, REM sleep behavior disorder and their first-degree relatives. Nature Communications. 2023;14(1):2501.\u003c/li\u003e\n\u003cli\u003eBokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2\u0026apos;s q2-feature-classifier plugin. Microbiome. 2018;6(1):90.\u003c/li\u003e\n\u003cli\u003eQuast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41(Database issue):D590-6.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cstrong\u003eSpecificity of LRB- and ETB-targeted primers evaluated using DNA from reference strains and clinical isolates\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReaction with the following primer set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLRB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eETB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003eReference strains\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eLachnospiraceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eLacrimsipora indolis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 25771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eRuminococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eRumonococcus gauvreauii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eJCM 14987\u003csup\u003eT\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eRuminococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eRuminococcus gnavus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 29149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eOscillospiraceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eRuminiclostridium cellobioparum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 15832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eOscillospiraceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003ePseudoflavonifractor capillosus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eJCM 32126\u003csup\u003eT\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eRikenellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eAlistipes indistinctus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eJCM 16068\u003csup\u003eT\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eBacteroidaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides fragilis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 25285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eBacteroidaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003ePhocaeicola vulgatus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 8482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eBacteroidaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides acidifaciens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eJCM 10556\u003csup\u003eT\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eBacteroidaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides uniforims\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 8492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eBacteroidaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides dorei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eJCM 13471\u003csup\u003eT\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eBacteroidaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides finegoldii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eJCM 13345\u003csup\u003eT\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eBacteroidaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides intestinales\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eJCM 13265\u003csup\u003eT\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eBacteroidaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides thetaiotaomicron\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 29148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eTannerellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eParabacteroides johnsoii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eJCM 13406\u003csup\u003eT\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eTannerellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eParabacteroides merdae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 43184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eClostridiaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eClostridium butyricum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 19398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eClostridiaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eClostridium sporogenes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 3584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eClostridiaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eClostridium ramosum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 25582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eClostridiaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eClostridium intestinales\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 49213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eClostridiaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eClostridium innocuum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 14501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eClostridiaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eClostridium spiroforme\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 29900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEggerthellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEggerthella lenta\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 25559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003ePeptoniphilaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eFinegoldia magna\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 14904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eLactobacillaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eLactobacillus casei\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eLactobacillaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eLactobacillus acidophilus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 4356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eLactobacillaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eLactobacillus gasseri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 33323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eLactobacillaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eLigilactobacillus animalis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 35046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eCorynebacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium striatum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 6940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eCorynebacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium ulcerans\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 51799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eCorynebacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium pseudodiphtheriticum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 10700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eStaphylococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus lugdunensis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 49576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eStaphylococcaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus saprophyticus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 15305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 35218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 13883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella oxytoca\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 700324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella varicola\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 31488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eProteus vulgaris\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 6380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEnterobacter cloacae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNCTC 13406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003ePasteurellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eHaemophilus influenzae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eATCC 43335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003eClinical isolates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella aerogenes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella aerogenes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella oxytoca\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella oxytoca\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eCitrobacter freundii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eEnterobacteriaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eRaoultella ornithinolytica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eMoraxellaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter\u0026nbsp;\u003c/em\u003especies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eAeromonadaceae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas veronii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eNo.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026minus;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLRB, Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae; ETB, Enterobacteriaceae\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Demographic and clinical characteristics of the study cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy\u0026nbsp;\u003c/strong\u003e(n = 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo ABX\u0026nbsp;\u003c/strong\u003e(n = 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eABX\u0026nbsp;\u003c/strong\u003e(n = 45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESBL non-carriers\u0026nbsp;\u003c/strong\u003e(n = 41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eESBL carriers\u0026nbsp;\u003c/strong\u003e(n = 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAge (y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e53.6 \u0026plusmn; 5.6 (43\u0026ndash;70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e57.2 \u0026plusmn; 25.1 (1\u0026ndash;92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e65.4 \u0026plusmn; 18.6 (0.5\u0026ndash;86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e63.4 \u0026plusmn; 19.3 (0.5\u0026ndash;92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e64.6 \u0026plusmn; 29.7 (1\u0026ndash;00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eMen, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e17 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e19 (42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e24 (58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e8 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eWomen, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e17 (85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e18 (51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e26 (57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e17 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e8 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eHospitalized patients, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e31 (88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e45 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e38 (92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e15 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eLength of hospital stay at the time of sampling (d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e25.7 \u0026plusmn; 33.7 (0\u0026ndash;145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e30.1 \u0026plusmn; 38.2 (0\u0026ndash;208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e21.8 \u0026plusmn; 38.0 (0\u0026ndash;208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e20.5 \u0026plusmn; 18.3 (0\u0026ndash;59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eSolid tumor, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e14 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e21 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e18 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e10 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eHematologic tumor, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eChemotherapy, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e9 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e13 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eRadiotherapy, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGastrointestinal disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11 (31.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e8 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e18 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e19 (42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e8 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8 (17.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eChronic heart disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e13 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e8 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eChronic lung disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eChronic renal disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e13 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eChronic liver disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eIntracranial vascular disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e13 (37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e14 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAutoimmune disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eImmune suppression, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4 (11.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAntibiotic use, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e23 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e10 (62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDuration of antibiotics at the time of sampling (d)\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e9.3 \u0026plusmn; 9.7 (1\u0026ndash;41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6.6 \u0026plusmn; 7.4 (1\u0026ndash;35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5.8 \u0026plusmn; 4.2 (1\u0026ndash;12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eESBL carriers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6 / 24 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10 / 33 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026ndash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAges (y), hospital stay duration (d), and duration\u0026nbsp;of antibiotics at the time of fecal sampling (d) are expressed as mean \u0026plusmn; standard deviation (SD; range). Other values are shown as number (percentage) of patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e One patient with secondary acute myeloid leukemia who received trimethoprim-sulfamethoxazole prophylaxis for \u003cem\u003ePneumocystis\u003c/em\u003e pneumonia was excluded.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gut microbiome, antimicrobial resistance, colonization resistance, dysbiosis, extended-spectrum β-lactamase, microbiota-based diagnostics, Enterobacteriaceae, Lachnospiraceae, Ruminococcaceae, Bacteroidaceae ","lastPublishedDoi":"10.21203/rs.3.rs-7842064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7842064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gut microbiome serves as a reservoir for antimicrobial-resistant (AMR) bacteria, making the prevention of silent colonization and transmission an urgent public health priority. Colonization risk often precedes established AMR colonization, yet no rapid method currently exists to identify individuals at risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed a microbiota-based AMR risk screening (MARS) method, comprising two complementary real-time PCR assays to quantify the bacterial loads of Lachnospiraceae, Ruminococcaceae, and Bacteroidaceae (LRB; indicators of colonization resistance) and Enterobacteriaceae (ETB; indicator of colonization permissiveness). Assay performance was evaluated in an antibiotic-treated mouse model and validated in clinical samples from 80 patients with diarrhea and 20 healthy controls. Both assays demonstrated high specificity and correlated with 16S rRNA gene sequencing. In mice, LRB assay cycle threshold (Ct) values measured just before inoculation with extended-spectrum β-lactamase-producing Escherichia coli (ESBL-Eco) correlated with fecal bacterial load and shedding duration. In patients, LRB bacterial loads declined during antibiotic treatment and recovered after discontinuation. Combined ETB and LRB assay results identified both high-risk individuals and ESBL carriers, with strong discriminatory performance (AUCs = 0.86 and 0.76).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MARS method provides a novel, rapid, and clinically applicable approach to assess AMR colonization risk and detect actual colonization, offering a valuable tool for early intervention in high-risk individuals before AMR carriage becomes established.\u003c/p\u003e","manuscriptTitle":"Microbiota-based Antimicrobial resistance Risk Screening (MARS): a novel PCR method for dynamic assessment of AMR colonization risk and ESBL carrier identification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 17:17:03","doi":"10.21203/rs.3.rs-7842064/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-12T22:46:59+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"76075059005495255908568348713795550455","date":"2026-03-03T23:31:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26083886499493335987236840473271809937","date":"2026-03-02T02:38:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T01:55:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249997898792919017838615358855609368970","date":"2026-02-28T17:03:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T15:06:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267488259616951923219803402382029235606","date":"2026-02-24T07:49:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223231988622620927909099040629356852481","date":"2026-02-10T06:16:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243485463785247613284829741423728226553","date":"2026-02-04T07:00:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T22:22:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302130710015632587414205769266305118410","date":"2025-12-08T15:36:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-18T00:43:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-31T18:26:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-14T06:32:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbiome","date":"2025-10-12T16:38:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35699df0-2eb7-4248-8c16-83d276594f8c","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T21:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 17:17:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7842064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7842064","identity":"rs-7842064","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}