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Record W3107414072 · doi:10.1172/jci.insight.142976

Fecal host biomarkers predicting severity of Clostridioides difficile infection

2020· article· en· W3107414072 on OpenAlexafffund
Makan Golizeh, Kaitlin Winter, Lucie Roussel, Marija Landekic, Mélanie J. Langelier, Vivian G. Loo, Momar Ndao, Donald C. Vinh

Bibliographic record

VenueJCI Insight · 2020
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsMcGill UniversityUniversité de MontréalMcGill University Health Centre
FundersUCB USConcordia UniversityMerck CanadaMcGill University Health CentreDirectorate for Mathematical and Physical SciencesRadboud UniversiteitConcordia University of EdmontonMedical Research and Materiel CommandCidara TherapeuticsMcGill UniversityCSL Behring
KeywordsClostridioidesMedicineDiarrheaFecesInternal medicineDiseaseBiomarkerCohortEpidemiologyHost (biology)GastroenterologySeverity of illnessImmunologyMicrobiologyBiology

Abstract

fetched live from OpenAlex

Clostridioides difficile is a major cause of health care-associated diarrhea. Severity ranges from mild to life-threatening, but this variability remains poorly understood. Microbiologic diagnosis of C. difficile infection (CDI) is straightforward but offers little insight into the patient's prognosis or into pathophysiologic determinants of clinical trajectory. The aim of this study was to discover host-derived, CDI-specific fecal biomarkers involved in disease severity. Subjects without and with CDI diarrhea were recruited. CDI severity was based on Infectious Diseases Society of America/Society for Healthcare Epidemiology of America criteria. We developed a liquid chromatography tandem mass spectrometry approach to identify host-derived protein biomarkers from stool and applied it to diagnostic samples for cohort-wise comparison (CDI-negative vs. nonsevere CDI vs. severe CDI). Selected biomarkers were orthogonally confirmed and subsequently verified in a CDI mouse model. We identified a protein signature from stool, consisting of alpha-2-macroglobulin (A2MG), matrix metalloproteinase-7 (MMP-7), and alpha-1-antitrypsin (A1AT), that not only discriminates CDI-positive samples from non-CDI ones but also is potentially associated with disease severity. In the mouse model, this signature with the murine homologs of the corresponding proteins was also identified. A2MG, MMP-7, and A1AT serve as biomarkers in patients with CDI and define novel components of the host response that may determine disease severity.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.147
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.038
GPT teacher head0.290
Teacher spread0.252 · 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.

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

Citations7
Published2020
Admission routes2
Has abstractyes

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