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Record W1973744237 · doi:10.1089/ten.teb.2008.0264

Assays on the Influence of Biomaterials on Allogeneic Rejection in Tissue Engineering

2008· review· en· W1973744237 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.

Bibliographic record

VenueTissue Engineering Part B Reviews · 2008
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTissue engineeringBiomaterialEx vivoImmunologyCell biologyInnate immune systemIn vivoImmune systemDendritic cellBiologyChemistryMedicineBiomedical engineeringBiotechnology

Abstract

fetched live from OpenAlex

In tissue engineering, innate responses to biomaterial scaffolds will affect rejection of allogeneic cells. Biomaterials directly influence innate and adaptive immune cell adhesion, reactive oxygen intermediate production, cytokine secretion, nuclear factor-kappa B nuclear translocation, gene expression, and cell surface markers, all of which are likely to affect allogeneic rejection responses. A major goal in tissue engineering is to induce transplant tolerance, potentially by manipulating the biomaterial component. This review describes methods of measuring responses of macrophages, dendritic cells, and T cells stimulated in vitro and in vivo and addresses key factors in assay development. Such tests include mixed leukocyte reactions, enzyme-linked immunosorbent spot assays, trans-vivo delayed-type hypersensitivity assays, and measurement of dendritic cell subsets and anti-donor antibodies; we propose extending these studies to tissue engineering.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.041
GPT teacher head0.298
Teacher spread0.256 · 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