MétaCan
Menu
Back to cohort

Gastrointestinal microbiota contributes to the development of murine transfusion-related acute lung injury

2018· article· en· W2862188711 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBlood Advances · 2018
Typearticle
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsHospital for Sick ChildrenCanadian Blood ServicesUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsTransfusion-related acute lung injuryGut floraLungImmunologyBiologyAntibodySpecific-pathogen-freeFecesMedicineMicrobiologyInternal medicinePulmonary edema

Abstract

fetched live from OpenAlex

Transfusion-related acute lung injury (TRALI) is a syndrome of respiratory distress upon blood transfusion and is the leading cause of transfusion-related fatalities. Whether the gut microbiota plays any role in the development of TRALI is currently unknown. We observed that untreated barrier-free (BF) mice suffered from severe antibody-mediated acute lung injury, whereas the more sterile housed specific pathogen-free (SPF) mice and gut flora-depleted BF mice were both protected from lung injury. The prevention of TRALI in the SPF mice and gut flora-depleted BF mice was associated with decreased plasma macrophage inflammatory protein-2 levels as well as decreased pulmonary neutrophil accumulation. DNA sequencing of amplicons of the 16S ribosomal RNA gene revealed a varying gastrointestinal bacterial composition between BF and SPF mice. BF fecal matter transferred into SPF mice significantly restored TRALI susceptibility in SPF mice. These data reveal a link between the gut flora composition and the development of antibody-mediated TRALI in mice. Assessment of gut microbial composition may help in TRALI risk assessment before transfusion.

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.

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)0.000
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 score0.048
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.007
GPT teacher head0.289
Teacher spread0.282 · 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