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Recipient Cells Form the Intimal Proliferative Lesion in the Rat Aortic Model of Allograft Arteriosclerosis

2002· article· en· W2088465176 on OpenAlexafffund
Paul M. Johnson, Michael Carpenter, Greg Hirsch, Tim Lee

Bibliographic record

VenueAmerican Journal of Transplantation · 2002
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsDalhousie University
FundersHeart and Stroke Foundation of Canada
KeywordsNeointimaMedicineArteriosclerosisPathologyTransplantationIntimal hyperplasiaImmunosuppressionLesionImmunologyRestenosisSmooth muscleInternal medicineStent

Abstract

fetched live from OpenAlex

Chronic rejection is the leading cause of late graft loss following solid organ transplantation and is characterized by a vasculopathy referred to as allograft arteriosclerosis. While the etiology of allograft arteriosclerosis remains unknown, it has been hypothesized that migration of donor medial smooth muscle cells into the intimal compartment is responsible for the formation of the occlusive lesion (neointima). In this study we have used aortic interposition grafts between fully histoincompatible rat strains (Brown Norway and Lewis) to investigate the origin of the neointimal cells. Three transplant paradigms were used: BN to Lew, Lew to BN and BN to Lew with immunosuppression. Neointimal cells were isolated from aortic transplant tissue through an EDTA wash/mechanical stripping technique. We have developed polymerase chain reaction primers to the MHC1 allele that are specific to each rat strains' DNA. Polymerase chain reaction analysis, using the strain-specific primers and purified neointimal cell DNA from transplanted aortic tissue from all three experimental groups, demonstrated that the neointimal cells are of recipient, and not donor origin.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.049
GPT teacher head0.301
Teacher spread0.252 · 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

Citations35
Published2002
Admission routes2
Has abstractyes

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