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Record W1956728406 · doi:10.1111/xen.12178

In vitro exposure of pig neonatal isletlike cell clusters to human blood

2015· article· en· W1956728406 on OpenAlexaff
Santosh Nagaraju, Suzanne Bertera, Takayuki Tanaka, Hidetaka Hara, Gina R. Rayat, Martin Wijkstrom, David Ayares, Massimo Trucco, David K. C. Cooper, Rita Bottino

Bibliographic record

VenueXenotransplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta
FundersNational Institute of Allergy and Infectious DiseasesUniversity of PittsburghNational Institutes of HealthUnited Therapeutics CorporationU.S. Public Health ServiceJuvenile Diabetes Research Foundation International
KeywordsXenotransplantationIsletAntibodyIn vitroImmunologyViability assayAndrologyCellBiologyMolecular biologyMedicineInternal medicineEndocrinologyDiabetes mellitusBiochemistryTransplantation

Abstract

fetched live from OpenAlex

BACKGROUND: Pig islet grafts have been successful in treating diabetes in animal models. One remaining question is whether neonatal pig isletlike cell clusters (NICC) are resistant to the early loss of islets from the instant blood-mediated inflammatory reaction (IBMIR). METHODS: Neonatal isletlike cell clusters were harvested from three groups of piglets-(i) wild-type (genetically unmodified), (ii) α1,3-galactosyltransferase gene-knockout (GTKO)/CD46, and (iii) GTKO/CD46/CD39. NICC samples were mixed with human blood in vitro, and the following measurements were made-antibody binding; complement activation; speed of islet-induced coagulation; C-peptide; glutamic acid decarboxylase (GAD65) release; viability. RESULTS: Time to coagulation and viability were both reduced in all groups compared to freshly drawn non-anticoagulated human blood and autologous combinations, respectively. Antibody binding to the NICC occurred in all groups. CONCLUSIONS: Neonatal isletlike cell clusters were subject to humoral injury with no difference associated to their genetic characteristics.

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 score0.006
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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