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Record W4406924121 · doi:10.1093/ofid/ofae631.2462

P-2310. <i>Gammaproteobacteria</i> Load Responses in the Intestinal Microbiota of Patients Undergoing Hematopoietic Cell Transplantation Predicts Resistant Gram-Negative Rod Colonization

2025· article· en· W4406924121 on OpenAlexaffabout
Leanne Mortimer, Nicole Janusz, Amanda C Carroll, Austin Yan, Tamara Leite, Andrew Purssell, Natasha Kekre, Michael Kennah, C. Arianne Buchan, Derek R. MacFadden

Bibliographic record

VenueOpen Forum Infectious Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsColonizationMedicineGammaproteobacteriaHematopoietic cellTransplantationGramHematopoietic stem cell transplantationHaematopoiesisDysbiosisImmunologyMicrobiologyGut floraInternal medicineBiologyBacteriaStem cell

Abstract

fetched live from OpenAlex

Abstract Background Hematopoietic cell transplant (HCT) patients are at high risk of Gram-negative rod (GNR) blood stream infections (BSIs) particularly before pre-engraftment. Globally, breakthrough infections with resistant GNRs (rGNRs) are increasing in HCT while rGNR epidemiology has become increasingly complex to predict. It’s recognized that individualized antimicrobial HCT therapy is needed but it is unclear what screening strategies are optimal to detect rGNR colonization. Single taxon dominance of certain Gammaproteobacteria (ɣ-bacteria) species in HCT intestinal microbiomes is a BSI risk factor where colonizing strains present prior to transplant are predominant aetiologies of GNR BSIs. In this study we sought to understand how ɣ-bacteria loads respond to HCT antimicrobial therapy. Methods We performed a prospective study of 60 adult patients undergoing HCT at The Ottawa Hospital from 2021-2022. A baseline fecal sample collected < 1 week before conditioning was screened by culture for colonization with rGNR to fluoroquinolones (FQ), 3rd generation cephalosporins (3GC) and carbapenems. Total- and ɣ-bacteria loads were measured by qPCR in baseline and serial specimens until engraftment, expressed as the Log10 (ɣ-bacteria 16S copies / total 16S copies) per ng fecal DNA. Ceftriaxone (CTX) and piperacillin-tazobactam (TZP) are used for prophylaxis and empiric FN therapy, respectively at our institution. Results High ɣ-bacteria loads were associated with baseline rGNR colonization compared to no rGNR colonization (Log10 (-1.85) vs Log10 (-2.39), p < 0.0095) (Fig. 1). Highest loads occurred when resistant Escherichia coli or Klebsiella pneumoniae colonized the intestinal microbiota (Log10 (-1.34), p < 0.001). ɣ-bacteria loads were refractory to CTX and TZP therapy when colonizing E. coli or K. pneumoniae carried 3GC resistance (Fig. 2a). In contrast, ɣ-bacteria loads decreased > 2 Log10 in response to CTX and TZP if at baseline rGNRs were FQ resistant, expressed chromosomal AmpC, or if no rGNRs were detected (Fig. 2b, c, d). Conclusion ɣ-bacteria load measurement during HCT may be useful for predicting colonization with rGNR strains and identifying patients at risk of GNR BSI. Disclosures All Authors: No reported disclosures

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.258
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes2
Has abstractyes

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