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Record W4389158505 · doi:10.1016/j.healun.2023.11.013

Molecular states associated with dysfunction and graft loss in heart transplants

2023· article· en· W4389158505 on OpenAlexafffund
Philip F. Halloran, Katelynn S. Madill-Thomsen, Martina Macková, A. Aliabadi-Zuckermann, Martín Cadeiras, María G. Crespo‐Leiro, E.C. DePasquale, Mario C. Deng, Johannes Gökler, Shelley Hall, Daniel H. Kim, Jon Kobashigawa, Peter S. Macdonald, Luciano Potena, Keyur B. Shah, Josef Stehlik, Andreas Zuckermann, J. Reeve

Bibliographic record

VenueThe Journal of Heart and Lung Transplantation · 2023
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Alberta
FundersMinistry of Advanced EducationCanada Foundation for InnovationUniversity of AlbertaNateraMinistry of Advanced Education and TechnologyMendez National Institute of Transplantation FoundationGenome Canada
KeywordsEjection fractionInflammationInternal medicineMedicinePathologyCardiologyOncologyHeart failureBiology

Abstract

fetched live from OpenAlex

BACKGROUND: We explored the changes in gene expression correlating with dysfunction and graft failure in endomyocardial biopsies. METHODS: Genome-wide microarrays (19,462 genes) were used to define mRNA changes correlating with dysfunction (left ventricular ejection fraction [LVEF] ≤ 55) and risk of graft loss within 3 years postbiopsy. LVEF data was available for 1,013 biopsies and survival data for 779 patients (74 losses). Molecular classifiers were built for predicting dysfunction (LVEF ≤ 55) and postbiopsy 3-year survival. RESULTS: Dysfunction is correlated with dedifferentiation-decreased expression of normal heart transcripts, for example, solute carriers, along with increased expression of inflammation genes. Many genes with reduced expression in dysfunction were matrix genes such as fibulin 1 and decorin. Gene ontology (GO) categories suggested matrix remodeling and inflammation, not rejection. Genes associated with the risk of failure postbiopsy overlapped dysfunction genes but also included genes affecting microcirculation, for example, arginase 2, which reduces NO production, and endothelin 1. GO terms also reflected increased glycolysis and response to hypoxia, but decreased VEGF and angiogenesis pathways. T cell-mediated rejection was associated with reduced survival and antibody-mediated rejection with relatively good survival, but the main determinants of survival were features of parenchymal injury. Both dysfunction and graft loss were correlated with increased biopsy expression of BNP (gene NPPB). Survival probability classifiers divided hearts into risk quintiles, with actuarial 3-year postbiopsy survival >95% for the highest versus 50% for the lowest. CONCLUSIONS: Dysfunction in transplanted hearts reflects dedifferentiation, decreased matrix genes, injury, and inflammation. The risk of short-term loss includes these changes but is also associated with microcirculation abnormalities, glycolysis, and response to hypoxia.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.291
Teacher spread0.278 · 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 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

Citations13
Published2023
Admission routes2
Has abstractno

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