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Record W3034309595 · doi:10.1038/s41598-020-66662-x

Malodorous biogenic amines in Escherichia coli-caused urinary tract infections in women—a metabolomics approach

2020· article· en· W3034309595 on OpenAlexaff
Scarlett Puebla-Barragan, Justin B. Renaud, Mark W. Sumarah, Gregor Reid

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsAgriculture and Agri-Food CanadaLawson Health Research InstituteWestern University
Fundersnot available
KeywordsTrimethylamineCadaverineUrineUrinary systemPutrescineEscherichia coliTrimethylamine N-oxideMicrobiologyChemistryGenitourinary systemMedicineFood scienceBiologyInternal medicineBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Many women suffer from urinary tract infections (UTIs). In addition to pain and increased urgency to urinate, malodour is a significant issue for these patients. The specific factors causing this malodour are unclear, and there are no targeted treatment options to counteract it effectively. We used a metabolomics approach to compare the chemical composition of metabolites in the urine of women with E. coli UTIs (n = 15) and those who are healthy (n = 10). The biogenic amines trimethylamine and putrescine, which cause malodour in other urogenital conditions, were significantly increased in UTI patients. Conversely, the precursor of trimethylamine, trimethylamine N-oxide, was lower. To further confirm the source of the malodorous compounds, in vitro experiments were conducted by incubating strains of uropathogenic E. coli in sterilized urine from healthy women. All tested strains accumulated trimethylamine and putrescine. Notably, cadaverine was also produced by E. coli strains in vitro; however, it was not significantly different between both groups. We confirmed that the malodorous amines TMA and putrescine are found in higher concentrations in the urine of patients with an E.coli-caused UTI.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.259
Teacher spread0.231 · 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 teacher head, not a consensus.

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".

Quick stats

Citations62
Published2020
Admission routes1
Has abstractyes

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