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Gene Expression Profiling for the Identification and Classification of Antibody-Mediated Heart Rejection

2017· article· en· W2584870882 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCirculation · 2017
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsThe Metabolomics Innovation CentreUniversity of Alberta
Fundersnot available
KeywordsMedicineHeart transplantationGene expression profilingImmunostainingLung transplantationPathologyImmunologyAntibodyBiopsyPathophysiologyGene expressionTransplantationImmunohistochemistryGeneInternal medicineBiology

Abstract

fetched live from OpenAlex

Background: Antibody-mediated rejection (AMR) contributes to heart allograft loss. However, an important knowledge gap remains in terms of the pathophysiology of AMR and how detection of immune activity, injury degree, and stage could be improved by intragraft gene expression profiling. Methods: We prospectively monitored 617 heart transplant recipients referred from 4 French transplant centers (January 1, 2006–January 1, 2011) for AMR. We compared patients with AMR (n=55) with a matched control group of 55 patients without AMR. We characterized all patients using histopathology (ISHLT [International Society for Heart and Lung Transplantation] 2013 grades), immunostaining, and circulating anti-HLA donor-specific antibodies at the time of biopsy, together with systematic gene expression assessments of the allograft tissue, using microarrays. Effector cells were evaluated with in vitro human cell cultures. We studied a validation cohort of 98 heart recipients transplanted in Edmonton, AB, Canada, including 27 cases of AMR and 71 controls. Results: A total of 240 heart transplant endomyocardial biopsies were assessed. AMR showed a distinct pattern of injury characterized by endothelial activation with microcirculatory inflammation by monocytes/macrophages and natural killer (NK) cells. We also observed selective changes in endothelial/angiogenesis and NK cell transcripts, including CD16A signaling and interferon-γ–inducible genes. The AMR-selective gene sets accurately discriminated patients with AMR from those without and included NK transcripts (area under the curve=0.87), endothelial activation transcripts (area under the curve=0.80), macrophage transcripts (area under the curve=0.86), and interferon-γ transcripts (area under the curve=0.84; P <0.0001 for all comparisons). These 4 gene sets showed increased expression with increasing pathological AMR (pAMR) International Society for Heart and Lung Transplantation grade ( P <0.001) and association with donor-specific antibody levels. The unsupervised principal components analysis demonstrated a high proportion of molecularly inactive pAMR1(I+), and there was significant molecular overlap between pAMR1(H + ) and full-blown pAMR2/3 cases. Endothelial activation transcripts, interferon-γ, and NK transcripts showed association with chronic allograft vasculopathy. The molecular architecture and selective AMR transcripts, together with gene set discrimination capacity for AMR identified in the discovery set, were reproduced in the validation cohort. Conclusions: Tissue-based measurements of specific pathogenesis-based transcripts reflecting NK burden, endothelial activation, macrophage burden, and interferon-γ effects accurately classify AMR and correlate with degree of injury and disease activity. This study illustrates the clinical potential of a tissue-based analysis of gene transcripts to refine diagnosis of heart transplant rejection.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.082
GPT teacher head0.392
Teacher spread0.310 · 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