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Porcine Endogenous Retroviral Nucleic Acid in Peripheral Tissues Is Associated with Migration of Porcine Cells Post Islet Transplant

2004· article· en· W1995057667 on OpenAlexaff
Tanya M. Binette, Karen Seeberger, James Lyon, Ray V. Rajotte, Gregory S. Korbutt

Bibliographic record

VenueAmerican Journal of Transplantation · 2004
Typearticle
Languageen
FieldMedicine
TopicXenotransplantation and immune response
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsXenotransplantationIsletVirologyImmune systemMessenger RNASplenocyteTransplantationMedicineNucleic acidDNAMolecular biologyEndogenyBiologyImmunologyGeneInsulinInternal medicine

Abstract

fetched live from OpenAlex

Porcine islets represent an alternative source of insulin-producing tissue, however, porcine endogenous retrovirus (PERV) remains a concern. In this study, SCID mice were transplanted with nonencapsulated (non-EC), microencapsulated (EC) or macroencapsulated (in a TheraCyte trade mark device) neonatal porcine islets (NPIs), and peripheral tissues were screened for presence of viral DNA and mRNA. To understand the role of an intact immune system in PERV incidence, mice with established NPI grafts were reconstituted with splenocytes. Peripheral tissues were screened for PERV and porcine DNA using PCR. Tissues with positive DNA were analyzed for PERV mRNA using RT-PCR. No significant difference was observed between non-EC and EC transplants regarding presence of PERV or porcine-specific DNA or mRNA. In reconstituted animals, little PERV or porcine DNA, and no PERV mRNA was detected. No PERV or porcine-specific DNA was observed in animals implanted with a TheraCyte trade mark device. In conclusion, an intact immune system significantly lowered the presence of PERV. Microencapsulation of islets did not alter PERV presence, however, macroencapsulation in the TheraCyte device did. Lower PERV incidence coincided with lower porcine DNA in peripheral tissues, linking the presence of PERV to migration of porcine cells.

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 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.234
Teacher spread0.224 · 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 teacher head, 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

Citations15
Published2004
Admission routes1
Has abstractyes

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