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Molecular Assessment of Heart Transplant Biopsies

2018· article· en· W2883148325 on OpenAlexaff
Michael Parkes, J. Reeve, Daniel Kim, Peter S. Macdonald, A.Z. Aliabadi, J. Goekler, Andreas Zuckermann, Patrick Bruneval, Alexandre Loupy, Luciano Potena, Martín Cadeiras, E.C. DePasquale, Mario C. Deng, Jon Kobashigawa, Philip F. Halloran

Bibliographic record

VenueTransplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhenotypeBiopsyPathologyMedicineMolecular pathologyAnatomical pathologyImmunohistochemistryInternal medicineBiologyGeneGenetics

Abstract

fetched live from OpenAlex

INTERHEART (ClinicalTrails.gov NCT02670408). Introduction We previously developed a molecular diagnostic system for assessment of rejection phenotypes in endomyocardial biopsies (EMB) based on expression of rejection-associated transcripts (RAT) derived in kidney (JHLT 36:1192, 2017). This system used archetypal analysis to identify 3 rejection-related molecular phenotypes among the biopsies (A1NoRejection, A2TCMR, A3ABMR), with each biopsy assigned scores relating them to the molecular phenotypes. We now explored whether there was another dimension beyond rejection that reflected acute parenchymal injury. Materials & Methods 889 single-piece EMBs from 462 heart transplant recipients at 8 centres in North America, Europe, and Australia were analyzed on Affymetrix microarrays. We used 2 methods to assess injury: archetypal analysis to assign groups, and expression of previously defined injury-repair transcripts (IRRAT). EMBs with A2TCMR scores ≥ 0.3 and A3ABMR scores ≥ 0.5 in the previously published 3 archetype model were designated “molecular rejection.” EMBs with A4 scores ≥ 0.4 in a new 4 archetype model were designated “molecular injury.” The rejection and injury designations were not mutually exclusive. Results EMBs characterized by archetypal analysis using 4 rather than 3 archetypes were distributed by principal component analysis based on RAT expression (Figure 1). PC1 reflected rejection, and PC2 reflected ABMR vs. TCMR (Figure 1A). The new group (A4) had an “acute injury” phenotype that was most apparent in PC3 (Figure 1B).A4 had high expression of macrophage transcripts and IRRAT. The median IRRAT score in EMBs with high injury and low rejection scores was high compared to relatively normal biopsies (0.79 vs. -0.19) (Table 1). Median IRRAT scores in EMBs with high rejection and low injury were also elevated, albeit to a lesser extent (0.21 vs. -0.19). The expression of rejection transcripts was somewhat elevated in acute injury without rejection, reflecting overlap between inflammatory processes activated in rejection and non- rejection-related injury.Many A4 EMBs were taken very early post-transplant (median time 30 days), probably reflecting injury induced in donation/implantation. Histology sometimes misdiagnosed rejection in A4 EMBs because of the macrophage infiltration: of 17 EMBs with high molecular injury and no molecular rejection, 9 were called rejection by histology. Conclusion Unsupervised analysis with 4 archetypes discovered a new group, acute injury, with high expression of macrophage and injury transcripts and early time post-transplant. Thus hearts often have an early acute injury phenotype that is inflamed, more so than in kidney transplants. Some of these biopsies have molecular rejection but some do not. Some with no molecular rejection are called rejection by histology, apparently reflecting injury-induced inflammation. We conclude that a molecular approach that independently assesses rejection and injury is needed for EMBs. Transcriptome Sciences, Inc.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.064

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0190.004

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 head0.023
GPT teacher head0.369
Teacher spread0.347 · 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".

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Citations3
Published2018
Admission routes1
Has abstractyes

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