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Record W2607289713 · doi:10.1038/s41598-017-00902-5

Growth Differentiation Factor-15 Deficiency Augments Inflammatory Response and Exacerbates Septic Heart and Renal Injury Induced by Lipopolysaccharide

2017· article· en· W2607289713 on OpenAlexafffund
Palida Abulizi, Neruja Loganathan, Duo Zhao, Tina Mele, Yixin Zhang, Terry Zwiep, Kexiang Liu, Xiufen Zheng

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicGDF15 and Related Biomarkers
Canadian institutionsLawson Health Research InstituteLondon Health Sciences CentreWestern University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsLipopolysaccharideInflammatory responseMedicineInflammationAcute kidney injurySystemic inflammatory response syndromeSepsisRenal injuryImmunologyInternal medicineKidney

Abstract

fetched live from OpenAlex

Septic acute kidney injury (AKI) and myocardial dysfunction are leading causes of mortality with no accepted method of therapy. In this study we demonstrate the role of growth differentiating factor 15 (GDF15) in septic AKI and myocardial dysfunction using a murine lipopolysaccharide (LPS)-induced sepsis model and an in vitro cell culture system. Data show that GDF15 deficiency augments inflammatory response and exacerbates renal and cardiac injury induced by LPS, while over-expression of GDF15 protects the kidney and heart from LPS-induced organ dysfunction. Therefore, this study highlights the therapeutic potential of GDF15 in the treatment of endotoxin-induced sepsis.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.263
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 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

Citations95
Published2017
Admission routes2
Has abstractyes

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