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Record W4403916378 · doi:10.1093/jac/dkae388

Establishment of a diverse pheno-genotypic challenge set of <i>Klebsiella pneumoniae</i> and <i>Pseudomonas aeruginosa</i> suitable for use in the murine pneumonia model

2024· article· en· W4403916378 on OpenAlexfundno aff
Andrew J Fratoni, Alissa M Padgett, Erin M Duffy, David P. Nicolau

Bibliographic record

VenueJournal of Antimicrobial Chemotherapy · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Institute of Allergy and Infectious DiseasesBiomedical Advanced Research and Development AuthorityNovo Nordisk FondenNovo NordiskBundesministerium für Bildung und ForschungPublic Health Agency of CanadaDepartment of Health and Social CareWellcome TrustPublic Health AgencyWellcomeCombating Antibiotic-Resistant Bacteria Biopharmaceutical AcceleratorBill and Melinda Gates FoundationPfizerNational Institutes of HealthShionogiU.S. Department of Health and Human ServicesAdministration for Strategic Preparedness and Response
KeywordsPseudomonas aeruginosaKlebsiella pneumoniaeMicrobiologyBiologyStreptococcus pneumoniaePneumoniaKlebsiella pneumoniaTobramycinAntibioticsBacteriaMedicineGentamicinEscherichia coliInternal medicineGene

Abstract

fetched live from OpenAlex

BACKGROUND: Preclinical murine infection models lack inter-laboratory uniformity, complicating result comparisons and data reproducibility. The European Innovative Medicines initiative-funded consortium (COMBINE) has developed a standardized murine neutropenic pneumonia protocol to address these concerns. While model methods have been standardized, a major obstacle to consistent results is the lack of available bacteria with defined viability and variability. Herein, we establish a diverse challenge set of Klebsiella pneumoniae and Pseudomonas aeruginosa suitable for use in the COMBINE protocol to further minimize experimental inconsistency and improve the interpretability of data generated among differing laboratories. MATERIALS AND METHODS: Sixty-six K. pneumoniae and 65 P. aeruginosa were phenotypically profiled against tigecycline (K. pneumoniae only), levofloxacin, meropenem, cefiderocol and tobramycin. Fifty-nine isolates were introduced into the COMBINE model to assess the sufficiency of the starting bacterial inoculation, resultant baseline bacterial burden, achievement of ≥1 log10cfu/lung growth at 24 h, time to and percentage mortality. Forty-five isolates displaying desirable minimum inhibitory concentration profiles were subjected to replicate in vivo testing to assess target parameters. RESULTS: 83% of K. pneumoniae reached the prerequisite growth at 24 h using a starting bacterial burden ≥7 log10cfu/lung. P. aeruginosa isolates grew well in the model: 90% achieved the growth target with a starting bacterial burden of 6 log10cfu/lung. Mortality was negligible for K. pneumoniae but high for P. aeruginosa. Poor or inconsistent achievement of the 24 h growth target was seen in 11/59 isolates. CONCLUSIONS: With this diverse cache of viable isolates established in the COMBINE pneumonia model, future translational studies can be undertaken to set efficacy benchmarks among laboratories.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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 designBench or experimental
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".

Quick stats

Citations6
Published2024
Admission routes1
Has abstractyes

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